spatstat/0000755000176200001440000000000014517671356012134 5ustar liggesusersspatstat/NAMESPACE0000644000176200001440000000272614514653072013352 0ustar liggesusers# spatstat NAMESPACE file importFrom(utils, RShowDoc, news, page, read.table) import(spatstat.utils) import(spatstat.data) import(spatstat.geom) import(spatstat.random) import(spatstat.explore) import(spatstat.model) import(spatstat.linnet) # no dynamic library # Do not edit the following. # It is generated automatically. # .................................................. # load dynamic library # (native routines are now registered in init.c) # .................................................. # There is no dynamic library # useDynLib(spatstat, .registration=TRUE) # .................................................. # Automatically-generated list of documented objects # .................................................. export("beginner") export("bugfixes") export("foo") export("latest.news") export("plot.foo") export("print.autoexec") export("print.bugtable") export("spatstat.family") # ....... Special cases ........... # ....... End of special cases ... # ......................................... # Automatically generated list of S3 methods # ......................................... S3method("plot", "foo") S3method("print", "autoexec") S3method("print", "bugtable") # ......................................... # Assignment methods # ......................................... # ......................................... # End of methods # ......................................... spatstat/demo/0000755000176200001440000000000014243060071013036 5ustar liggesusersspatstat/demo/spatstat.R0000644000176200001440000005604114330112453015031 0ustar liggesusersif(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && dev.interactive(orNone=TRUE)) oldoptions <- options(warn=-1) fanfare <- function(stuff) { plot(c(0,1),c(0,1),type="n",axes=FALSE, xlab="", ylab="") text(0.5,0.5, stuff, cex=2.5) } par(mar=c(1,1,2,1)+0.1) fanfare("Spatstat demonstration") fanfare("I. Types of data") plot(swedishpines, main="Point pattern") plot(demopat, cols=c("green", "blue"), main="Multitype point pattern") plot(longleaf, fg="blue", main="Marked point pattern") plot(finpines, main="Point pattern with multivariate marks") a <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) plot(a, main="Line segment pattern") marks(a) <- sample(letters[1:4], 20, replace=TRUE) plot(a, main="Multitype line segment pattern") marks(a) <- runif(20) plot(a, main="Marked line segment pattern", style="width") plot(owin(), main="Rectangular window") plot(letterR, main="Polygonal window") plot(as.mask(letterR), main="Binary mask window") Z <- as.im(function(x,y){ sqrt((x - 1)^2 + (y-1)^2)}, square(2)) plot(Z, main="Pixel image") X <- runifpoint(42) plot(dirichlet(X), main="Tessellation") plot(rpoispp3(100), main="Three-dimensional point pattern") plot(simplenet, main="Linear network (linnet)") X <- rpoislpp(20, simplenet) plot(X, main="Point pattern on linear network (lpp)", show.window=FALSE) V <- as.linim(function(x,y,seg,tp){x^2-y^2}, L=simplenet) plot(V, main="Pixel image on a linear network") fanfare("II. Graphics") par(mar=c(5,4,2,3)+0.1) plot(letterR, col="green", border="red", lwd=2, main="Polygonal window with colour fill") plot(letterR, hatch=TRUE, spacing=0.15, angle=30, main="Polygonal window with line shading") plot(letterR, hatch=TRUE, hatchargs=list(texture=8, spacing=0.12), main="Polygonal window with texture fill") plot(amacrine, chars=c(1,16), main="plot(X, chars = c(1,16))") plot(amacrine, cols=c("red","blue"), chars=16, main="plot(X, cols=c(\"red\", \"blue\"))") opa <- par(mfrow=c(1,2)) plot(longleaf, markscale=0.03, main="markscale=0.03") plot(longleaf, markscale=0.09, main="markscale=0.09") par(opa) plot(longleaf, pch=21, cex=1, bg=colourmap(terrain.colors(128), range=c(0,80)), main="colourmap for numeric mark values") Z <- as.im(function(x,y) { r <- sqrt(x^2+y^2); r * exp(-r) }, owin(c(-5,5),c(-5,5))) plot(Z, main="pixel image: image plot") plot(Z, main="pixel image: image plot (heat colours)", col=heat.colors(256)) plot(Z, main="pixel image: logarithmic colour map", log=TRUE, col=rainbow(128, end=5/6)) contour(Z, main="pixel image: contour plot", axes=FALSE) plot(Z, main="pixel image: image + contour plot") contour(Z, add=TRUE) persp(Z, colmap=terrain.colors(128), shade=0.3, phi=30,theta=100, main="pixel image: perspective plot") ct <- colourmap(rainbow(20), breaks=seq(-1,1,length=21)) plot(ct, main="Colour map for real numbers") ca <- colourmap(rainbow(8), inputs=letters[1:8]) plot(ca, main="Colour map for discrete values") Z <- as.im(nnfun(runifpoint(8))) plot(Z, main="colour image for discrete values") textureplot(Z, main="texture plot for discrete values") W <- owin(c(1,5),c(0,4.5)) Lout <- scaletointerval(distmap(rebound.owin(letterR, W))) Lin <- scaletointerval(distmap(complement.owin(letterR, W))) L <- scaletointerval(eval.im(Lin-Lout)) D <- scaletointerval(density(runifpoint(30, W), adjust=0.3)) X <- scaletointerval(as.im(function(x,y){ x }, W=W)) plot(listof(L=L, D=D, X=X), main="Multiple images") pairs(L, D, X, main="Multiple images: pairs plot") persp(L, colin=D, theta=-24, phi=35, box=FALSE, apron=TRUE, main="Two images:\nperspective + colours", shade=0.4, ltheta=225, lphi=10) plot(rgbim(D,X,L,maxColorValue=1), valuesAreColours=TRUE, main="Three images: RGB display") plot(hsvim(D,L,X), valuesAreColours=TRUE, main="Three images: HSV display") V <- as.linim(function(x,y,seg,tp){(y/1000)^2-(x/1000)^3}, L=domain(chicago)) plot(V, main="Pixel image on a linear network (colour plot)") plot(V, style="w", main="Pixel image on a linear network (width plot)") persp(V, phi=25, theta=-35, expand=0.2, main="Pixel image on a linear network (perspective plot)") XL <- rpoislpp(20, simplenet) plot(XL, main="Point pattern on a network (default plot)") plot(XL, shape="crossticks", size=0.04, main="Point pattern on a network (crossticks plot)") plot(chicago, cols=1:7, main="Multitype point pattern on a network (symbols and colours)") plot(dendrite, shape="crossticks", cols=2:4, size=8, leg.side="bottom", leg.args=list(lwd=3), main="Multitype point pattern on a network (crossticks and colours)") fanfare("III. Conversion between types") W <- as.owin(chorley) plot(W, "window W") plot(as.mask(W)) plot(as.mask(W, dimyx=1000)) plot(as.im(W, value=3)) plot(as.im(W, value=3, na.replace=0), ribbon=TRUE) plot(as.im(function(x,y) {x^2 + y}, W=square(1)), main="as.im(function(x,y){x^2+y})") V <- delaunay(runifpoint(12)) plot(V, main="Tessellation V") plot(as.im(V, dimyx=256), main="as.im(V)") plot(as.owin(V)) X <- swedishpines plot(X, "point pattern X") plot(as.im(X), col=c("white","red"), ribbon=FALSE, xlab="", ylab="") plot(as.owin(X), add=TRUE) fanfare("IV. Subsetting and splitting data") plot(X, "point pattern X") subset <- 1:20 plot(X[subset], main="subset operation: X[subset]") subwindow <- owin(poly=list(x=c(0,96,96,40,40),y=c(0,0,100,100,50))) plot(X[subwindow], main="subset operation: X[subwindow]") plot(lansing, "Lansing Woods data") plot(split(lansing), main="split operation: split(X)", mar.panel=c(0,0,2,0), hsep=1, pch=3) plot(longleaf, main="Longleaf Pines data") plot(cut(longleaf, breaks=3), main=c("cut operation", "cut(longleaf, breaks=3)"), cols=2:4) Z <- dirichlet(runifpoint(10)) X <- runifpoint(100) plot(cut(X,Z), main="points cut by tessellation", leg.side="left", cols=1:10) plot(Z, add=TRUE) plot(split(X, Z), main="points split by tessellation", mar.panel=c(0,0,2,2), hsep=1) W <- square(1) X <- as.im(function(x,y){sqrt(x^2+y^2)}, W) Y <- dirichlet(runifpoint(12, W)) plot(split(X,Y), main="image split by tessellation") fanfare("V. Tessellations") plot(quadrats(square(1), nx=5, ny=3)) plot(hextess(square(1), 0.07)) plot(quantess(letterR, "x", 7)) plot(polartess(letterR, nangular=6, radii=(0:4)/2, origin=c(2.8, 1.5)), do.col=TRUE) plot(delaunay(cells)) plot(cells, add=TRUE) plot(dirichlet(cells)) plot(cells, add=TRUE) plot(rpoislinetess(2.5), do.col=TRUE) fanfare("VI. Exploratory data analysis") par(mar=c(3,3,3,2)+0.1) plot(swedishpines, main="Quadrat counts", pch="+") tab <- quadratcount(swedishpines, 4) plot(tab, add=TRUE, lty=2, cex=2, col="blue") par(mar=c(5,3,3,2)+0.1) plot(swedishpines, main="", pch="+") title(main=expression(chi^2 * " test"), cex.main=2) tes <- quadrat.test(swedishpines, 3) tes plot(tes, add=TRUE, col="red", cex=1.5, lty=2, lwd=3) title(sub=paste("p-value =", signif(tes$p.value,3)), cex.sub=1.4) par(mar=c(4,4,3,2)+0.1) tesk <- cdf.test(nztrees, "x") tesk plot(tesk) mur <- lapply(murchison, rescale, s=1000) mur <- lapply(mur, "unitname<-", value="km") X <- mur$gold D <- distfun(mur$faults) plot(X, main="Murchison gold deposits", cols="blue") plot(mur$faults, add=TRUE, col="red") rh <- rhohat(X,D) plot(rh, main="Smoothed rate estimate", xlab="Distance to nearest fault (km)", legend=FALSE, xlim=c(0, 20)) plot(predict(rh), main="predict(rhohat(X,D))") Z <- density(cells, 0.07) plot(Z, main="Kernel smoothed intensity of point pattern") plot(cells, add=TRUE) plot(redwood, main="Redwood data") te <- scan.test(redwood, 0.1, method="poisson") plot(te, main=c("Scan Statistic for redwood data", paste("p-value =", signif(te$p.value,3)))) plot(redwood, add=TRUE) te X <- unique(unmark(shapley)) plot(X, "Shapley galaxy concentration", pch=".") coco <-colourmap(rev(rainbow(128, end=2/3)), range=c(0,1)) pa <- function(i, ...) { if(i == 1) list(chars=c(".", "+"), cols=1:2) else list(size=0.5, pch=16, col=coco) } plot(nnclean(X, k=17), panel.args=pa, mar.panel=c(0,1,1,0), nrows=2, main="Byers-Raftery nearest neighbour cleaning", cex.title=1.2) Y <- sharpen(X, sigma=0.5, edgecorrect=TRUE) plot(Y, main="Choi-Hall data sharpening", pch=".") owpa <- par(mfrow=c(1,2)) W <- grow.rectangle(as.rectangle(letterR), 1) X <- superimpose(runifpoint(300, letterR), runifpoint(50, W), W=W) plot(W, main="clusterset(X, 'm')") plot(clusterset(X, 'marks', fast=TRUE), add=TRUE, chars=c("o", "+"), cols=1:2) plot(letterR, add=TRUE) plot(W, main="clusterset(X, 'd')") plot(clusterset(X, 'domain', exact=FALSE), add=TRUE) plot(letterR, add=TRUE) par(owpa) D <- density(a, sigma=0.05) plot(D, main="Kernel smoothed intensity of line segment pattern") plot(a, add=TRUE) X <- runifpoint(42) plot(dirichlet(X)) plot(X, add=TRUE) plot(delaunay(X)) plot(X, add=TRUE) parsave <- par(mfrow=c(1,1), mar=0.2+c(0,1,3,1)) plot(listof("Longleaf Pines data"=longleaf, "Nearest mark"=nnmark(longleaf), "Kernel smoothing of marks"=Smooth(longleaf,10), "Inverse distance weighted\nsmoothing of marks"=idw(longleaf)), equal.scales=TRUE, halign=TRUE, valign=TRUE, main="", mar.panel=0.2+c(0,0,2,2)) par(parsave) fryplot(cells, main=c("Fry plot","cells data"), pch="+") miplot(longleaf, main="Morishita Index plot", pch=16, col="blue") plot(swedishpines, main="Swedish Pines data") K <- Kest(swedishpines) plot(K, main="K function for Swedish Pines", legendmath=TRUE) en <- envelope(swedishpines, fun=Kest, nsim=10, correction="translate") plot(en, main="Envelopes of K function based on CSR", shade=c("hi", "lo")) pc <- pcf(swedishpines) plot(pc, main="Pair correlation function") plot(swedishpines, main="nearest neighbours") m <- nnwhich(swedishpines) b <- swedishpines[m] arrows(swedishpines$x, swedishpines$y, b$x, b$y, angle=12, length=0.1, col="red") plot(swedishpines %mark% nndist(swedishpines), markscale=1, main="Stienen diagram", legend=FALSE, fg="blue") plot(Gest(swedishpines), main=c("Nearest neighbour distance function G", "Gest(swedishpines)"), legendmath=TRUE) Z <- distmap(swedishpines, dimyx=512) plot(swedishpines$window, main="Distance map") plot(Z, add=TRUE) points(swedishpines) plot(Fest(swedishpines), main=c("Empty space function F", "Fest(swedishpines)"), legendmath=TRUE) W <- rebound.owin(letterR, square(5)) plot(distmap(W), main="Distance map") plot(W, add=TRUE) a <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) contour(distmap(a), main="Distance map") plot(a, add=TRUE,col="red") plot(Jest(swedishpines), main=c("J-function", "J(r)=(1-G(r))/(1-F(r))")) X <- swedishpines X <- X[sample(1:npoints(X))] Z <- nnfun(X) plot(as.owin(X), main="Nearest neighbour map") plot(Z, add=TRUE) points(X) plot(allstats(swedishpines)) Fig4b <- residualspaper$Fig4b plot(Fig4b, main="Inhomogeneous point pattern") plot(Kinhom(Fig4b), main="Inhomogeneous K-function") plot(pcfinhom(Fig4b, stoyan=0.1), main="Inhomogeneous pair correlation") plot(Ginhom(Fig4b, sigma=0.06), main="Inhomogeneous G-function") plot(Jinhom(Fig4b, sigma=0.06), main="Inhomogeneous J-function") X <- unmark(bronzefilter) plot(X, "Bronze filter data") lam <- predict(ppm(X ~x)) plot(Kscaled(X, lam), xlim=c(0, 1.5), main="Locally-scaled K function") plot(urkiola) plot(split(urkiola), cex=0.5) plot(density(split(urkiola))) contour(density(split(urkiola)), panel.begin=as.owin(urkiola)) plot(relrisk(urkiola), main="Relative risk (cross-validated)") plot(bramblecanes) br <- rescale(bramblecanes) plot(alltypes(br, "K"), mar.panel=c(4,5,2,2)+0.1) ama <- rescale(amacrine) plot(alltypes(ama, Lcross, envelope=TRUE, nsim=9), . - r ~ r, ylim=c(-25, 5)) ponderosa.extra$plotit(main="Ponderosa Pines") L <- localL(ponderosa) pL <- plot(L, lty=1, col=1, legend=FALSE, main=c("neighbourhood density functions", "for Ponderosa Pines"), cex.main=0.8) parsave <- par(mfrow=c(1,2)) ponderosa.extra$plotit() par(pty="s") plot(L, iso007 ~ r, main="point B") par(mar=0.2+c(1,1,3,1)) ponderosa.extra$plotit() L12 <- localL(ponderosa, rvalue=12) P12 <- ponderosa %mark% L12 Z12 <- Smooth(P12, sigma=5, dimyx=128) plot(Z12, col=topo.colors(128), main=c("smoothed", "neighbourhood density"), cex.main=0.8) contour(Z12, add=TRUE) points(ponderosa, pch=16, cex=0.5) plot(amacrine, main="Amacrine cells data", cex.main=0.8) par(pty="s") mkc <- markcorr(amacrine, correction="translate", method="density", kernel="epanechnikov") plot(mkc, main="Mark correlation function", legend=FALSE, cex.main=0.8) par(parsave) par(mar=0.2+c(4,4,3,1)) plot(alltypes(amacrine, markconnect), title="Mark connection functions for amacrine cells") parsave <- par(mfrow=c(1,2)) parspruce2 <- par(mar=0.2+c(0,2,2,0)) plot(spruces, cex.main=0.8, markscale=10) par(pty="s", mar=0.2+c(2,3,2,0)) plot(markcorr(spruces), main="Mark correlation", legendpos="bottomright") par(parspruce2) plot(spruces, cex.main=0.8, markscale=10) par(pty="s", mar=0.2+c(2,3,2,0)) plot(markvario(spruces), main="Mark variogram", legendpos="topright") par(parsave) par(mar=0.2+c(4,4,3,2)) plot(listof("Emark(spruces)"=Emark(spruces), "Vmark(spruces)"=Vmark(spruces)), main="Independence diagnostics", ylim.covers=0, legendpos="bottom") par3 <- par(mfrow=c(1,2)) X <- rpoispp3(100) plot(X, main="3D point pattern X") plot(K3est(X), main="K-function in 3D") plot(X, main="3D point pattern X") plot(G3est(X), main="G-function in 3D", legendpos="bottomright") par(par3) par(mfrow=c(1,3)) X <- unmark(chicago) plot(X, col="green", cols="red", pch=16, main="Chicago Street Crimes", cex.main=0.75, show.window=FALSE) plot(density(X, 100, distance="e"), main="Kernel density estimate (Euclidean)") plot(density(X, 100, distance="p"), main="Kernel density estimate (shortest path)") plot(X, col="green", cols="red", pch=16, main="Chicago Street Crimes", cex.main=0.75, show.window=FALSE) plot(linearK(X, correction="none"), main="Network K-function", cex.main=0.75) plot(linearK(X, correction="Ang"), main="Corrected K-function", cex.main=0.75) par(mfrow=c(1,1)) fanfare("VII. Model-fitting") parsave <- par(mar=0.2+c(1,1,3,3)) plot(japanesepines) fit <- ppm(japanesepines ~1) print(fit) fit <- ppm(japanesepines ~polynom(x,y,2)) print(fit) plot(fit, how="image", se=FALSE, main=c("Inhomogeneous Poisson model", "fit by maximum likelihood", "Fitted intensity")) plot(fit, how="image", trend=FALSE, main=c("Standard error", "of fitted intensity")) plot(leverage(fit)) plot(influence(fit)) plot(mur$gold, main="Murchison gold deposits", cols="blue") plot(mur$faults, add=TRUE, col="red") fit <- ppm(mur$gold ~D, covariates=list(D=distfun(mur$faults))) par(mar=0.2+c(4,4,4,2)) plot(parres(fit, "D"), main="Partial residuals from loglinear Poisson model", xlab="Distance to nearest fault (km)", ylab="log intensity of gold", legend=FALSE) legend("bottomleft", legend=c("partial residual", "loglinear fit"), col=c(1,4), lty=c(1,4)) par(mar=rep(0.2, 4), mfrow=c(1,1)) fitT <- kppm(redwood ~1, clusters="Thomas") simT <- simulate(fitT)[[1]] plot(listof(redwood, simT), main.panel=c("Redwood", "simulation from\nfitted Thomas model"), main="", mar.panel=0.2, equal.scales=TRUE) mop <- par(mfrow=c(1,2), pty="s", mar=rep(4.4, 4)) plot(fitT, xname=c("Thomas model", "minimum contrast fit"), pause=FALSE) par(mop) oop <- par(pty="s", mar=0.2+c(4,4,4,2)) os <- objsurf(fitT) plot(os, main="Minimum contrast objective function", col=terrain.colors(128)) contour(os, add=TRUE) par(oop) parra <- par(mfrow=c(1,2), mar=0.2+c(3,3,4,2)) plot(swedishpines) fit <- ppm(swedishpines ~1, Strauss(r=7)) print(fit) plot(fit, how="image", main=c("Strauss model", "fit by maximum pseudolikelihood", "Conditional intensity plot")) # fitted interaction plot(swedishpines) fit <- ppm(swedishpines ~1, PairPiece(c(3,5,7,9,11,13))) plot(fitin(fit), legend=FALSE, main=c("Pairwise interaction model", "fit by maximum pseudolikelihood")) # simulation par(mfrow=c(1,1), mar=0.5+c(0,0,2,0)) Xsim <- rmh(model=fit, start=list(n.start=80), control=list(nrep=100)) plot(listof(swedishpines, Xsim), main="", main.panel=c("Swedish Pines", "Simulation from\nfitted Strauss model"), mar.panel=c(0,0,3,0),hsep=1,equal.scales=TRUE) # model compensator par(parra) par(mar=0.2+c(4,4,3,1)) plot(swedishpines) fit <- ppm(swedishpines ~1, Strauss(r=7)) plot(Kcom(fit), cbind(iso, icom, pois) ~ r, legend=FALSE, main="model compensators") legend("topleft", legend=c("empirical K function", "Strauss model compensator of K", "Poisson theoretical K"), lty=1:3, col=1:3, inset=0.05) par(parsave) # Multitype data dpat <- rescale(demopat, 8) unitname(dpat) <- c("mile", "miles") dpat plot(dpat, cols=c("red", "blue")) fit <- ppm(dpat ~marks + polynom(x,y,2), Poisson()) plot(fit, trend=TRUE, se=TRUE) # Linear network data plot(spiders) fit <- lppm(spiders ~ polynom(x,y,2)) anova(fit, test="Chi") (fit <- step(fit)) lam <- predict(fit) plot(lam, main="Point process model on linear network", ribscale=1000) plot(spiders, add=TRUE, pch=16, show.network=FALSE) fanfare("VIII. Simulation") plot(letterR, main="Poisson random points") lambda <- 10/area.owin(letterR) points(rpoispp(lambda, win=letterR)) points(rpoispp(9 * lambda, win=letterR)) points(rpoispp(90 * lambda, win=letterR)) plot(rpoispp(100)) plot(rpoispp(function(x,y){1000 * exp(-3*x)}, 1000), main="rpoispp(function)") plot(rMaternII(200, 0.05)) plot(rSSI(0.05, 200)) plot(rThomas(10, 0.2, 5)) plot(rMatClust(10, 0.05, 4)) plot(rCauchy(30, 0.01, 5)) plot(rVarGamma(30, 2, 0.02, 5)) plot(rGaussPoisson(30, 0.05, 0.5)) if(require(RandomFields) && RandomFieldsSafe()) { X <- rLGCP("exp", 4, var=0.2, scale=0.1) plot(attr(X, "Lambda"), main="log-Gaussian Cox process") plot(X, add=TRUE, pch=16) } plot(rStrauss(200, 0.3, 0.07)) plot(rDiggleGratton(200,0.03,0.08)) plot(rDGS(300, 0.05)) plot(redwood, main="random thinning - rthin()") points(rthin(redwood, 0.5), col="green", cex=1.4) plot(rcell(nx=15)) plot(rsyst(nx=5)) abline(h=(1:4)/5, lty=2) abline(v=(1:4)/5, lty=2) plot(rstrat(nx=5)) abline(h=(1:4)/5, lty=2) abline(v=(1:4)/5, lty=2) X <- rsyst(nx=10) plot(rjitter(X, 0.02)) Xg <- rmh(list(cif="geyer", par=list(beta=1.25, gamma=1.6, r=0.2, sat=4.5), w=c(0,10,0,10)), control=list(nrep=1e4), start=list(n.start=200)) plot(Xg, main=paste("Geyer saturation process\n", "rmh() with cif=\"geyer\"")) L <- as.psp(matrix(runif(20), 5, 4), window=square(1)) plot(L, main="runifpointOnLines(30, L)") plot(runifpointOnLines(30, L), add=TRUE, pch="+") plot(L, main="rpoisppOnLines(3, L)") plot(rpoisppOnLines(3, L), add=TRUE, pch="+") plot(runiflpp(20, simplenet)) plot(rpoislpp(5, simplenet)) plot(rpoisline(10)) plot(rlinegrid(30, 0.1)) spatstat.options(npixel=256) X <- dirichlet(runifpoint(30)) plot(rMosaicSet(X, 0.4), col="green", border=NA) plot(X, add=TRUE) plot(rMosaicField(X, runif)) plot(rMosaicSet(rpoislinetess(3), 0.5), col="green", border=NA, main="Switzer's random set") spatstat.options(npixel=100) plot(Halton(512, c(2,3)), main="quasirandom pattern") plot(Halton(16384, c(2,3)), main="quasirandom pattern", pch=".") fanfare("IX. Geometry") A <- letterR B <- shift(letterR, c(0.2,0.1)) plot(boundingbox(A,B), main="shift", type="n") plot(A, add=TRUE) plot(B, add=TRUE, border="red") B <- rotate(letterR, 0.2) plot(boundingbox(A,B), main="rotate", type="n") plot(A, add=TRUE) plot(B, add=TRUE, border="red") mat <- matrix(c(1.1, 0, 0.3, 1), 2, 2) B <- affine(letterR, mat=mat, vec=c(0.2,-0.1)) plot(boundingbox(A,B), main="affine", type="n") plot(A, add=TRUE) plot(B, add=TRUE, border="red") par1x2 <- par(mfrow=c(1,2)) L <- rpoisline(10, owin(c(1.5,4.5),c(0.2,3.6))) plot(L, main="Line segment pattern") plot(L$window, main="L[window]", type="n") plot(L[letterR], add=TRUE) plot(letterR, add=TRUE, border="red") par(par1x2) a <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) plot(a, main="Self-crossing points") plot(selfcrossing.psp(a), add=TRUE, col="red") a <- as.psp(matrix(runif(20), 5, 4), window=square(1)) b <- rstrat(square(1), 5) plot(a, lwd=3, col="green", main="project points to segments") plot(b, add=TRUE, col="red", pch=16) v <- project2segment(b, a) Xproj <- v$Xproj plot(Xproj, add=TRUE, pch=16) arrows(b$x, b$y, Xproj$x, Xproj$y, angle=10, length=0.15, col="red") plot(a, main="pointsOnLines(L)") plot(pointsOnLines(a, np=100), add=TRUE, pch="+") parry <- par(mfrow=c(1,3), mar=0.3+c(1,1,3,1)) X <- tess(xgrid=seq(2, 4, length=10), ygrid=seq(0, 3.5, length=8)) plot(X, cex.main=0.75) plot(letterR, cex.main=0.75) plot(intersect.tess(X, letterR), cex.main=0.75) X <- dirichlet(runifpoint(10)) plot(X) L <- infline(0.3,0.5) plot(owin(), main="L", cex.main=0.75) plot(L, col="red", lwd=2, cex.main=0.75) plot(chop.tess(X,L), cex.main=0.75) par(parry) W <- chorley$window plot(W, main="simplify.owin") WS <- simplify.owin(W, 2) plot(WS, add=TRUE, border="green") nopa <- par(mfrow=c(2,2)) Rbox <- grow.rectangle(as.rectangle(letterR), 0.3) v <- erosion.owin(letterR, 0.25) plot(Rbox, type="n", main="erode.owin", cex.main=0.75) plot(letterR, add=TRUE, col="red", cex.main=0.75) plot(v, add=TRUE, col="blue") v <- dilation.owin(letterR, 0.25) plot(Rbox, type="n", main="dilate.owin", cex.main=0.75) plot(v, add=TRUE, col="blue") plot(letterR, add=TRUE, col="red") v <- closing.owin(letterR, 0.3) plot(Rbox, type="n", main="closing.owin", cex.main=0.75) plot(v, add=TRUE, col="blue") plot(letterR, add=TRUE, col="red") v <- opening.owin(letterR, 0.3) plot(Rbox, type="n", main="opening.owin", cex.main=0.75) plot(letterR, add=TRUE, col="red") plot(v, add=TRUE, col="blue") par(nopa) laslett(heather$fine, main="Laslett's Transform") fanfare("X. Operations on pixel images") Z <- distmap(swedishpines, dimyx=512) plot(Z, main="An image Z") plot(levelset(Z, 4)) plot(cut(Z, 5)) plot(eval.im(sqrt(Z) - 3)) plot(solutionset(abs(Z - 6) <= 1)) nopa <- par(mfrow=c(1,2)) plot(Z) segments(0,0,96,100,lwd=2) plot(transect.im(Z)) par(nopa) d <- distmap(cells, dimyx=256) W <- levelset(d, 0.06) nopa <- par(mfrow=c(1,2)) plot(W) plot(connected(W)) par(nopa) Z <- as.im(function(x,y) { 4 * x^2 + 3 * y }, letterR) plot(Z) plot(letterR, add=TRUE) plot(blur(Z, 0.3, bleed=TRUE)) plot(letterR, add=TRUE) plot(blur(Z, 0.3, bleed=FALSE)) plot(letterR, add=TRUE) plot(blur(Z, 0.3, bleed=FALSE)) plot(letterR, add=TRUE) fanfare("XI. Programming tools") showoffK <- function(Y, current, ..., fullpicture,rad) { plot(fullpicture, main=c("Animation using `applynbd'", "explaining the K function")) points(Y, cex=2) ux <- current[["x"]] uy <- current[["y"]] points(ux,uy,pch="+",cex=3) theta <- seq(0,2*pi,length=100) polygon(ux + rad * cos(theta), uy + rad*sin(theta)) text(ux+rad/3,uy+rad/2,Y$n,cex=3) if(runif(1) < 0.2) Sys.sleep(runif(1, max=0.4)) return(npoints(Y)) } par(ask=FALSE) applynbd(redwood, R=0.2, showoffK, fullpicture=redwood, rad=0.2, exclude=TRUE) par(oldpar) options(oldoptions) spatstat/demo/data.R0000644000176200001440000000664514366376352014127 0ustar liggesusersif(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && dev.interactive(orNone=TRUE)) oldoptions <- options(warn=-1) plot(amacrine) plot(anemones, markscale=1) ants.extra$plotit() plot(austates) plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) plot(betacells) plot(bramblecanes, cols=1:3) plot(split(bramblecanes)) plot(bronzefilter,markscale=2) plot(subset(btb, select=spoligotype), cols=2:5, main="Bovine Tuberculosis, by spoligotype") plot(cells) plot(chicago, main="Chicago Street Crimes", col="grey", cols=c("red", "blue", "black", "blue", "red", "blue", "blue"), chars=c(16,2,22,17,24,15,6), leg.side="left", show.window=FALSE) chorley.extra$plotit() plot(clmfires, which.marks="cause", cols=2:5, cex=0.25, main="Castilla-La Mancha forest fires") plot(clmfires.extra$clmcov200, main="Covariates for forest fires") plot(concrete) plot(copper$Points, main="Copper") plot(copper$Lines, add=TRUE) plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) }), parargs=list(mar=rep(1,4))) plot(dendrite, leg.side="bottom", main="Dendritic spines", cex=0.75, cols=2:4) plot(demopat) plot(finpines, main="Finnish pines") wildM1 <- with(flu, virustype == "wt" & stain == "M2-M1") plot(flu[wildM1, 1, drop=TRUE], main=c("flu data", "wild type virus, M2-M1 stain"), chars=c(16,3), cex=0.4, cols=2:3) plot(gordon, main="People in Gordon Square", pch=16) plot(gorillas, which.marks=1, chars=c(1,3), cols=2:3, main="Gorilla nest sites") plot(hamster, cols=c(2,4)) plot(heather) plot(humberside) plot(hyytiala, cols=2:5) plot(japanesepines) plot(lansing) plot(split(lansing)) plot(longleaf) plot(mucosa, chars=c(1,3), cols=c("red", "green")) plot(mucosa.subwin, add=TRUE, lty=3) plot(murchison, main="Murchison data") plot(murchison$greenstone, main="Murchison data", col="lightgreen") plot(murchison$gold, add=TRUE, pch=3, col="blue") plot(murchison$faults, add=TRUE, col="red") plot(nbfires, use.marks=FALSE, pch=".") plot(split(nbfires), use.marks=FALSE, chars=".") plot(split(nbfires)$"2000", which.marks="fire.type", main=c("New Brunswick fires 2000", "by fire type"), cols=c("blue", "green", "red", "cyan"), leg.side="left") plot(nztrees) plot(trim.rectangle(as.owin(nztrees), c(0,5), 0), add=TRUE, lty=3) plot(osteo[1:10,], tick.marks=FALSE, xlab="", ylab="", zlab="") plot(paracou, cols=2:3, chars=c(16,3)) ponderosa.extra$plotit() pyr <- pyramidal pyr$grp <- abbreviate(pyramidal$group, minlength=7) plot(pyr, quote(plot(Neurons, pch=16, main=grp)), main="Pyramidal Neurons") rm(pyr) plot(redwood) plot(redwood3, add=TRUE, pch=20) redwoodfull.extra$plotit() plot(residualspaper$Fig1) plot(residualspaper$Fig4a) plot(residualspaper$Fig4b) plot(residualspaper$Fig4c) shapley.extra$plotit(main="Shapley") plot(simdat) plot(spiders, pch=16, show.window=FALSE) plot(sporophores, chars=c(16,1,2), cex=0.6) points(0,0,pch=16, cex=2) text(15,8,"Tree", cex=0.75) plot(spruces, maxsize=min(nndist(spruces))) plot(subset(stonetools, select=TYPE), main="Palaeolithic stone tools and bone fragments", cols=2:3, cex=0.5) plot(swedishpines) plot(urkiola, cex=0.5, cols=2:3) plot(waka, markscale=0.04, main=c("Waka national park", "tree diameters")) plot(waterstriders, main.panel="", equal.scales=TRUE, mar.panel=0, hsep=1, cex=0.75) par(oldpar) options(oldoptions) spatstat/demo/sumfun.R0000644000176200001440000000771414375270731014523 0ustar liggesusers## demonstration of all summary functions opa <- par(mfrow=c(1,1), mar=c(0,0,1,0)+0.2) ## Ripley's K-function plot(swedishpines) par(mar=c(4,4,2,1)+0.2) plot(Kest(swedishpines)) ## Besag's transformation plot(Lest(swedishpines)) ## pair correlation function plot(pcf(swedishpines)) par(mfrow=c(2,3), mar=c(0,0,1,0)+0.2) ## Showing the utility of the K-function plot(cells) plot(nztrees) plot(redwood) par(mar=c(4,4,2,1)+0.2) plot(Kest(cells)) plot(Kest(nztrees)) plot(Kest(redwood)) ## Showing the utility of the pair correlation function par(mar=c(0,0,1,0)+0.2) plot(cells) plot(nztrees) plot(redwood) par(mar=c(4,4,2,1)+0.2) plot(pcf(cells)) plot(pcf(nztrees)) plot(pcf(redwood)) ## par(mfrow=c(1,1)) ## Analogues for inhomogeneous patterns ## Reweighted K-function plot(japanesepines) fit <- ppm(japanesepines, ~polynom(x,y,2)) plot(predict(fit)) plot(Kinhom(japanesepines, fit)) plot(pcfinhom(japanesepines, fit)) plot(Linhom(japanesepines)) ## Rescaled K-function Bronze <- unmark(bronzefilter) plot(Bronze) plot(Kscaled(Bronze)) fit <- ppm(Bronze ~x) plot(predict(fit)) plot(Bronze, add=TRUE) plot(Kscaled(Bronze, fit)) plot(Lscaled(Bronze, fit)) ## Local indicators of spatial association plot(localL(swedishpines)) plot(localK(swedishpines)) ## anisotropic plot(Ksector(redwood, 0, 90)) plot(Rf <- pairorient(redwood, 0.05, 0.15)) rose(Rf, main="Rose diagram of pair orientation distribution") plot(deriv(Rf, spar=0.6, Dperiodic=TRUE)) rose(nnorient(redwood)) ## par(mfrow=c(2,3), mar=rep(0.2, 4)) ## Empty space function F plot(cells) plot(nztrees) plot(redwood) par(mar=c(4,4,2,1)+0.2) plot(Fest(cells)) plot(Fest(nztrees)) plot(Fest(redwood)) ## Nearest neighbour distance function G par(mar=rep(0.2, 4)) plot(cells) plot(nztrees) plot(redwood) par(mar=c(4,4,2,1)+0.2) plot(Gest(cells)) plot(Gest(nztrees)) plot(Gest(redwood)) ## J-function par(mar=rep(0.2, 4)) plot(cells) plot(nztrees) plot(redwood) par(mar=c(4,4,2,1)+0.2) plot(Jest(cells)) plot(Jest(nztrees)) plot(Jest(redwood)) par(mfrow=c(1,1), mar=c(4,4,2,1)+0.2) ## versions for inhomogeneous patterns plot(Finhom(japanesepines)) plot(Ginhom(japanesepines)) plot(Jinhom(japanesepines)) ## Display F,G,J,K plot(allstats(swedishpines)) ## Multitype patterns plot(amacrine) plot(Kcross(amacrine)) plot(Kdot(amacrine)) I <- (marks(amacrine) == "on") J <- (marks(amacrine) == "off") plot(Kmulti(amacrine, I, J)) plot(alltypes(amacrine, "K")) plot(Lcross(amacrine)) plot(Ldot(amacrine)) plot(pcfcross(amacrine)) plot(pcfdot(amacrine)) plot(pcfmulti(amacrine, I, J)) plot(Gcross(amacrine)) plot(Gdot(amacrine)) plot(Gmulti(amacrine, I, J)) plot(alltypes(amacrine, "G")) plot(Jcross(amacrine)) plot(Jdot(amacrine)) plot(Jmulti(amacrine,I,J)) plot(alltypes(amacrine, "J")) plot(alltypes(amacrine, "F")) plot(Iest(amacrine)) plot(markconnect(amacrine)) ## Multitype, inhomogeneous plot(Kcross.inhom(amacrine)) plot(Kdot.inhom(amacrine)) plot(Kmulti.inhom(amacrine, I, J)) plot(Lcross.inhom(amacrine)) plot(Ldot.inhom(amacrine)) plot(pcfcross.inhom(amacrine)) plot(pcfdot.inhom(amacrine)) plot(pcfmulti.inhom(amacrine, I, J)) ## Numerical marks plot(markcorr(longleaf)) plot(markvario(longleaf)) plot(Emark(longleaf)) plot(Vmark(longleaf)) ## Linear networks plot(chicago) plot(linearK(chicago)) plot(linearKcross(chicago)) plot(linearKdot(chicago)) plot(linearpcf(chicago)) plot(linearpcfcross(chicago)) plot(linearpcfdot(chicago)) lam <- rep(intensity(unmark(chicago)), npoints(chicago)) A <- split(chicago)$assault B <- split(chicago)$burglary lamA <- rep(intensity(A), npoints(A)) lamB <- rep(intensity(B), npoints(B)) plot(linearKinhom(chicago, lam)) plot(linearKcross.inhom(chicago, "assault", "burglary", lamA, lamB)) plot(linearKdot.inhom(chicago, "assault", lamA, lam)) plot(linearpcfinhom(chicago, lam)) plot(linearpcfcross.inhom(chicago, "assault", "burglary", lamA, lamB)) plot(linearpcfdot.inhom(chicago, "assault", lamA, lam)) plot(linearmarkconnect(chicago)) plot(linearmarkequal(chicago)) rm(I,J,fit) par(opa) spatstat/demo/00Index0000755000176200001440000000032414243060071014172 0ustar liggesusersspatstat Demonstration of spatstat library diagnose Demonstration of diagnostic capabilities for models in spatstat data Datasets in spatstat sumfun Demonstration of nonparametric summary functions in spatstat spatstat/demo/diagnose.R0000644000176200001440000001230014243060071014746 0ustar liggesusersif(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && (.Device %in% c("X11", "GTK", "windows", "Macintosh"))) par(mfrow=c(1,1)) oldoptions <- options(warn = -1) # ####################################################### # X <- rpoispp(function(x,y) { 1000 * exp(- 4 * x)}, 1000) plot(X, main="Inhomogeneous Poisson pattern") fit.hom <- ppm(X ~1, Poisson()) fit.inhom <- ppm(X ~x, Poisson()) diagnose.ppm(fit.inhom, which="marks", type="Pearson", main=c("Mark plot", "Circles for positive residual mass", "Colour for negative residual density")) par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="marks", main=c("Wrong model", "(homogeneous Poisson)", "raw residuals")) diagnose.ppm(fit.inhom, which="marks", main=c("Right model", "(inhomogeneous Poisson)", "raw residuals")) par(mfrow=c(1,1)) diagnose.ppm(fit.inhom, which="smooth", main="Smoothed residual field") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="smooth", main=c("Wrong model", "(homogeneous Poisson)", "Smoothed residual field")) diagnose.ppm(fit.inhom, which="smooth", main=c("Right model", "(inhomogeneous Poisson)", "Smoothed residual field")) par(mfrow=c(1,1)) diagnose.ppm(fit.inhom, which="x") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="x", main=c("Wrong model", "(homogeneous Poisson)", "lurking variable plot for x")) diagnose.ppm(fit.inhom, which="x", main=c("Right model", "(inhomogeneous Poisson)", "lurking variable plot for x")) par(mfrow=c(1,1)) diagnose.ppm(fit.hom, type="Pearson",main="standard diagnostic plots") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, main=c("Wrong model", "(homogeneous Poisson)")) diagnose.ppm(fit.inhom, main=c("Right model", "(inhomogeneous Poisson)")) par(mfrow=c(1,1)) # ####################################################### # LEVERAGE/INFLUENCE plot(leverage(fit.inhom)) plot(influence(fit.inhom)) plot(dfbetas(fit.inhom)) # ####################################################### # COMPENSATORS ## Takes a long time... CF <- compareFit(listof(hom=fit.hom, inhom=fit.inhom), Kcom, same="iso", different="icom") plot(CF, main="model compensators", legend=FALSE) legend("topleft", legend=c("empirical K function", "compensator of CSR", "compensator of inhomogeneous Poisson"), lty=1:3, col=1:3) # ####################################################### # Q - Q PLOTS # qqplot.ppm(fit.hom, 40) #conclusion: homogeneous Poisson model is not correct title(main="Q-Q plot of smoothed residuals") qqplot.ppm(fit.inhom, 40) # TAKES A WHILE... title(main=c("Right model", "(inhomogeneous Poisson)", "Q-Q plot of smoothed residuals")) # conclusion: fitted inhomogeneous Poisson model looks OK # ####################################################### # plot(cells) fitPoisson <- ppm(cells ~1, Poisson()) diagnose.ppm(fitPoisson, main=c("CSR fitted to cells data", "Raw residuals", "No suggestion of departure from CSR")) diagnose.ppm(fitPoisson, type="pearson", main=c("CSR fitted to cells data", "Pearson residuals", "No suggestion of departure from CSR")) # These diagnostic plots do NOT show evidence of departure from uniform Poisson plot(Kcom(fitPoisson), cbind(iso, icom) ~ r) plot(Gcom(fitPoisson), cbind(han, hcom) ~ r) # K compensator DOES show strong evidence of departure from uniform Poisson qqplot.ppm(fitPoisson, 40) title(main=c("CSR fitted to cells data", "Q-Q plot of smoothed raw residuals", "Strong suggestion of departure from CSR")) # Q-Q plot DOES show strong evidence of departure from uniform Poisson. # fitStrauss <- ppm(cells ~1, Strauss(r=0.1)) diagnose.ppm(fitStrauss, main=c("Strauss model fitted to cells data", "Raw residuals")) diagnose.ppm(fitStrauss, type="pearson", main=c("Strauss model fitted to cells data", "Pearson residuals")) plot(Kcom(fitStrauss), cbind(iso, icom) ~ r) plot(Gcom(fitStrauss), cbind(han, hcom) ~ r) # next line takes a LOOONG time ... qqplot.ppm(fitStrauss, 40, type="pearson") title(main=c("Strauss model fitted to cells data", "Q-Q plot of smoothed Pearson residuals", "Suggests adequate fit")) # Conclusion: Strauss model seems OK # ####################################################### # plot(nztrees) fit <- ppm(nztrees ~1, Poisson()) diagnose.ppm(fit, type="pearson") title(main=c("CSR fitted to NZ trees", "Pearson residuals")) diagnose.ppm(fit, type="pearson", cumulative=FALSE) title(main=c("CSR fitted to NZ trees", "Pearson residuals (non-cumulative)")) lurking(fit, expression(x), type="pearson", cumulative=FALSE, splineargs=list(spar=0.3)) # Sharp peak at right is suspicious qqplot.ppm(fit, 40, type="pearson") title(main=c("CSR fitted to NZ trees", "Q-Q plot of smoothed Pearson residuals")) # Slight suggestion of departure from Poisson at top right of pattern. par(oldpar) options(oldoptions) spatstat/man/0000755000176200001440000000000014243357531012677 5ustar liggesusersspatstat/man/foo.Rd0000644000176200001440000000237314243357531013756 0ustar liggesusers\name{foo} \alias{foo} \alias{plot.foo} \title{ Foo is Not a Real Name } \description{ The name \code{foo} is not a real name: it is a place holder, used to represent the name of any desired thing. The functions defined here simply print an explanation of the placeholder name \code{foo}. } \usage{ foo() \method{plot}{foo}(x, \dots) } \arguments{ \item{x}{Ignored.} \item{\dots}{Ignored.} } \details{ The name \code{foo} is used by computer scientists as a \emph{place holder}, to represent the name of any desired object or function. It is not the name of an actual object or function; it serves only as an example, to explain a concept. However, many users misinterpret this convention, and actually type the command \code{foo} or \code{foo()}. Then they email the package author to inform them that \code{foo} is not defined. To avoid this correspondence, we have now defined an object called \code{foo}. The function \code{foo()} prints a message explaining that \code{foo} is not really the name of a variable. The function can be executed simply by typing \code{foo} without parentheses. } \value{ Null. } \author{ \spatstatAuthors. } \seealso{ \code{\link{beginner}} } \examples{ foo } \keyword{documentation} spatstat/man/spatstat.family.Rd0000644000176200001440000000275014243357531016315 0ustar liggesusers\name{spatstat.family} \alias{spatstat.family} \title{Names of All Packages in the Spatstat Family} \description{ Provides the names of all the packages belonging to the \pkg{spatstat} family of packages. } \usage{ spatstat.family(subpackages=TRUE, extensions=FALSE) } \arguments{ \item{subpackages}{ Logical value specifying whether to include sub-packages. } \item{extensions}{ Logical value specifying whether to include extension packages. } } \value{ Character vector of package names. } \details{ This function returns a character vector containing the names of the packages that belong to the \pkg{spatstat} family. By default, only the sub-packages are listed, and not the extension packages. A \dQuote{sub-package} is a package which is implicitly loaded or imported when the command \code{library(spatstat)} is issued. Currently the sub-packages are: \itemize{ \item \code{spatstat.utils} \item \code{spatstat.data} \item \code{spatstat.sparse} \item \code{spatstat.geom} \item \code{spatstat.random} \item \code{spatstat.explore} \item \code{spatstat.model} \item \code{spatstat.linnet} \item \code{spatstat} } An \dQuote{extension package} is a package which must be loaded explicitly. The extension packages are: \itemize{ \item \code{spatstat.gui} \item \code{spatstat.local} \item \code{spatstat.Knet} } } \seealso{ \code{\link{latest.news}} } \author{ \spatstatAuthors. } \keyword{spatial} spatstat/man/bugfixes.Rd0000644000176200001440000000544114252774775015023 0ustar liggesusers\name{bugfixes} \alias{bugfixes} \title{ List Recent Bug Fixes } \description{ List all bug fixes in a package, starting from a certain date or version of the package. Fixes are sorted alphabetically by the name of the affected function. The default is to list bug fixes in the latest version of the \pkg{spatstat} family of packages. } \usage{ bugfixes(sinceversion = NULL, sincedate = NULL, package = spatstat.family(), show = TRUE) } \arguments{ \item{sinceversion}{ Earliest version of \code{package} for which bugs should be listed. The default is the current installed version. } \item{sincedate}{ Earliest release date of \code{package} for which bugs should be listed. A character string or a date-time object. } \item{package}{ The name of the package (or packages) for which bugs are to be listed. A character string or character vector. } \item{show}{ Logical value indicating whether to display the bug table on the terminal. } } \details{ Bug reports are extracted from the NEWS file of the specified \code{package}. Only those after a specified date, or after a specified version of the package, are retained. The bug reports are then sorted alphabetically, so that all bugs affecting a particular function are listed consecutively. Finally the table of bug reports is displayed (if \code{show=TRUE}) and returned invisibly. The argument \code{sinceversion} should be a character string like \code{"1.2-3"}. The default is the current installed version of the package. The argument \code{sincedate} should be a character string like \code{"2015-05-27"}, or a date-time object. If \code{sinceversion="all"} or \code{sincedate="all"} then all recorded bugs will be listed. The special options \code{sinceversion="book"} and \code{sincedate="book"} are interpreted to mean \code{sincedate="2015-06-05"}, which gives all bugs reported after publication of the book by Baddeley, Rubak and Turner (2015). Typing \code{bugfixes} without parentheses will display a table of all bugs that were fixed in the current installed version of \pkg{spatstat} and its sub-packages. By default, bugs in the \emph{extension} packages \pkg{spatstat.local}, \pkg{spatstat.Knet}, \pkg{spatstat.gui} are \emph{not} reported. To include these bugs as well, set \code{package=spatstat.family(TRUE,TRUE)}. } \value{ (Invisibly) a data frame, belonging to the class \code{"bugtable"}, which has a \code{print} method. } \author{ \adrian. } \seealso{ \code{\link{latest.news}}, \code{\link[utils]{news}}. } \references{ \baddrubaturnbook } \examples{ bugfixes ## show all bugs reported after publication of the spatstat book if(interactive()) bugfixes(sinceversion="book") } \keyword{documentation} spatstat/man/macros/0000755000176200001440000000000014243357531014163 5ustar liggesusersspatstat/man/macros/defns.Rd0000755000176200001440000001076314510477266015570 0ustar liggesusers%% macro definitions for spatstat man pages %% Authors \newcommand{\adrian}{Adrian Baddeley \email{Adrian.Baddeley@curtin.edu.au}} \newcommand{\rolf}{Rolf Turner \email{rolfturner@posteo.net}} \newcommand{\ege}{Ege Rubak \email{rubak@math.aau.dk}} \newcommand{\spatstatAuthors}{\adrian, \rolf and \ege} \newcommand{\spatstatAuthorsComma}{\adrian, \rolf, \ege} %% Contributors with emails \newcommand{\pavel}{Pavel Grabarnik \email{pavel.grabar@issp.serpukhov.su}} \newcommand{\dominic}{Dominic Schuhmacher \email{dominic.schuhmacher@mathematik.uni-goettingen.de}, URL \code{http://dominic.schuhmacher.name/}} \newcommand{\wei}{Ang Qi Wei \email{aqw07398@hotmail.com}} \newcommand{\colette}{Marie-Colette van Lieshout \email{Marie-Colette.van.Lieshout@cwi.nl}} \newcommand{\rasmus}{Rasmus Plenge Waagepetersen \email{rw@math.auc.dk}} \newcommand{\abdollah}{Abdollah Jalilian \email{jalilian@razi.ac.ir}} \newcommand{\ottmar}{Ottmar Cronie \email{ottmar@chalmers.se}} \newcommand{\stephenEglen}{Stephen Eglen \email{S.J.Eglen@damtp.cam.ac.uk}} \newcommand{\mehdi}{Mehdi Moradi \email{m2.moradi@yahoo.com}} \newcommand{\yamei}{Ya-Mei Chang \email{yamei628@gmail.com}} \newcommand{\martinH}{Martin Hazelton \email{Martin.Hazelton@otago.ac.nz}} \newcommand{\tilman}{Tilman Davies \email{Tilman.Davies@otago.ac.nz}} % Names with accents \newcommand{\Bogsted}{\ifelse{latex}{\out{B\o gsted}}{Bogsted}} \newcommand{\Cramer}{\ifelse{latex}{\out{Cram\'er}}{Cramer}} \newcommand{\Francois}{\ifelse{latex}{\out{Fran\c{c}ois}}{Francois}} \newcommand{\Frederic}{\ifelse{latex}{\out{Fr{\'e}d{\'e}ric}}{Frederic}} \newcommand{\Hogmander}{\ifelse{latex}{\out{H{\"o}gmander}}{Hogmander}} \newcommand{\Jyvaskyla}{\ifelse{latex}{\out{Jyv\"askyl\"a}}{Jyvaskyla}} \newcommand{\Lucia}{\ifelse{latex}{\out{Luc\'{\i{}}a}}{Lucia}} \newcommand{\Matern}{\ifelse{latex}{\out{Mat\'ern}}{Matern}} \newcommand{\Moller}{\ifelse{latex}{\out{M\o ller}}{Moller}} \newcommand{\Oehlschlaegel}{\ifelse{latex}{\out{Oehlschl\"{a}gel}}{Oehlschlaegel}} \newcommand{\Prokesova}{\ifelse{latex}{\out{Proke\u{s}ov{\'{a}}}}{Prokesova}} \newcommand{\Sarkka}{\ifelse{latex}{\out{S\"{a}rkk\"{a}}}{Sarkka}} \newcommand{\Sanchez}{\ifelse{latex}{\out{S\'{a}nchez}}{Sanchez}} \newcommand{\Martin}{\ifelse{latex}{\out{Mart\'{\i}n}}{Martin}} \newcommand{\Dominguez}{\ifelse{latex}{\out{Dom\'{\i}nguez}}{Dominguez}} \newcommand{\Rodriguez}{\ifelse{latex}{\out{Rodr\'{\i}guez}}{Rodriguez}} \newcommand{\Gonzalez}{\ifelse{latex}{\out{Gonz\'{a}lez}}{Gonzalez}} %% List of all Gibbs interactions \newcommand{\GibbsInteractionsList}{\code{\link[MPKG]{AreaInter}}, \code{\link[MPKG]{BadGey}}, \code{\link[MPKG]{Concom}}, \code{\link[MPKG]{DiggleGatesStibbard}}, \code{\link[MPKG]{DiggleGratton}}, \code{\link[MPKG]{Fiksel}}, \code{\link[MPKG]{Geyer}}, \code{\link[MPKG]{Hardcore}}, \code{\link[MPKG]{HierHard}}, \code{\link[MPKG]{HierStrauss}}, \code{\link[MPKG]{HierStraussHard}}, \code{\link[MPKG]{Hybrid}}, \code{\link[MPKG]{LennardJones}}, \code{\link[MPKG]{MultiHard}}, \code{\link[MPKG]{MultiStrauss}}, \code{\link[MPKG]{MultiStraussHard}}, \code{\link[MPKG]{OrdThresh}}, \code{\link[MPKG]{Ord}}, \code{\link[MPKG]{Pairwise}}, \code{\link[MPKG]{PairPiece}}, \code{\link[MPKG]{Penttinen}}, \code{\link[MPKG]{Poisson}}, \code{\link[MPKG]{Saturated}}, \code{\link[MPKG]{SatPiece}}, \code{\link[MPKG]{Softcore}}, \code{\link[MPKG]{Strauss}}, \code{\link[MPKG]{StraussHard}} and \code{\link[MPKG]{Triplets}}} %% List of interactions recognised by RMH code \newcommand{\rmhInteractionsList}{\code{\link[MPKG]{AreaInter}}, \code{\link[MPKG]{BadGey}}, \code{\link[MPKG]{DiggleGatesStibbard}}, \code{\link[MPKG]{DiggleGratton}}, \code{\link[MPKG]{Fiksel}}, \code{\link[MPKG]{Geyer}}, \code{\link[MPKG]{Hardcore}}, \code{\link[MPKG]{Hybrid}}, \code{\link[MPKG]{LennardJones}}, \code{\link[MPKG]{MultiStrauss}}, \code{\link[MPKG]{MultiStraussHard}}, \code{\link[MPKG]{PairPiece}}, \code{\link[MPKG]{Penttinen}}, \code{\link[MPKG]{Poisson}}, \code{\link[MPKG]{Softcore}}, \code{\link[MPKG]{Strauss}}, \code{\link[MPKG]{StraussHard}} and \code{\link[MPKG]{Triplets}}} %% Frequent references \newcommand{\baddrubaturnbook}{Baddeley, A., Rubak, E. and Turner, R. (2015) \emph{Spatial Point Patterns: Methodology and Applications with R}. Chapman and Hall/CRC Press. } %% Citations of recent articles that will change rapidly \newcommand{\baddchangclustersim}{Baddeley, A. and Chang, Y.-M. (2023) Robust algorithms for simulating cluster point processes. \emph{Journal of Statistical Computation and Simulation}. In Press. DOI \code{10.1080/00949655.2023.2166045}.} spatstat/man/spatstat-internal.Rd0000644000176200001440000000110314514653072016636 0ustar liggesusers\name{spatstat-internal} \title{Internal spatstat functions} \alias{print.autoexec} \alias{print.bugtable} %%%%%%% \description{ Internal spatstat functions. } \usage{ \method{print}{autoexec}(x, \dots) \method{print}{bugtable}(x, \dots) } \details{ These internal \pkg{spatstat} functions should not be called directly by the user. Their names and capabilities may change without warning from one version of \pkg{spatstat} to the next. } \value{ The return values of these functions are not documented, and may change without warning. } \keyword{internal} spatstat/man/latest.news.Rd0000644000176200001440000000417514243357531015444 0ustar liggesusers\name{latest.news} \alias{latest.news} \title{ Print News About Latest Version of Package } \description{ Prints the news documentation for the current version of \code{spatstat} or another specified package. } \usage{ latest.news(package = spatstat.family(), doBrowse=FALSE, major=TRUE) } \arguments{ \item{package}{ Name of package for which the latest news should be printed. A character string, or vector of character strings. } \item{doBrowse}{ Logical value indicating whether to display the results in a browser window instead of printing them. } \item{major}{ Logical value. If \code{TRUE} (the default), print all information for the current major version \code{"x.y"}. If \code{FALSE}, print only the information for the current minor version \code{"x.y-z"}. } } \details{ This function prints the news documentation about changes in the current installed version of a package. By default, it prints the latest news about all the sub-packages in the \pkg{spatstat} family. The function can be called simply by typing its name without parentheses (see the Examples). If \code{major=FALSE}, only information for the current minor version \code{"x.y-z"} will be printed. If \code{major=TRUE} (the default), all information for the current major version \code{"x.y"} will be printed, encompassing versions \code{"x.y-0"}, \code{"x.y-1"}, up to \code{"x.y-z"}. If \code{package} is given, then the function reads the news for the specified package from its \code{NEWS} file (if it has one) and prints only the entries that refer to the current version of the package. To see the news for all previous versions as well as the current version, use the \R utility \code{\link[utils]{news}}. See the Examples. } \value{ Null. } \author{ \spatstatAuthors. } \seealso{ \code{\link{spatstat.family}} lists the packages in the \pkg{spatstat} family. \code{\link{bugfixes}} lists bug fixes. \code{\link[utils]{news}} } \examples{ if(interactive()) { # current news latest.news # all news # news(package="spatstat") } } \keyword{documentation} spatstat/man/beginner.Rd0000644000176200001440000000142414243357531014760 0ustar liggesusers\name{beginner} \alias{beginner} \title{ Print Introduction For Beginners } \description{ Prints an introduction for beginners to the \code{spatstat} package, or another specified package. } \usage{ beginner(package = "spatstat") } \arguments{ \item{package}{ Name of package. } } \details{ This function prints an introduction for beginners to the \pkg{spatstat} package. The function can be executed simply by typing \code{beginner} without parentheses. If the argument \code{package} is given, then the function prints the beginner's help file \code{BEGINNER.txt} from the specified package (if it has one). } \value{ Null. } \author{\adrian and \rolf } \seealso{ \code{\link{latest.news}} } \examples{ beginner } \keyword{documentation} spatstat/man/spatstat-package.Rd0000644000176200001440000027667314514653072016447 0ustar liggesusers\name{spatstat-package} \alias{spatstat-package} \alias{spatstat} \docType{package} \title{The Spatstat Package} \description{ This is a summary of the features of \pkg{spatstat}, a family of \R packages for the statistical analysis of spatial point patterns. } \details{ \pkg{spatstat} is a family of \R packages for the statistical analysis of spatial data. Its main focus is the analysis of spatial patterns of points in two-dimensional space. \pkg{spatstat} is designed to support a complete statistical analysis of spatial data. It supports \itemize{ \item creation, manipulation and plotting of point patterns; \item exploratory data analysis; \item spatial random sampling; \item simulation of point process models; \item parametric model-fitting; \item non-parametric smoothing and regression; \item formal inference (hypothesis tests, confidence intervals); \item model diagnostics. } Apart from two-dimensional point patterns and point processes, \pkg{spatstat} also supports point patterns in three dimensions, point patterns in multidimensional space-time, point patterns on a linear network, patterns of line segments in two dimensions, and spatial tessellations and random sets in two dimensions. The package can fit several types of point process models to a point pattern dataset: \itemize{ \item Poisson point process models (by Berman-Turner approximate maximum likelihood or by spatial logistic regression) \item Gibbs/Markov point process models (by Baddeley-Turner approximate maximum pseudolikelihood, Coeurjolly-Rubak logistic likelihood, or Huang-Ogata approximate maximum likelihood) \item Cox/cluster point process models (by Waagepetersen's two-step fitting procedure and minimum contrast, composite likelihood, or Palm likelihood) \item determinantal point process models (by Waagepetersen's two-step fitting procedure and minimum contrast, composite likelihood, or Palm likelihood) } The models may include spatial trend, dependence on covariates, and complicated interpoint interactions. Models are specified by a \code{formula} in the \R language, and are fitted using a function analogous to \code{\link[stats]{lm}} and \code{\link[stats]{glm}}. Fitted models can be printed, plotted, predicted, simulated and so on. } \section{Getting Started}{ For a quick introduction to \pkg{spatstat}, read the package vignette \emph{Getting started with spatstat} installed with \pkg{spatstat}. To read that document, you can either \itemize{ \item visit \url{https://cran.r-project.org/package=spatstat} and click on \code{Getting Started with Spatstat} \item start \R, type \code{library(spatstat)} and \code{vignette('getstart')} \item start \R, type \code{help.start()} to open the help browser, and navigate to \code{Packages > spatstat > Vignettes}. } Once you have installed \pkg{spatstat}, start \R and type \code{library(spatstat)}. Then type \code{beginner} for a beginner's introduction, or \code{demo(spatstat)} for a demonstration of the package's capabilities. For a complete course on \pkg{spatstat}, and on statistical analysis of spatial point patterns, read the book by Baddeley, Rubak and Turner (2015). Other recommended books on spatial point process methods are Diggle (2014), Gelfand et al (2010) and Illian et al (2008). The \pkg{spatstat} package includes over 50 datasets, which can be useful when learning the package. Type \code{demo(data)} to see plots of all datasets available in the package. Type \code{vignette('datasets')} for detailed background information on these datasets, and plots of each dataset. For information on converting your data into \pkg{spatstat} format, read Chapter 3 of Baddeley, Rubak and Turner (2015). This chapter is available free online, as one of the sample chapters at the book companion website, \url{https://book.spatstat.org/}. % For information about handling data in \bold{shapefiles}, % see Chapter 3, or the Vignette % \emph{Handling shapefiles in the spatstat package}, % installed with \pkg{spatstat}, accessible as % \code{vignette('shapefiles')}. } \section{Structure of the spatstat family}{ The original \pkg{spatstat} package grew to be very large. It has now been divided into several \bold{sub-packages}: \itemize{ \item \pkg{spatstat.utils} containing basic utilities \item \pkg{spatstat.sparse} containing linear algebra utilities \item \pkg{spatstat.data} containing datasets \item \pkg{spatstat.geom} containing functionality for geometrical operations, and defining the main classes of spatial objects \item \pkg{spatstat.explore} containing the main functions for exploratory analysis of spatial data \item \pkg{spatstat.model} containing the main functions for parametric statistical modelling and analysis, and formal inference, for spatial data \item \pkg{spatstat.linnet} containing functions for spatial data on a linear network \item \pkg{spatstat}, which simply loads the other sub-packages listed above, and provides documentation. } The breakup has been done in such a way that the user should not notice any difference. Source code that worked with the old \pkg{spatstat} package should work with the new \pkg{spatstat} family. Code that is documented in our books, journal articles and vignettes should still work. When you install \pkg{spatstat}, the sub-packages listed above are also installed. Then if you load the \pkg{spatstat} package by typing \code{library(spatstat)}, the other sub-packages listed above will automatically be loaded or imported. This help file covers all the functionality and datasets that are provided in the sub-packages listed above. } \section{Extension packages}{ Additionally there are several \bold{extension packages:} \itemize{ \item \pkg{spatstat.gui} for interactive graphics \item \pkg{spatstat.local} for local likelihood (including geographically weighted regression) \item \pkg{spatstat.Knet} for additional, computationally efficient code for linear networks \item \pkg{spatstat.sphere} (under development) for spatial data on a sphere, including spatial data on the earth's surface } The extension packages must be installed separately and loaded explicitly if needed. They also have separate documentation. } \section{Updates}{ New versions of \pkg{spatstat} are released every 8 weeks. Users are advised to update their installation of \pkg{spatstat} regularly. Type \code{latest.news} to read the news documentation about changes to the current installed version of \pkg{spatstat}. See the Vignette \emph{Summary of recent updates}, installed with \pkg{spatstat}, which describes the main changes to \pkg{spatstat} since the book (Baddeley, Rubak and Turner, 2015) was published. It is accessible as \code{vignette('updates')}. Type \code{news(package="spatstat")} to read news documentation about all previous versions of the package. } \section{FUNCTIONS AND DATASETS}{ Following is a summary of the main functions and datasets in the \pkg{spatstat} package. Alternatively an alphabetical list of all functions and datasets is available by typing \code{library(help=spatstat)}. For further information on any of these, type \code{help(name)} or \code{?name} where \code{name} is the name of the function or dataset. } \section{CONTENTS:}{ \tabular{ll}{ I. \tab Creating and manipulating data \cr II. \tab Exploratory Data Analysis \cr III. \tab Model fitting (Cox and cluster models) \cr IV. \tab Model fitting (Poisson and Gibbs models) \cr V. \tab Model fitting (determinantal point processes)\cr VI. \tab Model fitting (spatial logistic regression)\cr VII. \tab Simulation \cr VIII. \tab Tests and diagnostics\cr IX. \tab Documentation } } \section{I. CREATING AND MANIPULATING DATA}{ \bold{Types of spatial data:} The main types of spatial data supported by \pkg{spatstat} are: \tabular{ll}{ \code{\link[spatstat.geom]{ppp}} \tab point pattern \cr \code{\link[spatstat.geom]{owin}} \tab window (spatial region) \cr \code{\link[spatstat.geom]{im}} \tab pixel image \cr \code{\link[spatstat.geom]{psp}} \tab line segment pattern \cr \code{\link[spatstat.geom]{tess}} \tab tessellation \cr \code{\link[spatstat.geom]{pp3}} \tab three-dimensional point pattern \cr \code{\link[spatstat.geom]{ppx}} \tab point pattern in any number of dimensions \cr \code{\link[spatstat.linnet]{lpp}} \tab point pattern on a linear network } \bold{To create a point pattern:} \tabular{ll}{ \code{\link[spatstat.geom]{ppp}} \tab create a point pattern from \eqn{(x,y)} and window information \cr \tab \code{ppp(x, y, xlim, ylim)} for rectangular window\cr \tab \code{ppp(x, y, poly)} for polygonal window \cr \tab \code{ppp(x, y, mask)} for binary image window \cr \code{\link[spatstat.geom]{as.ppp}} \tab convert other types of data to a \code{ppp} object \cr \code{\link[spatstat.geom]{clickppp}} \tab interactively add points to a plot \cr \code{\link[spatstat.geom]{marks<-}}, \code{\%mark\%} \tab attach/reassign marks to a point pattern } \bold{To simulate a random point pattern:} \tabular{ll}{ \code{\link[spatstat.random]{runifpoint}} \tab generate \eqn{n} independent uniform random points \cr \code{\link[spatstat.random]{rpoint}} \tab generate \eqn{n} independent random points \cr \code{\link[spatstat.random]{rmpoint}} \tab generate \eqn{n} independent multitype random points \cr \code{\link[spatstat.random]{rpoispp}} \tab simulate the (in)homogeneous Poisson point process \cr \code{\link[spatstat.random]{rmpoispp}} \tab simulate the (in)homogeneous multitype Poisson point process \cr \code{\link[spatstat.random]{runifdisc}} \tab generate \eqn{n} independent uniform random points in disc\cr \code{\link[spatstat.random]{rstrat}} \tab stratified random sample of points \cr \code{\link[spatstat.geom]{rsyst}} \tab systematic random sample of points \cr \code{\link[spatstat.geom]{rjitter}} \tab apply random displacements to points in a pattern\cr \code{\link[spatstat.random]{rMaternI}} \tab simulate the \Matern Model I inhibition process\cr \code{\link[spatstat.random]{rMaternII}} \tab simulate the \Matern Model II inhibition process\cr \code{\link[spatstat.random]{rSSI}} \tab simulate Simple Sequential Inhibition process\cr \code{\link[spatstat.random]{rStrauss}} \tab simulate Strauss process (perfect simulation)\cr \code{\link[spatstat.random]{rHardcore}} \tab simulate Hard Core process (perfect simulation)\cr \code{\link[spatstat.random]{rStraussHard}} \tab simulate Strauss-hard core process (perfect simulation)\cr \code{\link[spatstat.random]{rDiggleGratton}} \tab simulate Diggle-Gratton process (perfect simulation)\cr \code{\link[spatstat.random]{rDGS}} \tab simulate Diggle-Gates-Stibbard process (perfect simulation)\cr \code{\link[spatstat.random]{rPenttinen}} \tab simulate Penttinen process (perfect simulation)\cr \code{\link[spatstat.random]{rNeymanScott}} \tab simulate a general Neyman-Scott process\cr \code{\link[spatstat.random]{rPoissonCluster}} \tab simulate a general Poisson cluster process\cr \code{\link[spatstat.random]{rMatClust}} \tab simulate the \Matern Cluster process\cr \code{\link[spatstat.random]{rThomas}} \tab simulate the Thomas process \cr \code{\link[spatstat.random]{rGaussPoisson}} \tab simulate the Gauss-Poisson cluster process\cr \code{\link[spatstat.random]{rCauchy}} \tab simulate Neyman-Scott Cauchy cluster process \cr \code{\link[spatstat.random]{rVarGamma}} \tab simulate Neyman-Scott Variance Gamma cluster process \cr \code{\link[spatstat.random]{rthin}} \tab random thinning \cr \code{\link[spatstat.random]{rcell}} \tab simulate the Baddeley-Silverman cell process \cr \code{\link[spatstat.random]{rmh}} \tab simulate Gibbs point process using Metropolis-Hastings \cr \code{\link[spatstat.model]{simulate.ppm}} \tab simulate Gibbs point process using Metropolis-Hastings \cr \code{\link[spatstat.random]{runifpointOnLines}} \tab generate \eqn{n} random points along specified line segments \cr \code{\link[spatstat.random]{rpoisppOnLines}} \tab generate Poisson random points along specified line segments } \bold{To randomly change an existing point pattern:} \tabular{ll}{ \code{\link[spatstat.random]{rshift}} \tab random shifting of points \cr \code{\link[spatstat.geom]{rjitter}} \tab apply random displacements to points in a pattern\cr \code{\link[spatstat.random]{rthin}} \tab random thinning \cr \code{\link[spatstat.random]{rlabel}} \tab random (re)labelling of a multitype point pattern \cr \code{\link[spatstat.random]{quadratresample}} \tab block resampling } \bold{Standard point pattern datasets:} Datasets in \pkg{spatstat} are lazy-loaded, so you can simply type the name of the dataset to use it; there is no need to type \code{\link[utils]{data}(amacrine)} etc. Type \code{demo(data)} to see a display of all the datasets installed with the package. Type \code{vignette('datasets')} for a document giving an overview of all datasets, including background information, and plots. \tabular{ll}{ \code{\link[spatstat.data]{amacrine}} \tab Austin Hughes' rabbit amacrine cells \cr \code{\link[spatstat.data]{anemones}} \tab Upton-Fingleton sea anemones data\cr \code{\link[spatstat.data]{ants}} \tab Harkness-Isham ant nests data\cr \code{\link[spatstat.data]{bdspots}} \tab Breakdown spots in microelectrodes \cr \code{\link[spatstat.data]{bei}} \tab Tropical rainforest trees \cr \code{\link[spatstat.data]{betacells}} \tab Waessle et al. cat retinal ganglia data \cr \code{\link[spatstat.data]{bramblecanes}} \tab Bramble Canes data \cr \code{\link[spatstat.data]{bronzefilter}} \tab Bronze Filter Section data \cr \code{\link[spatstat.data]{cells}} \tab Crick-Ripley biological cells data \cr \code{\link[spatstat.data]{chicago}} \tab Chicago crimes \cr \code{\link[spatstat.data]{chorley}} \tab Chorley-Ribble cancer data \cr \code{\link[spatstat.data]{clmfires}} \tab Castilla-La Mancha forest fires \cr \code{\link[spatstat.data]{copper}} \tab Berman-Huntington copper deposits data \cr \code{\link[spatstat.data]{dendrite}} \tab Dendritic spines \cr \code{\link[spatstat.data]{demohyper}} \tab Synthetic point patterns\cr \code{\link[spatstat.data]{demopat}} \tab Synthetic point pattern \cr \code{\link[spatstat.data]{finpines}} \tab Finnish Pines data \cr \code{\link[spatstat.data]{flu}} \tab Influenza virus proteins \cr \code{\link[spatstat.data]{gordon}} \tab People in Gordon Square, London \cr \code{\link[spatstat.data]{gorillas}} \tab Gorilla nest sites \cr \code{\link[spatstat.data]{hamster}} \tab Aherne's hamster tumour data \cr \code{\link[spatstat.data]{humberside}} \tab North Humberside childhood leukaemia data \cr \code{\link[spatstat.data]{hyytiala}} \tab {Mixed forest in \ifelse{latex}{\out{Hyyti{\"a}l{\"a}}}{Hyytiala}, Finland}\cr \code{\link[spatstat.data]{japanesepines}} \tab Japanese Pines data \cr \code{\link[spatstat.data]{lansing}} \tab Lansing Woods data \cr \code{\link[spatstat.data]{longleaf}} \tab Longleaf Pines data \cr \code{\link[spatstat.data]{mucosa}} \tab Cells in gastric mucosa \cr \code{\link[spatstat.data]{murchison}} \tab Murchison gold deposits \cr \code{\link[spatstat.data]{nbfires}} \tab New Brunswick fires data \cr \code{\link[spatstat.data]{nztrees}} \tab Mark-Esler-Ripley trees data \cr \code{\link[spatstat.data]{osteo}} \tab Osteocyte lacunae (3D, replicated) \cr \code{\link[spatstat.data]{paracou}} \tab Kimboto trees in Paracou, French Guiana \cr \code{\link[spatstat.data]{ponderosa}} \tab Getis-Franklin ponderosa pine trees data \cr \code{\link[spatstat.data]{pyramidal}} \tab Pyramidal neurons from 31 brains \cr \code{\link[spatstat.data]{redwood}} \tab Strauss-Ripley redwood saplings data \cr \code{\link[spatstat.data]{redwoodfull}} \tab Strauss redwood saplings data (full set) \cr \code{\link[spatstat.data]{residualspaper}} \tab Data from Baddeley et al (2005) \cr \code{\link[spatstat.data]{shapley}} \tab Galaxies in an astronomical survey \cr \code{\link[spatstat.data]{simdat}} \tab Simulated point pattern (inhomogeneous, with interaction) \cr \code{\link[spatstat.data]{spiders}} \tab Spider webs on mortar lines of brick wall \cr \code{\link[spatstat.data]{sporophores}} \tab Mycorrhizal fungi around a tree \cr \code{\link[spatstat.data]{spruces}} \tab Spruce trees in Saxonia \cr \code{\link[spatstat.data]{swedishpines}} \tab Strand-Ripley Swedish pines data \cr \code{\link[spatstat.data]{urkiola}} \tab Urkiola Woods data \cr \code{\link[spatstat.data]{waka}} \tab Trees in Waka national park \cr \code{\link[spatstat.data]{waterstriders}} \tab Insects on water surface } \bold{To manipulate a point pattern:} \tabular{ll}{ \code{\link[spatstat.geom]{plot.ppp}} \tab plot a point pattern (e.g. \code{plot(X)}) \cr \code{spatstat.gui::iplot} \tab plot a point pattern interactively \cr \code{\link[spatstat.geom]{edit.ppp}} \tab interactive text editor \cr \code{\link[spatstat.geom]{[.ppp}} \tab extract or replace a subset of a point pattern \cr \tab \code{pp[subset]} or \code{pp[subwindow]} \cr \code{\link[spatstat.geom]{subset.ppp}} \tab extract subset of point pattern satisfying a condition \cr \code{\link[spatstat.geom]{superimpose}} \tab combine several point patterns \cr \code{\link[spatstat.geom]{by.ppp}} \tab apply a function to sub-patterns of a point pattern \cr \code{\link[spatstat.geom]{cut.ppp}} \tab classify the points in a point pattern \cr \code{\link[spatstat.geom]{split.ppp}} \tab divide pattern into sub-patterns \cr \code{\link[spatstat.geom]{unmark}} \tab remove marks \cr \code{\link[spatstat.geom]{npoints}} \tab count the number of points \cr \code{\link[spatstat.geom]{coords}} \tab extract coordinates, change coordinates \cr \code{\link[spatstat.geom]{marks}} \tab extract marks, change marks or attach marks \cr \code{\link[spatstat.geom]{rotate}} \tab rotate pattern \cr \code{\link[spatstat.geom]{shift} } \tab translate pattern \cr \code{\link[spatstat.geom]{flipxy} } \tab swap \eqn{x} and \eqn{y} coordinates \cr \code{\link[spatstat.geom]{reflect} } \tab reflect in the origin \cr \code{\link[spatstat.geom]{periodify} } \tab make several translated copies \cr \code{\link[spatstat.geom]{affine}} \tab apply affine transformation\cr \code{\link[spatstat.geom]{scalardilate}} \tab apply scalar dilation\cr \code{\link[spatstat.explore]{density.ppp}} \tab kernel estimation of point pattern intensity\cr \code{\link[spatstat.explore]{densityHeat.ppp}} \tab diffusion kernel estimation of point pattern intensity\cr \code{\link[spatstat.explore]{Smooth.ppp}} \tab kernel smoothing of marks of point pattern\cr \code{\link[spatstat.geom]{nnmark}} \tab mark value of nearest data point\cr \code{\link[spatstat.explore]{sharpen.ppp}} \tab data sharpening\cr \code{\link[spatstat.geom]{identify.ppp}} \tab interactively identify points \cr \code{\link[spatstat.geom]{unique.ppp}} \tab remove duplicate points \cr \code{\link[spatstat.geom]{duplicated.ppp}} \tab determine which points are duplicates \cr \code{\link[spatstat.geom]{uniquemap.ppp}} \tab map duplicated points to unique points \cr \code{\link[spatstat.geom]{connected.ppp}} \tab find clumps of points \cr \code{\link[spatstat.geom]{dirichlet}} \tab compute Dirichlet-Voronoi tessellation \cr \code{\link[spatstat.geom]{delaunay}} \tab compute Delaunay triangulation \cr \code{\link[spatstat.geom]{delaunayDistance}} \tab graph distance in Delaunay triangulation \cr \code{\link[spatstat.geom]{convexhull}} \tab compute convex hull \cr \code{\link[spatstat.geom]{discretise}} \tab discretise coordinates \cr \code{\link[spatstat.geom]{pixellate.ppp}} \tab approximate point pattern by pixel image \cr \code{\link[spatstat.geom]{as.im.ppp}} \tab approximate point pattern by pixel image } See \code{\link[spatstat.geom]{spatstat.options}} to control plotting behaviour. \bold{To create a window:} An object of class \code{"owin"} describes a spatial region (a window of observation). \tabular{ll}{ \code{\link[spatstat.geom]{owin}} \tab Create a window object \cr \tab \code{owin(xlim, ylim)} for rectangular window \cr \tab \code{owin(poly)} for polygonal window \cr \tab \code{owin(mask)} for binary image window \cr \code{\link[spatstat.geom]{Window}} \tab Extract window of another object \cr \code{\link[spatstat.geom]{Frame}} \tab Extract the containing rectangle ('frame') of another object \cr \code{\link[spatstat.geom]{as.owin}} \tab Convert other data to a window object \cr \code{\link[spatstat.geom]{square}} \tab make a square window \cr \code{\link[spatstat.geom]{disc}} \tab make a circular window \cr \code{\link[spatstat.geom]{ellipse}} \tab make an elliptical window \cr \code{\link[spatstat.geom]{ripras}} \tab Ripley-Rasson estimator of window, given only the points \cr \code{\link[spatstat.geom]{convexhull}} \tab compute convex hull of something \cr \code{\link[spatstat.data]{letterR}} \tab polygonal window in the shape of the \R logo \cr \code{\link[spatstat.geom]{clickpoly}} \tab interactively draw a polygonal window \cr \code{\link[spatstat.geom]{clickbox}} \tab interactively draw a rectangle } \bold{To manipulate a window:} \tabular{ll}{ \code{\link[spatstat.geom]{plot.owin}} \tab plot a window. \cr \tab \code{plot(W)}\cr \code{\link[spatstat.geom]{boundingbox}} \tab Find a tight bounding box for the window \cr \code{\link[spatstat.geom]{erosion}} \tab erode window by a distance r\cr \code{\link[spatstat.geom]{dilation}} \tab dilate window by a distance r\cr \code{\link[spatstat.geom]{closing}} \tab close window by a distance r\cr \code{\link[spatstat.geom]{opening}} \tab open window by a distance r\cr \code{\link[spatstat.geom]{border}} \tab difference between window and its erosion/dilation \cr \code{\link[spatstat.geom]{complement.owin}} \tab invert (swap inside and outside)\cr \code{\link[spatstat.geom]{simplify.owin}} \tab approximate a window by a simple polygon \cr \code{\link[spatstat.geom]{rotate}} \tab rotate window \cr \code{\link[spatstat.geom]{flipxy}} \tab swap \eqn{x} and \eqn{y} coordinates \cr \code{\link[spatstat.geom]{shift} } \tab translate window \cr \code{\link[spatstat.geom]{periodify} } \tab make several translated copies \cr \code{\link[spatstat.geom]{affine}} \tab apply affine transformation \cr \code{\link[spatstat.geom]{as.data.frame.owin}} \tab convert window to data frame } \bold{Digital approximations:} \tabular{ll}{ \code{\link[spatstat.geom]{as.mask}} \tab Make a discrete pixel approximation of a given window \cr \code{\link[spatstat.geom]{as.im.owin}} \tab convert window to pixel image \cr \code{\link[spatstat.geom]{pixellate.owin}} \tab convert window to pixel image \cr \code{\link[spatstat.geom]{commonGrid}} \tab find common pixel grid for windows \cr \code{\link[spatstat.geom]{nearest.raster.point}} \tab map continuous coordinates to raster locations\cr \code{\link[spatstat.geom]{raster.x}} \tab raster x coordinates \cr \code{\link[spatstat.geom]{raster.y}} \tab raster y coordinates \cr \code{\link[spatstat.geom]{raster.xy}} \tab raster x and y coordinates \cr \code{\link[spatstat.geom]{as.polygonal}} \tab convert pixel mask to polygonal window } See \code{\link[spatstat.geom]{spatstat.options}} to control the approximation \bold{Geometrical computations with windows:} \tabular{ll}{ \code{\link[spatstat.geom]{edges}} \tab extract boundary edges \cr \code{\link[spatstat.geom]{intersect.owin}} \tab intersection of two windows\cr \code{\link[spatstat.geom]{union.owin}} \tab union of two windows\cr \code{\link[spatstat.geom]{setminus.owin}} \tab set subtraction of two windows\cr \code{\link[spatstat.geom]{inside.owin}} \tab determine whether a point is inside a window\cr \code{\link[spatstat.geom]{area.owin}} \tab compute area \cr \code{\link[spatstat.geom]{perimeter}} \tab compute perimeter length \cr \code{\link[spatstat.geom]{diameter.owin}} \tab compute diameter\cr \code{\link[spatstat.geom]{incircle}} \tab find largest circle inside a window \cr \code{\link[spatstat.geom]{inradius}} \tab radius of incircle \cr \code{\link[spatstat.geom]{connected.owin}} \tab find connected components of window \cr \code{\link[spatstat.geom]{eroded.areas}} \tab compute areas of eroded windows\cr \code{\link[spatstat.geom]{dilated.areas}} \tab compute areas of dilated windows\cr \code{\link[spatstat.geom]{bdist.points}} \tab compute distances from data points to window boundary \cr \code{\link[spatstat.geom]{bdist.pixels}} \tab compute distances from all pixels to window boundary \cr \code{\link[spatstat.geom]{bdist.tiles}} \tab boundary distance for each tile in tessellation \cr \code{\link[spatstat.geom]{distmap.owin}} \tab distance transform image \cr \code{\link[spatstat.geom]{distfun.owin}} \tab distance transform \cr \code{\link[spatstat.geom]{centroid.owin}} \tab compute centroid (centre of mass) of window\cr \code{\link[spatstat.geom]{is.subset.owin}} \tab determine whether one window contains another \cr \code{\link[spatstat.geom]{is.convex}} \tab determine whether a window is convex \cr \code{\link[spatstat.geom]{convexhull}} \tab compute convex hull \cr \code{\link[spatstat.geom]{triangulate.owin}} \tab decompose into triangles \cr \code{\link[spatstat.geom]{as.mask}} \tab pixel approximation of window \cr \code{\link[spatstat.geom]{as.polygonal}} \tab polygonal approximation of window \cr \code{\link[spatstat.geom]{is.rectangle}} \tab test whether window is a rectangle \cr \code{\link[spatstat.geom]{is.polygonal}} \tab test whether window is polygonal \cr \code{\link[spatstat.geom]{is.mask}} \tab test whether window is a mask \cr \code{\link[spatstat.geom]{setcov}} \tab spatial covariance function of window \cr \code{\link[spatstat.geom]{pixelcentres}} \tab extract centres of pixels in mask \cr \code{\link[spatstat.geom]{clickdist}} \tab measure distance between two points clicked by user } \bold{Pixel images:} An object of class \code{"im"} represents a pixel image. Such objects are returned by some of the functions in \pkg{spatstat} including \code{\link[spatstat.explore]{Kmeasure}}, \code{\link[spatstat.geom]{setcov}} and \code{\link[spatstat.explore]{density.ppp}}. \tabular{ll}{ \code{\link[spatstat.geom]{im}} \tab create a pixel image\cr \code{\link[spatstat.geom]{as.im}} \tab convert other data to a pixel image\cr \code{\link[spatstat.geom]{pixellate}} \tab convert other data to a pixel image\cr \code{\link[spatstat.geom]{as.matrix.im}} \tab convert pixel image to matrix\cr \code{\link[spatstat.geom]{as.data.frame.im}} \tab convert pixel image to data frame\cr \code{\link[spatstat.geom]{as.function.im}} \tab convert pixel image to function\cr \code{\link[spatstat.geom]{plot.im}} \tab plot a pixel image on screen as a digital image\cr \code{\link[spatstat.geom]{contour.im}} \tab draw contours of a pixel image \cr \code{\link[spatstat.geom]{persp.im}} \tab draw perspective plot of a pixel image \cr \code{\link[spatstat.geom]{rgbim}} \tab create colour-valued pixel image \cr \code{\link[spatstat.geom]{hsvim}} \tab create colour-valued pixel image \cr \code{\link[spatstat.geom]{[.im}} \tab extract a subset of a pixel image\cr \code{\link[spatstat.geom]{[<-.im}} \tab replace a subset of a pixel image\cr \code{\link[spatstat.geom]{rotate.im}} \tab rotate pixel image \cr \code{\link[spatstat.geom]{shift.im}} \tab apply vector shift to pixel image \cr \code{\link[spatstat.geom]{affine.im}} \tab apply affine transformation to image \cr \code{X} \tab print very basic information about image \code{X}\cr \code{\link[spatstat.geom:summary.im]{summary}(X)} \tab summary of image \code{X} \cr \code{\link[spatstat.geom]{hist.im}} \tab histogram of image \cr \code{\link[spatstat.geom]{mean.im}} \tab mean pixel value of image \cr \code{\link[spatstat.geom]{integral.im}} \tab integral of pixel values \cr \code{\link[spatstat.geom]{quantile.im}} \tab quantiles of image \cr \code{\link[spatstat.geom]{cut.im}} \tab convert numeric image to factor image \cr \code{\link[spatstat.geom]{is.im}} \tab test whether an object is a pixel image\cr \code{\link[spatstat.geom]{interp.im}} \tab interpolate a pixel image\cr \code{\link[spatstat.explore]{blur}} \tab apply Gaussian blur to image\cr \code{\link[spatstat.explore]{Smooth.im}} \tab apply Gaussian blur to image\cr \code{\link[spatstat.geom]{connected.im}} \tab find connected components \cr \code{\link[spatstat.geom]{compatible.im}} \tab test whether two images have compatible dimensions \cr \code{\link[spatstat.geom]{harmonise.im}} \tab make images compatible \cr \code{\link[spatstat.geom]{commonGrid}} \tab find a common pixel grid for images \cr \code{\link[spatstat.geom]{eval.im}} \tab evaluate any expression involving images\cr \code{\link[spatstat.geom]{im.apply}} \tab evaluate a function of several images \cr \code{\link[spatstat.geom]{scaletointerval}} \tab rescale pixel values \cr \code{\link[spatstat.geom]{zapsmall.im}} \tab set very small pixel values to zero \cr \code{\link[spatstat.geom]{levelset}} \tab level set of an image\cr \code{\link[spatstat.geom]{solutionset}} \tab region where an expression is true \cr \code{\link[spatstat.geom]{imcov}} \tab spatial covariance function of image \cr \code{\link[spatstat.geom]{convolve.im}} \tab spatial convolution of images \cr \code{\link[spatstat.explore]{transect.im}} \tab line transect of image \cr \code{\link[spatstat.geom]{pixelcentres}} \tab extract centres of pixels \cr \code{\link[spatstat.geom]{transmat}} \tab convert matrix of pixel values \cr \tab to a different indexing convention \cr \code{\link[spatstat.random]{rnoise}} \tab random pixel noise } \bold{Line segment patterns} An object of class \code{"psp"} represents a pattern of straight line segments. \tabular{ll}{ \code{\link[spatstat.geom]{psp}} \tab create a line segment pattern \cr \code{\link[spatstat.geom]{as.psp}} \tab convert other data into a line segment pattern \cr \code{\link[spatstat.geom]{edges}} \tab extract edges of a window \cr \code{\link[spatstat.geom]{is.psp}} \tab determine whether a dataset has class \code{"psp"} \cr \code{\link[spatstat.geom]{plot.psp}} \tab plot a line segment pattern \cr \code{\link[spatstat.geom]{print.psp}} \tab print basic information \cr \code{\link[spatstat.geom]{summary.psp}} \tab print summary information \cr \code{\link[spatstat.geom]{[.psp}} \tab extract a subset of a line segment pattern \cr \code{\link[spatstat.geom]{subset.psp}} \tab extract subset of line segment pattern \cr \code{\link[spatstat.geom]{as.data.frame.psp}} \tab convert line segment pattern to data frame \cr \code{\link[spatstat.geom]{marks.psp}} \tab extract marks of line segments \cr \code{\link[spatstat.geom]{marks<-.psp}} \tab assign new marks to line segments \cr \code{\link[spatstat.geom]{unmark.psp}} \tab delete marks from line segments \cr \code{\link[spatstat.geom]{midpoints.psp}} \tab compute the midpoints of line segments \cr \code{\link[spatstat.geom]{endpoints.psp}} \tab extract the endpoints of line segments \cr \code{\link[spatstat.geom]{lengths_psp}} \tab compute the lengths of line segments \cr \code{\link[spatstat.geom]{angles.psp}} \tab compute the orientation angles of line segments \cr \code{\link[spatstat.geom]{superimpose}} \tab combine several line segment patterns \cr \code{\link[spatstat.geom]{flipxy}} \tab swap \eqn{x} and \eqn{y} coordinates \cr \code{\link[spatstat.geom]{rotate.psp}} \tab rotate a line segment pattern \cr \code{\link[spatstat.geom]{shift.psp}} \tab shift a line segment pattern \cr \code{\link[spatstat.geom]{periodify}} \tab make several shifted copies \cr \code{\link[spatstat.geom]{affine.psp}} \tab apply an affine transformation \cr \code{\link[spatstat.geom]{pixellate.psp}} \tab approximate line segment pattern by pixel image \cr \code{\link[spatstat.geom]{psp2mask}} \tab approximate line segment pattern by binary mask \cr \code{\link[spatstat.geom]{distmap.psp}} \tab compute the distance map of a line segment pattern \cr \code{\link[spatstat.geom]{distfun.psp}} \tab compute the distance map of a line segment pattern \cr \code{\link[spatstat.explore]{density.psp}} \tab kernel smoothing of line segments\cr \code{\link[spatstat.geom]{selfcrossing.psp}} \tab find crossing points between line segments \cr \code{\link[spatstat.geom]{selfcut.psp}} \tab cut segments where they cross \cr \code{\link[spatstat.geom]{crossing.psp}} \tab find crossing points between two line segment patterns \cr \code{\link[spatstat.geom]{extrapolate.psp}} \tab extrapolate line segments to infinite lines \cr \code{\link[spatstat.geom]{nncross}} \tab find distance to nearest line segment from a given point\cr \code{\link[spatstat.geom]{nearestsegment}} \tab find line segment closest to a given point \cr \code{\link[spatstat.geom]{project2segment}} \tab find location along a line segment closest to a given point \cr \code{\link[spatstat.geom]{pointsOnLines}} \tab generate points evenly spaced along line segment \cr \code{\link[spatstat.random]{rpoisline}} \tab generate a realisation of the Poisson line process inside a window\cr \code{\link[spatstat.geom]{rlinegrid}} \tab generate a random array of parallel lines through a window } \bold{Tessellations} An object of class \code{"tess"} represents a tessellation. \tabular{ll}{ \code{\link[spatstat.geom]{tess}} \tab create a tessellation \cr \code{\link[spatstat.geom]{quadrats}} \tab create a tessellation of rectangles\cr \code{\link[spatstat.geom]{hextess}} \tab create a tessellation of hexagons \cr \code{\link[spatstat.geom]{polartess}} \tab tessellation using polar coordinates \cr \code{\link[spatstat.geom]{quantess}} \tab quantile tessellation \cr \code{\link[spatstat.geom]{venn.tess}} \tab Venn diagram tessellation \cr \code{\link[spatstat.geom]{dirichlet}} \tab compute Dirichlet-Voronoi tessellation of points\cr \code{\link[spatstat.geom]{delaunay}} \tab compute Delaunay triangulation of points\cr \code{\link[spatstat.geom]{as.tess}} \tab convert other data to a tessellation \cr \code{\link[spatstat.geom]{plot.tess}} \tab plot a tessellation \cr \code{\link[spatstat.geom]{tiles}} \tab extract all the tiles of a tessellation \cr \code{\link[spatstat.geom]{[.tess}} \tab extract some tiles of a tessellation \cr \code{\link[spatstat.geom]{[<-.tess}} \tab change some tiles of a tessellation \cr \code{\link[spatstat.geom]{intersect.tess}} \tab intersect two tessellations \cr \tab or restrict a tessellation to a window \cr \code{\link[spatstat.geom]{chop.tess}} \tab subdivide a tessellation by a line \cr \code{\link[spatstat.random]{rpoislinetess}} \tab generate tessellation using Poisson line process \cr \code{\link[spatstat.geom]{tile.areas}} \tab area of each tile in tessellation \cr \code{\link[spatstat.geom]{bdist.tiles}} \tab boundary distance for each tile in tessellation \cr \code{\link[spatstat.geom]{connected.tess}} \tab find connected components of tiles \cr \code{\link[spatstat.geom]{shift.tess}} \tab shift a tessellation \cr \code{\link[spatstat.geom]{rotate.tess}} \tab rotate a tessellation \cr \code{\link[spatstat.geom]{reflect.tess}} \tab reflect about the origin \cr \code{\link[spatstat.geom]{flipxy.tess}} \tab reflect about the diagonal \cr \code{\link[spatstat.geom]{affine.tess}} \tab apply affine transformation } \bold{Three-dimensional point patterns} An object of class \code{"pp3"} represents a three-dimensional point pattern in a rectangular box. The box is represented by an object of class \code{"box3"}. \tabular{ll}{ \code{\link[spatstat.geom]{pp3}} \tab create a 3-D point pattern \cr \code{\link[spatstat.geom]{plot.pp3}} \tab plot a 3-D point pattern \cr \code{\link[spatstat.geom]{coords}} \tab extract coordinates \cr \code{\link[spatstat.geom]{as.hyperframe}} \tab extract coordinates \cr \code{\link[spatstat.geom]{subset.pp3}} \tab extract subset of 3-D point pattern \cr \code{\link[spatstat.geom]{unitname.pp3}} \tab name of unit of length \cr \code{\link[spatstat.geom]{npoints}} \tab count the number of points \cr \code{\link[spatstat.random]{runifpoint3}} \tab generate uniform random points in 3-D \cr \code{\link[spatstat.random]{rpoispp3}} \tab generate Poisson random points in 3-D \cr \code{\link[spatstat.explore]{envelope.pp3}} \tab generate simulation envelopes for 3-D pattern \cr \code{\link[spatstat.geom]{box3}} \tab create a 3-D rectangular box \cr \code{\link[spatstat.geom]{as.box3}} \tab convert data to 3-D rectangular box \cr \code{\link[spatstat.geom]{unitname.box3}} \tab name of unit of length \cr \code{\link[spatstat.geom]{diameter.box3}} \tab diameter of box \cr \code{\link[spatstat.geom]{volume.box3}} \tab volume of box \cr \code{\link[spatstat.geom]{shortside.box3}} \tab shortest side of box \cr \code{\link[spatstat.geom]{eroded.volumes}} \tab volumes of erosions of box } \bold{Multi-dimensional space-time point patterns} An object of class \code{"ppx"} represents a point pattern in multi-dimensional space and/or time. \tabular{ll}{ \code{\link[spatstat.geom]{ppx}} \tab create a multidimensional space-time point pattern \cr \code{\link[spatstat.geom]{coords}} \tab extract coordinates \cr \code{\link[spatstat.geom]{as.hyperframe}} \tab extract coordinates \cr \code{\link[spatstat.geom]{subset.ppx}} \tab extract subset \cr \code{\link[spatstat.geom]{unitname.ppx}} \tab name of unit of length \cr \code{\link[spatstat.geom]{npoints}} \tab count the number of points \cr \code{\link[spatstat.random]{runifpointx}} \tab generate uniform random points \cr \code{\link[spatstat.random]{rpoisppx}} \tab generate Poisson random points \cr \code{\link[spatstat.geom]{boxx}} \tab define multidimensional box \cr \code{\link[spatstat.geom]{diameter.boxx}} \tab diameter of box \cr \code{\link[spatstat.geom]{volume.boxx}} \tab volume of box \cr \code{\link[spatstat.geom]{shortside.boxx}} \tab shortest side of box \cr \code{\link[spatstat.geom]{eroded.volumes.boxx}} \tab volumes of erosions of box } \bold{Point patterns on a linear network} An object of class \code{"linnet"} represents a linear network (for example, a road network). \tabular{ll}{ \code{\link[spatstat.linnet]{linnet}} \tab create a linear network \cr \code{\link[spatstat.linnet]{clickjoin}} \tab interactively join vertices in network \cr \code{spatstat.gui::iplot.linnet} \tab interactively plot network \cr \code{\link[spatstat.data]{simplenet}} \tab simple example of network \cr \code{\link[spatstat.linnet]{lineardisc}} \tab disc in a linear network \cr \code{\link[spatstat.linnet]{delaunayNetwork}} \tab network of Delaunay triangulation \cr \code{\link[spatstat.linnet]{dirichletNetwork}} \tab network of Dirichlet edges \cr \code{\link[spatstat.linnet]{methods.linnet}} \tab methods for \code{linnet} objects\cr \code{\link[spatstat.linnet]{vertices.linnet}} \tab nodes of network \cr \code{\link[spatstat.linnet]{joinVertices}} \tab join existing vertices in a network \cr \code{\link[spatstat.linnet]{insertVertices}} \tab insert new vertices at positions along a network \cr \code{\link[spatstat.linnet]{addVertices}} \tab add new vertices, extending a network \cr \code{\link[spatstat.linnet]{thinNetwork}} \tab remove vertices or lines from a network \cr \code{\link[spatstat.linnet]{repairNetwork}} \tab repair internal format \cr \code{\link[spatstat.linnet]{pixellate.linnet}} \tab approximate by pixel image } An object of class \code{"lpp"} represents a point pattern on a linear network (for example, road accidents on a road network). \tabular{ll}{ \code{\link[spatstat.linnet]{lpp}} \tab create a point pattern on a linear network \cr \code{\link[spatstat.linnet]{methods.lpp}} \tab methods for \code{lpp} objects \cr \code{\link[spatstat.linnet]{subset.lpp}} \tab method for \code{subset} \cr \code{\link[spatstat.linnet]{rpoislpp}} \tab simulate Poisson points on linear network \cr \code{\link[spatstat.linnet]{runiflpp}} \tab simulate random points on a linear network \cr \code{\link[spatstat.data]{chicago}} \tab Chicago crime data \cr \code{\link[spatstat.data]{dendrite}} \tab Dendritic spines data \cr \code{\link[spatstat.data]{spiders}} \tab Spider webs on mortar lines of brick wall } \bold{Hyperframes} A hyperframe is like a data frame, except that the entries may be objects of any kind. \tabular{ll}{ \code{\link[spatstat.geom]{hyperframe}} \tab create a hyperframe \cr \code{\link[spatstat.geom]{as.hyperframe}} \tab convert data to hyperframe \cr \code{\link[spatstat.geom]{plot.hyperframe}} \tab plot hyperframe \cr \code{\link[spatstat.geom]{with.hyperframe}} \tab evaluate expression using each row of hyperframe \cr \code{\link[spatstat.geom]{cbind.hyperframe}} \tab combine hyperframes by columns\cr \code{\link[spatstat.geom]{rbind.hyperframe}} \tab combine hyperframes by rows\cr \code{\link[spatstat.geom]{as.data.frame.hyperframe}} \tab convert hyperframe to data frame \cr \code{\link[spatstat.geom]{subset.hyperframe}} \tab method for \code{subset} \cr \code{\link[spatstat.geom]{head.hyperframe}} \tab first few rows of hyperframe \cr \code{\link[spatstat.geom]{tail.hyperframe}} \tab last few rows of hyperframe } \bold{Layered objects} A layered object represents data that should be plotted in successive layers, for example, a background and a foreground. \tabular{ll}{ \code{\link[spatstat.geom]{layered}} \tab create layered object \cr \code{\link[spatstat.geom]{plot.layered}} \tab plot layered object\cr \code{\link[spatstat.geom]{[.layered}} \tab extract subset of layered object } \bold{Colour maps} A colour map is a mechanism for associating colours with data. It can be regarded as a function, mapping data to colours. Using a \code{colourmap} object in a plot command ensures that the mapping from numbers to colours is the same in different plots. \tabular{ll}{ \code{\link[spatstat.geom]{colourmap}} \tab create a colour map \cr \code{\link[spatstat.geom]{plot.colourmap}} \tab plot the colour map only\cr \code{\link[spatstat.geom]{tweak.colourmap}} \tab alter individual colour values \cr \code{\link[spatstat.geom]{interp.colourmap}} \tab make a smooth transition between colours \cr \code{\link[spatstat.geom]{beachcolourmap}} \tab one special colour map } } \section{II. EXPLORATORY DATA ANALYSIS}{ \bold{Inspection of data:} \tabular{ll}{ \code{\link[spatstat.geom:summary.ppp]{summary}(X)} \tab print useful summary of point pattern \code{X}\cr \code{X} \tab print basic description of point pattern \code{X} \cr \code{any(duplicated(X))} \tab check for duplicated points in pattern \code{X} \cr \code{spatstat.gui::istat(X)} \tab Interactive exploratory analysis \cr \code{spatstat.gui::View.ppp(X)} \tab spreadsheet-style viewer } \bold{Classical exploratory tools:} \tabular{ll}{ \code{\link[spatstat.explore]{clarkevans}} \tab Clark and Evans aggregation index \cr \code{\link[spatstat.explore]{fryplot}} \tab Fry plot \cr \code{\link[spatstat.explore]{miplot}} \tab Morisita Index plot } \bold{Smoothing:} \tabular{ll}{ \code{\link[spatstat.explore]{density.ppp}} \tab kernel smoothed density/intensity\cr \code{\link[spatstat.explore]{relrisk}} \tab kernel estimate of relative risk\cr \code{\link[spatstat.explore]{Smooth.ppp}} \tab spatial interpolation of marks \cr \code{\link[spatstat.explore]{bw.diggle}} \tab cross-validated bandwidth selection for \code{\link[spatstat.explore]{density.ppp}}\cr \code{\link[spatstat.explore]{bw.ppl}} \tab likelihood cross-validated bandwidth selection for \code{\link[spatstat.explore]{density.ppp}}\cr \code{\link[spatstat.explore]{bw.CvL}} \tab Cronie-Van Lieshout bandwidth selection for density estimation\cr \code{\link[spatstat.explore]{bw.scott}} \tab Scott's rule of thumb for density estimation\cr \code{\link[spatstat.explore]{bw.abram}} \tab Abramson's rule for adaptive bandwidths\cr \code{\link[spatstat.explore]{bw.relrisk}} \tab cross-validated bandwidth selection for \code{\link[spatstat.explore]{relrisk}} \cr \code{\link[spatstat.explore]{bw.smoothppp}} \tab cross-validated bandwidth selection for \code{\link[spatstat.explore]{Smooth.ppp}} \cr \code{\link[spatstat.explore]{bw.frac}} \tab bandwidth selection using window geometry\cr \code{\link[spatstat.explore]{bw.stoyan}} \tab Stoyan's rule of thumb for bandwidth for \code{\link[spatstat.explore]{pcf}} } \bold{Modern exploratory tools:} \tabular{ll}{ \code{\link[spatstat.explore]{clusterset}} \tab Allard-Fraley feature detection \cr \code{\link[spatstat.explore]{nnclean}} \tab Byers-Raftery feature detection \cr \code{\link[spatstat.explore]{sharpen.ppp}} \tab Choi-Hall data sharpening \cr \code{\link[spatstat.explore]{rhohat}} \tab Kernel estimate of covariate effect\cr \code{\link[spatstat.explore]{rho2hat}} \tab Kernel estimate of effect of two covariates\cr \code{\link[spatstat.explore]{spatialcdf}} \tab Spatial cumulative distribution function\cr \code{\link[spatstat.explore]{roc}} \tab Receiver operating characteristic curve } \bold{Summary statistics for a point pattern:} Type \code{demo(sumfun)} for a demonstration of many of the summary statistics. \tabular{ll}{ \code{\link[spatstat.geom]{intensity}} \tab Mean intensity \cr \code{\link[spatstat.geom]{quadratcount}} \tab Quadrat counts \cr \code{\link[spatstat.geom]{intensity.quadratcount}} \tab Mean intensity in quadrats \cr \code{\link[spatstat.explore]{Fest}} \tab empty space function \eqn{F} \cr \code{\link[spatstat.explore]{Gest}} \tab nearest neighbour distribution function \eqn{G} \cr \code{\link[spatstat.explore]{Jest}} \tab \eqn{J}-function \eqn{J = (1-G)/(1-F)} \cr \code{\link[spatstat.explore]{Kest}} \tab Ripley's \eqn{K}-function\cr \code{\link[spatstat.explore]{Lest}} \tab Besag \eqn{L}-function\cr \code{\link[spatstat.explore]{Tstat}} \tab Third order \eqn{T}-function \cr \code{\link[spatstat.explore]{allstats}} \tab all four functions \eqn{F}, \eqn{G}, \eqn{J}, \eqn{K} \cr \code{\link[spatstat.explore]{pcf}} \tab pair correlation function \cr \code{\link[spatstat.explore]{Kinhom}} \tab \eqn{K} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{Linhom}} \tab \eqn{L} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{pcfinhom}} \tab pair correlation for inhomogeneous patterns\cr \code{\link[spatstat.explore]{Finhom}} \tab \eqn{F} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{Ginhom}} \tab \eqn{G} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{Jinhom}} \tab \eqn{J} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{localL}} \tab Getis-Franklin neighbourhood density function\cr \code{\link[spatstat.explore]{localK}} \tab neighbourhood K-function\cr \code{\link[spatstat.explore]{localpcf}} \tab local pair correlation function\cr \code{\link[spatstat.explore]{localKinhom}} \tab local \eqn{K} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{localLinhom}} \tab local \eqn{L} for inhomogeneous point patterns \cr \code{\link[spatstat.explore]{localpcfinhom}} \tab local pair correlation for inhomogeneous patterns\cr \code{\link[spatstat.explore]{Ksector}} \tab Directional \eqn{K}-function\cr \code{\link[spatstat.explore]{Kscaled}} \tab locally scaled \eqn{K}-function \cr \code{\link[spatstat.explore]{Kest.fft}} \tab fast \eqn{K}-function using FFT for large datasets \cr \code{\link[spatstat.explore]{Kmeasure}} \tab reduced second moment measure \cr \code{\link[spatstat.explore]{envelope}} \tab simulation envelopes for a summary function \cr \code{\link[spatstat.explore]{varblock}} \tab variances and confidence intervals\cr \tab for a summary function \cr \code{\link[spatstat.explore]{lohboot}} \tab bootstrap for a summary function } Related facilities: \tabular{ll}{ \code{\link[spatstat.explore]{plot.fv}} \tab plot a summary function\cr \code{\link[spatstat.explore]{eval.fv}} \tab evaluate any expression involving summary functions\cr \code{\link[spatstat.explore]{harmonise.fv}} \tab make functions compatible \cr \code{\link[spatstat.explore]{eval.fasp}} \tab evaluate any expression involving an array of functions\cr \code{\link[spatstat.explore]{with.fv}} \tab evaluate an expression for a summary function\cr \code{\link[spatstat.explore]{Smooth.fv}} \tab apply smoothing to a summary function\cr \code{\link[spatstat.explore]{deriv.fv}} \tab calculate derivative of a summary function\cr \code{\link[spatstat.explore]{pool.fv}} \tab pool several estimates of a summary function\cr \code{\link[spatstat.geom]{nndist}} \tab nearest neighbour distances \cr \code{\link[spatstat.geom]{nnwhich}} \tab find nearest neighbours \cr \code{\link[spatstat.geom]{pairdist}} \tab distances between all pairs of points\cr \code{\link[spatstat.geom]{crossdist}} \tab distances between points in two patterns\cr \code{\link[spatstat.geom]{nncross}} \tab nearest neighbours between two point patterns \cr \code{\link[spatstat.geom]{exactdt}} \tab distance from any location to nearest data point\cr \code{\link[spatstat.geom]{distmap}} \tab distance map image\cr \code{\link[spatstat.geom]{distfun}} \tab distance map function\cr \code{\link[spatstat.geom]{nnmap}} \tab nearest point image \cr \code{\link[spatstat.geom]{nnfun}} \tab nearest point function \cr \code{\link[spatstat.explore]{density.ppp}} \tab kernel smoothed density\cr \code{\link[spatstat.explore]{densityHeat.ppp}} \tab diffusion kernel smoothed density\cr \code{\link[spatstat.explore]{Smooth.ppp}} \tab spatial interpolation of marks \cr \code{\link[spatstat.explore]{relrisk}} \tab kernel estimate of relative risk\cr \code{\link[spatstat.explore]{sharpen.ppp}} \tab data sharpening \cr \code{\link[spatstat.random]{rknn}} \tab theoretical distribution of nearest neighbour distance } \bold{Summary statistics for a multitype point pattern:} A multitype point pattern is represented by an object \code{X} of class \code{"ppp"} such that \code{marks(X)} is a factor. \tabular{ll}{ \code{\link[spatstat.explore]{relrisk}} \tab kernel estimation of relative risk \cr \code{\link[spatstat.explore]{scan.test}} \tab spatial scan test of elevated risk \cr \code{\link[spatstat.explore]{Gcross},\link[spatstat.explore]{Gdot},\link[spatstat.explore]{Gmulti}} \tab multitype nearest neighbour distributions \eqn{G_{ij}, G_{i\bullet}}{G[i,j], G[i.]} \cr \code{\link[spatstat.explore]{Kcross},\link[spatstat.explore]{Kdot}, \link[spatstat.explore]{Kmulti}} \tab multitype \eqn{K}-functions \eqn{K_{ij}, K_{i\bullet}}{K[i,j], K[i.]} \cr \code{\link[spatstat.explore]{Lcross},\link[spatstat.explore]{Ldot}} \tab multitype \eqn{L}-functions \eqn{L_{ij}, L_{i\bullet}}{L[i,j], L[i.]} \cr \code{\link[spatstat.explore]{Jcross},\link[spatstat.explore]{Jdot},\link[spatstat.explore]{Jmulti}} \tab multitype \eqn{J}-functions \eqn{J_{ij}, J_{i\bullet}}{J[i,j],J[i.]} \cr \code{\link[spatstat.explore]{pcfcross}} \tab multitype pair correlation function \eqn{g_{ij}}{g[i,j]} \cr \code{\link[spatstat.explore]{pcfdot}} \tab multitype pair correlation function \eqn{g_{i\bullet}}{g[i.]} \cr \code{\link[spatstat.explore]{pcfmulti}} \tab general pair correlation function \cr \code{\link[spatstat.explore]{markconnect}} \tab marked connection function \eqn{p_{ij}}{p[i,j]} \cr \code{\link[spatstat.explore]{alltypes}} \tab estimates of the above for all \eqn{i,j} pairs \cr \code{\link[spatstat.explore]{Iest}} \tab multitype \eqn{I}-function\cr \code{\link[spatstat.explore]{Kcross.inhom},\link[spatstat.explore]{Kdot.inhom}} \tab inhomogeneous counterparts of \code{Kcross}, \code{Kdot} \cr \code{\link[spatstat.explore]{Lcross.inhom},\link[spatstat.explore]{Ldot.inhom}} \tab inhomogeneous counterparts of \code{Lcross}, \code{Ldot} \cr \code{\link[spatstat.explore]{pcfcross.inhom},\link[spatstat.explore]{pcfdot.inhom}} \tab inhomogeneous counterparts of \code{pcfcross}, \code{pcfdot} \cr \code{\link[spatstat.explore]{localKcross},\link[spatstat.explore]{localKdot}} \tab local counterparts of \code{Kcross}, \code{Kdot} \cr \code{\link[spatstat.explore]{localLcross},\link[spatstat.explore]{localLdot}} \tab local counterparts of \code{Lcross}, \code{Ldot} \cr \code{\link[spatstat.explore]{localKcross.inhom},\link[spatstat.explore]{localLcross.inhom}} \tab local counterparts of \code{Kcross.inhom}, \code{Lcross.inhom} } \bold{Summary statistics for a marked point pattern:} A marked point pattern is represented by an object \code{X} of class \code{"ppp"} with a component \code{X$marks}. The entries in the vector \code{X$marks} may be numeric, complex, string or any other atomic type. For numeric marks, there are the following functions: \tabular{ll}{ \code{\link[spatstat.explore]{markmean}} \tab smoothed local average of marks \cr \code{\link[spatstat.explore]{markvar}} \tab smoothed local variance of marks \cr \code{\link[spatstat.explore]{markcorr}} \tab mark correlation function \cr \code{\link[spatstat.explore]{markcrosscorr}} \tab mark cross-correlation function \cr \code{\link[spatstat.explore]{markvario}} \tab mark variogram \cr \code{\link[spatstat.explore]{markmarkscatter}} \tab mark-mark scatterplot \cr \code{\link[spatstat.explore]{Kmark}} \tab mark-weighted \eqn{K} function \cr \code{\link[spatstat.explore]{Emark}} \tab mark independence diagnostic \eqn{E(r)} \cr \code{\link[spatstat.explore]{Vmark}} \tab mark independence diagnostic \eqn{V(r)} \cr \code{\link[spatstat.explore]{nnmean}} \tab nearest neighbour mean index \cr \code{\link[spatstat.explore]{nnvario}} \tab nearest neighbour mark variance index } For marks of any type, there are the following: \tabular{ll}{ \code{\link[spatstat.explore]{Gmulti}} \tab multitype nearest neighbour distribution \cr \code{\link[spatstat.explore]{Kmulti}} \tab multitype \eqn{K}-function \cr \code{\link[spatstat.explore]{Jmulti}} \tab multitype \eqn{J}-function } Alternatively use \code{\link[spatstat.geom]{cut.ppp}} to convert a marked point pattern to a multitype point pattern. \bold{Programming tools:} \tabular{ll}{ \code{\link[spatstat.geom]{applynbd}} \tab apply function to every neighbourhood in a point pattern \cr \code{\link[spatstat.geom]{markstat}} \tab apply function to the marks of neighbours in a point pattern \cr \code{\link[spatstat.explore]{marktable}} \tab tabulate the marks of neighbours in a point pattern \cr \code{\link[spatstat.geom]{pppdist}} \tab find the optimal match between two point patterns } \bold{Summary statistics for a point pattern on a linear network:} These are for point patterns on a linear network (class \code{lpp}). For unmarked patterns: \tabular{ll}{ \code{\link[spatstat.linnet]{linearK}} \tab \eqn{K} function on linear network \cr \code{\link[spatstat.linnet]{linearKinhom}} \tab inhomogeneous \eqn{K} function on linear network \cr \code{\link[spatstat.linnet]{linearpcf}} \tab pair correlation function on linear network \cr \code{\link[spatstat.linnet]{linearpcfinhom}} \tab inhomogeneous pair correlation on linear network } For multitype patterns: \tabular{ll}{ \code{\link[spatstat.linnet]{linearKcross}} \tab \eqn{K} function between two types of points \cr \code{\link[spatstat.linnet]{linearKdot}} \tab \eqn{K} function from one type to any type \cr \code{\link[spatstat.linnet]{linearKcross.inhom}} \tab Inhomogeneous version of \code{\link[spatstat.linnet]{linearKcross}} \cr \code{\link[spatstat.linnet]{linearKdot.inhom}} \tab Inhomogeneous version of \code{\link[spatstat.linnet]{linearKdot}} \cr \code{\link[spatstat.linnet]{linearmarkconnect}} \tab Mark connection function on linear network \cr \code{\link[spatstat.linnet]{linearmarkequal}} \tab Mark equality function on linear network \cr \code{\link[spatstat.linnet]{linearpcfcross}} \tab Pair correlation between two types of points \cr \code{\link[spatstat.linnet]{linearpcfdot}} \tab Pair correlation from one type to any type \cr \code{\link[spatstat.linnet]{linearpcfcross.inhom}} \tab Inhomogeneous version of \code{\link[spatstat.linnet]{linearpcfcross}} \cr \code{\link[spatstat.linnet]{linearpcfdot.inhom}} \tab Inhomogeneous version of \code{\link[spatstat.linnet]{linearpcfdot}} } Related facilities: \tabular{ll}{ \code{\link[spatstat.linnet]{pairdist.lpp}} \tab distances between pairs \cr \code{\link[spatstat.linnet]{crossdist.lpp}} \tab distances between pairs \cr \code{\link[spatstat.linnet]{nndist.lpp}} \tab nearest neighbour distances \cr \code{\link[spatstat.linnet]{nncross.lpp}} \tab nearest neighbour distances \cr \code{\link[spatstat.linnet]{nnwhich.lpp}} \tab find nearest neighbours \cr \code{\link[spatstat.linnet]{nnfun.lpp}} \tab find nearest data point \cr \code{\link[spatstat.linnet]{density.lpp}} \tab kernel smoothing estimator of intensity \cr \code{\link[spatstat.linnet]{densityHeat.lpp}} \tab diffusion kernel estimate \cr \code{\link[spatstat.linnet]{distfun.lpp}} \tab distance transform \cr \code{\link[spatstat.linnet]{envelope.lpp}} \tab simulation envelopes \cr \code{\link[spatstat.linnet]{rpoislpp}} \tab simulate Poisson points on linear network \cr \code{\link[spatstat.linnet]{runiflpp}} \tab simulate random points on a linear network } It is also possible to fit point process models to \code{lpp} objects. See Section IV. \bold{Summary statistics for a three-dimensional point pattern:} These are for 3-dimensional point pattern objects (class \code{pp3}). \tabular{ll}{ \code{\link[spatstat.explore]{F3est}} \tab empty space function \eqn{F} \cr \code{\link[spatstat.explore]{G3est}} \tab nearest neighbour function \eqn{G} \cr \code{\link[spatstat.explore]{K3est}} \tab \eqn{K}-function \cr \code{\link[spatstat.explore]{pcf3est}} \tab pair correlation function } Related facilities: \tabular{ll}{ \code{\link[spatstat.explore]{envelope.pp3}} \tab simulation envelopes \cr \code{\link[spatstat.geom]{pairdist.pp3}} \tab distances between all pairs of points \cr \code{\link[spatstat.geom]{crossdist.pp3}} \tab distances between points in two patterns \cr \code{\link[spatstat.geom]{nndist.pp3}} \tab nearest neighbour distances \cr \code{\link[spatstat.geom]{nnwhich.pp3}} \tab find nearest neighbours \cr \code{\link[spatstat.geom]{nncross.pp3}} \tab find nearest neighbours in another pattern } \bold{Computations for multi-dimensional point pattern:} These are for multi-dimensional space-time point pattern objects (class \code{ppx}). \tabular{ll}{ \code{\link[spatstat.geom]{pairdist.ppx}} \tab distances between all pairs of points \cr \code{\link[spatstat.geom]{crossdist.ppx}} \tab distances between points in two patterns \cr \code{\link[spatstat.geom]{nndist.ppx}} \tab nearest neighbour distances \cr \code{\link[spatstat.geom]{nnwhich.ppx}} \tab find nearest neighbours } \bold{Summary statistics for random sets:} These work for point patterns (class \code{ppp}), line segment patterns (class \code{psp}) or windows (class \code{owin}). \tabular{ll}{ \code{\link[spatstat.explore]{Hest}} \tab spherical contact distribution \eqn{H} \cr \code{\link[spatstat.explore]{Gfox}} \tab Foxall \eqn{G}-function \cr \code{\link[spatstat.explore]{Jfox}} \tab Foxall \eqn{J}-function } } \section{III. MODEL FITTING (COX AND CLUSTER MODELS)}{ Cluster process models (with homogeneous or inhomogeneous intensity) and Cox processes can be fitted by the function \code{\link[spatstat.model]{kppm}}. Its result is an object of class \code{"kppm"}. The fitted model can be printed, plotted, predicted, simulated and updated. \tabular{ll}{ \code{\link[spatstat.model]{kppm}} \tab Fit model\cr \code{\link[spatstat.model]{plot.kppm}} \tab Plot the fitted model\cr \code{\link[spatstat.model]{summary.kppm}} \tab Summarise the fitted model\cr \code{\link[spatstat.model]{fitted.kppm}} \tab Compute fitted intensity \cr \code{\link[spatstat.model]{predict.kppm}} \tab Compute fitted intensity \cr \code{\link[spatstat.model]{update.kppm}} \tab Update the model \cr \code{\link[spatstat.model]{improve.kppm}} \tab Refine the estimate of trend \cr \code{\link[spatstat.model]{simulate.kppm}} \tab Generate simulated realisations \cr \code{\link[spatstat.model]{vcov.kppm}} \tab Variance-covariance matrix of coefficients \cr \code{\link[spatstat.model:methods.kppm]{coef.kppm}} \tab Extract trend coefficients \cr \code{\link[spatstat.model:methods.kppm]{formula.kppm}} \tab Extract trend formula \cr \code{\link[spatstat.model]{parameters}} \tab Extract all model parameters \cr \code{\link[spatstat.model]{clusterfield.kppm}} \tab Compute offspring density \cr \code{\link[spatstat.model]{clusterradius.kppm}} \tab Radius of support of offspring density \cr \code{\link[spatstat.model]{Kmodel.kppm}} \tab \eqn{K} function of fitted model \cr \code{\link[spatstat.model]{pcfmodel.kppm}} \tab Pair correlation of fitted model } For model selection, you can also use the generic functions \code{\link[stats]{step}}, \code{\link[stats]{drop1}} and \code{\link[stats]{AIC}} on fitted point process models. For variable selection, see \code{\link[spatstat.explore]{sdr}}. The theoretical models can also be simulated, for any choice of parameter values, using \code{\link[spatstat.random]{rThomas}}, \code{\link[spatstat.random]{rMatClust}}, \code{\link[spatstat.random]{rCauchy}}, \code{\link[spatstat.random]{rVarGamma}}, and \code{\link[spatstat.random]{rLGCP}}. Lower-level fitting functions include: \tabular{ll}{ \code{\link[spatstat.model]{lgcp.estK}} \tab fit a log-Gaussian Cox process model\cr \code{\link[spatstat.model]{lgcp.estpcf}} \tab fit a log-Gaussian Cox process model\cr \code{\link[spatstat.model]{thomas.estK}} \tab fit the Thomas process model \cr \code{\link[spatstat.model]{thomas.estpcf}} \tab fit the Thomas process model \cr \code{\link[spatstat.model]{matclust.estK}} \tab fit the \Matern Cluster process model \cr \code{\link[spatstat.model]{matclust.estpcf}} \tab fit the \Matern Cluster process model \cr \code{\link[spatstat.model]{cauchy.estK}} \tab fit a Neyman-Scott Cauchy cluster process \cr \code{\link[spatstat.model]{cauchy.estpcf}} \tab fit a Neyman-Scott Cauchy cluster process\cr \code{\link[spatstat.model]{vargamma.estK}} \tab fit a Neyman-Scott Variance Gamma process\cr \code{\link[spatstat.model]{vargamma.estpcf}} \tab fit a Neyman-Scott Variance Gamma process\cr \code{\link[spatstat.model]{mincontrast}} \tab low-level algorithm for fitting models \cr \tab by the method of minimum contrast } } \section{IV. MODEL FITTING (POISSON AND GIBBS MODELS)}{ \bold{Types of models} Poisson point processes are the simplest models for point patterns. A Poisson model assumes that the points are stochastically independent. It may allow the points to have a non-uniform spatial density. The special case of a Poisson process with a uniform spatial density is often called Complete Spatial Randomness. Poisson point processes are included in the more general class of Gibbs point process models. In a Gibbs model, there is \emph{interaction} or dependence between points. Many different types of interaction can be specified. For a detailed explanation of how to fit Poisson or Gibbs point process models to point pattern data using \pkg{spatstat}, see Baddeley and Turner (2005b) or Baddeley (2008). \bold{To fit a Poisson or Gibbs point process model:} Model fitting in \pkg{spatstat} is performed mainly by the function \code{\link[spatstat.model]{ppm}}. Its result is an object of class \code{"ppm"}. Here are some examples, where \code{X} is a point pattern (class \code{"ppp"}): \tabular{ll}{ \emph{command} \tab \emph{model} \cr \code{ppm(X)} \tab Complete Spatial Randomness \cr \code{ppm(X ~ 1)} \tab Complete Spatial Randomness \cr \code{ppm(X ~ x)} \tab Poisson process with \cr \tab intensity loglinear in \eqn{x} coordinate \cr \code{ppm(X ~ 1, Strauss(0.1))} \tab Stationary Strauss process \cr \code{ppm(X ~ x, Strauss(0.1))} \tab Strauss process with \cr \tab conditional intensity loglinear in \eqn{x} } It is also possible to fit models that depend on other covariates. \bold{Manipulating the fitted model:} \tabular{ll}{ \code{\link[spatstat.model]{plot.ppm}} \tab Plot the fitted model\cr \code{\link[spatstat.model]{predict.ppm}} \tab Compute the spatial trend and conditional intensity\cr \tab of the fitted point process model \cr \code{\link[spatstat.model]{coef.ppm}} \tab Extract the fitted model coefficients\cr \code{\link[spatstat.model]{parameters}} \tab Extract all model parameters\cr \code{\link[spatstat.model]{formula.ppm}} \tab Extract the trend formula\cr \code{\link[spatstat.model]{intensity.ppm}} \tab Compute fitted intensity \cr \code{\link[spatstat.model]{Kmodel.ppm}} \tab \eqn{K} function of fitted model \cr \code{\link[spatstat.model]{pcfmodel.ppm}} \tab pair correlation of fitted model \cr \code{\link[spatstat.model]{fitted.ppm}} \tab Compute fitted conditional intensity at quadrature points \cr \code{\link[spatstat.model]{residuals.ppm}} \tab Compute point process residuals at quadrature points \cr \code{\link[spatstat.model]{update.ppm}} \tab Update the fit \cr \code{\link[spatstat.model]{vcov.ppm}} \tab Variance-covariance matrix of estimates\cr \code{\link[spatstat.model]{rmh.ppm}} \tab Simulate from fitted model \cr \code{\link[spatstat.model]{simulate.ppm}} \tab Simulate from fitted model \cr \code{\link[spatstat.model]{print.ppm}} \tab Print basic information about a fitted model\cr \code{\link[spatstat.model]{summary.ppm}} \tab Summarise a fitted model\cr \code{\link[spatstat.model]{effectfun}} \tab Compute the fitted effect of one covariate\cr \code{\link[spatstat.model]{logLik.ppm}} \tab log-likelihood or log-pseudolikelihood\cr \code{\link[spatstat.model]{anova.ppm}} \tab Analysis of deviance \cr \code{\link[spatstat.model]{model.frame.ppm}} \tab Extract data frame used to fit model \cr \code{\link[spatstat.model]{model.images}} \tab Extract spatial data used to fit model \cr \code{\link[spatstat.model]{model.depends}} \tab Identify variables in the model \cr \code{\link[spatstat.model]{as.interact}} \tab Interpoint interaction component of model \cr \code{\link[spatstat.model]{fitin}} \tab Extract fitted interpoint interaction \cr \code{\link[spatstat.model]{is.hybrid}} \tab Determine whether the model is a hybrid \cr \code{\link[spatstat.model]{valid.ppm}} \tab Check the model is a valid point process \cr \code{\link[spatstat.model]{project.ppm}} \tab Ensure the model is a valid point process } For model selection, you can also use the generic functions \code{\link[stats]{step}}, \code{\link[stats]{drop1}} and \code{\link[stats]{AIC}} on fitted point process models. For variable selection, see \code{\link[spatstat.explore]{sdr}}. See \code{\link[spatstat.geom]{spatstat.options}} to control plotting of fitted model. \bold{To specify a point process model:} The first order ``trend'' of the model is determined by an \R language formula. The formula specifies the form of the \emph{logarithm} of the trend. \tabular{ll}{ \code{X ~ 1} \tab No trend (stationary) \cr \code{X ~ x} \tab Loglinear trend \eqn{\lambda(x,y) = \exp(\alpha + \beta x)}{lambda(x,y) = exp(alpha + beta * x)} \cr \tab where \eqn{x,y} are Cartesian coordinates \cr \code{X ~ polynom(x,y,3)} \tab Log-cubic polynomial trend \cr \code{X ~ harmonic(x,y,2)} \tab Log-harmonic polynomial trend \cr \code{X ~ Z} \tab Loglinear function of covariate \code{Z} \cr \tab \eqn{\lambda(x,y) = \exp(\alpha + \beta Z(x,y))}{lambda(x,y) = exp(alpha + beta * Z(x,y))} } The higher order (``interaction'') components are described by an object of class \code{"interact"}. Such objects are created by: \tabular{ll}{ \code{\link[spatstat.model]{Poisson}()} \tab the Poisson point process\cr \code{\link[spatstat.model]{AreaInter}()} \tab Area-interaction process\cr \code{\link[spatstat.model]{BadGey}()} \tab multiscale Geyer process\cr \code{\link[spatstat.model]{Concom}()} \tab connected component interaction\cr \code{\link[spatstat.model]{DiggleGratton}() } \tab Diggle-Gratton potential \cr \code{\link[spatstat.model]{DiggleGatesStibbard}() } \tab Diggle-Gates-Stibbard potential \cr \code{\link[spatstat.model]{Fiksel}()} \tab Fiksel pairwise interaction process\cr \code{\link[spatstat.model]{Geyer}()} \tab Geyer's saturation process\cr \code{\link[spatstat.model]{Hardcore}()} \tab Hard core process\cr \code{\link[spatstat.model]{HierHard}()} \tab Hierarchical multiype hard core process\cr \code{\link[spatstat.model]{HierStrauss}()} \tab Hierarchical multiype Strauss process\cr \code{\link[spatstat.model]{HierStraussHard}()} \tab Hierarchical multiype Strauss-hard core process\cr \code{\link[spatstat.model]{Hybrid}()} \tab Hybrid of several interactions\cr \code{\link[spatstat.model]{LennardJones}() } \tab Lennard-Jones potential \cr \code{\link[spatstat.model]{MultiHard}()} \tab multitype hard core process \cr \code{\link[spatstat.model]{MultiStrauss}()} \tab multitype Strauss process \cr \code{\link[spatstat.model]{MultiStraussHard}()} \tab multitype Strauss/hard core process \cr \code{\link[spatstat.model]{OrdThresh}()} \tab Ord process, threshold potential\cr \code{\link[spatstat.model]{Ord}()} \tab Ord model, user-supplied potential \cr \code{\link[spatstat.model]{PairPiece}()} \tab pairwise interaction, piecewise constant \cr \code{\link[spatstat.model]{Pairwise}()} \tab pairwise interaction, user-supplied potential\cr \code{\link[spatstat.model]{Penttinen}()} \tab Penttinen pairwise interaction\cr \code{\link[spatstat.model]{SatPiece}()} \tab Saturated pair model, piecewise constant potential\cr \code{\link[spatstat.model]{Saturated}()} \tab Saturated pair model, user-supplied potential\cr \code{\link[spatstat.model]{Softcore}()} \tab pairwise interaction, soft core potential\cr \code{\link[spatstat.model]{Strauss}()} \tab Strauss process \cr \code{\link[spatstat.model]{StraussHard}()} \tab Strauss/hard core point process \cr \code{\link[spatstat.model]{Triplets}()} \tab Geyer triplets process } Note that it is also possible to combine several such interactions using \code{\link[spatstat.model]{Hybrid}}. \bold{Finer control over model fitting:} A quadrature scheme is represented by an object of class \code{"quad"}. To create a quadrature scheme, typically use \code{\link[spatstat.geom]{quadscheme}}. \tabular{ll}{ \code{\link[spatstat.geom]{quadscheme}} \tab default quadrature scheme \cr \tab using rectangular cells or Dirichlet cells\cr \code{\link[spatstat.geom]{pixelquad}} \tab quadrature scheme based on image pixels \cr \code{\link[spatstat.geom]{quad}} \tab create an object of class \code{"quad"} } To inspect a quadrature scheme: \tabular{ll}{ \code{plot(Q)} \tab plot quadrature scheme \code{Q}\cr \code{print(Q)} \tab print basic information about quadrature scheme \code{Q}\cr \code{\link[spatstat.geom:summary.quad]{summary}(Q)} \tab summary of quadrature scheme \code{Q} } A quadrature scheme consists of data points, dummy points, and weights. To generate dummy points: \tabular{ll}{ \code{\link[spatstat.geom]{default.dummy}} \tab default pattern of dummy points \cr \code{\link[spatstat.geom]{gridcentres}} \tab dummy points in a rectangular grid \cr \code{\link[spatstat.random]{rstrat}} \tab stratified random dummy pattern \cr \code{\link[spatstat.geom]{spokes}} \tab radial pattern of dummy points \cr \code{\link[spatstat.geom]{corners}} \tab dummy points at corners of the window } To compute weights: \tabular{ll}{ \code{\link[spatstat.geom]{gridweights}} \tab quadrature weights by the grid-counting rule \cr \code{\link[spatstat.geom]{dirichletWeights}} \tab quadrature weights are Dirichlet tile areas } \bold{Simulation and goodness-of-fit for fitted models:} \tabular{ll}{ \code{\link[spatstat.model]{rmh.ppm}} \tab simulate realisations of a fitted model \cr \code{\link[spatstat.model]{simulate.ppm}} \tab simulate realisations of a fitted model \cr \code{\link[spatstat.explore]{envelope}} \tab compute simulation envelopes for a fitted model } \bold{Point process models on a linear network:} An object of class \code{"lpp"} represents a pattern of points on a linear network. Point process models can also be fitted to these objects. Currently only Poisson models can be fitted. \tabular{ll}{ \code{\link[spatstat.linnet]{lppm}} \tab point process model on linear network \cr \code{\link[spatstat.linnet]{anova.lppm}} \tab analysis of deviance for \cr \tab point process model on linear network \cr \code{\link[spatstat.linnet]{envelope.lppm}} \tab simulation envelopes for \cr \tab point process model on linear network \cr \code{\link[spatstat.linnet]{fitted.lppm}} \tab fitted intensity values \cr \code{\link[spatstat.linnet]{predict.lppm}} \tab model prediction on linear network \cr \code{\link[spatstat.linnet]{linim}} \tab pixel image on linear network \cr \code{\link[spatstat.linnet]{plot.linim}} \tab plot a pixel image on linear network \cr \code{\link[spatstat.linnet]{eval.linim}} \tab evaluate expression involving images \cr \code{\link[spatstat.linnet]{linfun}} \tab function defined on linear network \cr \code{\link[spatstat.linnet]{methods.linfun}} \tab conversion facilities } } \section{V. MODEL FITTING (DETERMINANTAL POINT PROCESS MODELS)}{ Code for fitting \emph{determinantal point process models} has recently been added to \pkg{spatstat}. For information, see the help file for \code{\link[spatstat.model]{dppm}}. } \section{VI. MODEL FITTING (SPATIAL LOGISTIC REGRESSION)}{ \bold{Logistic regression} Pixel-based spatial logistic regression is an alternative technique for analysing spatial point patterns that is widely used in Geographical Information Systems. It is approximately equivalent to fitting a Poisson point process model. In pixel-based logistic regression, the spatial domain is divided into small pixels, the presence or absence of a data point in each pixel is recorded, and logistic regression is used to model the presence/absence indicators as a function of any covariates. Facilities for performing spatial logistic regression are provided in \pkg{spatstat} for comparison purposes. \bold{Fitting a spatial logistic regression} Spatial logistic regression is performed by the function \code{\link[spatstat.model]{slrm}}. Its result is an object of class \code{"slrm"}. There are many methods for this class, including methods for \code{print}, \code{fitted}, \code{predict}, \code{simulate}, \code{anova}, \code{coef}, \code{logLik}, \code{terms}, \code{update}, \code{formula} and \code{vcov}. For example, if \code{X} is a point pattern (class \code{"ppp"}): \tabular{ll}{ \emph{command} \tab \emph{model} \cr \code{slrm(X ~ 1)} \tab Complete Spatial Randomness \cr \code{slrm(X ~ x)} \tab Poisson process with \cr \tab intensity loglinear in \eqn{x} coordinate \cr \code{slrm(X ~ Z)} \tab Poisson process with \cr \tab intensity loglinear in covariate \code{Z} } \bold{Manipulating a fitted spatial logistic regression} \tabular{ll}{ \code{\link[spatstat.model]{anova.slrm}} \tab Analysis of deviance \cr \code{\link[spatstat.model]{coef.slrm}} \tab Extract fitted coefficients \cr \code{\link[spatstat.model]{vcov.slrm}} \tab Variance-covariance matrix of fitted coefficients \cr \code{\link[spatstat.model]{fitted.slrm}} \tab Compute fitted probabilities or intensity \cr \code{\link[spatstat.model]{logLik.slrm}} \tab Evaluate loglikelihood of fitted model \cr \code{\link[spatstat.model]{plot.slrm}} \tab Plot fitted probabilities or intensity \cr \code{\link[spatstat.model]{predict.slrm}} \tab Compute predicted probabilities or intensity with new data \cr \code{\link[spatstat.model]{simulate.slrm}} \tab Simulate model } There are many other undocumented methods for this class, including methods for \code{print}, \code{update}, \code{formula} and \code{terms}. Stepwise model selection is possible using \code{step} or \code{stepAIC}. For variable selection, see \code{\link[spatstat.explore]{sdr}}. } \section{VII. SIMULATION}{ There are many ways to generate a random point pattern, line segment pattern, pixel image or tessellation in \pkg{spatstat}. \bold{Random point patterns:} \tabular{ll}{ \code{\link[spatstat.random]{runifpoint}} \tab generate \eqn{n} independent uniform random points \cr \code{\link[spatstat.random]{rpoint}} \tab generate \eqn{n} independent random points \cr \code{\link[spatstat.random]{rmpoint}} \tab generate \eqn{n} independent multitype random points \cr \code{\link[spatstat.random]{rpoispp}} \tab simulate the (in)homogeneous Poisson point process \cr \code{\link[spatstat.random]{rmpoispp}} \tab simulate the (in)homogeneous multitype Poisson point process \cr \code{\link[spatstat.random]{runifdisc}} \tab generate \eqn{n} independent uniform random points in disc\cr \code{\link[spatstat.random]{rstrat}} \tab stratified random sample of points \cr \code{\link[spatstat.geom]{rsyst}} \tab systematic random sample (grid) of points \cr \code{\link[spatstat.random]{rMaternI}} \tab simulate the \Matern Model I inhibition process\cr \code{\link[spatstat.random]{rMaternII}} \tab simulate the \Matern Model II inhibition process\cr \code{\link[spatstat.random]{rSSI}} \tab simulate Simple Sequential Inhibition process\cr \code{\link[spatstat.random]{rHardcore}} \tab simulate hard core process (perfect simulation)\cr \code{\link[spatstat.random]{rStrauss}} \tab simulate Strauss process (perfect simulation)\cr \code{\link[spatstat.random]{rStraussHard}} \tab simulate Strauss-hard core process (perfect simulation)\cr \code{\link[spatstat.random]{rDiggleGratton}} \tab simulate Diggle-Gratton process (perfect simulation)\cr \code{\link[spatstat.random]{rDGS}} \tab simulate Diggle-Gates-Stibbard process (perfect simulation)\cr \code{\link[spatstat.random]{rPenttinen}} \tab simulate Penttinen process (perfect simulation)\cr \code{\link[spatstat.random]{rNeymanScott}} \tab simulate a general Neyman-Scott process\cr \code{\link[spatstat.random]{rMatClust}} \tab simulate the \Matern Cluster process\cr \code{\link[spatstat.random]{rThomas}} \tab simulate the Thomas process \cr \code{\link[spatstat.random]{rLGCP}} \tab simulate the log-Gaussian Cox process \cr \code{\link[spatstat.random]{rGaussPoisson}} \tab simulate the Gauss-Poisson cluster process\cr \code{\link[spatstat.random]{rCauchy}} \tab simulate Neyman-Scott process with Cauchy clusters \cr \code{\link[spatstat.random]{rVarGamma}} \tab simulate Neyman-Scott process with Variance Gamma clusters \cr \code{\link[spatstat.random]{rcell}} \tab simulate the Baddeley-Silverman cell process \cr \code{\link[spatstat.random]{runifpointOnLines}} \tab generate \eqn{n} random points along specified line segments \cr \code{\link[spatstat.random]{rpoisppOnLines}} \tab generate Poisson random points along specified line segments } \bold{Resampling a point pattern:} \tabular{ll}{ \code{\link[spatstat.random]{quadratresample}} \tab block resampling \cr \code{\link[spatstat.geom]{rjitter}} \tab apply random displacements to points in a pattern\cr \code{\link[spatstat.random]{rshift}} \tab random shifting of (subsets of) points\cr \code{\link[spatstat.random]{rthin}} \tab random thinning } See also \code{\link[spatstat.explore]{varblock}} for estimating the variance of a summary statistic by block resampling, and \code{\link[spatstat.explore]{lohboot}} for another bootstrap technique. \bold{Fitted point process models:} If you have fitted a point process model to a point pattern dataset, the fitted model can be simulated. Cluster process models are fitted by the function \code{\link[spatstat.model]{kppm}} yielding an object of class \code{"kppm"}. To generate one or more simulated realisations of this fitted model, use \code{\link[spatstat.model]{simulate.kppm}}. Gibbs point process models are fitted by the function \code{\link[spatstat.model]{ppm}} yielding an object of class \code{"ppm"}. To generate a simulated realisation of this fitted model, use \code{\link[spatstat.random]{rmh}}. To generate one or more simulated realisations of the fitted model, use \code{\link[spatstat.model]{simulate.ppm}}. \bold{Other random patterns:} \tabular{ll}{ \code{\link[spatstat.geom]{rlinegrid}} \tab generate a random array of parallel lines through a window \cr \code{\link[spatstat.random]{rpoisline}} \tab simulate the Poisson line process within a window \cr \code{\link[spatstat.random]{rpoislinetess}} \tab generate random tessellation using Poisson line process \cr \code{\link[spatstat.random]{rMosaicSet}} \tab generate random set by selecting some tiles of a tessellation \cr \code{\link[spatstat.random]{rMosaicField}} \tab generate random pixel image by assigning random values in each tile of a tessellation } \bold{Simulation-based inference} \tabular{ll}{ \code{\link[spatstat.explore]{envelope}} \tab critical envelope for Monte Carlo test of goodness-of-fit \cr \code{\link[spatstat.explore]{bits.envelope}} \tab critical envelope for balanced two-stage Monte Carlo test \cr \code{\link[spatstat.model]{qqplot.ppm}} \tab diagnostic plot for interpoint interaction \cr \code{\link[spatstat.explore]{scan.test}} \tab spatial scan statistic/test \cr \code{\link[spatstat.explore]{studpermu.test}} \tab studentised permutation test\cr \code{\link[spatstat.explore]{segregation.test}} \tab test of segregation of types } } \section{VIII. TESTS AND DIAGNOSTICS}{ \bold{Hypothesis tests:} \tabular{ll}{ \code{\link[spatstat.explore]{quadrat.test}} \tab \eqn{\chi^2}{chi^2} goodness-of-fit test on quadrat counts \cr \code{\link[spatstat.explore]{clarkevans.test}} \tab Clark and Evans test \cr \code{\link[spatstat.explore]{cdf.test}} \tab Spatial distribution goodness-of-fit test\cr \code{\link[spatstat.explore]{berman.test}} \tab Berman's goodness-of-fit tests\cr \code{\link[spatstat.explore]{envelope}} \tab critical envelope for Monte Carlo test of goodness-of-fit \cr \code{\link[spatstat.explore]{scan.test}} \tab spatial scan statistic/test \cr \code{\link[spatstat.explore]{dclf.test}} \tab Diggle-Cressie-Loosmore-Ford test \cr \code{\link[spatstat.explore]{mad.test}} \tab Mean Absolute Deviation test \cr \code{\link[spatstat.model]{anova.ppm}} \tab Analysis of Deviance for point process models } More recently-developed tests: \tabular{ll}{ \code{\link[spatstat.explore]{dg.test}} \tab Dao-Genton test \cr \code{\link[spatstat.explore]{bits.test}} \tab Balanced independent two-stage test \cr \code{\link[spatstat.explore]{dclf.progress}} \tab Progress plot for DCLF test \cr \code{\link[spatstat.explore]{mad.progress}} \tab Progress plot for MAD test \cr } \bold{Sensitivity diagnostics:} Classical measures of model sensitivity such as leverage and influence have been adapted to point process models. \tabular{ll}{ \code{\link[spatstat.model]{leverage.ppm}} \tab Leverage for point process model\cr \code{\link[spatstat.model]{influence.ppm}} \tab Influence for point process model\cr \code{\link[spatstat.model]{dfbetas.ppm}} \tab Parameter influence\cr \code{\link[spatstat.model]{dffit.ppm}} \tab Effect change diagnostic } \bold{Diagnostics for covariate effect:} Classical diagnostics for covariate effects have been adapted to point process models. \tabular{ll}{ \code{\link[spatstat.model]{parres}} \tab Partial residual plot\cr \code{\link[spatstat.model]{addvar}} \tab Added variable plot \cr \code{\link[spatstat.explore]{rhohat}} \tab Kernel estimate of covariate effect\cr \code{\link[spatstat.explore]{rho2hat}} \tab Kernel estimate of covariate effect (bivariate) } \bold{Residual diagnostics:} Residuals for a fitted point process model, and diagnostic plots based on the residuals, were introduced in Baddeley et al (2005) and Baddeley, Rubak and \Moller (2011). Type \code{demo(diagnose)} for a demonstration of the diagnostics features. \tabular{ll}{ \code{\link[spatstat.model]{diagnose.ppm}} \tab diagnostic plots for spatial trend\cr \code{\link[spatstat.model]{qqplot.ppm}} \tab diagnostic Q-Q plot for interpoint interaction\cr \code{\link[spatstat.data]{residualspaper}} \tab examples from Baddeley et al (2005) \cr \code{\link[spatstat.model]{Kcom}} \tab model compensator of \eqn{K} function \cr \code{\link[spatstat.model]{Gcom}} \tab model compensator of \eqn{G} function \cr \code{\link[spatstat.model]{Kres}} \tab score residual of \eqn{K} function \cr \code{\link[spatstat.model]{Gres}} \tab score residual of \eqn{G} function \cr \code{\link[spatstat.model]{psst}} \tab pseudoscore residual of summary function \cr \code{\link[spatstat.model]{psstA}} \tab pseudoscore residual of empty space function \cr \code{\link[spatstat.model]{psstG}} \tab pseudoscore residual of \eqn{G} function \cr \code{\link[spatstat.model]{compareFit}} \tab compare compensators of several fitted models } \bold{Resampling and randomisation procedures} You can build your own tests based on randomisation and resampling using the following capabilities: \tabular{ll}{ \code{\link[spatstat.random]{quadratresample}} \tab block resampling \cr \code{\link[spatstat.geom]{rjitter}} \tab apply random displacements to points in a pattern\cr \code{\link[spatstat.random]{rshift}} \tab random shifting of (subsets of) points\cr \code{\link[spatstat.random]{rthin}} \tab random thinning } } \section{IX. DOCUMENTATION}{ The online manual entries are quite detailed and should be consulted first for information about a particular function. The book Baddeley, Rubak and Turner (2015) is a complete course on analysing spatial point patterns, with full details about \pkg{spatstat}. Older material (which is now out-of-date but is freely available) includes Baddeley and Turner (2005a), a brief overview of the package in its early development; Baddeley and Turner (2005b), a more detailed explanation of how to fit point process models to data; and Baddeley (2010), a complete set of notes from a 2-day workshop on the use of \pkg{spatstat}. Type \code{citation("spatstat")} to get a list of these references. } \references{ Baddeley, A. (2010) \emph{Analysing spatial point patterns in R}. Workshop notes, Version 4.1. Online technical publication, CSIRO. \url{https://research.csiro.au/software/wp-content/uploads/sites/6/2015/02/Rspatialcourse_CMIS_PDF-Standard.pdf} Baddeley, A., Rubak, E. and Turner, R. (2015) \emph{Spatial Point Patterns: Methodology and Applications with R}. Chapman and Hall/CRC Press. Baddeley, A. and Turner, R. (2005a) Spatstat: an R package for analyzing spatial point patterns. \emph{Journal of Statistical Software} \bold{12}:6, 1--42. \code{DOI: 10.18637/jss.v012.i06}. Baddeley, A. and Turner, R. (2005b) Modelling spatial point patterns in R. In: A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan, editors, \emph{Case Studies in Spatial Point Pattern Modelling}, Lecture Notes in Statistics number 185. Pages 23--74. Springer-Verlag, New York, 2006. ISBN: 0-387-28311-0. Baddeley, A., Turner, R., \Moller, J. and Hazelton, M. (2005) Residual analysis for spatial point processes. \emph{Journal of the Royal Statistical Society, Series B} \bold{67}, 617--666. Baddeley, A., Rubak, E. and \Moller, J. (2011) Score, pseudo-score and residual diagnostics for spatial point process models. \emph{Statistical Science} \bold{26}, 613--646. Baddeley, A., Turner, R., Mateu, J. and Bevan, A. (2013) Hybrids of Gibbs point process models and their implementation. \emph{Journal of Statistical Software} \bold{55}:11, 1--43. \url{https://www.jstatsoft.org/v55/i11/} Diggle, P.J. (2003) \emph{Statistical analysis of spatial point patterns}, Second edition. Arnold. Diggle, P.J. (2014) \emph{Statistical Analysis of Spatial and Spatio-Temporal Point Patterns}, Third edition. {Chapman and Hall/CRC}. Gelfand, A.E., Diggle, P.J., Fuentes, M. and Guttorp, P., editors (2010) \emph{Handbook of Spatial Statistics}. CRC Press. Huang, F. and Ogata, Y. (1999) Improvements of the maximum pseudo-likelihood estimators in various spatial statistical models. \emph{Journal of Computational and Graphical Statistics} \bold{8}, 510--530. Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) \emph{Statistical Analysis and Modelling of Spatial Point Patterns.} Wiley. Waagepetersen, R. An estimating function approach to inference for inhomogeneous Neyman-Scott processes. \emph{Biometrics} \bold{63} (2007) 252--258. } \section{Licence}{ This library and its documentation are usable under the terms of the "GNU General Public License", a copy of which is distributed with the package. } \author{ \spatstatAuthors. } \section{Acknowledgements}{ Kasper Klitgaard Berthelsen, Ottmar Cronie, Tilman Davies, Yongtao Guan, Ute Hahn, Abdollah Jalilian, Marie-Colette van Lieshout, Greg McSwiggan, Tuomas Rajala, Suman Rakshit, Dominic Schuhmacher, Rasmus Waagepetersen and Hangsheng Wang made substantial contributions of code. Additional contributions and suggestions from Monsuru Adepeju, Corey Anderson, Ang Qi Wei, Ryan Arellano, Jens \ifelse{latex}{\out{{\AA}str{\" o}m}}{Astrom}, Robert Aue, Marcel Austenfeld, Sandro Azaele, Malissa Baddeley, Guy Bayegnak, Colin Beale, Melanie Bell, Thomas Bendtsen, Ricardo Bernhardt, Andrew Bevan, Brad Biggerstaff, Anders Bilgrau, Leanne Bischof, Christophe Biscio, Roger Bivand, Jose M. Blanco Moreno, Florent Bonneu, Jordan Brown, Ian Buller, Julian Burgos, Simon Byers, Ya-Mei Chang, Jianbao Chen, Igor Chernayavsky, Y.C. Chin, Bjarke Christensen, \Lucia Cobo Sanchez, Jean-\Francois Coeurjolly, Kim Colyvas, Hadrien Commenges, Rochelle Constantine, Robin Corria Ainslie, Richard Cotton, Marcelino de la Cruz, Peter Dalgaard, Mario D'Antuono, Sourav Das, Peter Diggle, Patrick Donnelly, Ian Dryden, Stephen Eglen, Ahmed El-Gabbas, Belarmain Fandohan, Olivier Flores, David Ford, Peter Forbes, Shane Frank, Janet Franklin, Funwi-Gabga Neba, Oscar Garcia, Agnes Gault, Jonas Geldmann, Marc Genton, Shaaban Ghalandarayeshi, Julian Gilbey, Jason Goldstick, Pavel Grabarnik, C. Graf, Ute Hahn, Andrew Hardegen, Martin \Bogsted Hansen, Martin Hazelton, Juha Heikkinen, Mandy Hering, Markus Herrmann, Maximilian Hesselbarth, Paul Hewson, Hamidreza Heydarian, Kassel Hingee, Kurt Hornik, Philipp Hunziker, Jack Hywood, Ross Ihaka, \ifelse{latex}{\out{\u{C}enk I\c{c}\"{o}s}}{Cenk Icos}, Aruna Jammalamadaka, Robert John-Chandran, Devin Johnson, Mahdieh Khanmohammadi, Bob Klaver, Lily Kozmian-Ledward, Peter Kovesi, Mike Kuhn, Jeff Laake, Robert Lamb, \Frederic Lavancier, Tom Lawrence, Tomas Lazauskas, Jonathan Lee, George Leser, Angela Li, Li Haitao, George Limitsios, Andrew Lister, Nestor Luambua, Bethany Macdonald, Ben Madin, Martin Maechler, Daniel Manrique-\ifelse{latex}{\out{Casta{\~n}o}}{Castano}, Kiran Marchikanti, Jeff Marcus, Robert Mark, Peter McCullagh, Monia Mahling, Jorge Mateu Mahiques, Ulf Mehlig, Frederico Mestre, Sebastian Wastl Meyer, Mi Xiangcheng, Lore De Middeleer, Robin Milne, Enrique Miranda, Jesper \Moller, Annie \ifelse{latex}{\out{Molli{\'e}}}{Mollie}, Ines Moncada, Mehdi Moradi, Virginia Morera Pujol, Erika Mudrak, Gopalan Nair, Nader Najari, Nicoletta Nava, Linda Stougaard Nielsen, Felipe Nunes, Jens Randel Nyengaard, Jens \Oehlschlaegel, Thierry Onkelinx, Sean O'Riordan, Evgeni Parilov, Jeff Picka, Nicolas Picard, Tim Pollington, Mike Porter, Sergiy Protsiv, Adrian Raftery, Suman Rakshit, Ben Ramage, Pablo Ramon, Xavier Raynaud, Nicholas Read, Matt Reiter, Ian Renner, Tom Richardson, Brian Ripley, Yonatan Rosen, Ted Rosenbaum, Barry Rowlingson, Jason Rudokas, Tyler Rudolph, John Rudge, Christopher Ryan, Farzaneh Safavimanesh, Aila \Sarkka, Cody Schank, Katja Schladitz, Sebastian Schutte, Bryan Scott, Olivia Semboli, \Francois \ifelse{latex}{\out{S\'{e}m\'{e}curbe}}{Semecurbe}, Vadim Shcherbakov, Shen Guochun, Shi Peijian, Harold-Jeffrey Ship, Tammy L Silva, Ida-Maria Sintorn, Yong Song, Malte Spiess, Mark Stevenson, Kaspar Stucki, Jan Sulavik, Michael Sumner, P. Surovy, Ben Taylor, Thordis Linda Thorarinsdottir, Leigh Torres, Berwin Turlach, Torben Tvedebrink, Kevin Ummer, Medha Uppala, Andrew van Burgel, Tobias Verbeke, Mikko Vihtakari, Alexendre Villers, Fabrice Vinatier, Maximilian Vogtland, Sasha Voss, Sven Wagner, Hao Wang, H. Wendrock, Jan Wild, Carl G. Witthoft, Selene Wong, Maxime Woringer, Luke Yates, Mike Zamboni and Achim Zeileis. } \keyword{spatial} \keyword{package} spatstat/DESCRIPTION0000644000176200001440000000760514517671356013652 0ustar liggesusersPackage: spatstat Version: 3.0-7 Date: 2023-10-30 Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests Authors@R: c(person("Adrian", "Baddeley", role = c("aut", "cre"), email = "Adrian.Baddeley@curtin.edu.au", comment = c(ORCID="0000-0001-9499-8382")), person("Rolf", "Turner", role = "aut", email="rolfturner@posteo.net", comment=c(ORCID="0000-0001-5521-5218")), person("Ege", "Rubak", role = "aut", email = "rubak@math.aau.dk", comment=c(ORCID="0000-0002-6675-533X"))) Maintainer: Adrian Baddeley Depends: R (>= 3.5.0), spatstat.data (>= 3.0-1), spatstat.geom (>= 3.2-7), spatstat.random (>= 3.2-1), spatstat.explore (>= 3.2-5), spatstat.model (>= 3.2-8), spatstat.linnet (>= 3.1-3), utils Imports: spatstat.utils (>= 3.0-3) Suggests: Description: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. License: GPL (>= 2) URL: http://spatstat.org/ NeedsCompilation: yes ByteCompile: true BugReports: https://github.com/spatstat/spatstat/issues Packaged: 2023-10-30 03:02:07 UTC; adrian Author: Adrian Baddeley [aut, cre] (), Rolf Turner [aut] (), Ege Rubak [aut] () Repository: CRAN Date/Publication: 2023-10-30 09:10:06 UTC spatstat/build/0000755000176200001440000000000014517616251013224 5ustar liggesusersspatstat/build/vignette.rds0000644000176200001440000000061614517616251015566 0ustar liggesusersRN0ͫؐ0~TQ 8!q"iw3PN|ز,\r\ a|5pY%1}%djEXk\1'EJC,HdUT~*)#DZXr@4Rs$Ve/8!bV  _>B?!(KBk*VۤӦy rdn!g?ž^TYyB_:qFיQ331L:̜I3P?F:1(ēRЯt 6jEB?<6[2`8#VϞOSS_]mޔE2HS~NvymټoWOb`}| naspatstat/vignettes/0000755000176200001440000000000014517616251014135 5ustar liggesusersspatstat/vignettes/updates.Rnw0000644000176200001440000044577614514714227016320 0ustar liggesusers\documentclass[10pt]{article} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage[bottom=0.5cm, right=1.5cm, left=1.5cm, top=1.5cm]{geometry} % \VignetteIndexEntry{Summary of Recent Updates to the Spatstat Family} % $Revision: 1.68 $ $Date: 2023/10/21 09:22:40 $ \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} <>= library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) @ \title{Summary of recent updates to \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{\today} \maketitle \thispagestyle{empty} <>= readSizeTable <- function(fname) { if(is.null(fname) || !file.exists(fname)) return(NULL) a <- read.table(fname, header=TRUE) a$date <- as.Date(a$date) return(a) } getSizeTable <- function(packagename="spatstat", tablename="packagesizes.txt") { fname <- system.file("doc", tablename, package=packagename) readSizeTable(fname) } RemoveDevel <- function(sizetable) { ## remove entries with fractional version numbers if(is.null(sizetable)) return(NULL) ver <- sizetable$version isdevel <- sapply(ver, function(x) { length(unlist(package_version(x))) > 3 }) st <- if(all(isdevel)) NULL else sizetable[!isdevel, , drop=FALSE] return(st) } counts <- c("nhelpfiles", "nobjects", "ndatasets", "Rlines", "srclines") mergeSizeTables <- function(a, b, breakupdate, allow.devel=FALSE) { #' a is the running total for spatstat; b is a sub-package. #' breakupdate is the date when the code in b was removed from spatstat #' so that the size of 'b' must be added to 'a' for all dates >= breakupdate if(!allow.devel) b <- RemoveDevel(b) if(is.null(b)) return(a) adates <- a$date bdates <- b$date alldates <- sort(unique(c(adates,bdates))) if(missing(breakupdate)) breakupdate <- min(bdates) #' functions to determine, for any given date, #' the relevant (latest) row of the table aok <- rev(!duplicated(rev(adates))) arowfun <- approxfun(adates[aok], seq_along(adates)[aok], method="constant", f=0, rule=2, yleft=0) bok <- rev(!duplicated(rev(bdates))) browfun <- approxfun(bdates[bok], seq_along(bdates)[bok], method="constant", f=0, rule=2, yleft=0) result <- NULL for(k in seq_along(alldates)) { thedate <- alldates[k] i <- arowfun(thedate) j <- browfun(thedate) #' i > 0 because spatstat's founding date is earlier than any sub-package nextrow <- a[i, ] if(j > 0 && thedate >= breakupdate) { #' add contribution from 'b' nextrow[, counts] <- nextrow[, counts] + b[j, counts] } result <- rbind(result, nextrow) } return(result) } ## Get histories of all sub-packages ## Package formerly known as 'spatstat' z <- getSizeTable() ## installed sub-packages - access via the installed sub-packages zutils <- getSizeTable("spatstat.utils") zdata <- getSizeTable("spatstat.data") zsparse <- getSizeTable("spatstat.sparse") zgeom <- getSizeTable("spatstat.geom") zrandom <- getSizeTable("spatstat.random") zexplore <- getSizeTable("spatstat.explore") zmodel <- getSizeTable("spatstat.model") zlinnet <- getSizeTable("spatstat.linnet") ## other sub-packages - access via stored copies of package size files ## defunct package spatstat.core zcore <- getSizeTable("spatstat", "spatstatcoresize.txt") ## extension packages zlocal <- getSizeTable("spatstat", "spatstatlocalsize.txt") zgui <- getSizeTable("spatstat", "spatstatguisize.txt") zKnet <- getSizeTable("spatstat", "spatstatKnetsize.txt") ## Merge histories starting at the 'split dates' z <- mergeSizeTables(z, zutils, "2017-03-22") z <- mergeSizeTables(z, zdata, "2017-09-23") z <- mergeSizeTables(z, zsparse, "2020-11-04") BigSplitDay <- "2020-12-14" z <- mergeSizeTables(z, zgeom, BigSplitDay) z <- mergeSizeTables(z, zcore, BigSplitDay) z <- mergeSizeTables(z, zlinnet, BigSplitDay) z <- mergeSizeTables(z, zrandom, "2022-02-12") CoreSplitDay <- "2020-05-25" # size of 'core' drops to 0 on this date z <- mergeSizeTables(z, zexplore, CoreSplitDay) z <- mergeSizeTables(z, zmodel, CoreSplitDay) ## extension packages: these never overlapped spatstat z <- mergeSizeTables(z, zlocal) z <- mergeSizeTables(z, zgui) z <- mergeSizeTables(z, zKnet) ## Now summarise currentcount <- z[nrow(z), counts] bookcount <- z[z$version == "1.42-0", counts] changes <- currentcount - bookcount newobj <- changes[["nobjects"]] newdat <- changes[["ndatasets"]] + 1 # counting rule doesn't detect redwood3 newcode <- changes[["Rlines"]] + changes[["srclines"]] bookcode <- bookcount[["Rlines"]] + bookcount[["srclines"]] currentcode <- currentcount[["Rlines"]] + currentcount[["srclines"]] growth <- signif((100 * newcode)/bookcode, digits=2) @ %$ This is a summary of changes to the \spst\ package that have occurred since the publication of the book \cite{baddrubaturn15} in 2015. Since then, the \spst\ family has grown by \Sexpr{growth}\%, including \Sexpr{newobj} new functions and \Sexpr{newdat} new datasets, and now contains more than \Sexpr{10 * floor(currentcode/10000)},000 lines of code. This document summarises the most important changes. <>= options(SweaveHooks=list(fig=function() par(mar=0.2+c(2,4,2,0)))) Plot <- function(fmla, ..., dat=z) { yvals <- eval(as.expression(fmla[[2]]), envir=dat) plot(fmla, ..., data=dat, type="l", xlab="", lwd=2, ylim=c(0, max(yvals))) } @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.5\textwidth} \centerline{ <>= Plot((Rlines + srclines)/1000 ~ date, ylab="Lines of code (x 1000)", main="Spatstat growth") lines(srclines/1000 ~ date, data=z) text(as.Date("2015-01-01"), 9.5, "C code") text(as.Date("2015-01-01"), 60, "R code") @ } \setcounter{tocdepth}{1} \tableofcontents \newpage \newpage \section{Version information} The book \cite{baddrubaturn15}, published in December 2015, covered \spst\ version \texttt{1.42-0}, released in May 2015. <>= ## Tabulate latest version numbers of packages vtable <- data.frame(package="spatstat", version=sversion, date=as.Date(sdate)) AppendVersion <- function(pkg, sizetable, v, allow.devel=FALSE) { if(!allow.devel) sizetable <- RemoveDevel(sizetable) if(is.null(sizetable)) return(v) lastrow <- sizetable[nrow(sizetable), , drop=FALSE] if(is.null(lastrow)) return(v) rbind(v, data.frame(package=pkg, version=lastrow[,"version"], date=as.Date(lastrow[,"date"]))) } vtable <- AppendVersion("spatstat.geom", zgeom, vtable) vtable <- AppendVersion("spatstat.random", zrandom, vtable) vtable <- AppendVersion("spatstat.explore", zexplore, vtable) vtable <- AppendVersion("spatstat.model", zmodel, vtable) ##vtable <- AppendVersion("spatstat.core", zcore, vtable) vtable <- AppendVersion("spatstat.linnet", zlinnet, vtable) vtable <- AppendVersion("spatstat.sparse", zsparse, vtable) vtable <- AppendVersion("spatstat.data", zdata, vtable) vtable <- AppendVersion("spatstat.utils", zutils, vtable) vtable <- AppendVersion("spatstat.local", zlocal, vtable) vtable <- AppendVersion("spatstat.Knet", zKnet, vtable) vtable <- AppendVersion("spatstat.gui", zgui, vtable) @ The current versions of the \spst\ family of packages (used to produce this document) are: <>= print(vtable[,c(3,1,2)], row.names=FALSE) @ \section{Package structure} The original \pkg{spatstat} package grew to be very large. It has now been split into a family of packages, to satisfy the requirements of CRAN. This should not affect the user: existing code will continue to work in the same way. Typing \code{library(spatstat)} will load the familiar \pkg{spatstat} package which can be used as before. \subsection{Sub-packages} Currently there are nine sub-packages, called \pkg{spatstat.utils}, \pkg{spatstat.data}, \pkg{spatstat.sparse}, \pkg{spatstat.geom}, \pkg{spatstat.random}, \pkg{spatstat.explore}, \pkg{spatstat.model}, \pkg{spatstat.linnet}, and \pkg{spatstat}. \begin{itemize} \item The \code{spatstat} package now contains only documentation and introductory material. It provides beginner's introductions, vignettes, interactive demonstration scripts, and a few help files summarising the package. \item The \pkg{spatstat.data} package now contains all the datasets for \pkg{spatstat}. \item The \pkg{spatstat.utils} package contains utility functions for \pkg{spatstat}. \item The \pkg{spatstat.sparse} package contains functions for manipulating sparse arrays and performing linear algebra. \item The \pkg{spatstat.geom} package contains definitions of spatial objects (such as point patterns, windows and pixel images) and code which performs geometrical operations. \item The \pkg{spatstat.random} package contains functions for random generation of spatial patterns and random simulation of models. \item The \pkg{spatstat.explore} package contains the code for exploratory data analysis and nonparametric analysis of spatial data. \item The \pkg{spatstat.model} package contains the code for model-fitting, model diagnostics, and formal inference. \item The \pkg{spatstat.linnet} package defines spatial data on a linear network, and performs geometrical operations and statistical analysis on such data. \end{itemize} \noindent\textbf{Installing:} If you install \pkg{spatstat}, then the system will install all the other sub-packages listed above. \noindent\textbf{Running:} If you type \code{library(spatstat)} in an \textsf{R} session, the system will automatically load \pkg{spatstat.data}, \pkg{spatstat.geom}, \pkg{spatstat.random}, \pkg{spatstat.explore}, \pkg{spatstat.model} and \pkg{spatstat.linnet}. It will also silently \textbf{``import''} \pkg{spatstat.utils} and \pkg{spatstat.sparse}. To access the functions in \pkg{spatstat.utils} directly, you would need to type \code{library(spatstat.utils)}. Similarly for \pkg{spatstat.sparse}. \subsection{Extension packages} There are also extension packages which provide additional capabilities and must be loaded explicitly when you need them. Currently there are three extension packages, with a fourth in development: \begin{itemize} \item \pkg{spatstat.local} for local model-fitting, \item \pkg{spatstat.Knet} provides additional code for analysing point patterns on a network. \item \pkg{spatstat.gui} containing interactive graphics functions, \item \pkg{spatstat.sphere} for analysing point patterns on a sphere (under development!) \end{itemize} %\pagebreak \section{Precis of all changes} Here is the text from the `overview' sections of the News and Release Notes for each update. \begin{itemize} \item The \spst\ family no longer depends on the packages \pkg{maptools}, \pkg{sp} and \pkg{RandomFields}. \item code for fitting and simulating log-Gaussian Cox models has changed. \item New vignette on function objects (class \texttt{"fv"} and \texttt{"envelope"}) \item Vignette on shapefiles temporarily removed. \item Integration of functions. \item \texttt{clarkevans.test} modified. %% \item The package \texttt{spatstat.core} has been split into two packages called %% \texttt{spatstat.explore} (for exploratory data analysis) %% and \texttt{spatstat.model} (for modelling and formal inference). %% \item The original \spst\ package has now been split into 9 sub-packages %% (\pkg{spatstat.utils}, \pkg{spatstat.data}, \pkg{spatstat.sparse}, %% \pkg{spatstat.geom}, \pkg{spatstat.random}, %% \pkg{spatstat.explore}, \pkg{spatstat.model}, \pkg{spatstat.linnet} %% and \pkg{spatstat}). \item Improvements to \texttt{envelope} methods. \item Conditional simulation for Matern cluster process. \item Improvements to \texttt{runifpoint} and \texttt{rpoispp}. \item Extension of distance transform algorithm. \item Improvement to progress reports. \item Suppress annoying warnings. \item Changed the calculation of standard errors in \texttt{density.ppp} and \texttt{relrisk.ppp}. \item Inline arithmetic for function tables (class \texttt{"fv"}) and arrays (class \texttt{"fasp"}) \item Standard error calculation for \texttt{Smooth.ppp} (experimental) \item multitype pair correlation functions can save numerator and denominator. \item multitype inhomogeneous $J$ functions. \item More support for automatic bandwidth selection. \item Standard errors are now available for \texttt{ppm} models fitted using \texttt{gam}. \item \texttt{linearKinhom} and \texttt{linearpcfinhom} now automatically estimate the intensity. \item \texttt{density.lpp} accepts bandwidth selection rules, and has a simple default bandwidth. \item Pair correlation functions allow more control over smoothing parameters. \item Extension to support for one-dimensional smoothing kernels. \item Improvements to \texttt{update} methods for point process models. \item New \texttt{update} methods for classes \texttt{dppm} and \texttt{rppm}. \item Generate truncated Poisson random variables. \item reciprocal moment of Poisson variable conditioned to be positive. \item Methods for \verb![[! and \verb![[<-! for hyperframes. \item Colour map for \textsf{pH} values. \item Restrict a colour map to a narrower range of values. \item Integral of a one-dimensional density estimate. \item \texttt{kppm} has been accelerated when \texttt{method="palm"} or \texttt{"clik2"}. \item \texttt{kppm} can save the history of the optimisation algorithm. \item Faster algorithms for simulating cluster processes. \item Penalised model-fitting for Neyman-Scott cluster process models. \item Index of the strength of clustering in a Neyman-Scott cluster process model. \item Probability of having any siblings. \item More information is printed about Neyman-Scott cluster process models. \item Palm intensity diagnostic plot. \item Convert several factors or factor-valued images to a common set of levels. \item Extension to \texttt{rjitter} \item Alternative to \texttt{rjitter} \item Quantile function as a function \item Periodic edge correction for \textit{K} function. \item Changed denominator in \textit{K} function and pair correlation function. \item Bandwidth selection for adaptive kernel estimation of intensity. \item U-shaped and inverted-U-shaped curves in \texttt{rhohat}. \item Radial cumulative integral of an image. \item New dataset \texttt{stonetools}. \item Regularized model-fitting in \texttt{ppm} and \texttt{kppm}. \item Residuals for recursively-partitioned models. \item Residuals for any observed point pattern and an estimate of its intensity. \item Weighted measures and weighted integrals. \item Improved approximation of intensity of Gibbs models. \item Experimental code to represent (theoretical) point process models \item Extract more information about a point process model. \item kernel smoothing on a linear network. \item linear network $K$ function and pair correlation function based on Euclidean distance. \item inhomogeneous linear network $J$ function. \item Terminal vertices of a network. \item A point pattern on a network can be plotted as cross-ticks. \item The interactive graphics functions \texttt{iplot} and \texttt{istat} have been removed from \spst\ into a new extension package \pkg{spatstat.gui}. \item The packages \pkg{tcltk} and \pkg{rpanel} are no longer Suggested by \spst. \item \spst\ now Imports the package \pkg{spatstat.sparse}. \item \spst\ now Imports the package \pkg{spatstat.utils}. \item \spst\ now requires the package \pkg{spatstat.data} which contains the datasets. \item \spst\ now suggests the package \pkg{fftwtools}. \item Conditional simulation in \texttt{kppm}. \item More diagnostics for spatial logistic regression models. \item Increased numerical stability in \texttt{kppm}. \item Simulation of the product shot noise Cox process. \item Information criteria for model selection in \texttt{kppm}. \item Estimation of the spatial covariance function of a pixel image \item Modified handling of covariates in \texttt{slrm} \item New options for \texttt{weighted.quantile} \item Buffer tessellation \item New function for jittering point patterns on a network. \item Extensions to \texttt{rhohat} \item \texttt{densityfun.ppp} handles query points outside original window \item Extension to \texttt{discretise}. \item Improvement to \texttt{densityEqualSplit}. \item summary method for spatial logistic regression models \item New options for \texttt{distmap.psp} \item Improved output in \texttt{summary.mppm} \item Increased speed for large datasets. \item Variance calculations handle larger datasets. \item Relative risk estimation on a network. \item Leave-one-out density estimation on a network. \item Add new vertices to a linear network. \item More support for multi-dimensional patterns. \item \texttt{predict.mppm} now works for multitype point process models. \item Improved handling of \texttt{newdata} in \texttt{predict.mppm} \item New datasets \texttt{concrete} and \texttt{btb}. \item Changed default value of \texttt{stringsAsFactors}. \item Function \texttt{lengths.psp} has been renamed \verb!lengths_psp!. \item Tessellations on a linear network can now have marks. \item More functions for manipulating tessellations on a linear network. \item New functions for simulating point processes on a linear network. \item Nearest Neighbour Index function can now return mark values. \item Index of repulsion strength for determinantal point process models. \item Nearest neighbours between two point patterns in any number of dimensions. \item More options for handling bad simulation outcomes in \texttt{envelope}. \item \texttt{mppm} accepts case weights. \item Bandwidth selectors warn about extreme values of bandwidth. \item Fast kernel estimation on a linear network using 2D kernels. \item Extension of Scott's rule for bandwidth selection. \item Cross-validated bandwidth selection on a linear network. \item Random thinning and random labelling of spatial patterns extended to different types of pattern. \item Confidence intervals for multitype $K$ function. \item Envelopes for balanced two-stage test \item Extensions to adaptive intensity estimators \item `Dartboard' tessellation using polar coordinates. \item Standard error calculation for inverse-distance weighting. \item Kernel estimate of intensity as a \texttt{function(x,y)}. \item Extract discrete and continuous components of a measure. \item Improvements and extensions to leverage and influence code. \item Plot a line segment pattern using line widths. \item Find connected components of each tile in a tessellation. \item Geometrical operations on \texttt{distfun} objects. \item Join vertices in a linear network. \item Distance map and contact distribution for rectangular structuring element. \item Lurking variable plot for models fitted to several point patterns. \item New dataset \code{cetaceans}. \item Gamma correction for colour maps and image plots. \item Class \code{units} has been renamed \code{unitname} to avoid package collision. \item More support for tessellations. \item Fixed longstanding bug in leverage and influence diagnostics. \item Improvements and bug fixes for leverage and influence diagnostics. \item Tighter bounding box for \code{psp}, \code{lpp}, \code{linnet} objects. \item Improved layout in \code{plot.solist} \item Tools to increase colour saturation. \item Connected components of a 3D point pattern. \item Accelerated computations on linear networks. \item Accelerated simulation of determinantal point processes. \item Improved printing of 3D point patterns. \item Minor corrections to handling of unitnames. \item Improvements to \texttt{ppm} and \texttt{update.ppm}. \item Correction to \texttt{lohboot} \item Numerous bug fixes for linear networks code. \item Now handles disconnected linear networks. \item Effect function is now available for all types of fitted model. \item Geometric-mean smoothing. \item A model can be fitted or re-fitted to a sub-region of data. \item New fast algorithm for kernel smoothing on a linear network. \item Leverage and influence diagnostics extended to Poisson/Gibbs models fitted by logistic composite likelihood. \item Two-stage Monte Carlo test. \item Dirichlet/Voronoi tessellation on a linear network. \item Thinning of point patterns on a linear network. \item More support for functions and tessellations on a linear network. \item Bandwidth selection for pair correlation function. \item Pooling operations improved. \item Operations on signed measures. \item Operations on lists of pixel images. \item Improved pixellation of point patterns. \item Stieltjes integral extended. \item Subset operators extended. \item Greatly accelerated \texttt{rmh} when using \texttt{nsave} \item Sufficient Dimension Reduction for point processes. \item Alternating Gibbs Sampler for point process simulation. \item New class of spatially sampled functions. \item ROC and AUC extended to other types of point patterns and models. \item More support for linear networks. \item More support for infinite straight lines. \item \spst\ now depends on the packages \pkg{nlme} and \pkg{rpart}. \item Important bug fix in \code{linearK}, \code{linearpcf} \item Changed internal format of \code{linnet} and \code{lpp} objects. \item Faster computation in linear networks. \item Bias correction techniques. \item Bounding circle of a spatial object. \item Option to plot marked points as arrows. \item Kernel smoothing accelerated. \item Workaround for bug in some graphics drivers affecting image orientation. \item Non-Gaussian smoothing kernels. \item Improvements to inhomogeneous multitype $K$ and $L$ functions. \item Variance approximation for pair correlation function. \item Leverage and influence for multitype point process models. \item Functions for extracting components of vector-valued objects. \item Recursive-partition point process models. \item Minkowski sum, morphological dilation and erosion with any shape. \item Minkowski sum also applicable to point patterns and line segment patterns. \item Important bug fix in Smooth.ppp \item Important bug fix in spatial CDF tests. \item More bug fixes for replicated patterns. \item Simulate a model fitted to replicated point patterns. \item Inhomogeneous multitype $F$ and $G$ functions. \item Summary functions recognise \texttt{correction="all"} \item Leverage and influence code handles bigger datasets. \item More support for pixel images. \item Improved progress reports. \item New dataset \texttt{redwood3} \item Fixed namespace problems arising when spatstat is not loaded. \item Important bug fix in leverage/influence diagnostics for Gibbs models. \item Surgery with linear networks. \item Tessellations on a linear network. \item Laslett's Transform. \item Colour maps for point patterns with continuous marks are easier to define. \item Pair correlation function estimates can be pooled. \item Stipulate a particular version of a package. \item More support for replicated point patterns. \item More support for tessellations. \item More support for multidimensional point patterns and point processes. \item More options for one-sided envelopes. \item More support for model comparison. \item Convexifying operation. \item Subdivide a linear network. \item Penttinen process can be simulated (by Metropolis-Hastings or CFTP). \item Calculate the predicted variance of number of points. \item Accelerated algorithms for linear networks. \item Quadrat counting accelerated, in some cases. \item Simulation algorithms have been accelerated; simulation outcomes are \emph{not} identical to those obtained from previous versions of \spst. \item Determinantal point process models. \item Random-effects and mixed-effects models for replicated patterns. \item Dao-Genton test, and corresponding simulation envelopes. \item Simulated annealing and simulated tempering. \item spatstat colour tools now handle transparent colours. \item Improvements to \verb![! and \texttt{subset} methods \item Extensions to kernel smoothing on a linear network. \item Support for one-dimensional smoothing kernels. \item Mark correlation function may include weights. \item Cross-correlation version of the mark correlation function. \item Penttinen pairwise interaction model. \item Improvements to simulation of Neyman-Scott processes. \item Improvements to fitting of Neyman-Scott models. \item Extended functionality for pixel images. \item Fitted intensity on linear network \item Triangulation of windows. \item Corrected an edge correction. \end{itemize} \section{New datasets} The following new datasets have been added. These are now provided in the sub-package \pkg{spatstat.data}. \begin{itemize} \item \texttt{austates}: The states and large mainland territories of Australia represented as polygonal regions forming a tessellation. \item \texttt{redwood3}: a more accurate version of the \texttt{redwood} data. \item \texttt{cetaceans}: point patterns of whale and dolphin sightings. \item \texttt{concrete}: air bubbles in concrete. \item \texttt{btb}: bovine tuberculosis occurrences. \item \texttt{stonetools}: palaeolithic stone tools and bone fragments. \end{itemize} \section{New classes} The following new classes of objects may be of use. \begin{itemize} \item \texttt{traj}: Trajectory (history of function evaluations) in a model that was fitted by optimisation. \item \texttt{metric}: Class of distance metrics. An object of class \texttt{metric} represents a distance metric between points in two-dimensional space. See \texttt{help(metric.object)}. \item \texttt{ssf}: Class of spatially sampled functions. An object of class \texttt{"ssf"} represents a spatial function which has been evaluated or sampled at an irregular set of points. See \texttt{help(ssf)}. \item \texttt{zclustermodel}: Experimental. An object of class \texttt{zclustermodel} represents a Neyman-Scott cluster point process model with specified parameter values (whereas \texttt{kppm} represents such a model fitted to data). \item \texttt{zgibbsmodel}: Experimental. An object of class \texttt{zgibbsmodel} represents a Gibbs point process model with specified parameter values (whereas \texttt{ppm} represents such a model fitted to data). \end{itemize} \section{New Functions} Following is a list of all the functions that have been added, starting with the most recent additions. \begin{itemize} \item \texttt{integral.fv}: Compute the integral of a function object. \item \texttt{compileCDF}: Low level utility for calculating cumulative distribution function of distance variable. \item \texttt{Math.fv}, \texttt{Complex.fv}, \texttt{Summary.fv}, \texttt{Ops.fv}: Methods for arithmetic operations for function tables (class \texttt{"fv"}) \item \texttt{Math.fasp}, \texttt{Complex.fasp}, \texttt{Summary.fasp}, \texttt{Ops.fasp}: Methods for arithmetic operations for function arrays (class \texttt{"fasp"}) \item \texttt{Gcross.inhom}, \texttt{Gdot.inhom}: Multitype $G$ functions for inhomogeneous point processes. \item \texttt{Jcross.inhom}, \texttt{Jdot.inhom}, \texttt{Jmulti.inhom}: Multitype $J$ functions for inhomogeneous point processes. \item \texttt{summary.bw.optim}, \texttt{print.summary.bw.optim}: Method for \texttt{summary} of optimised bandwidth objects (class \texttt{bw.optim}). These are the objects produced by the bandwidth selection functions such as \texttt{bw.diggle}, \texttt{bw.scott}, \texttt{bw.pcf} \item \texttt{psp2mask}: Function \texttt{as.mask.psp} has been renamed \texttt{psp2mask}. The old function \texttt{as.mask.psp} still exists but will soon be deprecated and later removed. \item \texttt{update.dppm}: Update method for determinantal point process models. \item \texttt{update.rppm}: Update method for recursively partitioned point process models. \item \verb![[.hyperframe!, \verb![[<-.hyperframe!: Methods for \verb![[! and \verb![[<-! for hyperframes. \item \texttt{pHcolourmap}, \texttt{pHcolour}: Colour map for values of pH \item \texttt{restrict.colourmap}: Restrict a colourmap to a narrower range of values. \item \texttt{integral.density}: Compute the integral of a one-dimensional kernel density estimate. \item \texttt{as.colourmap}: Extract colour information from an object. \item \texttt{panysib}: Probability that a point in a cluster process has \emph{any} siblings. \item \texttt{is.poissonclusterprocess}: Detects whether a given model is a Poisson cluster process (which includes Neyman-Scott processes). \item \texttt{traj}, \texttt{print.traj}, \texttt{plot.traj}, \texttt{lines.traj}: Extract, print and plot the trajectory of function evaluations. \item \texttt{rpoisnonzero}: Generate Poisson random variables conditioned to be positive. \item \texttt{rpoistrunc}: Generate `truncated' Poisson random variables, conditioned to be greater than or equal to a specified minimum value. \item \texttt{recipEnzpois}: Calculate the first reciprocal moment of nonzero Poisson variable. \item \texttt{rclusterBKBC}: (Advanced use) Internal algorithm to simulate any Neyman-Scott cluster process using either the naive, Brix-Kendall, or Baddeley-Chang algorithm. \item \texttt{palmdiagnose}, \texttt{plot.palmdiag}: Palm intensity diagnostic plot for cluster process models proposed by Tanaka, Ogata and Stoyan. \item \texttt{harmoniseLevels}: Given several factors or factor-valued pixel images, convert them all to have the same set of factor levels. \item \texttt{rexplode}: ``Explode'' a point pattern by randomly displacing each group of duplicated points to make a circular pattern around the original location. An alternative to \texttt{rjitter}. \item \texttt{quantilefun}: Return a function that computes any quantiles of a given dataset. \item \texttt{bw.CvL.adaptive}: Bandwidth selection for adaptive kernel estimation of intensity. \item \texttt{radcumint}: Radial cumulative integral of an image. \item \texttt{Smooth.lpp}: kernel smoothing on a linear network. \item \texttt{residuals.rppm}: Residual measure for a recursively-partitioned point process model. \item \texttt{residualMeasure}: Residual measure for any observed point pattern and any estimate of its intensity. \item \texttt{linearKEuclid, linearpcfEuclid, linearKEuclidInhom, linearpcfEuclidInhom}: Linear network $K$ function and pair correlation function based on Euclidean distances. \item \texttt{linearJinhom}: Inhomogeneous $J$ function on a linear network. \item \texttt{terminalvertices}: Extract the terminal vertices of a linear network. \item \texttt{bw.relrisk.lpp}: This function replaces \texttt{bw.relrisklpp} and is a method for the generic \texttt{bw.relrisk}. \item \texttt{measureWeighted}: weighted version of a measure. \item \texttt{harmonicmean, harmonicsum}: The harmonic mean of a set of numbers, calculated robustly. \item \texttt{which.min.fair, which.max.fair} (in \texttt{spatstat.utils}): Find the location of the minimum or maximum entry in a vector; if there are multiple minima or maxima, choose one of them at random. \item \texttt{hardcoredist}: Extract the hard core distance of a point process model. \item \texttt{interactionorder}: Extract the order of interpoint interaction of a point process model. \item \texttt{zgibbsmodel}: Experimental. Create an object of class \texttt{zgibbsmodel}. \item \texttt{print.zgibbsmodel}: Experimental. Print an object of class \texttt{zgibbsmodel}. \item \texttt{is.poisson.zgibbsmodel}, \texttt{is.stationary.zgibbsmodel}: Experimental. Methods for class \texttt{zgibbsmodel}. \item \texttt{indefinteg}: Numerically computes the indefinite integral of a function \item \texttt{framedist.pixels}: Computes distance from each pixel to the enclosing rectangle. \item \texttt{lurking.slrm}: Lurking variable plot for spatial logistic regression models. \item \texttt{eem.slrm}: Exponential energy marks for spatial logistic regression models. \item \texttt{eem.ppm}: Exponential energy marks for Gibbs and Poisson point process models (this function was previously called \texttt{eem}). \item \texttt{transformquantiles}: Transform the quantiles of a vector, matrix, array or pixel image. \item \texttt{convexmetric}: Distance metric based on a convex set. \texttt{invoke.metric}: Low level function to perform a desired operation using a given metric. \item \texttt{mean.ecdf, mean.ewcdf} Calculate the mean of an empirical cumulative distribution function. \item \texttt{rjitter.ppp} This function was previously called \texttt{rjitter}. It is now a method for the new generic function \texttt{rjitter}. \item \texttt{bufftess}: Distance buffer tessellation \item \texttt{ic}: Information criteria for model selection in ppm and kppm. Kindly contributed by Achmad Choiruddin, Jean-Francois Coeurjolly and Rasmus Waagepetersen. \item \texttt{rPSNCP}: Generate simulated realisations of the product shot noise Cox process. Contributed by Abdollah Jalilian, Yongtao Guan and Rasmus Waagepetersen. \item \texttt{spatcov}: Estimate the spatial covariance function of a pixel image. \item \texttt{summary.slrm}, \texttt{print.summary.slrm} Summary method for spatial logistic regression models \item \texttt{coef.summary.slrm}: Print the fitted coefficients, confidence interval and p-values for a spatial logistic regression model. \item \texttt{pairMean}: Compute the mean of a specified function of interpoint distance between random points in a window. \item \texttt{rjitterlpp}: Apply random displacements to the points on a linear network. \item \texttt{intersect.boxx}: Compute intersection of boxes in multi-dimensional space \item \texttt{scale.boxx}, \texttt{scale.ppx}: Methods for \texttt{scale} for boxes and patterns in multi-dimensional space \item \texttt{shift.boxx}, \texttt{shift.ppx}: Methods for \texttt{shift} for boxes and patterns in multi-dimensional space \item \texttt{is.boxx}: Determine whether an object is a multidimensional box \item \texttt{relrisk.lpp}: nonparametric estimation of relative risk on a network. \item \texttt{bw.relrisklpp}: Bandwidth selection for relative risk estimation on a network. \item \texttt{bw.lppl}: Bandwidth selection for kernel density estimation of point patterns on a linear network, using likelihood cross-validation. \item \texttt{densityfun.lpp}: a method for \texttt{densityfun} for point patterns on a linear network. \item \texttt{addVertices}: Add new vertices to a network, at locations outside the existing network. \item \verb!lengths_psp!: this is the new name of the function \texttt{lengths.psp}, which had to be changed because of a conflict with the generic \texttt{lengths}. \item \texttt{densityEqualSplit}: The equal-split algorithm for kernel density estimation on a network is now visible as a separate function. \item \texttt{densityHeat}: The heat-equation algorithm for kernel density estimation on a network is now visible as a separate function. It has also been extended to computing leave-one-out density estimates at the data points. \item \texttt{hotrod}: Compute the heat kernel $\kappa(u,v)$ on a one-dimensional line segment. \item \texttt{heatkernelapprox}: Calculate an approximation to the value of the heat kernel on a network evaluated at the source point, $\kappa(u,u)$. \item \texttt{is.linim}: test whether an object is a pixel image on a linear network (class \verb!"linim"!). \item \texttt{rcelllpp}: Simulate the cell point process on a linear network. \item \texttt{rSwitzerlpp}: Simulate the Switzer-type point process on a linear network. \item \texttt{intersect.lintess}: Form the intersection of two tessellations on a linear network. \item \texttt{chop.linnet}: Divide a linear network into tiles using infinite lines. \item \texttt{repairNetwork}: Detect and repair inconsistencies in internal data in a \texttt{linnet} or \texttt{lpp} object. \item \verb!marks<-.lintess!, \texttt{unmark.lintess}: Assign marks to the tiles of a tessellation on a linear network. \item \texttt{marks.lintess}: Extract the marks of the tiles of a tessellation on a linear network. \item \texttt{tilenames.lintess}: Extract the names of the tiles in a tessellation on a linear network \item \verb!tilenames<-.lintess!: Change the names of the tiles in a tessellation on a linear network \item \texttt{nobjects.lintess}: Count the number of tiles in a tessellation on a linear network \item \texttt{as.data.frame.lintess}: Convert a tessellation on a linear network into a data frame. \item \texttt{repul}: Repulsiveness index for a determinantal point process model. \item \texttt{reach.kppm}: Reach (interaction distance) for a Cox or cluster point process model. \item \texttt{summary.dppm}, \texttt{print.summary.dppm}: Summary method for determinantal point process models. \item \texttt{nncross.ppx}: Nearest neighbours between two point patterns in any number of dimensions. \item \texttt{rthinclumps}: Divide a spatial region into clumps and randomly delete some of them. \item \texttt{densityQuick.lpp}: Fast kernel estimator of point process intensity on a network using 2D smoothing kernel. \item \texttt{data.lppm}: Extract the original point pattern dataset (on a linear network) to which the model was fitted. \item \texttt{bw.scott.iso}: Isotropic version of Scott's rule (for point patterns in any dimension). \item \texttt{bits.envelope}: Global simulation envelope corresponding to \texttt{bits.test}, the balanced independent two-stage Monte Carlo test. \item \texttt{extrapolate.psp}: Extrapolate line segments to obtain infinite lines. \item \texttt{uniquemap}: Map duplicate points to unique representatives. Generic with methods for \texttt{ppp}, \texttt{lpp}, \texttt{ppx} \item \texttt{uniquemap.data.frame}, \texttt{uniquemap.matrix}: Map duplicate rows to unique representatives \item \texttt{localKcross}, \texttt{localLcross}, \texttt{localKdot}, \texttt{localLdot}, \texttt{localKcross.inhom}, \texttt{localLcross.inhom}: Multitype local $K$ functions. \item \texttt{polartess}: tessellation using polar coordinates. \item \texttt{densityVoronoi}: adaptive estimate of point process intensity using tessellation methods. \item \texttt{densityAdaptiveKernel}: adaptive estimate of point process intensity using variable kernel methods. \item \texttt{bw.abram}: compute adaptive smoothing bandwidths using Abramson's rule. \item \texttt{coords.quad}: method for \texttt{coords}, to extract the coordinates of the points in a quadrature scheme. \item \texttt{lineartileindex}: low-level function to classify points on a linear network according to which tile of a tessellation they fall inside. \item \texttt{markmarkscatter}: Mark--mark scatterplot. \item \texttt{bw.CvL}: Cronie-van Lieshout bandwidth selection for density estimation. \item \texttt{subset.psp}: subset method for line segment patterns. \item \texttt{densityfun}, \texttt{densityfun.ppp}: Compute a kernel estimate of intensity of a point pattern and return it as a function of spatial location. \item \texttt{as.im.densityfun}: Convert \texttt{function(x,y)} to a pixel image. \item \texttt{measureDiscrete}, \texttt{measureContinuous}: Extract the discrete and continuous components of a measure. \item \texttt{connected.tess}: Find connected components of each tile in a tessellation and make a new tessellation composed of these pieces. \item \texttt{dffit.ppm}: Effect change diagnostic \texttt{DFFIT} for spatial point process models. \item \texttt{shift.distfun}, \texttt{rotate.distfun}, \texttt{reflect.distfun}, \texttt{flipxy.distfun}, \texttt{affine.distfun}, \texttt{scalardilate.distfun}: Methods for geometrical operations on \texttt{distfun} objects. \item \texttt{rescale.distfun}: Change the unit of length in a \texttt{distfun} object. \item \texttt{plot.indicfun}: Plot method for indicator functions created by \texttt{as.function.owin}. \item \texttt{Smooth.leverage.ppm}, \texttt{Smooth.influence.ppm}: Smooth a leverage function or an influence measure. \item \texttt{integral.leverage.ppm}, \texttt{integral.influence.ppm}: Compute the integral of a leverage function or an influence measure. \item \texttt{mean.leverage.ppm}: Compute the mean value of a leverage function. \item \texttt{rectdistmap}: Distance map using rectangular metric. \item \texttt{rectcontact}: Contact distribution function using rectangular structuring element. \item \texttt{joinVertices}: Join specified vertices in a linear network. \item \code{summary.ssf}: Summary method for a spatially sampled function (class \code{ssf}). \item \code{unstack.tess}: Given a tessellation with multiple columns of marks, take the columns one at a time, and return a list of tessellations, each carrying only one of the original columns of marks. \item \code{contour.leverage.ppm}: Method for \code{contour} for leverage functions of class \code{leverage.ppm} \item \code{lurking}: New generic function for lurking variable plots. \item \code{lurking.ppp}, \code{lurking.ppm}: These are equivalent to the original function \code{lurking}. They are now methods for the new generic \code{lurking}. \item \code{lurking.mppm}: New method for class \code{mppm}. Lurking variable plot for models fitted to several point patterns. \item \code{print.lurk}: Prints information about the object returned by the function \code{lurking} representing a lurking variable plot. \item \code{model.matrix.mppm}: Method for \code{model.matrix} for models of class \code{mppm}. \item \code{test.crossing.psp}, \code{test.selfcrossing.psp}: Previously undocumented functions for testing whether segments cross. \item \code{to.saturated}: Convert a colour value to the corresponding fully-saturated colour. \item \code{intensity.psp}: Compute the average total length of segments per unit area. \item \code{boundingbox.psp}: Bounding box for line segment patterns. This produces a tighter bounding box than the previous default behaviour. \item \code{boundingbox.lpp}: Bounding box for point patterns on a linear network. This produces a tighter bounding box than the previous default behaviour. \item \code{boundingbox.linnet}: Bounding box for a linear network. This produces a tighter bounding box than the previous default behaviour. \item \verb!"Frame<-.default"!: New default method for assigning bounding frame to a spatial object. \item \code{connected.pp3}: Connected components of a 3D point pattern. \item \code{colouroutputs}, \verb!"colouroutputs<-"!: Extract or assign colour values in a colour map. (Documented a previously-existing function) \item \texttt{fitin.profilepl}: Extract the fitted interaction from a model fitted by profile likelihood. \item \verb![<-.linim!: Subset assignment method for pixel images on a linear network. \item \texttt{nnfromvertex}: Given a point pattern on a linear network, find the nearest data point from each vertex of the network. \item \texttt{tile.lengths}: Calculate the length of each tile in a tessellation on a network. \item \texttt{text.ppp}, \texttt{text.lpp}, \texttt{text.psp}: Methods for \texttt{text} for spatial patterns. \item \texttt{as.data.frame.envelope}: Extract function data from an envelope object, including the functions for the simulated data ('simfuns') if they were saved. \item \texttt{is.connected}, \texttt{is.connected.default}, \texttt{is.connected.linnet}: Determines whether a spatial object consists of one topologically connected piece, or several pieces. \item \texttt{is.connected.ppp}: Determines whether a point pattern is connected after all pairs of points closer than distance R are joined. \item \texttt{hist.funxy}: Histogram of values of a spatial function. \item \texttt{model.matrix.ippm}: Method for \texttt{model.matrix} which allows computation of regular and irregular score components. \item \texttt{harmonise.msr}: Convert several measures (objects of class \texttt{msr}) to a common quadrature scheme. \item \texttt{bits.test}: Balanced Independent Two-Stage Monte Carlo test, an improvement on the Dao-Genton test. \item \texttt{lineardirichlet}: Computes the Dirichlet-Voronoi tessellation associated with a point pattern on a linear network. \item \texttt{domain.lintess}, \texttt{domain.linfun}: Extract the linear network from a \texttt{lintess} or \texttt{linfun} object. \item \texttt{summary.lintess}: Summary of a tessellation on a linear network. \item \texttt{clicklpp}: Interactively add points on a linear network. \item \texttt{envelopeArray}: Generate an array of envelopes using a function that returns \texttt{fasp} objects. \item \texttt{bw.pcf}: Bandwidth selection for pair correlation function. \item \texttt{grow.box3}: Expand a three-dimensional box. \item \texttt{hexagon}, \texttt{regularpolygon}: Create regular polygons. \item \texttt{Ops.msr}: Arithmetic operations for measures. \item \texttt{Math.imlist}, \texttt{Ops.imlist}, \texttt{Summary.imlist}, \texttt{Complex.imlist}: Arithmetic operations for lists of pixel images. \item \texttt{measurePositive}, \texttt{measureNegative}, \texttt{measureVariation}, \texttt{totalVariation}: Positive and negative parts of a measure, and variation of a measure. \item \texttt{as.function.owin}: Convert a spatial window to a \texttt{function(x,y)}, the indicator function. \item \texttt{as.function.ssf}: Convert an object of class \texttt{ssf} to a \texttt{function(x,y)} \item \texttt{as.function.leverage.ppm} Convert an object of class \texttt{leverage.ppm} to a \texttt{function(x,y)} \item \texttt{sdr}, \texttt{dimhat}: Sufficient Dimension Reduction for point processes. \item \texttt{simulate.rhohat}: Simulate a Poisson point process with the intensity estimated by \texttt{rhohat}. \item \texttt{rlpp}: Random points on a linear network with a specified probability density. \item \texttt{cut.lpp}: Method for \texttt{cut} for point patterns on a linear network. \item \texttt{has.close}: Faster way to check whether a point has a close neighbour. \item \texttt{psib}: Sibling probability (index of clustering strength in a cluster process). \item \texttt{rags}, \texttt{ragsAreaInter}, \texttt{ragsMultiHard}: Alternating Gibbs Sampler for point processes. \item \texttt{bugfixes}: List all bug fixes in recent versions of a package. \item \texttt{ssf}: Create a spatially sampled function \item \texttt{print.ssf}, \texttt{plot.ssf}, \texttt{contour.ssf}, \texttt{image.ssf}: Display a spatially sampled function \item \texttt{as.im.ssf}, \texttt{as.ppp.ssf}, \texttt{marks.ssf}, \verb!marks<-.ssf!, \texttt{unmark.ssf}, \verb![.ssf!, \texttt{with.ssf}: Manipulate data in a spatially sampled function \item \texttt{Smooth.ssf}: Smooth a spatially sampled function \item \texttt{integral.ssf}: Approximate integral of spatially sampled function \item \texttt{roc.kppm}, \texttt{roc.lppm}, \texttt{roc.lpp}: Methods for \texttt{roc} for fitted models of class \texttt{"kppm"} and \texttt{"lppm"} and point patterns of class \texttt{"lpp"} \item \texttt{auc.kppm}, \texttt{auc.lppm}, \texttt{auc.lpp}: Methods for \texttt{auc} for fitted models of class \texttt{"kppm"} and \texttt{"lppm"} and point patterns of class \texttt{"lpp"} \item \texttt{timeTaken}: Extract the timing data from a \texttt{"timed"} object or objects. \item \texttt{rotate.infline}, \texttt{shift.infline}, \texttt{reflect.infline}, \texttt{flipxy.infline}: Geometrical transformations for infinite straight lines. \item \texttt{whichhalfplane}: Determine which side of an infinite line a point lies on. \item \texttt{matrixpower}, \texttt{matrixsqrt}, \texttt{matrixinvsqrt}: Raise a matrix to any power. \item \texttt{points.lpp}: Method for \texttt{points} for point patterns on a linear network. \item \texttt{pairs.linim}: Pairs plot for images on a linear network. \item \texttt{closetriples}: Find close triples of points. \item \texttt{anyNA.im}: Method for \texttt{anyNA} for pixel images. \item \texttt{bc}: Bias correction (Newton-Raphson) for fitted model parameters. \item \texttt{rex}: Richardson extrapolation for numerical integrals and statistical model parameter estimates. \item \texttt{boundingcircle}, \texttt{boundingcentre}: Find the smallest circle enclosing a window or point pattern. \item \verb![.linim! : Subset operator for pixel images on a linear network. \item \texttt{mean.linim}, \texttt{median.linim}, \texttt{quantile.linim}: The mean, median, or quantiles of pixel values in a pixel image on a linear network. \item \texttt{weighted.median}, \texttt{weighted.quantile}: Median or quantile of numerical data with associated weights. \item \verb!"[.linim"!: Subset operator for pixel images on a linear network. \item \texttt{mean.linim}, \texttt{median.linim}, \texttt{quantile.linim}: The mean, median, or quantiles of pixel values in a pixel image on a linear network. \item \texttt{boundingcircle}, \texttt{boundingcentre}: Smallest circle enclosing a spatial object. \item \texttt{split.msr}: Decompose a measure into parts. \item \texttt{unstack.msr}: Decompose a vector-valued measure into its component measures. \item \texttt{unstack.ppp}, \texttt{unstack.psp}, \texttt{unstack.lpp}: Given a spatial pattern with several columns of marks, separate the columns and return a list of spatial patterns, each having only one column of marks. \item \texttt{kernel.squint}: Integral of squared kernel, for the kernels used in density estimation. \item \texttt{as.im.data.frame}: Build a pixel image from a data frame of coordinates and pixel values. \item \texttt{covering}: Cover a window using discs of a given radius. \item \texttt{dilationAny}, \texttt{erosionAny}, \verb!%(-)%! : Morphological dilation and erosion by any shape. \item \texttt{FmultiInhom}, \texttt{GmultiInhom} Inhomogeneous multitype/marked versions of the summary functions \texttt{Fest}, \texttt{Gest}. \item \texttt{kernel.moment} Moment or incomplete moment of smoothing kernel. \item \texttt{MinkowskiSum}, \verb!%(+)%!: Minkowski sum of two windows: \verb!A %(+)% B!, or \texttt{MinkowskiSum(A,B)} \item \texttt{nobjects}: New generic function for counting the number of 'things' in a dataset. There are methods for \texttt{ppp}, \texttt{ppx}, \texttt{psp}, \texttt{tess}. \item \texttt{parameters.interact}, \texttt{parameters.fii}: Extract parameters from interpoint interactions. (These existing functions are now documented.) \item \texttt{ppmInfluence}: Calculate \texttt{leverage.ppm}, \texttt{influence.ppm} and \texttt{dfbetas.ppm} efficiently. \item \texttt{rppm}, \texttt{plot.rppm}, \texttt{predict.rppm}, \texttt{prune.rppm}: Recursive-partition point process models. \item \texttt{simulate.mppm} Simulate a point process model fitted to replicated point patterns. \item \texttt{update.interact}: Update the parameters of an interpoint interaction. [This existing function is now documented.] \item \texttt{where.max}, \texttt{where.min} Find the spatial location(s) where a pixel image achieves its maximum or minimum value. \item \texttt{compileK}, \texttt{compilepcf}: make a $K$ function or pair correlation function given the pairwise distances and their weights. [These existing internal functions are now documented.] \item \texttt{laslett}: Laslett's Transform. \item \texttt{lintess}: Tessellation on a linear network. \item \texttt{divide.linnet}: Divide a linear network into pieces demarcated by a point pattern. \item \texttt{insertVertices}: Insert new vertices in a linear network. \item \texttt{thinNetwork}: Remove vertices and/or segments from a linear network etc. \item \texttt{connected.linnet}: Find connected components of a linear network. \item \texttt{nvertices}, \texttt{nvertices.linnet}, \texttt{nvertices.owin}: Count the number of vertices in a linear network or vertices of the boundary of a window. \item \texttt{as.data.frame.linim}, \texttt{as.data.frame.linfun}: Extract a data frame of spatial locations and function values from an object of class \texttt{linim} or \texttt{linfun}. \item \texttt{as.linfun}, \texttt{as.linfun.linim}, \texttt{as.linfun.lintess}: Convert other kinds of data to a \texttt{linfun} object. \item \texttt{requireversion}: Require a particular version of a package (for use in stand-alone R scripts). \item \texttt{as.function.tess}: Convert a tessellation to a \texttt{function(x,y)}. The function value indicates which tile of the tessellation contains the point $(x,y)$. \item \texttt{tileindex}: Determine which tile of a tessellation contains a given point $(x,y)$. \item \texttt{persp.leverage.ppm}: Method for persp plots for objects of class \texttt{leverage.ppm} \item \texttt{AIC.mppm}, \texttt{extractAIC.mppm}: AIC for point process models fitted to replicated point patterns. \item \texttt{nobs.mppm}, \texttt{terms.mppm}, \texttt{getCall.mppm}: Methods for point process models fitted to replicated point patterns. \item \texttt{rPenttinen}: Simulate the Penttinen process using perfect simulation. \item \texttt{varcount}: Given a point process model, compute the predicted variance of the number of points falling in a window. \item \texttt{inside.boxx}: Test whether multidimensional points lie inside a specified multidimensional box. \item \texttt{lixellate}: Divide each segment of a linear network into smaller segments. \item \texttt{nsegments.linnet}, \texttt{nsegments.lpp}: Count the number of line segments in a linear network. \item \texttt{grow.boxx}: Expand a multidimensional box. \item \texttt{deviance.ppm}, \texttt{deviance.lppm}: Deviance for a fitted point process model. \item \texttt{pseudoR2}: Pseudo-R-squared for a fitted point process model. \item \texttt{tiles.empty} Checks whether each tile of a tessellation is empty or nonempty. \item \texttt{summary.linim}: Summary for a pixel image on a linear network. \item Determinantal Point Process models: \begin{itemize} \item \texttt{dppm}: Fit a determinantal point process model. \item \texttt{fitted.dppm}, \texttt{predict.dppm}, \texttt{intensity.dppm}: prediction for a fitted determinantal point process model. \item \texttt{Kmodel.dppm}, \texttt{pcfmodel.dppm}: Second moments of a determinantal point process model. \item \texttt{rdpp}, \texttt{simulate.dppm}: Simulation of a determinantal point process model. \item \texttt{logLik.dppm}, \texttt{AIC.dppm}, \texttt{extractAIC.dppm}, \texttt{nobs.dppm}: Likelihood and AIC for a fitted determinantal point process model. \item \texttt{print.dppm}, \texttt{reach.dppm}, \texttt{valid.dppm}: Basic information about a \texttt{dpp} model. \item \texttt{coef.dppm}, \texttt{formula.dppm}, \texttt{print.dppm}, \texttt{terms.dppm}, \texttt{labels.dppm}, \texttt{model.frame.dppm}, \texttt{model.matrix.dppm}, \texttt{model.images.dppm}, \texttt{is.stationary.dppm}, \texttt{reach.dppm}, \texttt{unitname.dppm}, \verb!unitname<-.dppm!, \texttt{Window.dppm}: Various methods for \texttt{dppm} objects. \item \texttt{parameters.dppm}: Extract meaningful list of model parameters. \item \texttt{objsurf.dppm}: Objective function surface of a \texttt{dppm} object. \item \texttt{residuals.dppm}: Residual measure for a \texttt{dppm} object. \end{itemize} \item Determinantal Point Process model families: \begin{itemize} \item \texttt{dppBessel}, \texttt{dppCauchy}, \texttt{dppGauss}, \texttt{dppMatern}, \texttt{dppPowerExp}: Determinantal Point Process family functions. \item \texttt{detpointprocfamilyfun}: Create a family function. \item \texttt{update.detpointprocfamily}: Set parameter values in a determinantal point process model family. \item \texttt{simulate.dppm}: Simulation. \item \texttt{is.stationary.detpointprocfamily}, \texttt{intensity.detpointprocfamily}, \texttt{Kmodel.detpointprocfamily}, \texttt{pcfmodel.detpointprocfamily}: Moments. \item \texttt{dim.detpointprocfamily}, \texttt{dppapproxkernel}, \texttt{dppapproxpcf}, \texttt{dppeigen}, \texttt{dppkernel}, \texttt{dppparbounds}, \texttt{dppspecdenrange}, \texttt{dppspecden}: Helper functions. \end{itemize} \item \texttt{dg.envelope}: Simulation envelopes corresponding to Dao-Genton test. \item \texttt{dg.progress}: Progress plot (envelope representation) for the Dao-Genton test. \item \texttt{dg.sigtrace}: significance trace for the Dao-Genton test. \item \texttt{markcrosscorr}: Mark cross-correlation function for point patterns with several columns of marks. \item \texttt{rtemper}: Simulated annealing or simulated tempering. \item \texttt{rgb2hsva}: Convert RGB to HSV data, like \texttt{rgb2hsv}, but preserving transparency. \item \texttt{superimpose.ppplist}, \texttt{superimpose.splitppp}: New methods for 'superimpose' for lists of point patterns. \item \texttt{dkernel}, \texttt{pkernel}, \texttt{qkernel}, \texttt{rkernel}: Probability density, cumulative probability, quantiles and random generation from distributions used in basic one-dimensional kernel smoothing. \item \texttt{kernel.factor}: Auxiliary calculations for one-dimensional kernel smoothing. \item \texttt{spatdim}: Spatial dimension of any object in the \spst\ package. \item \texttt{as.boxx}: Convert data to a multi-dimensional box. \item \texttt{intensity.ppx}: Method for \texttt{intensity} for multi-dimensional space-time point patterns. \item \texttt{fourierbasis}: Evaluate Fourier basis functions in any number of dimensions. \item \texttt{valid}: New generic function, with methods \texttt{valid.ppm}, \texttt{valid.lppm}, \texttt{valid.dppm}. \item \texttt{emend}, \texttt{emend.ppm}, \texttt{emend.lppm}: New generic function with methods for \texttt{ppm} and \texttt{lppm}. \texttt{emend.ppm} is equivalent to \texttt{project.ppm}. \item \texttt{Penttinen}: New pairwise interaction model. \item \texttt{quantile.density}: Calculates quantiles from kernel density estimates. \item \texttt{CDF.density}: Calculates cumulative distribution function from kernel density estimates. \item \texttt{triangulate.owin}: decompose a spatial window into triangles. \item \texttt{fitted.lppm}: fitted intensity values for a point process on a linear network. \item \texttt{parameters}: Extract all parameters from a fitted model. \end{itemize} \section{Alphabetical list of changes} Here is a list of all changes made to existing functions, listed alphabetically. \begin{itemize} %%A \item \texttt{adaptive.density}: This function can now perform adaptive estimation by three methods: tessellation-based methods, variable-bandwidth kernel estimation, and nearest-neighbour intensity estimation. The calculations are performed by \texttt{densityVoronoi}, \texttt{densityAdaptiveKernel} or \texttt{nndensity}. \item \texttt{affine.owin}: Allows transformation matrix to be singular, if the window is polygonal. \item \texttt{alltypes}: If \texttt{envelope=TRUE} and the envelope computation reaches the maximum permitted number of errors (\texttt{maxnerr}) in evaluating the summary function for the simulated patterns, then instead of triggering a fatal error, the envelope limits will be set to \texttt{NA}. \item \texttt{anova.mppm}: \begin{itemize} \item Now handles Gibbs models, and performs the adjusted composite likelihood ratio test. \item New argument \texttt{fine}. \item Issues a warning when applied to random-effects models (models fitted using the argument \texttt{random}). \end{itemize} \item \texttt{anyDuplicated.ppp}: Accelerated. \item \texttt{append.psp}: arguments may be \texttt{NULL}. \item \texttt{applynbd}: Now works for point patterns in three dimensions (class \texttt{"pp3"}) and point patterns on a network (class \texttt{"lpp"}). \item \texttt{as.function.tess}: New argument \texttt{values} specifies the function values. \item \texttt{as.im}: Many methods for \texttt{as.im} now have argument \texttt{rule.eps}. \item \texttt{as.im.distfun}: New argument \texttt{approx} specifies the choice of algorithm. \item \texttt{as.im.tess}: New argument \texttt{values}. \item \texttt{as.im.function}: \begin{itemize} \item New argument \texttt{strict}. \item New argument \texttt{stringsAsFactors}. \item The formal default value of \texttt{stringsAsFactors} has been changed to \texttt{NULL} to conform to changes in R. (The actual default value is \texttt{TRUE} for \texttt{R < 4.1.0} and \texttt{FALSE} for \texttt{R >= 4.1.0}). \end{itemize} \item \texttt{as.im.leverage.ppm}: New argument \texttt{what}. \item \texttt{as.im.nnfun}: New argument \texttt{approx} chooses between a fast, approximate algorithm and a slow, exact algorithm. \item \texttt{as.im.smoothfun}: New argument \texttt{approx} chooses between a fast, approximate algorithm and a slow, exact algorithm. \item \texttt{as.layered}: Default method now handles a (vanilla) list of spatial objects. \item \texttt{as.linfun.lintess}: \begin{itemize} \item New argument \texttt{values} specifies the function value for each tile. \item The default \texttt{values} are the marks, if present. \item New argument \texttt{navalue}. \item Computation accelerated. \end{itemize} \item \texttt{as.linim.default}: \begin{itemize} \item New arguments \texttt{delta} and \texttt{nd} control spacing of sample points in internal data. \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{as.linim.linfun}: \begin{itemize} \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{as.linnet.linnet}: New argument \texttt{maxsize}. \item \texttt{as.linnet.psp}: \begin{itemize} \item If the line segment pattern has marks, then the resulting linear network also carries these marks in the \verb!$lines! component. \item Computation accelerated. \item The resulting network has attribute \texttt{"camefrom"} indicating the provenance of each line segment in the network. \end{itemize} \item \texttt{as.lpp}: accepts more data formats: \begin{itemize} \item Now handles the case where coordinates \texttt{seg} and \texttt{tp} are given but \texttt{x} and \texttt{y} are missing. \item Now handles the case where \texttt{x} is a data frame with columns named \texttt{x,y,seg,tp} or \texttt{x,y} or \texttt{seg,tp}. \end{itemize} \item \texttt{as.mask:} New argument \texttt{rule.eps} specifies what to do when the desired pixel size is not a divisor of the frame size. \item \texttt{as.owin.default}: \begin{itemize} \item Now refuses to convert a \code{box3} to a two-dimensional window. \item Now accepts a structure with entries named \code{xmin},\code{xmax}, \code{ymin}, \code{ymax} in any order. This handles objects of class \code{bbox} in the \pkg{sf} package. \item Now detects objects of class \code{SpatialPolygons} and issues a more helpful error message. \end{itemize} \item \texttt{as.owin.data.frame}: New argument \texttt{step} \item \texttt{as.polygonal}: \begin{itemize} \item Can now repair errors in polygon data, if \texttt{repair=TRUE}. \item Accelerated when \texttt{w} is a pixel mask. \end{itemize} \item \texttt{as.psp}: now permits a data frame of marks to have only one column, instead of coercing it to a vector. \item \texttt{as.solist}: The argument \texttt{x} can now be a spatial object; \texttt{as.solist(cells)} is the same as \texttt{solist(cells)}. %%B \item \texttt{bdist.pixels}: Accelerated for polygonal windows. New argument \texttt{method}. \item \texttt{bdist.points}: Accelerated for polygonal windows. \item \texttt{beachcolours}: \begin{itemize} \item Improved positioning of the yellow colour band. \item If \texttt{sealevel} lies outside \texttt{srange}, then \texttt{srange} will be extended to include it (without a warning). \end{itemize} \item \texttt{beachcolourmap}: Improved positioning of the yellow colour band. \item \texttt{bilinearform}: This function has been moved to the sub-package \texttt{spatstat.sparse}. \item \texttt{bind.fv}: New argument \texttt{clip}. \item \texttt{blur}: New argument \texttt{kernel}. \item \texttt{bw.abram}: \begin{itemize} \item This function is now generic, with a method for class \texttt{ppp}. \item New argument \texttt{smoother} determines how the pilot estimate is computed. \item Formal arguments rearranged. \end{itemize} \item \texttt{bw.diggle}, \texttt{bw.ppl}, \texttt{bw.relrisk}, \texttt{bw.smoothppp}: \begin{itemize} \item These functions now extract and store the name of the unit of length from the point pattern dataset. When the bandwidth selection criterion is plotted, the name of the unit of length is shown on the x-axis. \item A warning is issued if the optimal value of the cross-validation criterion occurs at an endpoint of the search interval. New argument \texttt{warn}. \end{itemize} \item \texttt{bw.ppl}: \begin{itemize} \item New arguments \texttt{weights} and \texttt{sigma}. \item New argument \texttt{shortcut} allows faster computation. \item Additional arguments \verb!...! are now passed to \texttt{density.ppp}. \end{itemize} \item \texttt{bw.relrisk}: This function is now generic, with methods for class \texttt{"ppp"} and \texttt{"lpp"}. \item \texttt{bw.relrisk.lpp}: When \texttt{method="likelihood"}, the cross-validation criterion is now defined as the \underline{\emph{negative}} likelihood. This is consistent with \texttt{bw.relrisk.ppp}, and ensures that the optimum bandwidth is always found by minimising the cross-validation criterion. \item \texttt{bw.relrisk.ppp}: Additional arguments \texttt{...} are now passed to \texttt{density.ppp}. \item \texttt{bw.scott}: \begin{itemize} \item the two bandwidth values in the result now have names \texttt{sigma.x} and \texttt{sigma.y}. \item Now handles point patterns of any dimension. \item New arguments \texttt{isotropic} and \texttt{d}. \end{itemize} \item \texttt{bw.stoyan}: The rule has been modified so that, if the pattern is empty, it is now treated as if it contained 1 point, so that a finite bandwidth value is returned. %%C \item \texttt{cbind.hyperframe}: \begin{itemize} \item The result now retains the \texttt{row.names} of the original arguments. \item \end{itemize} \item \texttt{cdf.test}: \begin{itemize} \item Calculations are more robust against numerical rounding effects. \item The methods for classes \texttt{ppp}, \texttt{ppm}, \texttt{lpp}, \texttt{lppm}, \texttt{slrm} have a new argument \texttt{interpolate}. \item Monte Carlo test runs much faster. \item More jittering is applied when \texttt{jitter=TRUE}. Warnings about tied values should not occur any more. \end{itemize} \item \texttt{cdf.test.ppm}: \begin{itemize} \item Recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{cdf.test.mppm}: \begin{itemize} \item Now handles Gibbs models. \item Now recognises \texttt{covariate="x"} or \texttt{"y"}. \end{itemize} \item \texttt{circdensity}: Improved output of \texttt{print} method. \item \texttt{clarkevans}: The argument \texttt{correction="all"} is now recognised: it selects all the available options. [This is also the default.] \item \texttt{clarkevans.test}: \begin{itemize} \item The asymptotic test is now available for any choice of edge correction. \item New argument \texttt{method} determines whether to use the asymptotic test or Monte Carlo test. The default has changed to \texttt{method="asymptotic"}. \item Default edge correction has changed, to avoid bias. \end{itemize} \item \texttt{clickpoly}: The polygon is now drawn progressively as the user clicks new vertices. \item \texttt{closepairs.ppp}: New argument \code{periodic}. \item \texttt{closepairs.ppp}, \texttt{closepairs.pp3}: \begin{itemize} \item New arguments \texttt{distinct} and \texttt{neat} allow more options. \item Argument \texttt{ordered} has been replaced by \texttt{twice} (but \texttt{ordered} is still accepted, with a warning). \item Performance improved (computation time and memory requirements reduced.) This should improve the performance of many functions in \texttt{spatstat}. \end{itemize} \item \texttt{closepairs.pp3}: Argument \texttt{what} can take the value \texttt{"ijd"} \item \texttt{clusterset}: Improved behaviour. \item \texttt{clusterfit}: \begin{itemize} \item New argument \texttt{algorithm} specifies the choice of optimisation algorithm. \item Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments \texttt{q,p,rmax,rmin} now take precedence over entries with the same names in the list \texttt{ctrl}. \item New argument \texttt{verbose}. \end{itemize} \item \texttt{colourmap}: argument \texttt{col} have have length 1, representing a trivial colour map in which all data values are mapped to the same colour. \item \texttt{collapse.fv}: \begin{itemize} \item This is now treated as a method for the \texttt{nlme} generic \texttt{collapse}. Its syntax has been adjusted slightly. \item Recognises the abbreviations used by \texttt{fvnames()}. \end{itemize} \item \texttt{connected.im}: Now handles a logical-valued image properly. Arguments \texttt{...} now determine pixel resolution. \item \texttt{connected.owin}: Arguments \texttt{...} now determine pixel resolution. \item \texttt{contour.im}: \begin{itemize} \item New argument \texttt{col} specifies the colour of the contour lines. If \texttt{col} is a colour map, then the contours are drawn in different colours. \item New argument \texttt{log} specifies whether the contour lines should be equally spaced on a logarithmic scale. \end{itemize} \item \texttt{convolve.im}: the name of the unit of length is preserved. \item \texttt{crossdist.lpp}: \begin{itemize} \item Now handles much larger networks, using the sparse representation of the network. \item New argument \texttt{check}. \end{itemize} \item \texttt{crossing.psp}: New argument \texttt{details} gives more information about the intersections between the segments. \item \texttt{crosspairs.ppp}: \begin{itemize} \item New argument \texttt{periodic} specifies whether to use periodic (toroidal) distances. \item New arguments \texttt{iX}, \texttt{iY} make it possible to eliminate pairs in which the two points are identical. \end{itemize} \item \texttt{crosspairs.pp3}: Argument \texttt{what} can take the value \texttt{"ijd"} \item \texttt{cut.ppp}: Argument \texttt{z} can be \texttt{"x"} or \texttt{"y"} indicating one of the spatial coordinates. %%D \item \texttt{dclf.test, mad.test, dclf.progress, mad.progress,} \texttt{dclf.sigtrace, mad.sigtrace}, \texttt{dg.progress, dg.sigtrace}: \begin{itemize} \item New argument \texttt{clamp} determines the test statistic for one-sided tests. \item New argument \texttt{rmin} determines the left endpoint of the test interval. \item New argument \texttt{leaveout} specifies how to calculate discrepancy between observed and simulated function values. \item New argument \texttt{scale} allows summary function values to be rescaled before the comparison is performed. \item New argument \texttt{interpolate} supports interpolation of $p$-value. \item Function values which are infinite, \texttt{NaN} or \texttt{NA} are now ignored in the calculation (with a warning) instead of causing an error. Warning messages are more detailed. \end{itemize} \item \texttt{default.rmhcontrol, default.rmhexpand}: New argument \texttt{w}. \item \texttt{densityfun.ppp}: The resulting function can now handle query points which lie outside the window of the original data, and has argument \texttt{drop=TRUE} which specifies how to handle them. \item \texttt{densityEqualSplit}: New arguments \texttt{at} and \texttt{leaveoneout} for consistency with other functions. \item \texttt{densityHeat}: \begin{itemize} \item default behaviour has changed slightly. \item new argument \texttt{finespacing}. \end{itemize} \item \texttt{density.lpp}: \begin{itemize} \item Argument \texttt{sigma} can now be a function in the R language, assumed to provide a bandwidth selection rule. This function will be applied to the point pattern \texttt{x} to compute the bandwidth. \item Argument \texttt{sigma=NULL} is now accepted. The default value is one-eighth of the length of the shortest side of the bounding box of \texttt{x}. \item New fast algorithm (up to 1000 times faster) for the default case where \texttt{kernel="gaussian"} and \texttt{continuous=TRUE}. Generously contributed by Greg McSwiggan. \item Fast algorithm has been further accelerated. \item Further accelerated when the point pattern contains duplicated points. \item New argument \texttt{kernel} specifies the smoothing kernel. Any of the standard one-dimensional smoothing kernels can be used. \item Now supports both the `equal-split continuous' and `equal-split discontinuous' smoothers. New argument \texttt{continuous} determines the choice of smoother. \item New arguments \texttt{weights} and \texttt{old}. \item New argument \texttt{distance} offers a choice of different kernel methods. \item Infinite bandwidth (\texttt{sigma=Inf}) is now permitted, and results in a density estimate that is constant over all locations. \end{itemize} \item \texttt{density.ppp}: \begin{itemize} \item A non-Gaussian kernel can now be specified using the argument \texttt{kernel}. \item Standard error calculation is now available with any smoothing kernel. \item The interpretation of \texttt{weights} in the calculation of standard error has changed. New argument \texttt{wtype} controls this interpretation. \item Argument \texttt{weights} can now be a pixel image. \item Infinite bandwidth \texttt{sigma=Inf} is supported. \item Accelerated by about 30\% when \texttt{at="pixels"}. \item Accelerated by about 15\% in the case where \texttt{at="points"} and \texttt{kernel="gaussian"}. \item Accelerated in the cases where weights are given or \texttt{diggle=TRUE}. \item New argument \texttt{verbose}. \end{itemize} \item \texttt{densityQuick.lpp}: Argument \texttt{X} changed to \texttt{x} for consistency. \item \texttt{density.psp}: \begin{itemize} \item New argument \texttt{method}. \item Accelerated by 1 to 2 orders of magnitude. \end{itemize} \item \texttt{density.splitppp}: New argument \texttt{weights}. \item \texttt{dfbetas.ppm}: \begin{itemize} \item For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. \item Increased the default resolution of the pixel images. Spatial resolution can now be controlled by the arguments \code{dimyx}, \code{eps}. \item Recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{diagnose.ppm}: \begin{itemize} \item Infinite values of \texttt{rbord} are now ignored and treated as zero. This ensures that \texttt{diagnose.ppm} has a sensible default when the fitted model has infinite reach. \item Accelerated, when \texttt{type="inverse"}, for models without a hard core. \end{itemize} \item \texttt{diagnose.ppm, plot.diagppm}: \begin{itemize} \item New arguments \texttt{col.neg, col.smooth} control the colour maps. \item Accelerated, when \texttt{type="inverse"}, for models without a hard core. \end{itemize} \item \texttt{dilation.ppp}: Improved geometrical accuracy. Now accepts arguments to control resolution of polygonal approximation. \item \texttt{dirichletEdges}: New argument \texttt{clip}. \item \texttt{discretise}: \begin{itemize} \item New argument \texttt{move.points} determines whether the point coordinates are also discretised. \item New argument \texttt{rule.eps} \end{itemize} \item \texttt{discs}: \begin{itemize} \item Now accepts a single numeric value for \texttt{radii}. \item New argument \texttt{npoly}. \item Accelerated in some cases. \end{itemize} \item \texttt{distcdf}: \begin{itemize} \item Arguments which are \texttt{NULL} will be treated as missing. \item New argument \texttt{savedenom}. \end{itemize} \item \texttt{distfun}: \begin{itemize} \item When the user calls a distance function that was created by \texttt{distfun}, the user may now give a \texttt{ppp} or \texttt{lpp} object for the argument \texttt{x}, instead of giving two coordinate vectors \texttt{x} and \texttt{y}. \item New argument \texttt{rule.eps} \end{itemize} \item \texttt{distfun.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item Computation accelerated. \end{itemize} \item \texttt{distmap.owin}: \begin{itemize} \item New argument \texttt{connect}. \item Behaviour has been altered so that, when \texttt{X} is a binary mask, the results of \texttt{distmap(X, invert=TRUE)} and \texttt{distmap(complement.owin(X))} are identical. This affects a few pixels close to the edge of the frame. \end{itemize} \item \texttt{distmap.ppp}: New argument \texttt{clip}. \item \texttt{distmap.psp}: New arguments \texttt{extras} and \texttt{clip}. \item \texttt{dppm}: Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments \texttt{q,p,rmax,rmin} now take precedence over entries with the same names in the list \texttt{ctrl}. \item \texttt{duplicated.ppp}: accelerated. %%E \item \texttt{edge.Trans}: New argument \texttt{gW} for efficiency. \item \texttt{eem}: The function \texttt{eem} is now generic, with methods for \texttt{ppm} and \texttt{slrm}. The function previously named \texttt{eem} is now called \texttt{eem.ppm}. \item \texttt{effectfun}: \begin{itemize} \item Now works for \texttt{ppm}, \texttt{kppm}, \texttt{lppm}, \texttt{dppm}, \texttt{rppm} and \texttt{profilepl} objects. \item New argument \texttt{nvalues}. \item Standard error calculation (\texttt{se.fit=TRUE}) now works for models fitted with \texttt{use.gam=TRUE}. \end{itemize} \item \texttt{envelope}: \begin{itemize} \item All methods for \texttt{envelope} now accept a summary function in which the function argument is not named \texttt{r}. This includes functions such as \texttt{transect.im} and \texttt{roc}. \item New argument \texttt{clamp} gives greater control over one-sided envelopes. \item New argument \texttt{funargs} \item New argument \texttt{scale} allows global envelopes to have width proportional to a specified function of $r$, rather than constant width. \item New argument \texttt{funYargs} contains arguments to the summary function when applied to the data pattern only. \item The argument \texttt{simulate} can now be a function (such as \texttt{rlabel}). The function will be applied repeatedly to the original data pattern. \item \texttt{rejectNA} and \texttt{silent}. \end{itemize} \item \texttt{envelope.lpp}, \texttt{envelope.lppm}: \begin{itemize} \item New arguments \texttt{fix.n} and \texttt{fix.marks} allow envelopes to be computed using simulations conditional on the observed number of points. \item New arguments \texttt{maxnerr}, \texttt{rejectNA} and \texttt{silent}. \end{itemize} \item \texttt{eval.im}: New argument \texttt{warn}. \item \texttt{eval.linim}: New argument \texttt{warn}. \item \texttt{eval.fasp}: automatically generated labels have been improved. \item \texttt{ewcdf}: \begin{itemize} \item Argument \texttt{weights} can now be \texttt{NULL}. \item New arguments \texttt{normalise} and \texttt{adjust}. \item Computation accelerated. \item The result does not inherit class \texttt{"ecdf"} if \texttt{normalise=FALSE}. \end{itemize} %%F \item \texttt{Fest}: Additional checks for errors in input data. \item \texttt{Finhom}: \begin{itemize} \item A warning is issued if bias is likely to occur because of undersmoothing. \item New arguments \texttt{warn.bias} and \texttt{savelambda}. \end{itemize} \item \texttt{fitted.lppm}: New argument \texttt{leaveoneout} allows leave-one-out computation of fitted value. \item \texttt{fitted.ppm}: \begin{itemize} \item New option, \texttt{type="link"}. \item New argument \code{ignore.hardcore}. \end{itemize} \item \texttt{funxy}: \begin{itemize} \item When the user calls a function that was created by \texttt{funxy}, the user may now give a \texttt{ppp} or \texttt{lpp} object for the argument \texttt{x}, instead of giving two coordinate vectors \texttt{x} and \texttt{y}. \item Functions of class \texttt{"funxy"} can now be applied to quadrature schemes. \item The result now has a \texttt{unitname}, inherited from the argument \texttt{W}. \end{itemize} %%G \item \texttt{Gcross}: Function labels (shown on the plot legend) have been improved when \texttt{i = j}. \item \texttt{Geyer}: The saturation parameter \texttt{sat} can now be less than 1. \item \texttt{Ginhom}: \begin{itemize} \item A warning is issued if bias is likely to occur because of undersmoothing. \item New arguments \texttt{warn.bias} and \texttt{savelambda}. \end{itemize} \item \texttt{grow.rectangle}: New argument \texttt{fraction}. %%H \item \texttt{harmonise.im}: The result belongs to classes \texttt{solist} and \texttt{imlist} so that it can be plotted. \item \texttt{Hest}: \begin{itemize} \item Argument \texttt{X} can now be a pixel image with logical values. \item New argument \texttt{W}. [Based on code by Kassel Hingee.] \item Additional checks for errors in input data. \end{itemize} \item \texttt{hist.im}: New argument \texttt{xname}. \item \texttt{hyperframe}: \begin{itemize} \item The formal default value of \texttt{stringsAsFactors} has been changed to \texttt{NULL} to conform to changes in R. (The actual default value is \texttt{TRUE} for \texttt{R < 4.1.0} and \texttt{FALSE} for \texttt{R >= 4.1.0}). \end{itemize} %%I \item \texttt{identify.psp}: Improved placement of labels. Arguments can be passed to \texttt{text.default} to control the plotting of labels. \item \texttt{idw}: Standard errors can now be calculated by setting \texttt{se=TRUE}. \item \texttt{imcov}: the name of the unit of length is preserved. \item \texttt{im.apply}: \begin{itemize} \item Computation accelerated \item New argument \texttt{fun.handles.na} \item New argument \texttt{check} \end{itemize} \item \texttt{influence.ppm}: For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. \item \texttt{integral.im}: \begin{itemize} \item New argument \texttt{weight} specifies a weight function for the integration. \item Accelerated in the case where \texttt{domain} is a tessellation. \end{itemize} \item \texttt{integral.linfun}: \begin{itemize} \item New argument \texttt{weight} specifies a weight function for the integration. \item New argument \texttt{delta} controls step length of approximation to integral. \item New argument \texttt{nd} controls approximate number of sample points used to calculate integral. \item Argument \code{domain} can be a tessellation. \item Now handles complex-valued functions. \end{itemize} \item \texttt{integral.linim}: \begin{itemize} \item New argument \texttt{weight} specifies a weight function for the integration. \item Argument \code{domain} can be a tessellation. \item Now handles complex-valued functions. \end{itemize} \item \texttt{integral.msr}: New argument \texttt{weight} specifies a weight (integrand) for the integration. \item \texttt{integral.ssf}: Argument \code{domain} can be a tessellation. \item \texttt{intensity.ppm}: \begin{itemize} \item Intensity approximation is now implemented for area-interaction model, and Geyer saturation model. \item Can now calculate the Coeurjolly-Lavancier DPP approximation of intensity. [Code kindly contributed by Frederic Lavancier] \item New argument \texttt{approx} specifies the choice of approximation. \end{itemize} \item \texttt{interp.im}: New argument \texttt{bilinear}. \item \texttt{intersect.tess}: New argument \texttt{keepempty}. \item \texttt{invoke.symbolmap}: new argument \texttt{angleref}. \item \texttt{iplot}, \texttt{iplot.ppp}, \texttt{iplot.layered}, \texttt{iplot.linnet}, \texttt{iplot.default}: These interactive plotting functions have been removed from \pkg{spatstat} into a new package \pkg{spatstat.gui}. \item \texttt{ippm}: \begin{itemize} \item Accelerated. \item The internal format of the result has been extended slightly. \item Improved defaults for numerical algorithm parameters. \end{itemize} \item \texttt{istat}: This interactive analysis function has been removed from \pkg{spatstat} into a new package \pkg{spatstat.gui}. %%J \item \texttt{Jcross}: Function labels (shown on the plot legend) have been improved when \texttt{i = j}. \item \texttt{Jfox}: new argument \texttt{warn.trim}. \item \texttt{Jinhom}: \begin{itemize} \item A warning is issued if bias is likely to occur because of undersmoothing. \item New arguments \texttt{warn.bias} and \texttt{savelambda}. \end{itemize} %%K \item \texttt{Kcross}: \begin{itemize} \item Function labels (shown on the plot legend) have been improved when \texttt{i = j}. \item Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \end{itemize} \item \texttt{Kcross.inhom}, \texttt{Kdot.inhom}, \texttt{Kmulti.inhom}: \begin{itemize} \item These functions now allow intensity values to be given by a fitted point process model. \item New arguments \texttt{update}, \texttt{leaveoneout}, \texttt{lambdaX}. \item Leave-one-out calculation is now implemented when \texttt{lambbdaX} is a fitted model of class \texttt{"dppm"}. \end{itemize} \item \texttt{Kdot}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{Kest} \begin{itemize} \item Accelerated computation (for translation and rigid corrections) when window is an irregular shape. \item Calculation of isotropic edge correction for polygonal windows has changed slightly. Results are believed to be more accurate. Computation has been accelerated by about 20 percent in typical cases. \item Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \end{itemize} \item \texttt{Kest.fft}: Now has \verb!...! arguments allowing control of spatial resolution. \item \texttt{Kinhom}: \begin{itemize} \item New argument \texttt{ratio}. \item Stops gracefully if \texttt{lambda} contains any zero values. \item Leave-one-out calculation is implemented when \texttt{lambda} is a fitted model of class \texttt{"dppm"}. \end{itemize} \item \texttt{Kmulti}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{kernel.moment}: \begin{itemize} \item New arguments \texttt{mean} and \texttt{sd}. \item Computation accelerated for \texttt{kernel='cosine'} or \texttt{'optcosine'}. \item All cases are now computed using analytic expressions, for \texttt{m=0,1,2}. \end{itemize} \item \texttt{kppm}: \begin{itemize} \item The code for fitting log-Gaussian Cox process models (\texttt{clusters="LGCP"}) has been re-implemented without using the package \pkg{RandomFields}. The current code supports the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance models. \item Computation accelerated when \texttt{method="palm"} or \texttt{method="clik2"}. [Kindly contributed by Bethany Macdonald.] \item New argument \texttt{trajectory} specifies whether to save the history of function evaluations performed by the optimization algorithm. \item New argument \texttt{penalised} supports penalised model-fitting with a penalty against extremely large or small values of the cluster scale. \item New arguments \texttt{ppm.improve.type} and \texttt{ppm.improve.args}. \item The first order trend is fitted using a regularized fitting algorithm when \texttt{ppm.improve.type="enet"}. \item New default settings ensure greater numerical stability of the optimization algorithm against the effects of the scale of the spatial coordinates. New argument \texttt{stabilize} specifies whether the optimization algorithm should be numerically stabilized. \item Fitting a model with \texttt{clusters="LGCP"} no longer requires the package \pkg{RandomFields} to be loaded explicitly. \item New argument \texttt{algorithm} specifies the choice of optimisation algorithm. \item Left hand side of formula can now involve entries in the list \texttt{data}. \item refuses to fit a log-Gaussian Cox model with anisotropic covariance. \item A warning about infinite values of the summary function no longer occurs when the default settings are used. Also affects \texttt{mincontrast}, \texttt{cauchy.estpcf}, \texttt{lgcp.estpcf}, \texttt{matclust.estpcf}, \texttt{thomas.estpcf}, \texttt{vargamma.estpcf}. \item Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments \texttt{q,p,rmax,rmin} now take precedence over entries with the same names in the list \texttt{ctrl}. \item Improved printed output. \item Improved numerical robustness. \end{itemize} %%L \item \texttt{latest.news}: Now prints news documentation for the current major version, by default. New argument \texttt{major}. \item \texttt{Lcross}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{Ldot}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{Lcross.inhom}, \texttt{Ldot.inhom}: These functions now allow intensity values to be given by a fitted point process model. New arguments \texttt{update}, \texttt{leaveoneout}, \texttt{lambdaX}. \item \texttt{lengths.psp}: \begin{itemize} \item New argument \texttt{squared}. \item This function will soon be Deprecated in favour of the new name \verb!lengths_psp!. \end{itemize} \item \texttt{Lest}, \texttt{Linhom}, \texttt{Ldot}, \texttt{Lcross}, \texttt{Ldot.inhom}, \texttt{Lcross.inhom}: These summary functions now have explicit argument \texttt{"correction"}. \item \texttt{leverage.ppm}: \begin{itemize} \item For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. \item Increased the default resolution of the pixel images. Spatial resolution can now be controlled by the arguments \code{dimyx}, \code{eps}. \item Recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: \begin{itemize} \item These methods now work for models that were fitted by logistic composite likelihood (\texttt{method='logi'}). \item Computation has been vastly accelerated for models with Geyer interaction fitted using isotropic or translation edge corrections. \item Faster computation in many cases. \item Virtually all models and edge corrections are now supported, using a ``brute force'' algorithm. This can be slow in some cases. \end{itemize} \item \texttt{lgcp.estK}, \texttt{lgcp.estpcf}: This code for fitting log-Gaussian Cox process models has been re-implemented without using the package \pkg{RandomFields}. The current code supports the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance models. \item \texttt{lineardisc}: \begin{itemize} \item New argument \texttt{add}. \item Default plotting behaviour has changed. \end{itemize} \item \texttt{linearK}, \texttt{linearpcf} and relatives: \\ \begin{itemize} \item substantially accelerated. \item ratio calculations are now supported. \item new argument \texttt{ratio}. \end{itemize} \item \texttt{linearKEuclidInhom}, \texttt{linearpcfEuclidInhom}: Argument \texttt{lambda=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. A warning is issued that this is different from the previous behaviour. \item \texttt{linearKinhom}: new argument \texttt{normpower}. \item \texttt{linearKdot.inhom}, \texttt{linearpcfdot.inhom} Argument \texttt{lambdaI=NULL} or \texttt{lambdadot=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. \item \texttt{linearKcross.inhom}, \texttt{linearpcfcross.inhom} Argument \texttt{lambdaI=NULL} or \texttt{lambdaJ=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. \item \texttt{linearKinhom}, \texttt{linearpcfinhom}: \begin{itemize} \item Argument \texttt{lambda=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. A warning is issued that this is different from the previous behaviour. \item Changed behaviour when \texttt{lambda} is a fitted model. \item New arguments \texttt{update} and \texttt{leaveoneout}. \end{itemize} \item \texttt{linearpcf}: new argument \texttt{normpower}. \item \texttt{linearpcfinhom}: New arguments \texttt{adjust.sigma}, \texttt{bw} and \texttt{adjust.bw}. \item \texttt{linearpcfcross.inhom}, \texttt{linearpcfdot.inhom}: New arguments \texttt{adjust.sigma}, \texttt{bw} and \texttt{adjust.bw}. \item \texttt{linearpcfEuclidInhom}: New arguments \texttt{adjust.sigma}, \texttt{bw} and \texttt{adjust.bw}. \item \texttt{linim}: \begin{itemize} \item The image \texttt{Z} is now automatically restricted to the network. \item New argument \texttt{restrict}. \end{itemize} \item \texttt{linnet}: \begin{itemize} \item The internal format of a \texttt{linnet} (linear network) object has been changed. Existing datasets of class \texttt{linnet} are still supported. However, computation will be faster if they are converted to the new format. To convert a linnet object \texttt{L} to the new format, use \verb!L <- as.linnet(L)!. \item If the argument \texttt{edges} is given, then this argument now determines the ordering of the sequence of line segments. For example, the \texttt{i}-th row of \texttt{edges} specifies the \texttt{i}-th line segment in \texttt{as.psp(L)}. \item New argument \texttt{warn}. \item When argument \texttt{edges} is specified, the code now checks whether any edges are duplicated. \end{itemize} \item \texttt{lintess}: \begin{itemize} \item Argument \texttt{df} can be missing or \texttt{NULL}, resulting in a tesellation with only one tile. \item Tessellations can now have marks. New argument \texttt{marks}. \end{itemize} \item \texttt{localpcf}: New argument \texttt{rvalue}. \item \texttt{localpcfinhom}: \begin{itemize} \item New arguments \texttt{update}, \texttt{leaveoneout}, \texttt{rvalue}. \end{itemize} \item \texttt{logLik.ppm}: \begin{itemize} \item New argument \texttt{absolute}. \item The warning about pseudolikelihood (`log likelihood not available') is given only once, and is not repeated in subsequent calls, within a spatstat session. \end{itemize} \item \texttt{logLik.mppm}: new argument \texttt{warn}. \item \texttt{lohboot}: \begin{itemize} \item Algorithm has been corrected and extended thanks to Christophe Biscio and Rasmus Waagepetersen. \item New arguments \texttt{block}, \texttt{basicboot}, \texttt{Vcorrection}. \item Accelerated when the window is a rectangle. \item Now works for multitype $K$ functions \texttt{Kcross}, \texttt{Kdot}, \texttt{Lcross}, \texttt{Ldot}, \texttt{Kcross.inhom}, \texttt{Lcross.inhom} \item Confidence bands for \texttt{Lest}, \texttt{Linhom}, \texttt{Lcross}, \texttt{Ldot}, \texttt{Lcross.inhom} are now computed differently. First a confidence band is computed for the corresponding $K$ function \texttt{Kest}, \texttt{Kinhom}, \texttt{Kcross}, \texttt{Kdot}, \texttt{Kcross.inhom} respectively. Then this is transformed to a confidence band for the $L$ function by applying the square root transformation. \end{itemize} \item \texttt{lpp}: \begin{itemize} \item The internal format of an \texttt{lpp} object has been changed. Existing datasets of class \texttt{lpp} are still supported. However, computation will be faster if they are converted to the new format. To convert an \texttt{lpp} object \texttt{X} to the new format, use \verb!X <- as.lpp(X)!. \item \texttt{X} can be missing or \texttt{NULL}, resulting in an empty point pattern. \item Now handles the case where coordinates \texttt{seg} and \texttt{tp} are given but \texttt{x} and \texttt{y} are missing. \end{itemize} \item \texttt{lppm}: \begin{itemize} \item Covariates can be objects of class \texttt{lintess}. \item New argument \texttt{random} controls placement of dummy points. \item Computation accelerated. \end{itemize} \item \texttt{lurking.ppm}: accelerated. \item \texttt{lut}: argument \texttt{outputs} may have length 1, representing a lookup table in which all data values are mapped to the same output value. %%M \item \texttt{markconnect}: Accepts the argument \texttt{weights} which is passed to \texttt{markcorr}. \item \texttt{markcorr}: \begin{itemize} \item New argument \texttt{weights} allows computation of the weighted version of the mark correlation function. Weights can be an expression to be evaluated, or a function, or a pixel image, or a numeric vector. \item Now allows negative mark values, when \texttt{normalise=FALSE}. \end{itemize} \item \texttt{markcrosscorr}: Now allows negative mark values, when \texttt{normalise=FALSE}. \item \texttt{markstat}: Now works for point patterns in three dimensions (class \texttt{"pp3"}) and point patterns on a network (class \texttt{"lpp"}). \item \texttt{marktable}: Now works for point patterns in three dimensions (class \texttt{"pp3"}) and point patterns on a network (class \texttt{"lpp"}). \item \texttt{markvario}: Accepts the argument \texttt{weights} which is passed to \texttt{markcorr}. \item \texttt{mincontrast}: New argument \texttt{action.bad.values} specifies what action is taken when the summary function produces \texttt{NA} or \texttt{NaN} or infinite values. \item \texttt{minnndist}, \texttt{maxnndist}: New argument \texttt{by} makes it possible to find the minimum or maximum nearest neighbour distance between each pair of possible types in a multitype pattern. \item \texttt{mppm}: \begin{itemize} \item Now handles models with a random effect component. (This is covered in \cite[Chap.\ 16]{baddrubaturn15}.) \item New argument \texttt{random} is a formula specifying the random effect. (This is covered in \cite[Chap.\ 16]{baddrubaturn15}.) \item Performs more checks for consistency of the input data. \item New arguments \texttt{gcontrol} and \texttt{reltol.pql} control the fitting algorithm. \item New argument \texttt{weights} specifies case weights for each row of data. \end{itemize} \item \texttt{msr}: Infinite and \texttt{NA} values are now detected (if \texttt{check=TRUE}) and are reset to zero, with a warning. %%N \item \texttt{nbfires}: \begin{itemize} \item the unit of length for the coordinates is now specified in this dataset. \item This dataset now includes information about the different land and sea borders of New Brunswick. \end{itemize} \item \texttt{nncorr,nnmean,nnvario}: New argument \texttt{na.action}. \item \texttt{nncross.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item Computation accelerated. \end{itemize} \item \texttt{nncross.ppp}: When \texttt{X} is a point pattern and \texttt{Y} is a line segment pattern, higher order neighbours (\verb!k > 1!) are now supported. \item \texttt{nndist.pp3}: New argument \texttt{by} allows computation of the nearest distance to each group of points. \item \texttt{nndist.ppx}: New argument \texttt{by} allows computation of the nearest distance to each group of points. \item \texttt{nndist.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item new argument \texttt{by} allows computation of the nearest distance to each group of points. \item Computation accelerated. \end{itemize} \item \texttt{nnwhich.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item new argument \texttt{by} allows computation of the nearest distance to each group of points. \item Computation accelerated. \end{itemize} \item \texttt{nnfun}: new argument \texttt{rule.eps}. \texttt{nnfun.lpp}: \begin{itemize} \item New argument \texttt{k}. \item New argument \texttt{value} specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. \end{itemize} \texttt{nnfun.ppp}: \begin{itemize} \item New argument \texttt{value} specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. \end{itemize} \texttt{nnfun.psp}: \begin{itemize} \item New argument \texttt{value} specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. \end{itemize} %%O \item \texttt{owin}: If argument \texttt{mask} is a logical matrix, \texttt{NA} entries will be accepted, and converted to \texttt{FALSE}. \item \texttt{owin2mask}: New options \texttt{op="majority"} and \texttt{op="minority"}. If \texttt{op="majority"}, a pixel belongs to the resulting mask if at least half of the pixel area is covered by the window. %%P \item \texttt{padimage}: New argument \texttt{W} allows an image to be padded out to fill any window. \item \texttt{pairdist.lpp}: Now handles much larger networks, using the sparse representation of the network. \item \texttt{pairorient}: Default edge corrections now include \texttt{"bord.modif"}. \item \texttt{pairs.im}: new argument \texttt{drop}. \item \texttt{parres}: the argument \texttt{covariate} is allowed to be missing if the model only depends on one covariate. \item \texttt{pcf.ppp}: \begin{itemize} \item New argument \code{close} for advanced use. \item New argument \texttt{ratio} allows several estimates of pcf to be pooled. \item Now calculates an analytic approximation to the variance of the estimate of the pair correlation function (when \texttt{var.approx=TRUE}). \item Now returns the smoothing bandwidth used, as an attribute of the result. \item New argument \texttt{close} for advanced use. \item Now accepts \texttt{correction="none"}. \end{itemize} \item \texttt{pcfcross}, \texttt{pcfdot}, \texttt{pcfmulti}: New argument \texttt{ratio} makes it possible to save the numerator and denominator of the function estimates, so that estimates can be pooled. \item \texttt{pcfcross.inhom}, \texttt{pcfdor.inhom}: New arguments \texttt{adjust.sigma} and \texttt{adjust.bw} allow separate adjustment of the one-dimensional smoothing bandwidth \texttt{bw} and the spatial smoothing bandwidth \texttt{sigma}. \item \texttt{pcfinhom}: \begin{itemize} \item New arguments \texttt{adjust.sigma} and \texttt{adjust.bw} allow separate adjustment of the one-dimensional smoothing bandwidth \texttt{bw} and the spatial smoothing bandwidth \texttt{sigma}. \item New argument \code{close} for advanced use. \item Default behaviour is changed when \texttt{lambda} is a fitted model. The default is now to re-fit the model to the data before computing pcf. New arguments \texttt{update} and \texttt{leaveoneout} control this. \item New argument \texttt{close} for advanced use. \item Now handles \texttt{correction="good"} \item Leave-one-out calculation is implemented when \texttt{lambda} is a fitted model of class \texttt{"dppm"}. \end{itemize} \item \code{persp.funxy}: Improved $z$-axis label. \item \texttt{pixellate.ppp}: \begin{itemize} \item If the pattern is empty, the result is an integer-valued image (by default) for consistency with the results for non-empty patterns. \item Accelerated in the case where weights are given. \item New arguments \texttt{fractional} and \texttt{preserve} for more accurate discretisation. \item New argument \texttt{savemap}. \end{itemize} \item \texttt{plot.anylist}: \begin{itemize} \item If a list entry \verb!x[[i]]! belongs to class \texttt{"anylist"}, it will be expanded so that each entry \verb!x[[i]][[j]]! will be plotted as a separate panel. \item New arguments \texttt{panel.begin.args}, \texttt{panel.end.args} \item Result is now an (invisible) list containing the result from executing the plot of each panel. \end{itemize} \item \texttt{plot.bermantest}: Improved layout for plots of Berman's \texttt{Z2} test. \item \texttt{plot.colourmap}: \begin{itemize} \item Now handles a colour map for a zero-length interval [a,a] \item New argument \texttt{increasing} specifies whether the colours are displayed in order left-to-right/bottom-to-top. \item Changed default behaviour for discrete colour maps when \texttt{vertical=FALSE}. \end{itemize} \item \texttt{plot.fv}: New argument \texttt{clip.xlim}. \item \texttt{plot.im}: \begin{itemize} \item New argument \texttt{addcontour} specifies that contour lines should be drawn over the image plot. \item Now handles complex-valued images. \item New argument \texttt{workaround} to avoid a bug in some MacOS device drivers that causes the image to be displayed in the wrong spatial orientation. \item The number of tick marks in the colour ribbon can now be controlled using the argument \texttt{nint} in \texttt{ribargs}. \item Improved behaviour when all pixel values are \texttt{NA}. \item Improved handling of tickmarks on colour ribbon. \item Improved behaviour when the image values are almost constant. \item New argument \texttt{riblab}. \item Axes are prevented from extending outside the image rectangle. \item New argument \texttt{zap}. \item Some warnings are suppressed when \texttt{do.plot=FALSE}. \end{itemize} \item \texttt{plot.imlist}: Result is now an (invisible) list containing the result from executing the plot of each panel. \item \texttt{plot.influence.ppm}: New argument \texttt{multiplot}. \item \texttt{plot.kppm}: \begin{itemize} \item New arguments \texttt{pause} and \texttt{xname}. \item The argument \texttt{what="all"} is now recognised: it selects all the available options. [This is also the default.] \end{itemize} \item \texttt{plot.leverage.ppm}: \begin{itemize} \item New arguments \texttt{multiplot} and \code{what}. \item A contour line showing the average value of leverage is now drawn on the colour ribbon, as well as on the main image. New argument \texttt{args.contour}. \end{itemize} \item \texttt{plot.linfun}: \begin{itemize} \item Now passes arguments to the function being plotted. \item A scale bar is now plotted when \texttt{style="width"}. \item New argument \texttt{legend}. \item The return value has a different format. \end{itemize} \item \texttt{plot.linim}: \begin{itemize} \item The return value has a different format. \item New argument \texttt{fatten} improves visual appearance when \texttt{style="colour"}. \item A scale bar is now plotted when \texttt{style="width"}. \item When \texttt{style="width"}, negative values are plotted in red (by default). New argument \texttt{negative.args} controls this. \item New argument \texttt{zlim} specifies the range of values to be mapped. \item New explicit argument \texttt{box} determines whether to plot a bounding box; default is \texttt{FALSE} in all cases. \end{itemize} \item \texttt{plot.lintess}: \begin{itemize} \item Improved plot method, with more options. \item Modified to display the marks attached to the tiles. \item Options: \verb!style=c("colour", "width", "image")!. \end{itemize} \item \texttt{plot.lpp}: \begin{itemize} \item New argument \texttt{show.network}. \item For a point pattern with continuous marks (``real numbers'') the colour arguments \texttt{cols}, \texttt{fg}, \texttt{bg} can now be vectors of colour values, and will be used to determine the default colour map for the marks. \item If \texttt{shape="crossticks"}, the points will be drawn as short line segments perpendicular to the network. \end{itemize} \item \texttt{plot.mppm}: \begin{itemize} \item New argument \texttt{main}. \item New argument \texttt{se}. \end{itemize} \item \texttt{plot.msr}: \begin{itemize} \item Now handles multitype measures. \item New argument \texttt{multiplot}. \item New argument \texttt{massthresh}. \item New arguments \texttt{equal.markscale} and \texttt{equal.ribbon}. \end{itemize} \item \texttt{plot.onearrow:} Graphical parameters, specified when the object was created, are now taken as the defaults for graphical parameters to the plot. \item \texttt{plot.owin:} New argument \texttt{use.polypath} controls how to plot a filled polygon when it has holes. \item \texttt{plot.profilepl}: This function has now been documented, and the graphics improved. \item \texttt{plot.psp}: \begin{itemize} \item Segments can be plotted with widths proportional to their mark values. \item New argument \texttt{style}. \item New argument \texttt{col} gives control over the colour map representing the values of marks attached to the segments. \item The code for \texttt{style="width"} has been completely rewritten, so that it no longer depends on \texttt{plot.linim}, and is much more efficient. \item The formal argument list has been extended. \end{itemize} \item \texttt{plot.pp3}: New arguments \texttt{box.front}, \texttt{box.back} control plotting of the box. \item \texttt{plot.ppp}: \begin{itemize} \item For multitype point patterns, a warning is issued if the plot legend does not represent every possible type of point due to space restrictions. \item The default colour for the points is now a transparent grey, if this is supported by the plot device. \item For a point pattern with continuous marks (``real numbers'') the colour arguments \texttt{cols}, \texttt{fg}, \texttt{bg} can now be vectors of colour values, and will be used to determine the default colour map for the marks. \item Now recognises graphics parameters for text, such as \texttt{family} and \texttt{srt} \item When \texttt{clipwin} is given, any parts of the boundary of the window of \texttt{x} that lie inside \texttt{clipwin} will also be plotted. \item Improved placement of symbol map legend when argument \texttt{symap} is given. \end{itemize} \item \code{plot.tess}: \begin{itemize} \item This plot method can now fill each tile with a different colour. \item New arguments \code{do.col}, \code{values}, \code{col} and \code{ribargs}. Old argument \code{col} has been renamed \code{border} for consistency. \item Now generates a separate plot panel for each column of marks, if \texttt{do.col=TRUE}. \item New argument \texttt{multiplot}. \end{itemize} \item \texttt{plot.palmdiag}: Improved calculation of $y$ axis limits. \item \texttt{plot.profilepl} ,\texttt{plot.quadratcount}, \texttt{plot.quadrattest}, \texttt{plot.tess}: Now recognise graphics parameters for text, such as \texttt{family} and \texttt{srt} \item \texttt{plot.solist}: \begin{itemize} \item When \texttt{equal.ribbon=TRUE}, the images may now be factor-valued or character-valued. Character-valued images will be converted to factor-valued images. The common colour map will combine the levels of all the factor images. \item New arguments \texttt{panel.begin.args}, \texttt{panel.end.args} \item Result is now an (invisible) list containing the result from executing the plot of each panel. \end{itemize} \item \texttt{plot.studpermutest}: This existing function now has a help file. \item \texttt{plot.symbolmap}: \begin{itemize} \item New argument \texttt{colour.only} makes it possible to display only the colour map information in a symbol map. \item New argument \texttt{warn}. \item Issues a warning if the plot of a discrete symbol map does not represent every possible input value, due to space restrictions. \item New argument \texttt{nsymbols} controls the number of symbols plotted. \end{itemize} \item \code{ponderosa}: In this installed dataset, the function \code{ponderosa.extra\$plotit} has changed slightly (to accommodate the dependence on the package \pkg{spatstat.utils}). \item \texttt{polynom}: This function now has a help file. \item \texttt{pool.fv}: \begin{itemize} \item The default plot of the pooled function no longer includes the variance curves. \item New arguments \texttt{relabel} and \texttt{variance}. \end{itemize} \item \texttt{pool.rat}: New arguments \texttt{weights}, \texttt{relabel} and \texttt{variance}. \item \texttt{ppm}: \begin{itemize} \item Now supports regularized model-fitting. \item Huang-Ogata approximate maximum likelihood can be applied to logistic fits. \item New argument \texttt{improve.type}. \begin{itemize} \item Option \texttt{method="ho"} is replaced by \texttt{improve.type="ho"}. \item Regularized model-fitting is performed when \texttt{improve.type="enet"}. \item Huang-Ogata approximate maximum likelihood can be applied to logistic fits by setting \texttt{method="logi"} and \texttt{improve.type="ho"}. \end{itemize} \item Argument \code{interaction} can now be a function that makes an interaction, such as \code{Poisson}, \code{Hardcore}, \code{MultiHard}. \item Argument \texttt{subset} can now be a window (class \texttt{"owin"}) specifying the sub-region of data to which the model should be fitted. \end{itemize} \item \texttt{ppm.ppp, ppm.quad}: \begin{itemize} \item New argument \texttt{emend}, equivalent to \texttt{project}. \item New arguments \texttt{subset} and \texttt{clipwin}. \end{itemize} \item \code{ppmInfluence}: The result now belongs to class \code{ppmInfluence}, for which there are methods for \code{leverage}, \code{influence}, \code{dfbetas} which extract the desired component. \item \texttt{ppp}: \begin{itemize} \item New argument \texttt{checkdup}. \item If the coordinate vectors \code{x} and \code{y} contain \code{NA}, \code{NaN} or infinite values, these points are deleted with a warning, instead of causing a fatal error. \end{itemize} \item \texttt{pp3}: New argument \texttt{marks}. \item \texttt{predict.kppm, residuals.kppm} Now issues a warning when the calculation ignores the cluster/Cox component and treats the model as if it were Poisson. (This currently happens in predict.kppm when se=TRUE or interval != "none", and in residuals.kppm when type != "raw"). \item \texttt{predict.lppm}: Argument \texttt{locations} can now be an \texttt{lpp} object. \item \texttt{predict.mppm}: \begin{itemize} \item The argument \texttt{type="all"} is now recognised: it selects all the available options. [This is also the default.] \item Now supports multitype point process models. \item Improved handling of argument \texttt{newdata}. \end{itemize} \item \texttt{predict.ppm}: \begin{itemize} \item Now recognises the arguments \code{dimyx} and \code{eps} for specifying the resolution of the grid of prediction points. \item New argument \code{ignore.hardcore}. \item Accelerated for models fitted with \texttt{method="VBlogi"} \item Standard error calculation (\texttt{se=TRUE}) now works for models fitted with \texttt{use.gam=TRUE}. \end{itemize} \item \texttt{predict.rhohat}: New argument \texttt{what} determines which value should be calculated: the function estimate, the upper/lower confidence limits, or the standard error. \item \texttt{print.kppm}: Additional characteristics of the fitted model are reported, including the cluster strength \texttt{phi} and the sibling probability. \item \texttt{print.linim}: More information is printed. \item \texttt{print.lintess}: Output includes information about marks. \item \texttt{print.lppm}: The name of the original point pattern dataset (to which the model was fitted) is now printed. \item \texttt{print.quad}: More information is printed. \item \texttt{print.rmhmodel}: More information is printed. \item \texttt{progressreport} \begin{itemize} \item The estimated time of completion is also printed, if the remaining time is longer than 10 minutes. \item Behaviour improved. \item New arguments \texttt{state}, \texttt{tick}, \texttt{showtime}. \item New option: \verb!style="tk"! \item New argument \texttt{formula} controls the calculation of estimated time remaining. \item New argument \texttt{savehistory} specifies whether to save the elapsed times when the function was called. \end{itemize} \item \code{pseudoR2.ppm}, \code{pseudoR2.lppm}: \begin{itemize} \item The null model now includes any offset terms, by default. \item New argument \code{keepoffset}. \end{itemize} %%Q \item \texttt{quadform}: This function has been moved to the sub-package \texttt{spatstat.sparse}. \item \texttt{quadratcount.ppp}: Computation accelerated in some cases. \item \texttt{quadrat.test.ppm}: Computation accelerated in some cases. \item \texttt{quantess}: \begin{itemize} \item The covariate \texttt{Z} can now be \texttt{"rad"} or \texttt{"ang"} representing polar coordinates. \item New argument \texttt{origin} specifies the origin of polar coordinates. \item New argument \texttt{eps} controls the accuracy of the calculation. \end{itemize} \item \texttt{quantile.ewcdf}: The function is now normalised to the range \verb![0,1]! before the quantiles are computed. This can be suppressed by setting \texttt{normalise=FALSE}. \item \texttt{qqplot.ppm} Argument \texttt{expr} can now be a list of point patterns, or an envelope object containing a list of point patterns. %%R \item Most random generators: now accept \texttt{nsim=0} and return a zero-length list. \item \texttt{rbind.hyperframe}: The result now retains the \texttt{row.names} of the original arguments. \item \texttt{rcellnumber}: New argument \texttt{mu}. \item \texttt{rebound.owin}: Now preserves unitnames of the objects. \item \texttt{rescale.owin}, \texttt{rescale.ppp}, \texttt{rescale.psp}: The geometrical type of the window is now preserved in all cases. (Previously if the window was polygonal but was equivalent to a rectangle, the rescaled window was a rectangle.) \item \texttt{rgbim, hsvim}: New argument \texttt{A} controls the alpha (transparency) channel. \item \texttt{rgb2hex, col2hex, paletteindex, is.colour, samecolour,} \texttt{complementarycolour, is.grey, to.grey} These colour tools now handle transparent colours. \item \texttt{rgb2hex}: New argument \texttt{maxColorValue} \item \texttt{relrisk.ppp}: \begin{itemize} \item If \texttt{se=TRUE} and \texttt{at="pixels"}, the result belongs to class \texttt{solist}. \item The arguments \texttt{adjust}, \texttt{edge}, \texttt{diggle} are now explicit formal arguments. \item New argument \texttt{weights}. \item Ratios which are close to 0/0 are handled more effectively, reducing the likelihood of strange-looking plots when \texttt{sigma} is very small. \item Issues a warning if numerical underflow is detected. \item The interpretation of \texttt{weights} in the calculation of standard error has changed. New argument \texttt{wtype} controls this interpretation. \item New argument \texttt{fudge} specifies a constant numeric value that will be added to each estimate of point process intensity before calculation of relative risk. \end{itemize} \texttt{rhohat}: \begin{itemize} \item The result now includes the ``average'' intensity $\overline\rho$. \item New options \texttt{smoother="piecewise"} computes a piecewise-constant estimate of $\rho(z)$. \item Nonparametric maximum likelihood estimation is now supported, assuming the intensity is a monotone function of the covariate. \item New options \texttt{smoother="increasing"} and \texttt{smoother="decreasing"} for estimating a monotone increasing or monotone decreasing curve. \item New options \texttt{smoother="mountain"} and \texttt{smoother="valley"} for estimating a unimodal function (U-shaped or inverted-U-shaped curve). \item New argument \texttt{subset} allows computation for a subset of the data. \item New argument \texttt{positiveCI} specifies whether confidence limits should always be positive. \item If the covariate is a \texttt{distfun}, the name of the unit of length is saved and displayed on the plot. \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{rhohat.lpp}: \begin{itemize} \item New argument \texttt{random} controls placement of dummy points. \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{rhohat.lppm}: \begin{itemize} \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item\texttt{rhohat.ppm}: New argument \texttt{rule.eps} passed to \texttt{as.mask}. \item \texttt{rjitter.ppp}: If \texttt{trim=TRUE}, the displacement radius will be constrained to be less than or equal to the distance from the data point to the window boundary. This guarantees that all displaced points fall inside the window, and accelerates the computation. \item \texttt{rlabel}: \begin{itemize} \item New argument \texttt{group} specifies that the points are divided into several groups, and that relabelling is applied within each group. \item New arguments \texttt{nsim} and \texttt{drop}. \item \texttt{X} can now be a point pattern of any type (\texttt{ppp}, \texttt{lpp}, \texttt{pp3}, \texttt{ppx}) or a line segment pattern (\texttt{psp}). \end{itemize} \item \texttt{rlabel.ppp}: New argument \texttt{group} specifies that the points are divided into several groups, and that relabelling is applied within each group. \item \texttt{rLGCP}: \begin{itemize} \item This function has been completely re-implemented so that it no longer requires the package \pkg{RandomFields}, which is defunct (and sadly missed). \item The current implementation supports only the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance functions. \item Now recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{rMaternI, rMaternII}: These functions can now generate random patterns in three dimensions and higher dimensions, when the argument \texttt{win} is of class \texttt{box3} or \texttt{boxx}. \item \texttt{rMatClust}: \begin{itemize} \item Can now perform conditional simulation given a fixed number of points. \item New arguments \texttt{n.cond} and \texttt{w.cond}. \end{itemize} \item \texttt{rmh}: \begin{itemize} \item Accelerated, in the case where multiple patterns are saved using \texttt{nsave}. \item The printed output of the debugger (invoked by \texttt{snoop=TRUE}) has been improved. \end{itemize} \item \texttt{rmh.ppm, rmhmodel.ppm, simulate.ppm}: A model fitted using the \texttt{Penttinen} interaction can now be simulated. \item \texttt{rmh.default, rmhmodel.default}: \begin{itemize} \item These functions now recognise \verb!cif='penttinen'! for the Penttinen interaction. \item New arguments \texttt{nsim}, \texttt{saveinfo}. \item The printed output of the debugger (invoked by \texttt{snoop=TRUE}) has been improved. \end{itemize} \item \texttt{rmhcontrol}: \begin{itemize} \item New parameter \texttt{pstage} determines when to generate random proposal points. \item The parameter \texttt{nsave} can now be a vector of integers. \end{itemize} \item \texttt{rNeymanScott}: \begin{itemize} \item Argument \texttt{lmax} has been replaced by \texttt{kappamax}. \item New argument 'mumax'. \end{itemize} \item \texttt{rose.default} New argument \texttt{weights}. \item \texttt{rose} New arguments \texttt{start} and \texttt{clockwise} specify the convention for measuring and plotting angles. \item \texttt{rotmean}: \begin{itemize} \item New argument \texttt{padzero}. \item Default behaviour has changed. \item Improved algorithm stability. \item The result now has the same \texttt{unitname} as the input object. \item New argument \texttt{adjust} controls the smoothing bandwidth. \end{itemize} \item \texttt{rpoint}: New argument \texttt{forcewin} forces the code to use the window \texttt{win} when \texttt{f} is a pixel image. \item \texttt{rpoispp}: Accelerated, when \texttt{lambda} is a pixel image. \item \texttt{rpoisppx}: New argument \code{drop}. \item \texttt{rpoisline}: Also returns information about the original infinite random lines. \item \texttt{rpoislpp}: If \texttt{lambda} is a list of \texttt{"linim"} or \texttt{"linfun"} objects, then the argument \texttt{L} can be omitted. \item \texttt{rPoissonCluster}: Argument \texttt{lmax} has been replaced by \texttt{kappamax}. \item \texttt{rshift.ppp}, \texttt{rshift.splitppp}: new argument \texttt{nsim}. \item \texttt{rSSI}: Accelerated. \item \texttt{rStrauss, rHardcore, rStraussHard, rDiggleGratton, rDGS, rPenttinen:} New argument \texttt{drop}. \item \texttt{rtemper:} new argument \texttt{track}. \item \texttt{rthin} \begin{itemize} \item Accelerated, when \texttt{P} is a single number. \item \texttt{X} can now be a point pattern of any type (\texttt{ppp}, \texttt{lpp}, \texttt{pp3}, \texttt{ppx}) or a line segment pattern (\texttt{psp}). \end{itemize} \item \texttt{rThomas, rMatClust, rCauchy, rVarGamma}: \begin{itemize} \item These algorithms have been accelerated by several orders of magnitude in the case where the cluster radius is large. \item These functions now offer a choice of simulation algorithms. \item Formal arguments have changed. \item When the model is approximately Poisson, it is simulated using rpoispp. This avoids computations which would require huge amounts of memory. New argument \texttt{poisthresh} controls this behaviour. \item New argument \texttt{saveparents}. \end{itemize} \item \texttt{runiflpp}, \texttt{rpoislpp}: The simulation parameters can be determined from an example point pattern, given as the argument \texttt{ex}. \item \texttt{runifpointOnLines}, \texttt{rpoisppOnLines}: New argument \code{drop}. \item \texttt{runifpointx}: New argument \code{drop}. %%S \item \texttt{selfcut.psp}: \begin{itemize} \item Computation accelerated. \item The result now has an attribute \texttt{"camefrom"} indicating the provenance of each segment in the result. \item No longer checks for validity of the resulting segments. \end{itemize} \item \texttt{sessionInfo}: Output now includes a list of packages that are imported but not loaded. \item \texttt{sessionLibs}: Package names are now sorted alphabetically \item \texttt{setcov}: the name of the unit of length is preserved. \item \code{shapley}: In this installed dataset, the function \code{shapley.extra\$plotit} has changed slightly (to accommodate the dependence on the package \pkg{spatstat.utils}). \item \texttt{shift.im}, \texttt{shift.owin}, \texttt{shift.ppp}, \texttt{shift.psp}: More options for the argument \texttt{origin}. \item Simulation: Several basic simulation algorithms have been accelerated. Consequently, simulation outcomes are not identical to those obtained with previous versions of \spst, even when the same random seed is used. To ensure compatibility with previous versions of spatstat, revert to the slower code by setting \texttt{spatstat.options(fastthin=FALSE, fastpois=FALSE)}. \item \texttt{simulate.kppm}: \begin{itemize} \item For log-Gaussian Cox process models (\texttt{clusters="LGCP"}) the simulation algorithm has been completely re-implemented without using the package \pkg{RandomFields}. The current code supports the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance models. \item Conditional simulation of the model, given a fixed number of points, is now supported using the new arguments \texttt{n.cond} and \texttt{w.cond}. \item Additional arguments \verb!...! are now passed to the function that performs the simulation. \end{itemize} \item \texttt{simulate.ppm}: \begin{itemize} \item New argument \texttt{w} controls the window of the simulated patterns. \item New argument \texttt{verbose}. \item Now recognises the argument \texttt{window} as an alternative to \texttt{w}. \end{itemize} \item \texttt{slrm}: \begin{itemize} \item In the default case (where \texttt{dataAtPoints} is not given) all spatial covariates, including the spatial coordinates \texttt{x} and \texttt{y}, are now evaluated at the centre of each pixel. This improves consistency with other implementations of spatial logistic regression. \item Silently ignores any arguments \verb!'...'! that are not recognised by \texttt{as.mask} \end{itemize} \item \texttt{Smooth.ppp}: \begin{itemize} \item A non-Gaussian kernel can now be specified using the argument \texttt{kernel}. \item Argument \texttt{weights} can now be a pixel image, a function, a numeric vector or an expression to be evaluated. \item Infinite bandwidth \texttt{sigma=Inf} is supported. \item Accelerated by about 30\% in the case where \texttt{at="pixels"}. \item Accelerated by about 15\% in the case where \texttt{at="points"} and \texttt{kernel="gaussian"}. \item Now exits gracefully if any mark values are \texttt{NA}, \texttt{NaN} or \texttt{Inf}. \item New argument \texttt{geometric} supports geometric-mean smoothing. \item The arguments \texttt{adjust}, \texttt{edge}, \texttt{diggle} and \texttt{kernel} are now explicit formal arguments. \item Standard error calculation is now supported (Experimental). \end{itemize} \item \texttt{solist}: New argument \verb!.NameBase! \item \texttt{spatialcdf}: \begin{itemize} \item Computation accelerated. \item The result does not inherit class \texttt{"ecdf"} if \texttt{normalise=FALSE}. \end{itemize} \item \texttt{spatstat.options} New options \texttt{fastthin} and \texttt{fastpois} enable fast simulation algorithms. Set these options to \texttt{FALSE} to reproduce results obtained with previous versions of \spst. \item \texttt{split.ppp}, \texttt{split.ppx}: The splitting variable \texttt{f} can now be a logical vector. \item \verb!split<-.ppp!: The default for argument \texttt{un} in \verb!split<-.ppp! now agrees with the default for the same argument in \texttt{split.ppp}. \item \texttt{square}: Handles a common error in the format of the arguments. \item \texttt{step}: now works for models of class \texttt{"mppm"}. \item \texttt{stieltjes}: Argument \texttt{M} can be a stepfun object (such as an empirical CDF). \item \texttt{subset.ppp}, \texttt{subset.lpp}, \texttt{subset.pp3}, \texttt{subset.ppx}: The argument \texttt{subset} can now be any argument acceptable to the \verb!"["! method. \item \texttt{Summary.linim} (methods for the operations \texttt{range}, \texttt{max}, \texttt{min} etc): Recognises the argument \texttt{finite} so that \texttt{range(x, finite=TRUE)} works for a linim object \texttt{x}. \item summary functions: The argument \texttt{correction="all"} is now recognised: it selects all the available options. \begin{quote} This applies to \texttt{Fest}, \texttt{F3est}, \texttt{Gest}, \texttt{Gcross}, \texttt{Gdot}, \texttt{Gmulti}, \texttt{G3est}, \texttt{Gfox}, \texttt{Gcom}, \texttt{Gres}, \texttt{Hest}, \texttt{Jest}, \texttt{Jmulti}, \texttt{Jcross}, \texttt{Jdot}, \texttt{Jfox}, \texttt{Kest}, \texttt{Kinhom}, \texttt{Kmulti}, \texttt{Kcross}, \texttt{Kdot}, \texttt{Kcom}, \texttt{Kres}, \texttt{Kmulti.inhom}, \texttt{Kcross.inhom}, \texttt{Kdot.inhom}, \texttt{Kscaled}, \texttt{Ksector}, \texttt{Kmark}, \texttt{K3est}, \texttt{Lscaled}, \texttt{markcorr}, \texttt{markcrosscorr}, \texttt{nnorient}, \texttt{pairorient}, \texttt{pcfinhom}, \texttt{pcfcross.inhom}, \texttt{pcfcross}, \texttt{pcf}, \texttt{Tstat}. \end{quote} \item \texttt{summary.distfun}, \texttt{summary.funxy}: \begin{itemize} \item More information is printed. \item Pixel resolution can now be controlled. \end{itemize} \item \texttt{summary.im}: Output improved when the image is empty (i.e. when all pixel values are undefined). \item \texttt{summary.kppm}: prints more information about algorithm convergence. \item \texttt{summary.lintess}: prints information about marks. \item \texttt{summary.lppm}: The name of the original point pattern dataset (to which the model was fitted) is now printed. \item \texttt{summary.mppm}: Improved summary of the dependence of the interpoint interaction on the covariates. \item \texttt{summary.ppm}: New argument \texttt{fine} selects the algorithm for variance estimation. \item \texttt{summary.owin}, \texttt{summary.im}: The fraction of frame area that is occupied by the window/image is now reported. \item \texttt{sumouter}: \begin{itemize} \item New argument \texttt{y} allows computation of asymmetric outer products. \item This function has now been moved to the sub-package \texttt{spatstat.sparse} \end{itemize} \item \texttt{symbolmap}: \begin{itemize} \item Now accepts a vector of colour values for the arguments \texttt{col}, \texttt{cols}, \texttt{fg}, \texttt{bg} if the argument \texttt{range} is given. \item New option: \texttt{shape="arrows"}. \end{itemize} %%T \item \texttt{tess}: Argument \texttt{window} is ignored when xgrid, ygrid are given. \item \texttt{texturemap}: Argument \texttt{textures} can be missing or NULL. \item \texttt{textureplot}: Argument \texttt{x} can now be something acceptable to \texttt{as.im}. \item \texttt{tilenames}, \verb!tilenames<-!: These functions are now generic, with methods for \texttt{tess} and \texttt{lintess}. \item \texttt{to.grey} New argument \texttt{transparent}. \item \texttt{transect.im}: new argument \texttt{nsample}. %%U \item \texttt{union.owin}: Improved behaviour when there are more than 2 windows. \item \texttt{unnormdensity}: \begin{itemize} \item Suppress annoying warning messages from \texttt{density.default}. This affects many functions in the \spst\ family of packages. \item Argument \texttt{weights} may have length 1. \item New argument \texttt{defaults}. \item Computation accelerated. \end{itemize} \item \texttt{unstack.lintess}: now handles marks. \item \texttt{update}: now works for models of class \texttt{"mppm"}. \item \texttt{update.kppm}: \begin{itemize} \item New argument \texttt{evaluate}. \item Now handles additional arguments in any order, with or without names. \item Changed arguments. \item Improved behaviour. \end{itemize} \item \texttt{update.ppm}: For the case \texttt{update(model, X)} where \texttt{X} is a point pattern, if the window of \texttt{X} is different from the original window, then the model is re-fitted from scratch (i.e. \texttt{use.internal=FALSE}). %%V \item \texttt{valid.ppm} This is now a method for the generic function \texttt{valid}. \item \texttt{varcount}: New argument \texttt{relative} supports calculation of the overdispersion index. \item \texttt{vcov.mppm}: \begin{itemize} \item Now handles models with Gibbs interactions. \item New argument \texttt{nacoef.action} specifies what to do if some of the fitted coefficients are \texttt{NA}, \texttt{NaN} or \texttt{Inf}. \end{itemize} \item \texttt{vcov.ppm}: \begin{itemize} \item Performance slightly improved, for Gibbs models. \item Variance calculations now handle larger datasets because they use sparse arrays, by default. \item New argument \texttt{nacoef.action} specifies what to do if some of the fitted model coefficients are \texttt{NA}, \texttt{NaN} or infinite. \end{itemize} %%W %%X %%Y %%Z \item \verb![<-.hyperframe!: Improved error message when the format of the index is not supported. \item \verb![<-.im! \begin{itemize} \item Accepts an array for \texttt{value}. \item The subset index \texttt{i} can now be a linear network. Then the result of \verb!x[i, drop=FALSE]! is a pixel image of class \texttt{linim}. \item New argument \texttt{drop} controls behaviour when indices are missing as in \verb!x[] <- value! \end{itemize} \item \verb![.layered!: \begin{itemize} \item Subset index \texttt{i} can now be an \texttt{owin} object. \item Additional arguments \verb!...! are now passed to other methods. \end{itemize} \item \verb![.leverage.ppm!: New argument \texttt{update}. \item \verb![.linnet!: \begin{itemize} \item New argument \texttt{snip} determines what to do with segments of the network that cross the boundary of the window. Default behaviour has changed. \item More robust against artefacts when the subset index is a pixel mask. \end{itemize} \item \verb![.linim!: \begin{itemize} \item More robust against artefacts. \item Accelerated. \end{itemize} \item \verb![.lpp!: New argument \texttt{snip} determines what to do with segments of the network that cross the boundary of the window. Default behaviour has changed. \item \verb![.ppx!: \begin{itemize} \item The subset index \texttt{i} may now be a spatial domain of class \texttt{boxx} or \texttt{box3}. \item New argument \texttt{clip}. \end{itemize} \item \verb![.ppp!: \begin{itemize} \item New argument \texttt{clip} determines whether the window is clipped. \item The previously-unused argument \texttt{drop} now determines whether to remove unused levels of a factor. \end{itemize} \item \verb![.pp3!, \verb![.lpp!, \verb![.ppx!, \texttt{subset.ppp, subset.pp3, subset.lpp, subset.ppx}: These methods now have an argument \texttt{drop} which determines whether to remove unused levels of a factor. \item \verb![.psp!: \begin{itemize} \item accelerated. \item New argument \texttt{fragments} specifies whether to keep fragments of line segments that are cut by the new window, or only to retain segments that lie entirely inside the window. \end{itemize} \item \verb![.solist!: Subset index \texttt{i} can now be an \texttt{owin} object. \end{itemize} \begin{thebibliography}{1} \bibitem{badd10wshop} A.~Baddeley. \newblock Analysing spatial point patterns in {{R}}. \newblock Technical report, CSIRO, 2010. \newblock Version 4. \newblock URL \texttt{https://research.csiro.au/software/r-workshop-notes/} \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \end{thebibliography} \vspace*{\fill} \noindent Current total package size: \Sexpr{currentcount[["ndatasets"]]} datasets, \Sexpr{currentcount[["nobjects"]]} functions, \Sexpr{currentcount[["Rlines"]]} lines of R, \Sexpr{currentcount[["srclines"]]} lines of C, \Sexpr{currentcode} total lines of code. \end{document} spatstat/vignettes/mask.eps0000644000176200001440000001077314243060071015576 0ustar liggesusers%!PS-Adobe-3.0 %%DocumentNeededResources: font Helvetica %%+ font Helvetica-Bold %%+ font Helvetica-Oblique %%+ font Helvetica-BoldOblique %%+ font Symbol %%DocumentMedia: a4 595 841 0 () () %%Title: R Graphics Output %%Creator: R Software %%Pages: (atend) %%Orientation: Landscape %%BoundingBox: 46 61 550 781 %%EndComments %%BeginProlog /bp { gs sRGB 595.00 0 translate 90 rotate gs } def % begin .ps.prolog /gs { gsave } bind def /gr { grestore } bind 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\newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} <>= library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) @ \title{Bugs fixed in \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle \thispagestyle{empty} This vignette lists all \emph{important} bugs detected and fixed in the \spst\ package since 2010. It also explains how to search the list of all recorded bugs in the \spst\ family of packages. <>= nbugs <- nrow(bugfixes("all", show=FALSE)) nbugssince <- nrow(bugfixes("book", show=FALSE)) @ \tableofcontents \pagebreak \section{Bug history} Thousands of bugs have been detected and fixed in \spst\ since it was first released in 2001. We started recording the bug history in 2010. \subsection{Documentation of bugs} Bugs that may have affected the user are listed in the package \texttt{NEWS} file, and can be searched using the \R\ command \texttt{news} or the \spst\ command \texttt{bugfixes}. To see the bugs which have just been fixed in the latest version of \spst, type <>= bugfixes @ To see all bugs which were fixed after a particular version of \spst, for example, bugs that were fixed in version \texttt{1.50-0} or later, type <>= bugfixes(sinceversion="1.50-0") @ To see all bugs in \spst\ that were fixed after a particular date, for example 30 June 2017, type <>= bugfixes(sincedate="2017-06-30") @ To see all bugs fixed after the book \cite{baddrubaturn15} was written, type <>= bugfixes("book") @ To see all bugs in the entire recorded history of \spst, type <>= bugfixes("all") @ which currently produces a list of \Sexpr{nbugs} bugs, of which \Sexpr{nbugssince} were detected after publication of the book \cite{baddrubaturn15}. \subsection{Bugs in the \texttt{spatstat} family of packages} Recently \spst\ was divided into a family of sub-packages. The command \texttt{bugfixes} now covers the bug history in all of these sub-packages. See the help for \texttt{bugfixes} for further details. <>= getstuff <- function(pkg) { x <- read.dcf(file=system.file("DESCRIPTION", package=pkg), fields=c("Version", "Date")) xversion <- as.character(x[,"Version"]) xdate <- as.character(x[,"Date"]) data.frame(date=as.Date(xdate), package=pkg, version=xversion) } vtable <- do.call(rbind, lapply(c("spatstat.utils", "spatstat.data", "spatstat.sparse", "spatstat.geom", "spatstat.random", "spatstat.explore", "spatstat.model", "spatstat.linnet", "spatstat"), getstuff)) @ The current versions of the \spst\ family of packages (used to produce this document) are: <>= print(vtable, row.names=FALSE) @ \pagebreak \section{List of bugs} Following is a list of the {\bf most serious bugs}, in decreasing order of potential impact. A bug is classified as ``serious'' if it produced incorrect results without the user's knowledge. Bugs which cause an error exit are not listed here, because the presence of a bug is obvious, and the bug would not have misled the user. \newcommand\bugger[4]{% \\ {} % {\small (Bug introduced in \texttt{spatstat {#1}}, {#2}; % fixed in \texttt{spatstat {#3}}, {#4})}% } \newcommand\bugin[4]{% \\ {} % {\small (Bug introduced in \texttt{{#1}}, {#2}; % fixed in \texttt{{#3}}, {#4})}% } \newcommand\bugbefore[4]{% \\ {} % {\small (Bug introduced before \texttt{{#1}}, {#2}; % fixed in \texttt{{#3}}, {#4})}% } \newcommand\bugalways[2]{\bugbefore{spatstat 1.19-0}{may 2010}{#1}{#2}} %%% LEVEL 1 \subsection{Serious Bugs, Always Wrong, Broad Impact} \begin{itemize} \item \texttt{nncross.ppp}: Results were completely incorrect if $k > 1$. \bugger{1.31-2}{april 2013}{1.35-0}{december 2013} \item \texttt{nncross.pp3}: Results were completely incorrect in some cases. \bugger{1.32-0}{august 2013}{1.34-0}{october 2013} \item \texttt{cdf.test.ppm}: Calculation of $p$-values was incorrect for Gibbs models: $1-p$ was computed instead of $p$. \bugger{1.40-0}{december 2014}{1.45-2}{may 2016} \item \texttt{Smooth.ppp}: Results of \verb!Smooth(X, at="points", leaveoneout=FALSE)! were completely incorrect. \bugger{1.20-5}{august 2010}{1.46-0}{july 2016} \item \texttt{rmh}: \begin{itemize} \item Simulation was completely incorrect in the case of a multitype point process with an interaction that does not depend on the marks, such as \verb!ppm(betacells, ~marks, Strauss(60))! due to a coding error in the \texttt{C} interface. \bugger{1.22-3}{march 2010}{1.22-3}{june 2011} \item Simulation of the Area-Interaction model was completely incorrect. \bugger{1.23-6}{october 2011}{1.31-0}{january 2013} \item Simulation of the Geyer saturation process was completely incorrect. \bugger{1.31-0}{january 2013}{1.31-1}{march 2013} \item Simulation of the Strauss-Hard Core process was partially incorrect, giving point patterns with a slightly lower intensity. \bugger{1.31-0}{january 2013}{1.37-0}{may 2014} \item Simulation of the \emph{multitype} hard core model was completely incorrect (the interaction was effectively removed, changing the model into a Poisson process). \bugger{1.31-0}{january 2013}{1.63-0}{january 2020} \item The result of simulating a model with a hard core did not necessarily respect the hard core constraint, and simulation of a model with strong inhibition did not necessarily converge. This only happened if the first order trend was large, the starting state (\texttt{n.start} or \texttt{x.start}) was not given, and the number of iterations \texttt{nrep} was not very large. It occurred because of a poor choice for the default starting state. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.40-0}, december 2014)} \item Simulation was incorrect in the case of an inhomogeneous multitype model with \texttt{fixall=TRUE} (i.e.\ with a fixed number of points of each type) if the model was segregated (i.e.\ if different types of points had different first order trend). The effect of the error was that all types of points had the same first order trend. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.43-0}, september 2015)} \item Simulation of the Geyer saturation process was incorrectly initialised, so that the results of a short run (i.e. small value of \texttt{nrep}) were incorrect, while long runs were correct. \bugger{1.17-0}{october 2009}{1.31-1}{march 2013} \end{itemize} \item \texttt{nnmark, as.im.ssf}: If \code{marks(X)} was a matrix rather than a data frame, the results were completely incorrect. \bugger{1.32-0}{august 2013}{1.55-1}{april 2018} \item \texttt{rVarGamma}: Simulations were incorrect; they were generated using the wrong value of the parameter \texttt{nu.ker}. \bugger{1.25-0}{december 2011}{1.35-0}{december 2013} \item \texttt{rCauchy}: Simulations were incorrect; they were generated using the wrong value of the parameter \texttt{omega}. \bugger{1.25-0}{december 2011}{1.25-2}{january 2012} \item \texttt{lppm}: For multitype patterns, the fitted model was completely incorrect due to an error in constructing the quadrature scheme. \bugger{1.23-0}{july 2011}{1.30-0}{december 2012} \item \verb![.lpp!: The local coordinate \texttt{seg} was completely incorrect, when \texttt{i} was a window. \bugger{1.31-2}{april 2013}{1.45-0}{march 2016} \item \texttt{lohboot}: Implementation was completely incorrect. \bugger{1.26-1}{april 2012}{1.53-2}{october 2017} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were incorrect for non-Poisson processes due to a mathematical error. \bugger{1.25-0}{december 2011}{1.51-0}{may 2017} \end{itemize} %%% LEVEL 2 \subsection{Serious Bugs, Often Completely Wrong, Moderate Impact} \begin{itemize} \item \texttt{pairdist.lpp}: Results could have been completely incorrect, due to an internal bug, if the linear network data was in the non-sparse representation. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.linnet 3.0-3}{january 2023} \item \texttt{matrixsqrt}, \texttt{matrixinvsqrt}, \texttt{matrixpower}: If the result was a complex-valued matrix, the values were completely incorrect. \bugin{spatstat 1.48-0}{december 2016}{spatstat.sparse 2.1-1}{february 2021} \item \texttt{pcf.ppp}: Estimates were incorrectly scaled (they were incorrectly multiplied by the area of the window.) Spotted by Maximilian Hesselbarth. \bugin{spatstat.explore 3.0-0}{may 2022}{spatstat.explore 3.0-6}{january 2023} \item \texttt{rLGCP}, \texttt{simulate.kppm}: Simulation results for log-Gaussian Cox processes were incorrect unless the pixel dimensions and pixel spacings were identical on the horizontal and vertical axes. (If pixel dimensions were not specified, then the results were incorrect whenever the Frame of the simulation window was not a square.) \bugger{1.22-2}{june 2011}{1.65-0}{december 2020} \item \texttt{deviance.lppm}, \texttt{pseudoR2.lppm}: Results were completely incorrect, due to a coding error. \bugger{1.44-0}{december 2015}{1.64-2}{november 2020} \item \texttt{kppm}: Results were sometimes incorrect for method='clik2' and method='palm' because the log composite likelihood was erroneously truncated to positive values. Any fitted model for which \verb!logLik(model) = 2.2e-16! should be suspected of being incorrect. \bugin{spatstat.core 2.0-0}{march 2021}{spatstat.core 2.3-2}{november 2021} \item \texttt{bw.pcf}: Results were totally incorrect due to a typo. \bugger{1.51-0}{may 2017}{1.52-0}{august 2017} \item \texttt{density.ppp}: edge correction factors were calculated incorrectly when the window was not a rectangle, causing a negative bias in the estimated intensity. \bugger{1.57-0}{oct 2018}{1.64-0}{april 2020}. \item \texttt{density.ppp}: The standard error (calculated when \texttt{se=TRUE}) was incorrect when \texttt{sigma} was a single numeric value. The output was equal to \texttt{sqrt(sigma)} times the correct answer. \bugger{1.41-1}{february 2015}{1.57-0}{october 2018} \item \texttt{density.ppp}: The standard error (calculated when \texttt{se=TRUE}) was incorrect for non-Gaussian kernels. \bugbefore{spatstat 1.57-0}{october 2018}{spatstat.explore 3.2-0}{may 2023} \item \texttt{relrisk.ppp}: The standard error (calculated when \texttt{se=TRUE}) was incorrect for non-Gaussian kernels. \bugbefore{spatstat 1.57-0}{october 2018}{spatstat.explore 3.2-0}{may 2023} \item \texttt{rthin}: If \texttt{P} was close to 1, the result was sometimes an empty point pattern when it should have been identical to \texttt{X}. \bugger{1.43-0}{october 2015}{1.57-0}{october 2018} \item \texttt{predict.mppm}: If the model included random effects, and if the library \pkg{MASS} was not loaded, the predictions were on the log scale (i.e.\ they were logarithms of the correct values). \bugger{1.43-0}{october 2015}{1.55-1}{april 2018} \item \texttt{nnmap}, \texttt{nnmark}: Values were incorrect if the resulting pixel image had unequal numbers of rows and columns. \bugger{1.35-0}{december 2013}{1.55-0}{january 2018} \item \texttt{vcov.mppm}: Format was incorrect (rows/columns were omitted) in some cases. \bugger{1.45-1}{may 2016}{1.55-0}{january 2018} \item \texttt{model.matrix.ppm}, \texttt{model.frame.ppm}: Values were sometimes incorrect when applied to the result of \texttt{subfits}. To be precise, if \texttt{fit} was an \texttt{mppm} object fitted to a hyperframe that included ``design covariates'' (covariates that take a constant value in each row of the hyperframe), and if \verb!futs <- subfits(fit)!, then \verb!model.matrix(futs[[i]])! gave incorrect values in the columns corresponding to the design covariates. \bugger{1.45-1}{may 2016}{1.55-0}{january 2018} \item \texttt{predict.rhohat}, \texttt{simulate.rhohat}: Results were incorrect for a \texttt{rhohat} object computed from linear network data (class \texttt{"lpp"} or \texttt{"lppm"}). \bugger{1.31-0}{march 2013}{1.63-1}{february 2020} \item \texttt{predict.rho2hat}: Results were incorrect for a \texttt{rho2hat} object computed from a point pattern. \bugger{1.42-0}{may 2015}{1.52-0}{august 2017} \item \texttt{density.ppp}: Result was incorrect for non-Gaussian kernels when \texttt{at="points"} and \texttt{leaveoneout=FALSE}. \bugger{1.47-0}{october 2016}{1.57-0}{october 2018} \item \texttt{envelope.ppm}: If the model was an inhomogeneous Poisson process, the resulting envelope object was incorrect (the simulations were correct, but the envelopes were calculated assuming the model was CSR). \bugger{1.23-5}{september 2011}{1.23-6}{october 2011} \item \texttt{linearK}, \texttt{linearpcf}, \texttt{linearKinhom}, \texttt{linearpcfinhom} and multitype versions: These functions were sometimes greatly underestimated when the network had segments shorter than 10 coordinate units. \bugger{1.44-0}{december 2015}{1.46-2}{july 2016} \item \texttt{nncross}, \texttt{distfun}, \texttt{AreaInter}: Results of \texttt{nncross} were possibly incorrect when \code{X} and \code{Y} did not have the same window. This bug affected values of \texttt{distfun} and may also have affected ppm objects with interaction \texttt{AreaInter}. \bugger{1.9-4}{june 2006}{1.25-2}{january 2012} \item \texttt{update.kppm}: \begin{itemize} \item Did not function correctly when several additional arguments were given. \bugger{1.42-2}{june 2015}{1.54-0}{november 2017} \item If the call to \texttt{update} did not include a formula argument or a point pattern argument, then all arguments were ignored. Example: \texttt{update(fit, improve.type="quasi")} was identical to \texttt{fit}. \bugger{1.42-2}{june 2015}{1.45-0}{march 2016} \end{itemize} \item \texttt{markcorrint}: Results were completely incorrect. \bugger{1.39-0}{october 2014}{1.40-0}{december 2014} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were slightly incorrect for models with a hard core, due to a mathematical error. \bugger{1.51-0}{may 2017}{1.55-1}{april 2018} \item \texttt{Ops.msr}: If the input data contained a pixel image of the smoothed density, this image was not updated; it was copied to the output unchanged. Plots of the resulting measure were incorrect. \bugger{1.52-0}{august 2017}{1.55-1}{april 2018} \item \verb![.linnet!: in calculating \verb!L[W]! where \texttt{W} is a window, the code ignored segments of \code{L} that crossed \code{W} without having a vertex in \code{W}. \bugger{1.53-0}{september 2017}{1.55-1}{april 2015} \item \verb!as.im.function!: if the function domain was not a rectangle and the function values were categorical (factor) values, the result was an empty image. \bugger{1.42-0}{may 2015}{1.57-0}{october 2018} \item \texttt{density.lpp}: If \texttt{weights} were given, the results were completely incorrect if \texttt{leaveoneout=TRUE} (the default) and \texttt{at="points"}. \bugin{spatstat 1.51-0}{may 2017}{spatstat.linnet 3.0-0}{june 2022} \end{itemize} %%% LEVEL 3 \subsection{Bugs, Substantially Incorrect, Moderate Impact} \begin{itemize} \item \texttt{rpoislinetess}: Results were incorrect unless the window was centred at the origin. \bugbefore{spatstat 1.42-0}{may 2015}{spatstat.random 3.2-1}{october 2023} \item \texttt{closepairs.ppp}: If \texttt{distinct=FALSE} and \texttt{what="all"}, the resulting vectors \texttt{yi} and \texttt{yj} contained incorrect values, and had the wrong length. \bugbefore{spatstat 1.56-0}{june 2018}{spatstat.geom 2.4-0}{march 2022} \item \texttt{nncross.ppp}: When \texttt{k > 1}, distance values were incorrectly replaced by \texttt{Inf} in some cases. \bugbefore{spatstat 1.56-0}{june 2018}{spatstat.geom 2.4-0}{march 2022} \item \texttt{nncross.ppp}: If the argument \texttt{by} was given, some of the results were incorrect. [Spotted by Hank Stevens.] \bugger{1.32-0}{august 2013}{2.2-0}{june 2021} \item \verb!"[<-.im"!: Incorrect values were assigned in \verb!x[] <- v! when \texttt{x} and \texttt{v} were both factor-valued but with different sets of levels. \bugbefore{spatstat 1.56-0}{june 2018}{spatstat.geom 3.0-5}{january 2023} \item \texttt{vcov.mppm}: For Gibbs (non-Poisson) models, the variance matrix was calculated incorrectly in some cases. \bugin{spatstat 1.45-1}{may 2016}{spatstat.core 2.4-1}{may 2022} \item \texttt{vcov.mppm}: Results were sometimes incorrect if the two models had different interactions (e.g. Strauss vs Poisson). \bugin{spatstat 1.45-1}{may 2016}{spatstat.core 2.4-1}{may 2022} \item \texttt{distmap.owin}: If the window was a binary mask, the distance values were slightly too large (by a factor \texttt{1 + 1/n} where \texttt{n} is the pixel grid dimension). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{distfun.owin}: If the window was a binary mask, the distance values were slightly too small (typically reduced by 1/20 of a pixel width). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{nncross.ppp}, \texttt{nncross.pp3}: If \texttt{iX} and \texttt{iY} were given, some of the results were incorrect. \bugger{1.32-0}{august 2013}{2.2-0}{june 2021} \item \texttt{vcov.ppm}: Result was sometimes incorrect for Gibbs models. The Fisher information was slightly underestimated. \bugger{1.31-1}{march 2013}{1.64-1}{may 2020} \item \texttt{as.linnet.psp}: Sometimes produced a network with duplicated segments. [Such objects can be repaired using \texttt{repairNetwork}.] \bugger{1.41-1}{february 2015}{1.62-0}{december 2019} \item \texttt{addvar}: If the covariate contained \texttt{NA}, \texttt{NaN} or \texttt{Inf} values, the calculations were sometimes incorrect. \bugger{1.45-0}{march 2016}{.core 2.4-1}{march 2022} \item \texttt{rlpp}: The resulting pattern was unmarked even when it should have been multitype. \bugger{1.48-0}{december 2016}{1.63-0}{january 2020} \item \texttt{spatialcdf}: Argument \texttt{weights} was ignored, unless it was a fitted model. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{ppp}: Points inside the window were erroneously rejected as lying outside the window, if the window was a polygon equivalent to a rectangle with sides longer than $10^6$ units. {\small (Bug was present since the beginning. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{inside.owin}: All results were \texttt{FALSE} if the window was a polygon equivalent to a rectangle with sides longer than $10^6$ units. {\small (Bug was present since the beginning. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{sumouter}: result was incorrect (all entries were zero) if \texttt{w} was missing and \texttt{y} was given. \bugger{1.47-0}{october 2016}{1.59-0}{march 2019} \item \texttt{simulate.dppm}, \texttt{simulate.detpointprocfamily}: In dimensions higher than 2, the result was shifted so that it was centred at the origin. \bugger{1.54-0}{december 2017}{1.55-0}{january 2018} \item \texttt{integral.msr}: If the result was a matrix, it was the transpose of the correct answer. \bugger{1.35-0}{december 2012}{1.55-1}{april 2018} \item \texttt{density.ppp}: Values of \verb!density(X, at="points")! and \verb!Smooth(X, at="points")! were sometimes incorrect, due to omission of the contribution from the data point with the smallest $x$ coordinate. \bugger{1.26-0}{april 2012}{1.46-1}{july 2016} \item \texttt{multiplicity.default}: The first occurrence of any value in the input was incorrectly assigned a multiplicity of 1. \bugger{1.32-0}{december 2013}{1.57-1}{november 2018} \item \texttt{update.ppm}: If the argument \texttt{Q} was given, the results were usually incorrect, or an error was generated. \bugger{1.38-0}{august 2014}{1.38-1}{august 2014} \item \texttt{subfits}: The interaction coefficients of the submodels were incorrect for Gibbs models with a multitype interaction (\texttt{MultiStrauss}, etc). \bugger{1.35-0}{december 2013}{1.45-2}{may 2016} \item \texttt{F3est}: Estimates of $F(r)$ for the largest value of $r$ were wildly incorrect. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.48-0}, december 2016)} \item \texttt{kppm}, \texttt{matclust.estpcf}, \texttt{pcfmodel}: The pair correlation function of the M\'atern Cluster Process was evaluated incorrectly at distances close to 0. This could have affected the fitted parameters in \texttt{matclust.estpcf()} or \texttt{kppm(clusters="MatClust")}. \bugger{1.20-2}{august 2010}{1.33-0}{september 2013} \item \texttt{ppm}: Results were incorrect for the Geyer saturation model with a non-integer value of the saturation parameter \texttt{sat}. \bugger{1.20-0}{july 2010}{1.31-2}{april 2013} \item \texttt{clip.infline}: Results were incorrect unless the midpoint of the window was the coordinate origin. \bugger{1.15-1}{april 2009}{1.48-0}{december 2016} \item \texttt{intensity.ppm}: Result was incorrect for Gibbs models if the model was exactly equivalent to a Poisson process (i.e. if all interaction coefficients were exactly zero). \bugger{1.28-1}{june 2012}{1.47-0}{october 2016} \item \texttt{idw}: Results were incorrect if \texttt{se=TRUE} and \verb!at="pixels"! and \texttt{power} was not equal to 2. The pixel values of \verb!$estimate! were all equal to zero. \bugger{1.58-0}{january 2019}{1.63-0}{january 2020} \item \texttt{funxy}: Did not correctly handle one-line functions. The resulting objects evaluated the wrong function in some cases. \bugger{1.45-0}{march 2016}{1.46-0}{july 2016} \item \texttt{kernel.moment}: Result was incorrect for \texttt{kernel="cosine"} and \texttt{kernel="optcosine"}. \bugger{1.45-2}{may 2016}{1.56-0}{june 2018} \item \verb![.msr!: Format was mangled if the subset contained exactly one quadrature point. \bugger{1.21-3}{january 2011}{1.56-0}{june 2018} \item \texttt{hyperframe}: Did not correctly handle date-time values (columns of class \texttt{"Date"}, etc). \bugger{1.19-1}{may 2010}{1.63-0}{january 2020} \item \texttt{tess}: If a list of tiles was given, and the tiles were pixel images or masks, their pixel resolutions were ignored, and reset to the default $128 \times 128$. {\small (Bug fixed in \texttt{spatstat 1.56-0}, june 2018)} \item \texttt{nnorient}: crashed if the point pattern was empty. \bugger{1.40-0}{december 2015}{1.57-0}{october 2018} \item \verb!as.im.data.frame!: Results were incorrect for factor-valued data. \bugger{1.45-2}{may 2016}{1.63-0}{january 2020} \item \texttt{predict.ppm}: Argument \texttt{new.coef} was ignored in calculating the standard error when \texttt{se=TRUE}. \bugin{spatstat 1.29-0}{october 2012}{spatstat.model 3.0-0}{june 2022} \item \texttt{predict.ppm}: Argument \texttt{new.coef} was ignored in calculating the standard error (and therefore the width of the interval) when \texttt{type="count"} and (\texttt{interval="confidence"} or \texttt{interval="prediction"}). \bugin{spatstat 1.29-0}{october 2012}{spatstat.model 3.0-0}{june 2022} \item \texttt{unnormdensity}: If \texttt{weights} was a single numerical value \texttt{w}, the calculation incorrectly assigned the weight for each observation to be \texttt{w/n} where \texttt{n=length(x)}. \bugalways{spatstat.geom 3.0-7}{march 2023} \end{itemize} %% LEVEL 4: \subsection{Partially Incorrect} \begin{itemize} \item \texttt{kernel.squint}: The return value was incorrect if the argument \texttt{bw} was given. \bugin{spatstat 1.43-0}{october 2015}{spatstat.explore 3.2-4}{october 2023} \item \texttt{pcf} The variance approximation was calculated incorrectly (due to a bug in \texttt{kernel.squint}). \bugin{spatstat 1.43-0}{october 2015}{spatstat.explore 3.2-4}{october 2023} \item \texttt{rjitter.ppp}: If \texttt{retry=FALSE}, marks were ignored. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.geom 3.0-5}{january 2023} \item \texttt{rhohat.lpp}: The argument \texttt{subset} was not handled correctly in the internal data. The estimated function \texttt{rho} was correct, but if \texttt{predict.rhohat} was applied, predictions were computed only in the \texttt{subset}, and were possibly incorrect values. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.linnet 3.0-0}{june 2022} \item \texttt{rhohat.ppp}: The argument \texttt{subset} was not handled correctly in the internal data. The estimated function \texttt{rho} was correct, but if \texttt{predict.rhohat} was applied, predictions were computed only in the \texttt{subset}, and were possibly incorrect values. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.explore 3.0-0}{june 2022} \item \texttt{density.lpp}: The result had the wrong length if \texttt{x} contained duplicated points when \texttt{weights} were given and \texttt{at="points"}. [Spotted by Andrea Gilardi] \bugger{1.55-1}{april 2018}{2.2-0}{june 2021} \item \texttt{crossdist.pp3}: Results with \texttt{periodic=TRUE} were partially incorrect. \bugger{1.34-1}{dec 2013}{1.65-0}{dec 2020} \item \texttt{mppm}: Internal data were malformed if the interaction was \texttt{Hardcore} or \texttt{MultiHard} or a hybrid involving these interactions. This caused various errors when the fitted model was used. \bugger{1.61-0}{september 2019}{1.64-0}{april 2020}. \item \texttt{mppm}: Ignored the arguments \texttt{nd} and \texttt{eps} controlling the quadrature scheme. \bugger{1.35-0}{december 2013}{1.64-0}{april 2020}. \item \texttt{edge.Ripley}: Results were incorrect for data points lying exactly at the corners of a rectangle. \bugbefore{spatstat 1.21-0}{november 2010}{spatstat.core 2.3-2}{november 2021} \item \texttt{kppm}, \texttt{AIC}: For kppm models fitted with \verb!method='clik2'!, the resulting value of \texttt{logLik()} was equal to $1/2$ of the correct value. This would have affected model comparison using AIC, and model selection using \texttt{step}. \bugger{1.42-0}{may 2015}{1.63-0}{january 2020}. \item \texttt{edge.Ripley}, \texttt{Kest}, \texttt{Kinhom}: Isotropic correction weights for polygonal windows were sometimes incorrect for small radius \texttt{r} if the polygon contained many small segments or if the polygon was very long and thin. \bugger{1.60-0}{june 2019}{1.62-0}{december 2019}. \item \texttt{edge.Ripley}, \texttt{Kest}, \texttt{Kinhom}: Isotropic edge correction weight was computed incorrectly for a data point lying exactly on a corner of a rectangular window. \bugin{spatstat 1.60-0}{june 2019}{spatstat.explore 3.0-4}{november 2022} \item \texttt{beachcolours}, \texttt{beachcolourmap}: The number of colours was not always equal to \texttt{ncolours}. \bugger{1.32-0}{august 2013}{1.59-0}{march 2019} \item \texttt{extractbranch.lpp}: Point pattern coordinates were sometimes erroneously set to \texttt{NA}. \bugger{1.42-0}{may 2015}{1.59-0}{march 2019} \item \texttt{rotmean}: When \texttt{result="im"} the resulting image did not have the same dimensions as the input. \bugger{1.42-2}{june 2015}{1.58-0}{january 2019} \item \texttt{quadratcount.ppp}: Sometimes issued an incorrect warning that data points were outside the tessellation, when \texttt{tess} was a tessellation represented by a pixel image. {\small (Bug fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{quadrat.test}: the $p$-value was \texttt{NA} if one of the observed counts was zero, for the Cressie-Read tests with \texttt{CR} not equal to $1$ or $-1$. \bugger{1.38-0}{august 2014}{1.59-0}{march 2019} \item \texttt{quadrat.test}: argument \texttt{CR} was ignored if \texttt{method="MonteCarlo"}. \bugger{1.38-0}{august 2014}{1.61-0}{september 2019} \item \texttt{rotmean}: If argument \texttt{origin} was given, and if \texttt{result="im"} was specified, the resulting image was wrongly displaced. \bugger{1.42-2}{june 2015}{1.58-0}{january 2019} \item \texttt{runifpointx}: Result was mangled when \texttt{n=0} or \texttt{n=1}. \bugger{1.50-0}{march 2017}{1.58-0}{january 2019} \item \texttt{model.matrix.ppm}: The attribute \texttt{assign} was omitted in some cases. \bugger{1.45-1}{may 2016}{1.55-0}{january 2018} \item \texttt{model.matrix.mppm}: Sometimes returned a matrix with the wrong number of rows. \bugger{1.55-0}{january 2018}{1.63-0}{january 2020} \item \texttt{density.ppp}: If the smoothing bandwidth \texttt{sigma} was very small (e.g.\ less than the width of a pixel), results were inaccurate if the default resolution was used, and completely incorrect if a user-specified resolution was given. \bugger{1.26-0}{april 2012}{1.52-0}{august 2017} \item \texttt{selfcrossing.psp}: $y$ coordinate values were incorrect. \bugger{1.23-2}{august 2011}{1.25-3}{february 2012} \item \texttt{Geyer}: For point process models with the \texttt{Geyer} interaction, \texttt{vcov.ppm} and \texttt{suffstat} sometimes gave incorrect answers. \bugger{1.27-0}{may 2012}{1.30-0}{december 2012} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Calculations were incorrect for a Geyer model fitted using an edge correction other than \texttt{"border"} or \texttt{"none"}. \bugger{1.25-0}{december 2011}{1.51-0}{may 2017} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were slightly incorrect for models fitted using the border correction. \bugger{1.25-0}{december 2011}{1.54-0}{november 2017} \item \texttt{leverage.ppm}: The mean leverage value (shown as a contour level in \texttt{plot.leverage.ppm}) was slightly incorrect for Gibbs models. \bugger{1.25-0}{december 2011}{1.54-0}{november 2017} \item \texttt{vcov.ppm}, \texttt{suffstat}: These functions sometimes gave incorrect values for marked point process models. \bugger{1.27-0}{may 2012}{1.29-0}{october 2012} \item \texttt{diagnose.ppm}: When applied to a model obtained from \texttt{subfits()}, in the default case (\texttt{oldstyle=FALSE}) the variance calculations were incorrect. Consequently the dotted lines representing significance bands were incorrect. An error or warning about negative variances occurred sometimes. However, calculations with \texttt{oldstyle=TRUE} were correct. The default has now been changed to \texttt{oldstyle=TRUE} for such models. \bugger{1.35-0}{december 2013}{1.45-0}{march 2016} \item \texttt{Smooth.ppp}: Results for \verb!at="points"! were garbled, for some values of \texttt{sigma}, if \texttt{X} had more than one column of marks. \bugger{1.38-0}{october 2014}{1.46-0}{july 2016} \item \texttt{linearK}, \texttt{linearKinhom}: If any data points were located exactly at a vertex of the linear network, the weights for Ang's correction were incorrect, due to numerical error. This sometimes produced infinite or NA values of the linear $K$ function. \bugger{1.23-0}{july 2011}{1.27-0}{may 2012} \item \texttt{Kinhom}, \texttt{Linhom}: the results were not renormalised (even if \texttt{renormalise=TRUE}) in some cases. \bugger{1.21-0}{december 2010}{1.37-0}{may 2014} \item \texttt{Kinhom}, \texttt{Linhom}: Ignored argument \texttt{reciplambda2} in some cases. \bugger{1.39-0}{october 2014}{1.40-0}{december 2014} \item \texttt{Kinhom}, \texttt{Linhom}: Calculations were incorrect if \texttt{lambda} was a fitted point process model. \bugger{1.38-0}{august 2014}{1.38-1}{august 2014} \item \texttt{integral.linim}, \texttt{integral.linfun}: \begin{itemize} \item results were inaccurate because of a bias in the distribution of sample points. \bugger{1.41-0}{february 2015}{1.47-0}{october 2016} \item results were inaccurate if many of the segment lengths were shorter than the width of a pixel. \bugger{1.41-0}{february 2015}{1.48-0}{december 2016} \item results were wildly inaccurate in some extreme cases where many segments were very short. \bugger{1.41-0}{february 2015}{1.54-0}{november 2017} \end{itemize} \item \texttt{predict.ppm}: Calculation of the conditional intensity omitted the edge correction if \texttt{correction='translate'} or \texttt{correction='periodic'}. \bugger{1.17-0}{october 2009}{1.31-3}{may 2013} \item \texttt{varblock}: Calculations were incorrect if more than one column of edge corrections was computed. \bugger{1.21-1}{november 2010}{1.39-0}{october 2014} \item \texttt{scan.test} Results were sometimes incorrect due to numerical instability (a 'Gibbs phenomenon'). \bugger{1.24-1}{october 2011}{1.26-1}{april 2012} \item \texttt{relrisk}: When \verb!at="pixels"!, a small fraction of pixel values were sometimes wildly inaccurate, due to numerical errors. This affected the range of values in the result, and therefore the appearance of plots. {\small (Bug fixed in \texttt{spatstat 1.40-0}, december 2014)} \item \texttt{predict.slrm}: Results of \texttt{predict(object, newdata)} were incorrect if the spatial domain of \texttt{newdata} was larger than the original domain. \bugger{1.21-0}{november 2010}{1.25-3}{february 2012} \item \texttt{Lest}: The variance approximations (Lotwick-Silverman and Ripley) obtained with \texttt{var.approx=TRUE} were incorrect for \texttt{Lest} (although they were correct for \texttt{Kest}) due to a coding error. \bugger{1.24-1}{october 2011}{1.24-2}{november 2011} \item \texttt{bw.diggle}: Bandwidth was too large by a factor of 2. \bugger{1.23-4}{september 2011}{1.23-5}{september 2011} \item pair correlation functions (\texttt{pcf.ppp}, \texttt{pcfdot}, \texttt{pcfcross} etc:) The result had a negative bias at the maximum $r$ value, because contributions to the pcf estimate from interpoint distances greater than \texttt{max(r)} were mistakenly omitted. {\small (Bugs fixed in \texttt{spatstat 1.35-0}, december 2013)} \item \texttt{Kest}, \texttt{Lest}: Gave incorrect values in very large datasets, due to numerical overflow. `Very large' typically means about 1 million points in a random pattern, or 100,000 points in a tightly clustered pattern. [Overflow cannot occur unless there are at least 46,341 points.] \item \texttt{bw.relrisk}: Implementation of \texttt{method="weightedleastsquares"} was incorrect and was equivalent to \texttt{method="leastsquares"}. \bugger{1.21-0}{november 2010}{1.23-4}{september 2011} \item \texttt{triangulate.owin}: Results were incorrect in some special cases. \bugger{1.42-2}{june 2015}{1.44-0}{december 2015} \item \texttt{crosspairs}: If \texttt{X} and \texttt{Y} were identical point patterns, the result was not necessarily symmetric (on some machines) due to numerical artifacts. \bugger{1.35-0}{december 2013}{1.44-0}{december 2015} \item \texttt{bdist.tiles}: Values were incorrect in some cases due to numerical error. {\small (Bug fixed in \texttt{spatstat 1.29-0}, october 2012)} \item \texttt{Kest.fft}: Result was incorrectly normalised. \bugger{1.21-2}{january 2011}{1.44-0}{december 2015} \item \texttt{crossdist.ppp}: Ignored argument \texttt{squared} if \texttt{periodic=FALSE}. {\small (Bug fixed in \texttt{spatstat 1.38-0}, july 2014)} \item polygon geometry: The point-in-polygon test gave the wrong answer in some boundary cases. {\small (Bug fixed in \texttt{spatstat 1.23-2}, august 2011)} \item \texttt{MultiStraussHard}: If a fitted model with \texttt{MultiStraussHard} interaction was invalid, \texttt{project.ppm} sometimes yielded a model that was still invalid. {\small (Bug fixed in \texttt{spatstat 1.42-0}, may 2015)} \item \texttt{pool.envelope}: Did not always respect the value of \texttt{use.theory}. \bugger{1.23-5}{september 2011}{1.43-0}{september 2015} \item \texttt{nncross.lpp}, \texttt{nnwhich.lpp}, \texttt{distfun.lpp}: Sometimes caused a segmentation fault. \bugger{1.44-0}{december 2015}{1.44-1}{december 2015} \item \texttt{anova.ppm}: If a single \texttt{object} was given, and it was a Gibbs model, then \texttt{adjust} was effectively set to \texttt{FALSE}. \bugger{1.39-0}{october 2014}{1.44-1}{december 2015} \item \verb![.linim!: the result sometimes had the wrong class. \bugger{1.53-0}{september 2017}{1.55-1}{april 2015} \item \verb![.linim!: factor values were erroneously converted to integers, in some cases. \bugger{1.53-0}{september 2017}{1.61-0}{september 2019} \item \verb!is.subset.owin!: sometimes gave the wrong result for polygonal windows due to numerical rounding error. {\small (Bug was always present. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{plot.tess}: the legend showed the tile names in lexicographical order, rather than their original order. \bugger{1.55-1}{april 2018}{1.59-0}{march 2019} \item \texttt{rThomas}, \texttt{rMatClust}, \texttt{rCauchy}, \texttt{rVarGamma}: If the simulation window was not a rectangle, the attribute \texttt{Lambda} was a numeric vector, rather than a pixel image as intended. \bugger{1.43-0}{october 2015}{1.59-0}{march 2019} \item \texttt{effectfun}: In a multitype point process model, \texttt{effectfun} ignored any user-specified value of \texttt{marks}. \bugger{1.52-0}{august 2017}{1.61-0}{september 2019} \item \verb!"[<-.hyperframe"!: Some classes of objects were not handled correctly. \bugger{1.37-0}{may 2014}{1.61-0}{september 2019} \item \texttt{relrisk.ppp}: Crashed if there were more than 2 types of points and \texttt{method = "leastsquares"} or \texttt{method = "weightedleastsquares"}. \bugger{1.23-4}{september 2011}{1.63-0}{january 2020} \item \texttt{nncross.ppp}: Format of output was incorrect if \texttt{X} was an empty pattern. \bugger{1.56-0}{june 2018}{1.63-0}{january 2020} \item \texttt{rmh}, \texttt{rmh.default}: For a marked point process, the debugger did not display the marks. (The \texttt{rmh} debugger is invoked by calling \texttt{rmh} with \texttt{snoop=TRUE}). \bugger{1.31-1}{march 2013}{1.63-0}{january 2020} \item \texttt{model.matrix.mppm}: If the model was fitted using \texttt{gam}, the resulting matrix did not have an \texttt{"assign"} attribute. \bugger{1.55-0}{march 2018}{2.2-0}{june 2021} \item \texttt{update.slrm}: Failed to find covariates that were provided in \texttt{env}. \bugger{1.33-0}{september 2013}{2.2-0}{june 2021} \item \texttt{distmap.owin}: Values were incorrect if X was an empty window (\texttt{is.empty(X) = TRUE}). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{distmap.ppp}, \texttt{distmap.psp}: Values were incorrect if X was an empty pattern (\texttt{npoints(X) = 0}). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{distmap.psp}: Values were incorrect if X was an empty pattern (\texttt{nsegments(X) = 0}). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{envelope} methods: Results were malformed if the name of the function argument was not \texttt{r}. \bugalways{spatstat.explore 3.2-3}{september 2023} \end{itemize} \begin{thebibliography}{1} \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \end{thebibliography} \end{document} spatstat/vignettes/hexagon.pdf0000644000176200001440000000473214243060071016254 0ustar liggesusers%PDF-1.4 %쏢 5 0 obj <> stream x-1As^1/@`X^` 2E0T YA=qNOc?4M 9,T;SvzI!ŕehV)OkbJ#S|8xh+] bendstream endobj 6 0 obj 118 endobj 4 0 obj <> /Contents 5 0 R >> endobj 3 0 obj << /Type /Pages /Kids [ 4 0 R ] /Count 1 >> endobj 1 0 obj <> endobj 7 0 obj <>endobj 8 0 obj <> endobj 9 0 obj <>stream 2013-12-23T19:49:36+08:00 2013-12-23T19:49:36+08:00 fig2dev Version 3.2 Patchlevel 5d hexagon.fig endstream endobj 2 0 obj <>endobj xref 0 10 0000000000 65535 f 0000000410 00000 n 0000001982 00000 n 0000000351 00000 n 0000000222 00000 n 0000000015 00000 n 0000000203 00000 n 0000000474 00000 n 0000000515 00000 n 0000000544 00000 n trailer << /Size 10 /Root 1 0 R /Info 2 0 R /ID [<7169BA68125AE1AEC0984268ECC4E10A><7169BA68125AE1AEC0984268ECC4E10A>] >> startxref 2169 %%EOF spatstat/vignettes/datasets.Rnw0000644000176200001440000007110614366376353016452 0ustar liggesusers\documentclass[11pt]{article} % \VignetteIndexEntry{Datasets Provided for the Spatstat Package} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \usepackage{graphicx} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \newcommand{\sdat}{\pkg{spatstat.data}} \newcommand{\Sdat}{\pkg{Spatstat.data}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") spatstat.options(transparent=FALSE) options(useFancyQuotes=FALSE) @ \title{Datasets provided for \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle This document is an overview of the spatial datasets that are provided for the \spst\ package. To flick through a nice display of all the data sets that come with \spst\ type \texttt{demo(data)}. To see information about a given data set, type \texttt{help({\em name})} where \emph{name} is the name of the data set. To plot a given data set, type \texttt{plot({\em name})}. Datasets in \spst\ are ``lazy-loaded'', which means that they can be accessed simply by typing their name. Not all packages do this; in some packages you have to type \texttt{data({\em name})} in order to access a data set. To list all the datasets in \spst, you need to type \texttt{data(package="spatstat.data")}. This is because, for efficiency, the datasets are actually installed in a sub-package \sdat. This is the only time you should ever need to mention \sdat\ explicitly. When the \spst\ package is loaded by the command \texttt{library(spatstat)}, the sub-package \sdat\ is automatically loaded. \section{List of datasets} \subsection{Point patterns in 2D} Here is a list of the standard point pattern data sets that are supplied with the current installation of \sdat: \newcommand{\recto}{\framebox{\hphantom{re}\vphantom{re}}} \newcommand{\irregpoly}{\includegraphics*[width=6mm]{irregpoly}} \newcommand{\masque}{\includegraphics*[width=6mm]{mask}} \newcommand{\convpoly}{\includegraphics*[width=4mm]{hexagon}} \newcommand{\disc}{$\bigcirc$} \newcommand{\nomarks}{$\cdot$} \newcommand{\nocov}{$\cdot$} \begin{tabular}{l|l|ccc} {\sf name} & {\sf description} & {\sf marks} & {\sf covariates} & {\sf window} \\ \hline {\tt amacrine} & rabbit amacrine cells & cell type & \nocov & \recto \\ {\tt anemones} & sea anemones & diameter & \nocov & \recto \\ {\tt ants} & ant nests& species & zones & \convpoly \\ {\tt bdspots} & breakdown spots & \nomarks & \nocov & \disc \\ {\tt bei} & rainforest trees & \nomarks & topography & \recto \\ {\tt betacells} & cat retinal ganglia & cell type, area & \nocov & \recto \\ {\tt bramblecanes} & bramble canes & age & \nocov & \recto \\ {\tt bronzefilter} & bronze particles & diameter & \nocov & \recto \\ {\tt btb} & bovine tuberculosis & type, year & \nocov & \irregpoly \\ {\tt cells} & biological cells & \nomarks &\nocov & \recto \\ {\tt chorley} & cancers & case/control &\nocov & \irregpoly \\ {\tt clmfires} & forest fires & cause, size, date & \shortstack[c]{elevation, orientation,\\ slope, land use} & \irregpoly \\ {\tt concrete} & air bubbles in concrete & \nomarks & \nocov & \masque \\ {\tt copper} & copper deposits & \nomarks & fault lines & \recto \\ {\tt demopat} & artificial data & type & \nocov & \irregpoly \\ {\tt finpines} & trees & diam, height & \nocov & \recto \\ {\tt gordon} & people in a park & \nomarks & \nocov & \irregpoly \\ {\tt gorillas} & gorilla nest sites & group, season & \shortstack[c]{terrain, vegetation,\\ heat, water} & \irregpoly \\ {\tt hamster} & hamster tumour cells & cell type &\nocov & \recto \\ {\tt humberside} & child leukaemia & case/control & \nocov & \irregpoly\\ {\tt hyytiala} & mixed forest & species &\nocov & \recto \\ {\tt japanesepines} & Japanese pines & \nomarks &\nocov & \recto \\ {\tt lansing} & mixed forest & species & \nocov & \recto \\ {\tt longleaf} & trees & diameter & \nocov & \recto \\ {\tt mucosa} & gastric mucosa cells & cell type & \nocov & \recto \\ {\tt murchison} & gold deposits & \nomarks & faults, rock type & \irregpoly \\ {\tt nbfires} & wildfires & several & \nocov & \irregpoly \\ {\tt nztrees} & trees & \nomarks & \nocov & \recto \\ {\tt paracou} & trees & adult/juvenile & \nocov & \recto \\ {\tt ponderosa} & trees & \nomarks & \nocov & \recto \\ {\tt redwood} & saplings & \nomarks & \nocov & \recto \\ {\tt redwood3} & saplings & \nomarks & \nocov & \recto \\ {\tt redwoodfull} & saplings & \nomarks & zones & \recto \\ {\tt shapley} & galaxies & magnitude, recession, SE & \nocov & \convpoly \\ {\tt simdat} & simulated pattern & \nomarks & \nocov & \recto \\ {\tt sporophores} & fungi & species & \nocov & \disc \\ {\tt spruces} & trees & diameter & \nocov & \recto \\ {\tt stonetools} & palaeolithic tools & type, depth & \nocov & \irregpoly \\ {\tt swedishpines} & trees & \nomarks & \nocov & \recto \\ {\tt urkiola} & mixed forest & species & \nocov & \irregpoly \\ {\tt vesicles} & synaptic vesicles & \nomarks & zones & \irregpoly \\ {\tt waka} & trees & diameter & \nocov & \recto \\ \hline \end{tabular} \bigskip \noindent The shape of the window containing the point pattern is indicated by the symbols \recto\ (rectangle), \disc\ (disc), \convpoly\ (convex polygon), \irregpoly\ (irregular polygon) and \masque (binary mask). Additional information about the data set \texttt{\em name} may be stored in a separate list \texttt{{\em name}.extra}. Currently these are the available options: \begin{tabular}[!h]{ll} {\sc Name} & {\sc Contents} \\ \hline {\tt ants.extra} & field and scrub subregions; \\ & additional map elements; plotting function \\ {\tt bei.extra} & covariate images \\ {\tt chorley.extra} & incinerator location; plotting function \\ {\tt gorillas.extra} & covariate images\\ {\tt nbfires.extra} & inscribed rectangle; border type labels \\ {\tt ponderosa.extra} & data points of interest; plotting function\\ {\tt redwoodfull.extra} & subregions; plotting function \\ {\tt shapley.extra} & individual survey fields; plotting function \\ {\tt vesicles.extra} & anatomical regions \\ \hline \end{tabular} For demonstration and instruction purposes, raw data files are available for the datasets \texttt{vesicles}, \texttt{gorillas} and \texttt{osteo}. \subsection{Other Data Types} There are also the following spatial data sets which are not 2D point patterns: \begin{tabular}[c]{l|l|l} {\sf name} & {\sf description} & {\sf format} \\ \hline {\tt austates} & Australian states & tessellation \\ {\tt cetaceans} & marine survey & replicated 2D point patterns \\ {\tt chicago} & crimes & point pattern on linear network \\ {\tt demohyper} & simulated data & replicated 2D point patterns with covariates\\ {\tt dendrite} & dendritic spines & point pattern on linear network \\ {\tt flu} & virus proteins & replicated 2D point patterns \\ {\tt heather} & heather mosaic & binary image (three versions) \\ {\tt osteo} & osteocyte lacunae & replicated 3D point patterns with covariates\\ {\tt pyramidal} & pyramidal neurons & replicated 2D point patterns in 3 groups\\ {\tt residualspaper} & data \& code from Baddeley et al (2005) & 2D point patterns, \R\ function \\ {\tt simba} & simulated data & replicated 2D point patterns in 2 groups\\ {\tt spiders} & spider webs & point pattern on linear network \\ {\tt waterstriders} & insects on water & replicated 2D point patterns\\ \hline \end{tabular} Additionally there is a dataset \texttt{Kovesi} containing several colour maps with perceptually uniform contrast. \section{Information on each dataset} Here we give basic information about each dataset. For further information, consult the help file for the particular dataset. <>= opa <- par() ## How to set all margins to zero and eliminate all outer spaces zeromargins <- function() { par( mar=rep(0,4), omd=c(0,1,0,1), xaxs="i", yaxs="i" ) invisible(NULL) } ## Set 'mar' setmargins <- function(...) { x <- c(...) x <- rep(x, 4)[1:4] par(mar=x) invisible(NULL) } @ \subsubsection*{\texttt{amacrine}: Amacrine cells} Locations of displaced amacrine cells in the retina of a rabbit. There are two types of points, ``on'' and ``off''. \SweaveOpts{width=5.5,height=3}\setkeys{Gin}{width=0.8\textwidth} <>= plot(amacrine) @ <>= setmargins(0,1,2,0) plot(amacrine) @ \subsubsection*{\texttt{anemones}: Sea Anemones} These data give the spatial locations and diameters of sea anemones on a boulder near sea level. \SweaveOpts{width=7,height=4.5}\setkeys{Gin}{width=0.8\textwidth} <>= plot(anemones, markscale=1) @ <>= setmargins(0,0,2,0) plot(anemones, markscale=1) @ \subsubsection*{\texttt{ants}: Ants' nests} Spatial locations of nests of two species of ants at a site in Greece. The full dataset (supplied here) has an irregular polygonal boundary, while most analyses have been confined to two rectangular subsets of the pattern (also supplied here). % Parameters for Ants data with key at right \SweaveOpts{width=6.3,height=4}\setkeys{Gin}{width=0.7\textwidth} <>= ants.extra$plotit() @ %$ <>= setmargins(0,0,1,0) ants.extra$plotit() @ %$ \subsubsection*{\texttt{austates}: Australian states} The states and large mainland territories of Australia are represented as polygonal regions forming a tessellation. <>= plot(austates) @ \subsubsection*{\texttt{bdspots}: Breakdown spots} A list of three point patterns, each giving the locations of electrical breakdown spots on a circular electrode in a microelectronic capacitor. \SweaveOpts{width=12,height=6}\setkeys{Gin}{width=\textwidth} <>= plot(bdspots, equal.scales=TRUE, pch="+", panel.args=function(i)list(cex=c(0.15, 0.2, 0.7)[i])) @ <>= zeromargins() plot(bdspots, equal.scales=TRUE, pch="+", main="", mar.panel=0, hsep=1, panel.args=function(i)list(cex=c(0.15, 0.2, 0.7)[i])) @ \subsubsection*{\texttt{bei}: Beilschmiedia data} Locations of 3605 trees in a tropical rain forest. Accompanied by covariate data giving the elevation (altitude) and slope of elevation in the study region. \SweaveOpts{width=12,height=6}\setkeys{Gin}{width=0.8\textwidth} <>= plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) @ <>= setmargins(0,0,2,0) plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) @ The following command gives a perspective display similar to the front cover of Baddeley, Rubak and Turner (2015): <>= M <- persp(bei.extra$elev, theta=-45, phi=18, expand=7, border=NA, apron=TRUE, shade=0.3, box=FALSE, visible=TRUE, main="") perspPoints(bei, Z=bei.extra$elev, M=M, pch=16, cex=0.3) @ \subsubsection*{\texttt{betacells}: Beta ganglion cells} Locations of beta ganglion cells in cat retina, each cell classified as `on' or `off' and also labelled with the cell profile area. <>= plot(betacells) @ \subsubsection*{\texttt{bramblecanes}: Bramble canes} <>= plot(bramblecanes, cols=1:3) @ Try the following <>= plot(split(bramblecanes)) @ \subsubsection*{\texttt{bronzefilter}: Bronze filter section profiles} Spatially inhomogeneous pattern of circular section profiles of particles, observed in a longitudinal plane section through a gradient sinter filter made from bronze powder. <>= plot(bronzefilter,markscale=2) @ \subsubsection*{\texttt{btb}: bovine tuberculosis} Locations of farms where bovine tuberculosis was detected, marked by year of detection and spoligotype of tuberculosis. <>= plot(btb, which.marks="spoligotype", cols=2:5, chars=1:4) @ \subsubsection*{\texttt{cells}: Biological cells} Locations of the centres of 42 biological cells observed under optical microscopy in a histological section. Often used as a demonstration example. <>= plot(cells) @ \subsubsection*{\texttt{cetaceans}: Survey of marine species} Recorded sightings of whales, dolphins and other marine species in a series of surveys. Replicated 2D marked point patterns. <>= plot(cetaceans.extra$patterns, main="Cetaceans data", cols=1:5, hsep=1) @ \subsubsection*{\texttt{chicago}: Chicago crimes} Locations (street addresses) of crimes reported in a two-week period in an area close to the University of Chicago. A multitype point pattern on a linear network. <>= plot(chicago, main="Chicago Crimes", col="grey", cols=c("red", "blue", "black", "blue", "red", "blue", "blue"), chars=c(16,2,22,17,24,15,6), leg.side="left", show.window=FALSE) @ \subsubsection*{\texttt{chorley}: Chorley-Ribble cancer data} Spatial locations of cases of cancer of the larynx and cancer of the lung, and the location of a disused industrial incinerator. A marked point pattern, with an irregular window and a simple covariate. <>= chorley.extra$plotit() @ %$ \subsubsection*{\texttt{clmfires}: Castilla-La Mancha Fires} Forest fires in the Castilla-La Mancha region of Spain between 1998 and 2007. A point pattern with 4 columns of marks: \begin{tabular}{ll} \texttt{cause} & cause of fire\\ \texttt{burnt.area} & total area burned, in hectares \\ \texttt{date} & date of fire \\ \texttt{julian.date} & date of fire in days since 1.1.1998 \end{tabular} <>= plot(clmfires, which.marks="cause", cols=2:5, cex=0.25, main="Castilla-La Mancha forest fires") @ The accompanying dataset \texttt{clmfires.extra} is a list of two items \texttt{clmcov100} and \texttt{clmcov200} containing covariate information for the entire Castilla-La Mancha region. Each of these two elements is a list of four pixel images named \texttt{elevation}, \texttt{orientation}, \texttt{slope} and \texttt{landuse}. <>= plot(clmfires.extra$clmcov100$elevation, main="Elevation") @ %$ \subsubsection*{\texttt{concrete}: Air bubbles in concrete} Prof.\ Shin-ichi Igarashi's data: a point pattern of the locations of centroids of air bubbles seen in a cross-section of concrete. Air bubbles are present in the matrix of cement paste which surrounds the particles of aggregate. The outline of the aggregate, and the locations of the centroids of the bubble profiles, are recorded. <>= plot(concrete,chars="+",cols="blue",col="yellow") @ \subsubsection*{\texttt{copper}: Queensland copper data} These data come from an intensive geological survey in central Queensland, Australia. They consist of 67 points representing copper ore deposits, and 146 line segments representing geological `lineaments', mostly faults. <>= plot(copper$Points, main="Copper") plot(copper$Lines, add=TRUE) @ \subsubsection*{\texttt{demohyper}} A synthetic example of a \texttt{hyperframe} for demonstration purposes. <>= plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) }), parargs=list(mar=rep(1,4))) @ \subsubsection*{\texttt{demopat}} A synthetic example of a point pattern for demonstration purposes. <>= plot(demopat) @ \subsubsection*{\texttt{dendrite}} Dendrites are branching filaments which extend from the main body of a neuron (nerve cell) to propagate electrochemical signals. Spines are small protrusions on the dendrites. This dataset gives the locations of 566 spines observed on one branch of the dendritic tree of a rat neuron. The spines are classified according to their shape into three types: mushroom, stubby or thin. <>= plot(dendrite, leg.side="bottom", main="", cex=0.75, cols=2:4) @ \subsubsection*{\texttt{finpines}: Finnish pine saplings} Locations of 126 pine saplings in a Finnish forest, their heights and their diameters. <>= plot(finpines, main="Finnish pines") @ \subsubsection*{\texttt{flu}: Influenza virus proteins} The \texttt{flu} dataset contains replicated spatial point patterns giving the locations of two different virus proteins on the membranes of cells infected with influenza virus. It is a \texttt{hyperframe} containing point patterns and explanatory variables. <>= wildM1 <- with(flu, virustype == "wt" & stain == "M2-M1") plot(flu[wildM1, 1, drop=TRUE], main=c("flu data", "wild type virus, M2-M1 stain"), chars=c(16,3), cex=0.4, cols=2:3) @ \subsubsection*{\texttt{gordon}: People in Gordon Square} Locations of people sitting on a grass patch on a sunny afternoon. <>= plot(gordon, main="People in Gordon Square", pch=16) @ \subsubsection*{\texttt{gorillas}: Gorilla nesting sites} Locations of nesting sites of gorillas, and associated covariates, in a National Park in Cameroon. \texttt{gorillas} is a marked point pattern (object of class \texttt{"ppp"}) representing nest site locations. \texttt{gorillas.extra} is a named list of 7 pixel images (objects of class \texttt{"im"}) containing spatial covariates. It also belongs to the class \texttt{"listof"}. <>= plot(gorillas, which.marks=1, chars=c(1,3), cols=2:3, main="Gorilla nest sites") @ The \texttt{vegetation} covariate is also available as a raw ASCII format file, <>= system.file("rawdata/gorillas/vegetation.asc", package="spatstat") @ \subsubsection*{\texttt{hamster}: Hamster kidney cells} Cell nuclei in hamster kidney, each nucleus classified as either `dividing' or `pyknotic'. A multitype point pattern. <>= plot(hamster, cols=c(2,4)) @ \subsubsection*{\texttt{heather}: Heather mosaic} The spatial mosaic of vegetation of the heather plant, recorded in a 10 by 20 metre sampling plot in Sweden. A list with three entries, representing the same data at different spatial resolutions. <>= plot(heather$coarse) @ Type the following to see all three images: <>= plot(heather) @ \subsubsection*{\texttt{humberside}: Childhood Leukemia and Lymphoma} Spatial locations of cases of childhood leukaemia and lymphoma, and randomly-selected controls, in North Humberside. A marked point pattern. <>= plot(humberside) @ The dataset \texttt{humberside.convex} is an object of the same format, representing the same point pattern data, but contained in a larger, 5-sided convex polygon. \subsubsection*{\texttt{hyytiala}: Mixed forest} Spatial locations and species classification for trees in a Finnish forest. <>= plot(hyytiala, cols=2:5) @ \subsubsection*{\texttt{japanesepines}: Japanese black pine saplings} Locations of Japanese black pine saplings in a square sampling region in a natural forest. Often used as a standard example. <>= plot(japanesepines) @ \subsubsection*{\texttt{lansing}: Lansing Woods} Locations and botanical classification of trees in a forest. A multitype point pattern with 6 different types of points. Includes duplicated points. <>= plot(lansing) @ Type the following to see 6 plots, each showing the location of one of the types of points: <>= plot(split(lansing)) @ \subsubsection*{\texttt{longleaf}: Longleaf Pines} Locations and diameters of Longleaf pine trees. <>= plot(longleaf) @ \subsubsection*{\texttt{mucosa}: Gastric Mucosa Cells} A bivariate inhomogeneous point pattern, giving the locations of the centres of two types of cells in a cross-section of the gastric mucosa of a rat. <>= plot(mucosa, chars=c(1,3), cols=c("red", "green")) plot(mucosa.subwin, add=TRUE, lty=3) @ \subsubsection*{\texttt{murchison}: Murchison Gold Deposits} Spatial locations of gold deposits and associated geological features in the Murchison area of Western Australia. A list of three elements: \begin{itemize} \item \texttt{gold}, the point pattern of gold deposits; \item \texttt{faults}, the line segment pattern of geological faults; \item \texttt{greenstone}, the subregion of greenstone outcrop. \end{itemize} Type the following to see the full Murchison dataset: <>= plot(murchison$greenstone, main="Murchison data", col="lightgreen") plot(murchison$gold, add=TRUE, pch=3, col="blue") plot(murchison$faults, add=TRUE, col="red") @ Some analysis of the data uses a rectangle close to the abandoned town of Reedy: <<>>= reedy <- owin(c(580, 650) * 1000, c(6986, 7026) * 1000) @ Here are the data in that area: <>= plot(murchison$greenstone[reedy], main="Murchison data", col="lightgreen") plot(murchison$gold[reedy], add=TRUE, pch=3, col="blue") plot(murchison$faults[reedy], add=TRUE, col="red") @ \subsubsection*{\texttt{nbfires}: New Brunswick Fires} Fires in New Brunswick (Canada) with marks giving information about each fire. <>= plot(nbfires, use.marks=FALSE, pch=".") @ The following command would show the data for each year in a separate panel: <>= plot(split(nbfires), use.marks=FALSE, chars=".") @ <>= par(mar=c(0,0,2,0)) plot(split(nbfires)$"2000", which.marks="fire.type", main=c("New Brunswick fires 2000", "by fire type"), cols=c("blue", "green", "red", "cyan"), leg.side="left") @ \subsubsection*{\texttt{nztrees}: New Zealand Trees} Locations of trees in a forest plot in New Zealand. Often used as a demonstration example. <>= plot(nztrees) plot(trim.rectangle(as.owin(nztrees), c(0,5), 0), add=TRUE, lty=3) @ \subsubsection*{\texttt{osteo}: Osteocyte Lacunae} Replicated three-dimensional point patterns: the three-dimensional locations of osteocyte lacunae observed in rectangular volumes of solid bone using a confocal microscope. A \texttt{hyperframe} containing 3D point patterns and explanatory variables. <>= plot(osteo[1:4,], main.panel="", pch=21, bg='white') @ For demonstration and instruction purposes, the raw data from the 36th point pattern are available in a plain ascii file in the \texttt{spatstat} installation, <>= system.file("rawdata/osteo/osteo36.txt", package="spatstat") @ \subsubsection*{\texttt{paracou}: Kimboto trees} Point pattern of adult and juvenile Kimboto trees recorded at Paracou in French Guiana. A bivariate point pattern. <>= plot(paracou, cols=2:3, chars=c(16,3)) @ \subsubsection*{\texttt{ponderosa}: Ponderosa Pines} Locations of Ponderosa Pine trees in a forest. Several special points are identified. <>= ponderosa.extra$plotit() @ %$ \subsubsection*{\texttt{pyramidal}: Pyramidal Neurons in Brain} Locations of pyramidal neurons in sections of human brain. There is one point pattern from each of 31 human subjects. The subjects are divided into three groups: controls (12 subjects), schizoaffective (9 subjects) and schizophrenic (10 subjects). To reduce space, we show only the odd-numbered patterns: <<>>= pyr <- pyramidal[c(FALSE,TRUE), ] @ <>= pyr$grp <- abbreviate(pyr$group, minlength=7) plot(pyr, quote(plot(Neurons, pch=16, main=grp)), main="Pyramidal Neurons") @ \subsubsection*{\texttt{redwood}, \texttt{redwood3}, \texttt{redwoodfull}: Redwood seedlings and saplings} California Redwood seedlings and saplings in a forest. There are two versions of this dataset: \texttt{redwood} and \texttt{redwoodfull}. The \texttt{redwoodfull} dataset is the full data. It is spatially inhomogeneous in density and spacing of points. The \texttt{redwood} dataset is a subset of the full data, selected because it is apparently homogeneous, and has often been used as a demonstration example. This comes in two versions commonly used in the literature: \texttt{redwood} (coordinates given to 2 decimal places) and \texttt{redwood3} (coordinates given to 3 decimal places). <>= plot(redwood) plot(redwood3, add=TRUE, pch=20) @ <>= redwoodfull.extra$plotit() @ %$ \subsubsection*{\texttt{residualspaper}: Data from residuals paper} Contains the point patterns used as examples in \begin{quote} A. Baddeley, R. Turner, J. M{\o}ller and M. Hazelton (2005) Residual analysis for spatial point processes. \emph{Journal of the Royal Statistical Society, Series B} \textbf{67}, 617--666 \end{quote} along with {\sf R} code. <>= plot(as.solist(residualspaper[c("Fig1", "Fig4a", "Fig4b", "Fig4c")]), main="") @ \subsubsection*{\texttt{shapley}: Shapley Galaxy Concentration} Sky positions of 4215 galaxies in the Shapley Supercluster (mapped by radioastronomy). <>= shapley.extra$plotit(main="Shapley") @ %$ \subsubsection*{\texttt{simdat}: Simulated data} Another simulated dataset used for demonstration purposes. <>= plot(simdat) @ \subsubsection*{\texttt{spiders}: Spider webs} Spider webs across the mortar lines of a brick wall. A point pattern on a linear network. <>= plot(spiders, pch=16, show.window=FALSE) @ \subsubsection*{\texttt{sporophores}: Sporophores} Sporophores of three species of fungi around a tree. <>= plot(sporophores, chars=c(16,1,2), cex=0.6) points(0,0,pch=16, cex=2) text(15,8,"Tree", cex=0.75) @ \subsubsection*{\texttt{stonetools}: Palaeolithic stone tools and bone fragments} Palaeolithic tools and bone fragments uncovered in an archaeological dig in Tanzania. Each find is marked by its type (either \texttt{BONE} or \texttt{LITHIC}) and by the height $z$ (above a reference level) of the location in the soil where it was found. <>= plot(stonetools, which.marks=2, cols=c(2,3), chars=c(1,3), cex=0.5) @ \subsubsection*{\texttt{spruces}: Spruces in Saxony} Locations of Norwegian spruce trees in a natural forest stand in Saxonia, Germany. Each tree is marked with its diameter at breast height. <>= plot(spruces, maxsize=min(nndist(spruces))) @ \subsubsection*{\texttt{swedishpines}: Swedish Pines} Locations of pine saplings in a Swedish forest. Often used as a demonstration example. <>= plot(swedishpines) @ \subsubsection*{\texttt{urkiola}: trees in a wood} Locations of birch and oak trees in a secondary wood in Urkiola Natural Park (Basque country, northern Spain). Irregular window, bivariate point pattern. <>= plot(urkiola, cex=0.5, cols=2:3) @ \subsubsection*{\texttt{waka}: trees in Waka National Park} Spatial coordinates of each tree, marked by the tree diameter at breast height. <>= par(mar=c(0,0,2,0)) plot(waka, markscale=0.04, main=c("Waka national park", "tree diameters")) @ \subsubsection*{\texttt{vesicles}: synaptic vesicles} Point pattern of synaptic vesicles observed in rat brain tissue. <>= v <- rotate(vesicles, pi/2) ve <- lapply(vesicles.extra, rotate, pi/2) plot(v, main="Vesicles") plot(ve$activezone, add=TRUE, lwd=3) @ The auxiliary dataset \texttt{vesicles.extra} is a list with entries\\ \begin{tabular}{ll} \texttt{presynapse} & outer polygonal boundary of presynapse \\ \texttt{mitochondria} & polygonal boundary of mitochondria \\ \texttt{mask} & binary mask representation of vesicles window \\ \texttt{activezone} & line segment pattern representing the active zone. \end{tabular} For demonstration and training purposes, the raw data files for this dataset are also provided in the \pkg{spatstat} package installation:\\ \begin{tabular}{ll} \texttt{vesicles.txt} & spatial locations of vesicles \\ \texttt{presynapse.txt} & vertices of \texttt{presynapse} \\ \texttt{mitochondria.txt} & vertices of \texttt{mitochondria} \\ \texttt{vesiclesimage.tif} & greyscale microscope image \\ \texttt{vesiclesmask.tif} & binary image of \texttt{mask} \\ \texttt{activezone.txt} & coordinates of \texttt{activezone} \end{tabular} The files are in the folder \texttt{rawdata/vesicles} in the \texttt{spatstat} installation directory. The precise location of the files can be obtained using \texttt{system.file}, for example <>= system.file("rawdata/vesicles/mitochondria.txt", package="spatstat") @ \subsubsection*{\texttt{waterstriders}: Insects on a pond} Three independent replications of a point pattern formed by insects on the surface of a pond. <>= plot(waterstriders) @ \end{document} spatstat/vignettes/irregpoly.pdf0000644000176200001440000000524114243060071016633 0ustar liggesusers%PDF-1.4 %쏢 5 0 obj <> stream xm=n1 =O0?}n #EJH6@ob%^EsMo? 'P*fvjqp}]",D <qe]X/pRiuS\ *qX2B*XkӜ\lDU1J卼PX]rrZ0CP$!7D,(ƃ[Q5C2)RHQ5A70=x"EuZ1m=@4)YJ9VL,6}9f,q^ѕ:'1V8'3&R[UXߑX |endstream endobj 6 0 obj 311 endobj 4 0 obj <> /Contents 5 0 R >> endobj 3 0 obj << /Type /Pages /Kids [ 4 0 R ] /Count 1 >> endobj 1 0 obj <> endobj 7 0 obj <>endobj 8 0 obj <> endobj 9 0 obj <>stream 2013-12-23T19:50:47+08:00 2013-12-23T19:50:47+08:00 fig2dev Version 3.2 Patchlevel 5d irregpoly.fig endstream endobj 2 0 obj <>endobj xref 0 10 0000000000 65535 f 0000000605 00000 n 0000002179 00000 n 0000000546 00000 n 0000000415 00000 n 0000000015 00000 n 0000000396 00000 n 0000000669 00000 n 0000000710 00000 n 0000000739 00000 n trailer << /Size 10 /Root 1 0 R /Info 2 0 R /ID [] >> startxref 2368 %%EOF spatstat/vignettes/getstart.Rnw0000644000176200001440000003126414243060071016455 0ustar liggesusers\documentclass[11pt]{article} % \VignetteIndexEntry{Getting Started with Spatstat} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \usepackage{graphicx} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) @ \title{Getting started with \texttt{spatstat}} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle Welcome to \spst, a package in the \R\ language for analysing spatial point patterns. This document will help you to get started with \spst. It gives you a quick overview of \spst, and some cookbook recipes for doing basic calculations. \section*{What kind of data does \spst\ handle?} \Spst\ is mainly designed for analysing \emph{spatial point patterns}. For example, suppose you are an ecologist studying plant seedlings. You have pegged out a $10 \times 10$ metre rectangle for your survey. Inside the rectangle you identify all the seedlings of the species you want, and record their $(x,y)$ locations. You can plot the $(x,y)$ locations: <>= data(redwood) plot(redwood, pch=16, main="") @ This is a \emph{spatial point pattern} dataset. Methods for analysing this kind of data are summarised in the highly recommended book by Diggle \cite{digg03}, or our own book \cite{baddrubaturn15}, or other references in the bibliography below. \nocite{handbook10,bivapebegome08} Alternatively the points could be locations in one dimension (such as road accidents recorded on a road network) or in three dimensions (such as cells observed in 3D microscopy). You might also have recorded additional information about each seedling, such as its height, or the number of fronds. Such information, attached to each point in the point pattern, is called a \emph{mark} variable. For example, here is a stand of pine trees, with each tree marked by its diameter at breast height (dbh). The circle radii represent the dbh values (not to scale). <>= data(longleaf) plot(longleaf, main="") @ You might also have recorded supplementary data, such as the terrain elevation, which might serve as explanatory variables. These data can be in any format. \Spst\ does not usually provide capabilities for analysing such data in their own right, but \spst\ does allow such explanatory data to be taken into account in the analysis of a spatial point pattern. \Spst\ is \underline{\bf not} designed to handle point data where the $(x,y)$ locations are fixed (e.g.\ temperature records from the state capital cities in Australia) or where the different $(x,y)$ points represent the same object at different times (e.g.\ hourly locations of a tiger shark with a GPS tag). These are different statistical problems, for which you need different methodology. \section*{What can \spst\ do?} \Spst\ supports a very wide range of popular techniques for statistical analysis for spatial point patterns, for example \begin{itemize} \item kernel estimation of density/intensity \item quadrat counting and clustering indices \item detection of clustering using Ripley's $K$-function \item spatial logistic regression \item model-fitting \item Monte Carlo tests \end{itemize} as well as some advanced statistical techniques. \Spst\ is one of the largest packages available for \R, containing over 1000 commands. It is the product of 25 years of software development by leading researchers in spatial statistics. \section*{How do I start using \spst?} \begin{enumerate} \item Install \R\ on your computer \begin{quote} Go to \texttt{r-project.org} and follow the installation instructions. \end{quote} \item Install the \spst\ package in your \R\ system \begin{quote} Start \R\ and type \verb!install.packages("spatstat")!. If that doesn't work, go to \texttt{r-project.org} to learn how to install Contributed Packages. \end{quote} \item Start \R\ \item Type \texttt{library(spatstat)} to load the package. \item Type \texttt{help(spatstat)} for information. \end{enumerate} \section*{How do I get my data into \spst?} <>= data(finpines) mypattern <- unmark(finpines) mydata <- round(as.data.frame(finpines), 2) @ Here is a cookbook example. Suppose you've recorded the $(x,y)$ locations of seedlings, in an Excel spreadsheet. You should also have recorded the dimensions of the survey area in which the seedlings were mapped. \begin{enumerate} \item In Excel, save the spreadsheet into a comma-separated values (CSV) file. \item Start \R\ \item Read your data into \R\ using \texttt{read.csv}. \begin{quote} If your CSV file is called \texttt{myfile.csv} then you could type something like <>= mydata <- read.csv("myfile.csv") @ to read the data from the file and save them in an object called \texttt{mydata} (or whatever you want to call it). You may need to set various options inside the \texttt{read.csv()} command to get this to work for your file format: type \texttt{help(read.csv)} for information. \end{quote} \item Check that \texttt{mydata} contains the data you expect. \begin{quote} For example, to see the first few rows of data from the spreadsheet, type <<>>= head(mydata) @ To select a particular column of data, you can type \texttt{mydata[,3]} to extract the third column, or \verb!mydata$x! to extract the column labelled \texttt{x}. \end{quote} \item Type \texttt{library(spatstat)} to load the \spst\ package \item Now convert the data to a point pattern object using the \spst\ command \texttt{ppp}. \begin{quote} Suppose that the \texttt{x} and \texttt{y} coordinates were stored in columns 3 and 7 of the spreadsheet. Suppose that the sampling plot was a rectangle, with the $x$ coordinates ranging from 100 to 200, and the $y$ coordinates ranging from 10 to 90. Then you would type <>= mypattern <- ppp(mydata[,3], mydata[,7], c(100,200), c(10,90)) @ The general form is <>= ppp(x.coordinates, y.coordinates, x.range, y.range) @ Note that this only stores the seedling locations. If you have additional columns of data (such as seedling height, seedling sex, etc) these can be added as \emph{marks}, later. \end{quote} \item Check that the point pattern looks right by plotting it: <>= plot(mypattern) @ \item Now you are ready to do some statistical analysis. Try the following: \begin{itemize} \item Basic summary of data: type <>= summary(mypattern) @ \item Ripley's $K$-function: <>= options(SweaveHooks=list(fig=function() par(mar=rep(4,4)+0.1))) @ <>= plot(Kest(mypattern)) @ For more information, type \texttt{help(Kest)} \item Envelopes of $K$-function: <>= plot(envelope(mypattern,Kest)) @ <>= env <- envelope(mypattern,Kest, nsim=39) @ <>= plot(env, main="envelope(mypattern, Kest)") @ <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ For more information, type \texttt{help(envelope)} \item kernel smoother of point density: <>= plot(density(mypattern)) @ For more information, type \texttt{help(density.ppp)} \end{itemize} \item Next if you have additional columns of data recording (for example) the seedling height and seedling sex, you can add these data as \emph{marks}. Suppose that columns 5 and 9 of the spreadsheet contained such values. Then do something like <>= marks(mypattern) <- mydata[, c(5,9)] @ <>= mypattern <-finpines @ Now you can try things like the kernel smoother of mark values: <>= plot(Smooth(mypattern)) @ \setkeys{Gin}{width=0.8\textwidth} <>= plot(Smooth(mypattern, sigma=1.2), main="Smooth(mypattern)") @ \setkeys{Gin}{width=0.4\textwidth} \item You are airborne! Now look at the book \cite{baddrubaturn15} for more hints. \end{enumerate} \section*{How do I find out which command to use?} Information sources for \spst\ include: \begin{itemize} \item the Quick Reference guide: a list of the most useful commands. \begin{quote} To view the quick reference guide, start \R, then type \texttt{library(spatstat)} and then \texttt{help(spatstat)}. Alternatively you can download a pdf of the Quick Reference guide from the website \texttt{www.spatstat.org} \end{quote} \item online help: \begin{quote} The online help files are useful --- they give detailed information and advice about each command. They are available when you are running \spst. To get help about a particular command \texttt{blah}, type \texttt{help(blah)}. There is a graphical help interface, which you can start by typing \texttt{help.start()}. Alternatively you can download a pdf of the entire manual (1000 pages!) from the website \texttt{www.spatstat.org}. \end{quote} \item vignettes: \begin{quote} \Spst\ comes installed with several `vignettes' (introductory documents with examples) which can be accessed using the graphical help interface. They include a document about \texttt{Handling shapefiles}. \end{quote} \item book: \begin{quote} Our book \cite{baddrubaturn15} contains a complete course on \texttt{spatstat}. \end{quote} \item website: \begin{quote} Visit the \spst\ package website \texttt{www.spatstat.org} \end{quote} \item forums: \begin{quote} Join the forum \texttt{R-sig-geo} by visiting \texttt{r-project.org}. Then email your questions to the forum. Alternatively you can ask the authors of the \spst\ package (their email addresses are given in the package documentation). \end{quote} \end{itemize} \begin{thebibliography}{10} % \bibitem{badd10wshop} % A. Baddeley. % \newblock Analysing spatial point patterns in {{R}}. % \newblock Technical report, CSIRO, 2010. % \newblock Version 4. % \newblock URL \texttt{https://research.csiro.au/software/r-workshop-notes/} % \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \bibitem{bivapebegome08} R. Bivand, E.J. Pebesma, and V. G{\'{o}}mez-Rubio. \newblock {\em Applied spatial data analysis with {R}}. \newblock Springer, 2008. \bibitem{cres93} N.A.C. Cressie. \newblock {\em Statistics for Spatial Data}. \newblock {John Wiley and Sons}, {New York}, second edition, 1993. \bibitem{digg03} P.J. Diggle. \newblock {\em Statistical Analysis of Spatial Point Patterns}. \newblock Hodder Arnold, London, second edition, 2003. \bibitem{fortdale05} M.J. Fortin and M.R.T. Dale. \newblock {\em Spatial analysis: a guide for ecologists}. \newblock Cambridge University Press, Cambridge, UK, 2005. \bibitem{fothroge09handbook} A.S. Fotheringham and P.A. Rogers, editors. \newblock {\em The {SAGE} {H}andbook on {S}patial {A}nalysis}. \newblock SAGE Publications, London, 2009. \bibitem{gaetguyo09} C. Gaetan and X. Guyon. \newblock {\em Spatial statistics and modeling}. \newblock Springer, 2009. \newblock Translated by Kevin Bleakley. \bibitem{handbook10} A.E. Gelfand, P.J. Diggle, M. Fuentes, and P. Guttorp, editors. \newblock {\em Handbook of Spatial Statistics}. \newblock CRC Press, 2010. \bibitem{illietal08} J. Illian, A. Penttinen, H. Stoyan, and D. Stoyan. \newblock {\em Statistical Analysis and Modelling of Spatial Point Patterns}. \newblock John Wiley and Sons, Chichester, 2008. \bibitem{mollwaag04} J. M{\o}ller and R.P. Waagepetersen. \newblock {\em Statistical Inference and Simulation for Spatial Point Processes}. \newblock Chapman and Hall/CRC, Boca Raton, 2004. \bibitem{pfeietal08} D.U. Pfeiffer, T. Robinson, M. Stevenson, K. Stevens, D. Rogers, and A. Clements. \newblock {\em Spatial analysis in epidemiology}. \newblock Oxford University Press, Oxford, UK, 2008. \bibitem{wallgotw04} L.A. Waller and C.A. Gotway. \newblock {\em Applied spatial statistics for public health data}. \newblock Wiley, 2004. \end{thebibliography} \end{document} spatstat/vignettes/replicated.Rnw0000644000176200001440000014174114243060071016736 0ustar liggesusers\documentclass[11pt]{article} % \VignetteIndexEntry{Analysing Replicated Point Patterns in Spatstat} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\indicate}[1]{\boldmaths{1}\{ {#1} \}} \newcommand{\dee}[1]{\, {\rm d}{#1}} \newcommand{\boldmaths}[1]{{\ensuremath\boldsymbol{#1}}} \newcommand{\xx}{\boldmaths{x}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) @ \title{Analysing replicated point patterns in \texttt{spatstat}} \author{Adrian Baddeley} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle \begin{abstract} This document describes \spst's capabilities for fitting models to replicated point patterns. More generally it applies to data from a designed experiment in which the response from each unit is a spatial point pattern. \end{abstract} \tableofcontents \newpage %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} `Replicated point patterns' are datasets consisting of several point patterns which can be regarded as independent repetitions of the same experiment. For example, three point patterns taken from micrographs of three pipette samples of the same jug of milk, could be assumed to be replicated observations. More generally we could have several experimental groups, with replicated point pattern data in each group. For example there may be two jugs of milk that were treated differently, and we take three pipette samples from each jug. Even more generally our point patterns could be the result of a designed experiment involving control and treatment groups, covariates such as temperature, and even spatial covariates (such as image data). This document describes some capabilities available in the \spst\ package for analysing such data. \textbf{For further detail, see Chapter 16 of the spatstat book \cite{TheBook}.} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Overview of software} The main components needed are: \begin{itemize} \item the model-fitting function \texttt{mppm}, an extension of the \texttt{spatstat} function \texttt{ppm}, that will fit Gibbs point process models to multiple point pattern datasets; \item support for the class \texttt{"mppm"} of point process models fitted by \texttt{mppm} (e.g. functions to print and plot the fitted model, analysis of deviance for Poisson models) \item some tools for exploratory data analysis; \item basic support for the data from such experiments by storing the data in a \emph{``hyperframe''}. A hyperframe is like a data frame, except that each entry in a column can be a point pattern or a pixel image, as well as a single number or categorical value. \item four example datasets. \end{itemize} \section{Formulating the problem} We view the experiment as involving a series of {\em `units'\/}. Each unit is subjected to a known set of experimental conditions (described by the values of the {\em covariates\/}), and each unit yields a {\em response\/} which is a spatial point pattern. The value of a particular covariate for each unit can be either a single value (numerical, logical or factor), or a pixel image. Three important cases are: \begin{description} \item[independent replicates:] We observe $n$ different point patterns that can be regarded as independent replicates, i.e.\ independent realisations of the same point process. The `responses' are the point patterns; there are no covariates. \item[replication in groups:] there are $K$ different experimental groups (e.g. control, aspirin, nurofen). In group $k$ ($k=1,\ldots,K$) we observe $n_k$ point patterns which can be regarded as independent replicates within this group. We regard this as an experiment with $n = \sum_k n_k$ units. The responses are the point patterns; there is one covariate which is a factor (categorical variable) identifying which group each point pattern belongs to. \item[general case:] there are covariates other than factors that influence the response. The point patterns are assumed to be independent, but no two patterns have the same distribution. \end{description} Examples of these three cases are given in the datasets \texttt{waterstriders}, \texttt{pyramidal} and \texttt{demohyper} respectively, which are installed in \spst. \section{Installed datasets} The following datasets are currently installed in \spst. \begin{itemize} \item \texttt{waterstriders}: Penttinen's \cite{pent84} waterstriders data recording the locations of insect larvae on a pond in 3 independent experiments. \item \texttt{pyramidal}: data from Diggle, Lange and Benes \cite{digglangbene91} on the locations of pyramidal neurons in human brain, 31 human subjects grouped into 3 groups (controls, schizoaffective and schizophrenic). \item \texttt{flu}: data from Chen et al \cite{chenetal08} giving the locations of two different virus proteins on the membranes of cells infected with influenza virus; 41 multitype point patterns divided into two virus types (wild and mutant) and two stain types. \item \texttt{simba}: simulated data from an experiment with two groups and 5 replicate point patterns per group. \item \texttt{demohyper}: simulated data from an experiment with two groups in which each experimental unit has a point pattern response and a pixel image covariate. \end{itemize} \section{Lists of point patterns} First we need a convenient way to store the \emph{responses} from all the units in an experiment. An individual point pattern is stored as an object of class \verb!"ppp"!. The easiest way to store all the responses is to form a list of \verb!"ppp"! objects. \subsection{Waterstriders data} The \texttt{waterstriders} data are an example of this type. The data consist of 3 independent point patterns representing the locations of insect larvae on a pond. See \texttt{help(waterstriders)}. <<>>= waterstriders @ The \texttt{waterstriders} dataset is a list of point patterns. It is a list, each of whose entries is a point pattern (object of class \verb!"ppp"!). Note that the observation windows of the three point patterns are {\tt not\/} identical. \subsection{The class \texttt{listof}} For convenience, the \texttt{waterstriders} dataset also belongs to the class \verb!"listof"!. This is a simple mechanism to allow us to handle the list neatly --- for example, we can provide special methods for printing, plotting and summarising the list. \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= plot(waterstriders, main="") @ Notice that the plot method displays each entry of the list in a separate panel. There's also the summary method: <<>>= summary(waterstriders) @ \subsection{Creating a \texttt{listof} object} For example, here is a simulated dataset containing three independent realisations of the Poisson process with intensity 100. <<>>= X <- listof(rpoispp(100), rpoispp(100), rpoispp(100)) @ Then it can be printed and plotted. <>= plot(X) X @ To convert an existing list to the class \code{listof}, use \code{as.listof}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Hyperframes} A \emph{hyperframe} is like a data frame, except that its entries can be objects of any kind. A hyperframe is effectively a two-dimensional array in which each column consists of values of one type (as in a data frame) or consists of objects of one class. The entries in a hyperframe can be point patterns, pixel images, windows, or any other objects. To analyse an experiment, we will store {\bf all} the data from the experiment in a single hyperframe. The rows of the hyperframe will correspond to different experimental units, while the columns represent different variables (response variables or covariates). \subsection{Creating hyperframes} The function \texttt{hyperframe} will create a hyperframe. <>= hyperframe(...) @ The arguments \verb!...! are any number of arguments of the form \texttt{tag=value}. Each \texttt{value} will become a column of the array. The \texttt{tag} determines the name of the column. Each \texttt{value} can be either \begin{itemize} \item an atomic vector or factor (i.e. numeric vector, integer vector, character vector, logical vector, complex vector or factor) \item a list of objects which are all of the same class \item one atomic value, which will be replicated to make an atomic vector or factor \item one object, which will be replicated to make a list of identical objects. \end{itemize} All columns (vectors, factors and lists) must be of the same length, if their length is greater than 1. For example, here is a hyperframe containing a column of numbers and a column of \emph{functions}: <<>>= H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) H @ Note that a column of character strings will be converted to a factor, unless you set \texttt{stringsAsFactors=FALSE} in the call to \code{hyperframe}. This is the same behaviour as for the function \code{data.frame}. <<>>= G <- hyperframe(X=1:3, Y=letters[1:3], Z=factor(letters[1:3]), W=list(rpoispp(100),rpoispp(100), rpoispp(100)), U=42, V=rpoispp(100), stringsAsFactors=FALSE) G @ This hyperframe has 3 rows and 6 columns. The columns named \texttt{U} and \texttt{V} are constant (all entries in a column are the same). The column named \texttt{Y} is a character vector while \texttt{Z} is a factor. \subsection{Hyperframes of data} To analyse an experiment, we will store {\bf all} the data from the experiment in a single hyperframe. The rows of the hyperframe will correspond to different experimental units, while the columns represent different variables (response variables or covariates). Several examples of hyperframes are provided with the package, including \texttt{demohyper}, \texttt{flu}, \texttt{simba} and \texttt{pyramidal}, described above. The \texttt{simba} dataset contains simulated data from an experiment with a `control' group and a `treatment' group, each group containing 5 experimental units. The responses in the control group are independent Poisson point patterns with intensity 80. The responses in the treatment group are independent realisations of a Strauss process (see \texttt{help(simba)} for details). The \texttt{simba} dataset is a hyperframe with 10 rows and 2 columns: \texttt{Points} (the point patterns) and \texttt{group} (a factor with levels \texttt{control} and \texttt{treatment}). <<>>= simba @ The \texttt{pyramidal} dataset contains data from Diggle, Lange and Benes \cite{digglangbene91} on the locations of pyramidal neurons in human brain. One point pattern was observed in each of 31 human subjects. The subjects were classified into 3 groups (controls, schizoaffective and schizophrenic). The \texttt{pyramidal} dataset is a hyperframe with 31 rows and 2 columns: \code{Neurons} (the point patterns) and \code{group} (a factor with levels \texttt{control}, \texttt{schizoaffective} and \texttt{schizophrenic}). <<>>= pyramidal @ The \texttt{waterstriders} dataset is not a hyperframe; it's just a list of point patterns. It can easily be converted into a hyperframe: <<>>= ws <- hyperframe(Striders=waterstriders) @ \subsection{Columns of a hyperframe} Individual columns of a hyperframe can be extracted using \verb!$!: <<>>= H$X H$Y @ The result of \verb!$! is a vector or factor if the column contains atomic values; otherwise it is a list of objects (with class \texttt{"listof"} to make it easier to print and plot). Individual columns can also be assigned (overwritten or created) using \verb!$<-!: <<>>= H$U <- letters[1:3] H @ This can be used to build up a hyperframe column-by-column: <<>>= G <- hyperframe() G$X <- waterstriders G$Y <- 1:3 G @ \subsection{Subsets of a hyperframe} Other subsets of a hyperframe can be extracted with \verb![!: <<>>= H[,1] H[2,] H[2:3, ] H[1,1] @ The result of \verb![! is a hyperframe, unless you set \verb!drop=TRUE! and the subset consists of only one element or one column: <<>>= H[,1,drop=TRUE] H[1,1,drop=TRUE] H[1,2,drop=TRUE] @ There is also a method for \verb![<-! that allows you to assign values to a subset of a hyperframe. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Plotting} \subsection{Plotting a \code{listof} object} The plot method for \code{listof} objects has formal arguments <>= plot.listof(x, ..., main, arrange = TRUE, nrows = NULL, ncols = NULL) @ where \code{main} is a title for the entire page. If \code{arrange=TRUE} then the entries of the list are displayed in separate panels on the same page (with \code{nrows} rows and \code{ncols} columns of panels), while if \code{arrange=FALSE} then the entries are just plotted as a series of plot frames. The extra arguments \verb!...! control the individual plot panels. These arguments will be passed to the plot method that displays each entry of the list. Suitable arguments depend on the type of entries. <>= plot(waterstriders, pch=16, nrows=1) @ \subsection{Plotting a hyperframe} \subsubsection{Plotting one column} If \code{h} is a hyperframe, then the default action of \code{plot(h)} is to extract the first column of \code{h} and plot each of the entries in a separate panel on one page (actually using the plot method for class \verb!"listof"!). \SweaveOpts{width=7,height=5} \setkeys{Gin}{width=0.9\textwidth} <>= plot(simba) @ This only works if the entries in the first column are objects for which a plot method is defined (for example, point patterns, images, windows). To select a different column, use \verb!$! or \verb![!: \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) plot(H$Y) @ The plot can be controlled using the arguments for \code{plot.listof} (and, in this case, \code{plot.function}, since \verb!H$Y! consists of functions). \subsubsection{Complex plots} More generally, we can display any kind of higher-order plot involving one or more columns of a hyperframe: <>= plot(h, e) @ where \code{h} is a hyperframe and \code{e} is an \R\ language call or expression that must be evaluated in each row to generate each plot panel. \SweaveOpts{width=9,height=5} \setkeys{Gin}{width=0.9\textwidth} <>= plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) })) @ Note the use of \code{quote}, which prevents the code inside the braces from being evaluated immediately. To plot the $K$-functions of each of the patterns in the \code{waterstriders} dataset, \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= H <- hyperframe(Bugs=waterstriders) plot(H, quote(plot(Kest(Bugs))), marsize=1) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Data analysis} \subsection{Computing with hyperframes} Often we want to perform some computation on each row of a hyperframe. In a data frame, this can be done using the command \code{with}: <<>>= df <- data.frame(A=1:10, B=10:1) with(df, A-B) @ In this example, the expression \code{A-B} is evaluated in each row of the data frame, and the result is a vector containing the computed values for each row. The function \code{with} is generic, and has a method for data frames, \code{with.data.frame}. The computation above was executed by \code{with.data.frame}. The same syntax is available for hyperframes using the method \code{with.hyperframe}: <>= with(h,e) @ Here \code{h} is a hyperframe, and \code{e} is an {\sf R} language construct involving the names of columns in \code{h}. For each row of \code{h}, the expression \code{e} will be evaluated in such a way that each entry in the row is identified by its column name. <<>>= H <- hyperframe(Bugs=waterstriders) with(H, npoints(Bugs)) with(H, distmap(Bugs)) @ The result of \code{with.hyperframe} is a list of objects (of class \verb!"listof"!), or a vector or factor if appropriate. Notice that (unlike the situation for data frames) the operations in the expression \code{e} do not have to be vectorised. For example, \code{distmap} expects a single point pattern, and is not vectorised to deal with a list of point patterns. Instead, the expression \code{distmap(Bugs)} is evaluated separately in each row of the hyperframe. \subsection{Summary statistics} One application of \code{with.hyperframe} is to calculate summary statistics for each row of a hyperframe. For example, the number of points in a point pattern \code{X} is returned by \code{npoints(X)}. To calculate this for each of the responses in the \code{simba} dataset, <<>>= with(simba, npoints(Points)) @ The summary statistic can be any kind of object. For example, to compute the empirical $K$-functions for each of the patterns in the \code{waterstriders} dataset, <<>>= H <- hyperframe(Bugs=waterstriders) K <- with(H, Kest(Bugs)) @ To plot these $K$-functions you can then just type \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= plot(K) @ The summary statistic for each row could be a numeric vector: <<>>= H <- hyperframe(Bugs=waterstriders) with(H, nndist(Bugs)) @ The result is a list, each entry being a vector of nearest neighbour distances. To find the minimum interpoint distance in each pattern: <<>>= with(H, min(nndist(Bugs))) @ \subsection{Generating new columns} New columns of a hyperframe can be created by computation from the existing columns. For example, I can add a new column to the \code{simba} dataset that contains pixel images of the distance maps for each of the point pattern responses. <>= simba$Dist <- with(simba, distmap(Points)) @ \subsection{Simulation} This can be useful for simulation. For example, to generate Poisson point patterns with different intensities, where the intensities are given by a numeric vector \code{lambda}: \SweaveOpts{width=6,height=6} \setkeys{Gin}{width=0.7\textwidth} <>= lambda <- rexp(6, rate=1/50) H <- hyperframe(lambda=lambda) H$Points <- with(H, rpoispp(lambda)) plot(H, quote(plot(Points, main=paste("lambda=", signif(lambda, 4))))) @ It's even simpler to generate 10 independent Poisson point patterns with the \emph{same} intensity 50, say: <>= H$X <- with(H, rpoispp(50)) @ \noindent The expression \code{rpoispp(50)} is evaluated once in each row, yielding a different point pattern in each row because of the randomness. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Exploratory data analysis} Before fitting models to the data, it is prudent to explore the data to detect unusual features and to suggest appropriate models. \subsection{Exploring spatial trend and covariate effects} Points may be distributed non-uniformly either because they are intrinsically non-uniform (``spatial trend'') or because their abundance depends on a spatial covariate (``covariate effects''). Non-uniformity of a point pattern can be investigated using the kernel smoothed intensity. This is the convolution of the point pattern with a smooth density called the kernel. Effectively each point in the pattern is replaced by a copy of the kernel, and the sum of all copies of the kernel is the kernel-smoothed intensity function. It is computed by \texttt{density.ppp} separately for each point pattern. <>= plot(simba, quote(plot(density(Points), main="")), nrows=2) @ Covariate effects due to a real-valued spatial covariate (a real-valued pixel image) can be investigated using the command \code{rhohat}. This uses a kernel smoothing technique to fit a model of the form \[ \lambda(u) = \rho(Z(u)) \] where $\lambda(u)$ is the point process intensity at a location $u$, and $Z(u)$ is the value of the spatial covariate at that location. Here $\rho$ is an unknown, smooth function which is to be estimated. The function $\rho$ expresses the effect of the spatial covariate on the point process intensity. If $\rho$ turns out to be constant, then the covariate has no effect on point process intensity (and the constant value of $\rho$ is the constant intensity of the point process). <>= rhos <- with(demohyper, rhohat(Points, Image)) plot(rhos) @ \SweaveOpts{width=6,height=4} \setkeys{Gin}{width=0.9\textwidth} \subsection{Exploring interpoint interaction} Still to be written. See Chapter 16 of \cite{baddrubaturn15}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Fitting models of spatial trend} The command \code{mppm} fits models to multiple point patterns. Its syntax is very similar to that of \code{lm} and \code{glm}: <>= mppm(formula, data, interaction, ...) @ where \code{formula} is a formula describing the systematic trend part of the model, \code{data} is a hyperframe containing all the data (responses and covariates), and \code{interaction} determines the stochastic interpoint interaction part of the model. For example: <>= mppm(Points ~ group, simba, Poisson()) @ Note that the formula has a left hand side, which identifies the response. This should be the name of a column of \code{data}. \subsection{Trend formula} The right side of \code{formula} is an expression for the linear predictor (effectively the {\bf logarithm} of the spatial trend). The variables appearing in the right hand side of \code{formula} should be either \begin{itemize} \item names of columns in \code{data} \item objects in the {\sf R} global environment (such as \code{pi} and \code{log}) \item the reserved names \code{x}, \code{y} (representing Cartesian coordinates), \code{marks} (representing mark values attached to points) or \code{id} (a factor representing the row number in the hyperframe). \end{itemize} \subsubsection{Design covariates} The variables in the trend could be `design covariates'. For example, to fit a model to the \code{simba} dataset in which all patterns are independent replicates of the same uniform Poisson process, with the same constant intensity: <<>>= mppm(Points ~ 1, simba) @ To fit a model in which the two groups of patterns (control and treatment groups) each consist of independent replicates of a uniform Poisson process, but with possibly different intensity in each group: <<>>= mppm(Points ~ group, simba) @ To fit a uniform Poisson process to each pattern, with different intensity for each pattern: <<>>= mppm(Points ~ id, simba) @ \subsubsection{Spatial covariates} The variables in the trend could be `spatial covariates'. For example, the \code{demohyper} dataset has a column \code{Image} containing pixel images. <<>>= mppm(Points ~ Image, data=demohyper) @ This model postulates that each pattern is a Poisson process with intensity of the form \[ \lambda(u) = \exp(\beta_0 + \beta_1 Z(u)) \] at location $u$, where $\beta_0, \beta_1$ are coefficients to be estimated, and $Z(u)$ is the value of the pixel image \code{Image} at location $u$. It may or may not be appropriate to assume that the intensity of the points is an exponential function of the image pixel value $Z$. If instead we wanted the intensity $\lambda(u)$ to be \emph{proportional} to $Z(u)$, the appropriate model is <>= mppm(Points ~ offset(log(Image)), data=demohyper) @ which corresponds to an intensity proportional to \code{Image}, \[ \lambda(u) = \exp(\beta_0 + \log Z(u)) = e^{\beta_0} \; Z(u). \] The \code{offset} indicates that there is no coefficient in front of $\log Z(u)$. Alternatively we could allow a coefficient: <>= mppm(Points ~ log(Image), data=demop) @ which corresponds to a gamma transformation of \code{Image}, \[ \lambda(u) = \exp(\beta_0 + \beta_1 \log Z(u)) = e^{\beta_0} \; Z(u)^{\beta_1}. \] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Interpoint interaction} The stochastic interpoint interaction in a point process model is specified by the arguments \code{interaction} and (optionally) \code{iformula} in <>= mppm(formula, data, interaction, ..., iformula=NULL) @ \subsection{Same interaction for all patterns} In the simplest case, the argument \texttt{interaction} is one of the familiar objects that describe the point process interaction structure. It is an object of class \texttt{"interact"} created by calling one of the functions \begin{center} \begin{tabular}{rl} \texttt{Poisson()} & the Poisson point process\\ \texttt{Hardcore()} & the hard core process \\ \texttt{Strauss()} & the Strauss process \\ \texttt{StraussHard()} & the Strauss/hard core point process\\ \texttt{Softcore()} & pairwise interaction, soft core potential\\ \texttt{PairPiece()} & pairwise interaction, piecewise constant \\ \texttt{DiggleGatesStibbard() } & Diggle-Gates-Stibbard pair potential \\ \texttt{DiggleGratton() } & Diggle-Gratton pair potential \\ \texttt{Fiksel() } & Fiksel pair potential \\ \texttt{LennardJones() } & Lennard-Jones pair potential \\ \texttt{Pairwise()} & pairwise interaction, user-supplied potential\\ \texttt{AreaInter()} & area-interaction potential\\ \texttt{Geyer()} & Geyer's saturation process\\ \texttt{BadGey()} & multiscale Geyer saturation process\\ \texttt{Saturated()} & Saturated pair model, user-supplied potential\\ \texttt{OrdThresh()} & Ord process, threshold potential\\ \texttt{Ord()} & Ord model, user-supplied potential \\ \texttt{MultiStrauss()} & multitype Strauss process \\ \texttt{MultiStraussHard()} & multitype Strauss/hard core process \\ \texttt{Concom()} & connected component interaction \\ \texttt{Hybrid()} & hybrid of several interactions \\ \end{tabular} \end{center} In this `simple' usage of \texttt{mppm}, the point process model assumes that all point patterns have exactly the same interpoint interaction, (with the same interaction parameters), and only differ in their spatial trend. \subsection{Hyperframe of interactions} More generally the argument \code{interaction} can be a hyperframe containing objects of class \texttt{"interact"}. For example, we might want to fit a Strauss process to each point pattern, but with a different Strauss interaction radius for each pattern. <>= radii <- with(simba, mean(nndist(Points))) @ Then \code{radii} is a vector of numbers which we could use as the values of the interaction radius for each case. First we need to make the interaction objects: <<>>= Rad <- hyperframe(R=radii) Str <- with(Rad, Strauss(R)) @ Then we put them into a hyperframe and fit the model: <<>>= Int <- hyperframe(str=Str) mppm(Points ~ 1, simba, interaction=Int) @ An important constraint is that all of the interaction objects in one column must be \emph{instances of the same process} (e.g. Strauss) albeit possibly having different parameter values. For example, you cannot put Poisson and Strauss processes in the same column. \subsection{Interaction formula} If \code{interaction} is a hyperframe, then the additional argument \code{iformula} may be used to fully specify the interaction. (An \code{iformula} is also required if \code{interaction} has more than one column.) The \code{iformula} should be a formula without a left hand side. Variables on the right hand side are typically the names of columns in \code{interaction}. \subsubsection{Selecting one column} If the right hand side of \code{iformula} is a single name, then this identifies the column in \code{interaction} to be used as the interpoint interaction structure. <<>>= h <- hyperframe(Y=waterstriders) g <- hyperframe(po=Poisson(), str4 = Strauss(4), str7= Strauss(7)) mppm(Y ~ 1, data=h, interaction=g, iformula=~str4) @ \subsubsection{Interaction depending on design} The \code{iformula} can also involve columns of \code{data}, but only those columns that are vectors or factors. This allows us to specify an interaction that depends on the experimental design. [This feature is {\bf experimental}.] For example <<>>= fit <- mppm(Points ~ 1, simba, Strauss(0.07), iformula = ~Interaction*group) @ Since \code{Strauss(0.1)} is not a hyperframe, it is first converted to a hyperframe with a single column named \code{Interaction}. The \code{iformula = ~Interaction*group} specifies (since \code{group} is a factor) that the interpoint interaction shall have a different coefficient in each experimental group. That is, we fit a model which has two different values for the Strauss interaction parameter $\gamma$, one for the control group and one for the treatment group. When you print the result of such a fit, the package tries to do `automatic interpretation' of the fitted model (translating the fitted interaction coefficients into meaningful numbers like $\gamma$). This will be successful in \emph{most} cases: <<>>= fit @ <>= co <- coef(fit) si <- function(x) { signif(x, 4) } @ Thus we see that the estimate of the Strauss parameter $\gamma$ for the control group is \Sexpr{si(exp(co[2]))}, and for the treatment group \Sexpr{si(exp(sum(co[c(2,4)])))} (the correct values in this simulated dataset were $1$ and $0.5$). The fitted model can also be interpreted directly from the fitted canonical coefficients: <<>>= coef(fit) @ The last output shows all the coefficients $\beta_j$ in the linear predictor for the (log) conditional intensity. The interpretation of the model coefficients, for any fitted model in \R, depends on the \emph{contrasts} which were applicable when the model was fitted. This is part of the core {\sf R} system: see \code{help(contrasts)} or \code{options(contrasts)}. If you did not specify otherwise, the default is to use \emph{treatment contrasts}. This means that, for an explanatory variable which is a \texttt{factor} with $N$ levels, the first level of the factor is used as a baseline, and the fitted model coefficients represent the factor levels $2, 3, \ldots, N$ relative to this baseline. In the output above, there is a coefficient for \code{(Intercept)} and one for \code{grouptreatment}. These are coefficients related to the \code{group} factor. According to the ``treatment contrasts'' rule, the \code{(Intercept)} coefficient is the estimated effect for the control group, and the \code{grouptreatment} coefficient is the estimated difference between the treatment and control groups. Thus the fitted first order trend is $\exp(\Sexpr{si(co[1])}) = \Sexpr{si(exp(co[1]))}$ for the control group and $\exp(\Sexpr{si(co[1])} + \Sexpr{si(co[3])}) = \Sexpr{si(exp(sum(co[c(1,3)])))}$ for the treatment group. The correct values in this simulated dataset were $80$ and $100$. The remaining coefficients in the output are \code{Interaction} and \code{Interaction:grouptreatment}. Recall that the Strauss process interaction term is $\gamma^{t(u,\xx)} = \exp(t(u,\xx) \log\gamma)$ at a spatial location $u$, for a point pattern $\xx$. Since we're using treatment contrasts, the coefficient \code{Interaction} is the estimate of $\log\gamma$ for the control group. The coefficient \code{Interaction:grouptreatment} is the estimate of the difference in $\log\gamma$ between the treatment and control groups. Thus the estimated Strauss interaction parameter $\gamma$ is $\exp(\Sexpr{si(co[2])}) = \Sexpr{si(exp(co[2]))}$ for the control group and $\exp(\Sexpr{si(co[2])} + (\Sexpr{si(co[4])})) = \Sexpr{si(exp(co[2]+co[4]))}$ for the treatment group. The correct values were $1$ and $0.5$. \subsubsection{Completely different interactions for different cases} In the previous example, when we fitted a Strauss model to all point patterns in the \code{simba} dataset, the fitted model for the patterns in the control group was close to Poisson ($\gamma \approx 1$). Suppose we now want to fit a model which {\it is} Poisson in the control group, and Strauss in the treatment group. The Poisson and Strauss interactions must be given as separate columns in a hyperframe of interactions: <>= interaction=hyperframe(po=Poisson(), str=Strauss(0.07)) @ What do we write for the \code{iformula}? The following \emph{will not} work: <>= iformula=~ifelse(group=="control", po, str) @ This does not work because the Poisson and Strauss models are `incompatible' inside such expressions. The canonical sufficient statistics for the Poisson and Strauss processes do not have the same dimension. Internally in \code{mppm} we translate the symbols \code{po} and \code{str} into matrices; the dimensions of these matrices are different, so the \code{ifelse} expression cannot be evaluated. Instead we need something like the following: <>= iformula=~I((group=="control")*po) + I((group=="treatment") * str) @ The letter \code{I} here is a standard R function that prevents its argument from being interpreted as a formula (thus the \code{*} is interpreted as multiplication instead of a model interaction). The expression \code{(group=="control")} is logical, and when multiplied by the matrix \code{po}, yields a matrix. So the following does work: <<>>= g <- hyperframe(po=Poisson(), str=Strauss(0.07)) fit2 <- mppm(Points ~ 1, simba, g, iformula=~I((group=="control")*po) + I((group=="treatment") * str)) fit2 @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %#%^!ifdef RANDOMEFFECTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Random effects} \subsection{Mixed effects models} It is also possible to fit models that include `random effects'. Effectively, some of the coefficients in the model are assumed to be Normally-distributed random variables instead of constants. \subsubsection{Mixed Poisson model} Consider the simplest model of a uniform Poisson process which we fitted to the 3 point patterns of waterstriders. It might be sensible to assume that each pattern is a realisation of a Poisson process, but with {\em random intensity\/}. In each realisation the intensity $\lambda$ is constant across different locations, but it is a different, random value in different realisations. This example is called a `mixed Poisson process' and belongs to the class of `Cox processes' (Poisson processes with random intensity functions). Let's assume further that the log-intensity is a Normal random variable. Then the model is a (very degenerate) special case of a `log-Gaussian Cox process'. To fit such a model we use the standard techniques of mixed effects models \cite{lairware82,davigilt95,pinhbate00}. The mixed Poisson process which we discussed above would be written in standard form \begin{equation} \label{mixPois} \lambda_i(u) = \exp(\mu + Z_i) \end{equation} for the $i$th point pattern, where $\mu$ is a parameter to be estimated (the `fixed effect') and $Z_i \sim N(0, \sigma^2)$ is a zero-mean Normal random variable (the `random effect' for point pattern $i$). In the simplest case we would assume that $Z_1, \ldots, Z_n$ are independent. The variance $\sigma^2$ of the random effects would be estimated. One can also estimate the individual realised values $z_i$ of the random effects for each point pattern, although these are usually not of such great interest. Since the model includes both fixed and random effects, it is called a ``mixed-effects'' model. \subsubsection{Dependence structure} When we formulate a random-effects or mixed-effects model, we must specify the dependence structure of the random effects. In the model above we assumed that the $Z_i$ are independent for all point patterns $i$. If the experiment consists of two groups, we could alternatively assume that $Z_i = Z_j$ whenever $i$ and $j$ belong to the same group. In other words all the patterns in one group have the same value of the random effect. So the random effect is associated with the group rather than with individual patterns. This could be appropriate if, for example, the groups represent different batches of a chemical. Each batch is prepared under slightly different conditions so we believe that there are random variations between batches, but within a batch we believe that the chemical is well-mixed. \subsubsection{Random effects are coefficients} In the mixed Poisson model (\ref{mixPois}), the random effect is an additive constant (with a random value) in the log-intensity. In general, a random effect is a \emph{coefficient} of one of the covariates. For example if $v$ is a real-valued design covariate (e.g. `temperature'), with value $v_i$ for the $i$th point pattern, then we could assume \begin{equation} \label{ranef2} \lambda_i(u) = \exp(\mu + Z_i v_i) \end{equation} where $Z_i \sim N(0, \sigma^2)$ are independent for different $i$. This model has a random effect in the dependence on $v$. We could also have a random effect for a spatial covariate $V$. Suppose $V_i$ is a real-valued image for the $i$th pattern (so that $V_i(u)$ is the value of some covariate at the location $u$ for the $i$th case). Then we could assume \begin{equation} \label{ranef3} \lambda_i(u) = \exp(\mu + Z_i V_i(u)) \end{equation} where $Z_i \sim N(0, \sigma^2)$ are independent for different $i$. This kind of random effect would be appropriate if, for example, the images $V_i$ are not `normalised' or `standardised' relative to each other (e.g.\ they are images taken under different illumination). Then the coefficients $Z_i$ effectively include the rescaling necessary to standardise the images. \subsection{Fitting a mixed-effects model} The call to \texttt{mppm} can also include the argument \texttt{random}. This should be a formula (with no left-hand side) describing the structure of random effects. The formula for random effects must be recognisable to \texttt{lme}. It is typically of the form \begin{verbatim} ~x1 + ... + xn | g \end{verbatim} or \begin{verbatim} ~x1 + ... + xn | g1/.../gm \end{verbatim} where \verb!x1 + ... + xn! specifies the covariates for the random effects and \texttt{g} or \verb!g1/.../gm! determines the grouping (dependence) structure. Here \code{g} or \code{g1, \ldots, gm} should be factors. To fit the mixed Poisson model (\ref{mixPois}) to the waterstriders, we want to have a random intercept coefficient (so \texttt{x} is \texttt{1}) that varies for different point patterns (so \texttt{g} is \texttt{id}). The reserved name \code{id} is a factor referring to the individual point pattern. Thus <<>>= H <- hyperframe(P=waterstriders) mppm(P ~ 1, H, random=~1|id) @ To fit the mixed effects model (\ref{ranef2}) to the coculture data with the \code{AstroIm} covariate, with a random effect associated with each well, <>= mppm(Neurons ~ AstroIm, random=~AstroIm|WellNumber) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %#%^!endif %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Studying the fitted model} Fitted models produced by \code{mppm} can be examined and validated in many ways. \subsection{Fits for each pattern} \subsubsection{Subfits} The command \code{subfits} takes an \code{mppm} object and extracts, for each individual point pattern, the fitted point process model for that pattern \emph{that is implied by the overall fit}. It returns a list of objects of class \code{ppm}. <<>>= H <- hyperframe(W=waterstriders) fit <- mppm(W ~ 1, H) subfits(fit) @ In this example the result is a list of three \code{ppm} objects representing the implied fits for each of the three point patterns in the \code{waterstriders} dataset. Notice that {\bf the fitted coefficients are the same} in all three models. Note that there are some unresolved difficulties with the implementation of \code{subfits}. Two completely different implementations are supplied in the package; they are called \code{subfits.old} %(used in versions 0.1--1 and earlier) and \code{subfits.new}.% (introduced in 0.1--2). The old version would occasionally crash. Unfortunately the newer version \code{subfits.new} is quite memory-hungry and sometimes causes R to hang. We're still working on this problem. So for the time being, \code{subfits} is the same as \code{subfits.old}. You can change this simply by reassigning, e.g. <>= subfits <- subfits.new @ \subsubsection{Fitting separately to each pattern} For comparison, we could fit a point process model separately to each point pattern dataset using \code{ppm}. The easy way to do this is with \code{with.hyperframe}. To fit a \emph{separate} uniform Poisson point process to each of the three waterstriders patterns, <<>>= H <- hyperframe(W=waterstriders) with(H, ppm(W)) @ The result is again a list of three fitted point process models (objects of class \code{ppm}), but now the fitted coefficients are different. \subsection{Residuals} One standard way to check a fitted model is to examine the residuals. \subsubsection{Point process residuals} Some recent papers \cite{baddetal05,baddmollpake08} have defined residuals for a fitted point process model (fitted to a \emph{single} point pattern). These residuals are implemented in \code{spatstat} as \code{residuals.ppm} and apply to an object of class \code{ppm}, that is, a model fitted to a \emph{single} point pattern. The command \code{residuals.mppm} computes the point process residuals for an \code{mppm} object. <<>>= fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) @ The result is a list, with one entry for each of the point pattern datasets. Each list entry contains the point process residuals for the corresponding point pattern dataset. Each entry in the list is a signed measure (object of class \code{"msr"}) as explained in the help for \code{residuals.ppm}). It can be plotted: <>= plot(res) @ You probably want the smoothed residual field: <>= smor <- with(hyperframe(res=res), Smooth(res, sigma=4)) plot(smor) @ \subsubsection{Sums of residuals} It would be useful to have a residual that is a single value for each point pattern (representing how much that point pattern departs from the model fitted to all the point patterns). That can be computed by \emph{integrating} the residual measures using the function \code{integral.msr}: <<>>= fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) totres <- sapply(res, integral.msr) @ In designed experiments we can plot these total residuals against the design covariates: <>= fit <- mppm(Points~Image, data=demohyper) resids <- residuals(fit, type="Pearson") totres <- sapply(resids, integral.msr) areas <- with(demohyper, area.owin(as.owin(Points))) df <- as.data.frame(demohyper[, "Group"]) df$resids <- totres/areas plot(resids~Group, df) @ \subsubsection{Four-panel diagnostic plots} Sometimes a more useful tool is the function \code{diagnose.ppm} which produces a four-panel diagnostic plot based on the point process residuals. However, it is only available for \code{ppm} objects. To obtain a four-panel diagnostic plot for each of the point patterns, do the following: \begin{enumerate} \item fit a model to multiple point patterns using \code{mppm}. \item extract the individual fits using \code{subfits}. \item plot the residuals of the individual fits. \end{enumerate} For example: <>= fit <- mppm(P ~ 1, hyperframe(P=waterstriders)) sub <- hyperframe(Model=subfits(fit)) plot(sub, quote(diagnose.ppm(Model))) @ (One could also do this for models fitted separately to the individual point patterns.) \subsubsection{Residuals of the parameter estimates} We can also compare the parameter estimates obtained by fitting the model simultaneously to all patterns (using \code{mppm}) with those obtained by fitting the model separately to each pattern (using \code{ppm}). <<>>= H <- hyperframe(P = waterstriders) fitall <- mppm(P ~ 1, H) together <- subfits(fitall) separate <- with(H, ppm(P)) Fits <- hyperframe(Together=together, Separate=separate) dr <- with(Fits, unlist(coef(Separate)) - unlist(coef(Together))) dr exp(dr) @ One could also try deletion residuals, etc. \subsection{Goodness-of-fit tests} \subsubsection{Quadrat count test} The $\chi^2$ goodness-of-fit test based on quadrat counts is implemented for objects of class \code{ppm} (in \code{quadrat.test.ppm}) and also for objects of class \code{mppm} (in \code{quadrat.test.mppm}). This is a goodness-of-fit test for a fitted {\bf Poisson} point process model only. The model could be uniform or non-uniform and the intensity might depend on covariates. <<>>= H <- hyperframe(X=waterstriders) # Poisson with constant intensity for all patterns fit1 <- mppm(X~1, H) quadrat.test(fit1, nx=2) # uniform Poisson with different intensity for each pattern fit2 <- mppm(X ~ id, H) quadrat.test(fit2, nx=2) @ See the help for \code{quadrat.test.ppm} and \code{quadrat.test.mppm} for further details. \subsubsection{Kolmogorov-Smirnov test} The Kolmogorov-Smirnov test of goodness-of-fit of a Poisson point process model compares the observed and predicted distributions of the values of a spatial covariate. We want to test the null hypothesis $H_0$ that the observed point pattern ${\mathbf x}$ is a realisation from the Poisson process with intensity function $\lambda(u)$ (for locations $u$ in the window $W$). Let $Z(u)$ be a given, real-valued covariate defined at each spatial location $u$. Under $H_0$, the \emph{observed} values of $Z$ at the data points, $Z(x_i)$ for each $x_i \in {\mathbf x}$, are independent random variables with common probability distribution function \[ F_0(z) = \frac{\int_W \lambda(u) \indicate{Z(u) \le z} \dee u} {\int_W \lambda(u) \dee u}. \] We can therefore apply the Kolmogorov-Smirnov test of goodness-of-fit. This compares the empirical cumulative distribution of the observed values $Z(x_i)$ to the predicted c.d.f. $F_0$. The test is implemented as \code{kstest.ppm}. The syntax is <>= kstest.mppm(model, covariate) @ where \code{model} is a fitted model (of class \texttt{"mppm"}) and \code{covariate} is either \begin{itemize} \item a \code{function(x,y)} making it possible to compute the value of the covariate at any location \code{(x,y)} \item a pixel image containing the covariate values \item a list of functions, one for each row of the hyperframe of original data \item a list of pixel images, one for each row of the hyperframe of original data \item a hyperframe with one column containing either functions or pixel images. \end{itemize} See Chapter 16 of \cite{baddrubaturn15} for further information. \newpage \addcontentsline{toc}{section}{Bibliography} %\bibliography{% %extra,% %extra2,% %biblio/badd,% %biblio/bioscience,% %biblio/censoring,% %biblio/mcmc,% %biblio/spatstat,% %biblio/stat,% %biblio/stochgeom% %} \begin{thebibliography}{1} \bibitem{baddmollpake08} A. Baddeley, J. M{\o}ller, and A.G. Pakes. \newblock Properties of residuals for spatial point processes. \newblock {\em Annals of the Institute of Statistical Mathematics}, 60:627--649, 2008. \bibitem{TheBook} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with R}. \newblock Chapman \& Hall/CRC Press, 2015. \bibitem{statpaper} A. Baddeley, I. Sintorn, L. Bischof, R. Turner, and S. Heggarty. \newblock Analysing designed experiments where the response is a spatial point pattern. \newblock In preparation. \bibitem{baddetal05} A. Baddeley, R. Turner, J. M{\o}ller, and M. Hazelton. \newblock Residual analysis for spatial point processes (with discussion). \newblock {\em Journal of the Royal Statistical Society, series B}, 67(5):617--666, 2005. \bibitem{chenetal08} B.J. Chen, G.P. Leser, D. Jackson, and R.A. Lamb. \newblock The influenza virus {M2} protein cytoplasmic tail interacts with the {M1} protein and influences virus assembly at the site of virus budding. \newblock {\em Journal of Virology}, 82:10059--10070, 2008. %#%^!ifdef RANDOMEFFECTS \bibitem{davigilt95} M. Davidian and D.M. Giltinan. \newblock {\em Nonlinear Mixed Effects Models for Repeated Measurement Data}. \newblock Chapman and Hall, 1995. %#%^!endif \bibitem{digglangbene91} P.J. Diggle, N. Lange, and F. M. Benes. \newblock Analysis of variance for replicated spatial point patterns in clinical neuroanatomy. \newblock {\em Journal of the {A}merican {S}tatistical {A}ssociation}, 86:618--625, 1991. %#%^!ifdef RANDOMEFFECTS \bibitem{lairware82} N.M. Laird and J.H. Ware. \newblock Random-effects models for longitudinal data. \newblock {\em Biometrics}, 38:963--974, 1982. %#%^!endif \bibitem{pent84} A. Penttinen. \newblock {\em Modelling Interaction in Spatial Point Patterns: Parameter Estimation by the Maximum Likelihood Method}. \newblock Number 7 in {Jyv\"askyl\"a} Studies in Computer Science, Economics and Statistics. University of {Jyv\"askyl\"a}, 1984. %#%^!ifdef RANDOMEFFECTS \bibitem{pinhbate00} J.C. Pinheiro and D.M. Bates. \newblock {\em Mixed-Effects Models in {S} and {S-PLUS}}. \newblock Springer, 2000. %#%^!endif \end{thebibliography} %\addcontentsline{toc}{section}{Index} %\printindex \end{document} spatstat/vignettes/hexagon.eps0000644000176200001440000000577014243060071016275 0ustar liggesusers%!PS-Adobe-2.0 EPSF-2.0 %%Title: hexagon.fig %%Creator: fig2dev Version 3.2 Patchlevel 5a %%CreationDate: Tue Nov 23 11:04:35 2010 %%BoundingBox: 0 0 98 98 %Magnification: 1.0000 %%EndComments %%BeginProlog /$F2psDict 200 dict def $F2psDict begin $F2psDict /mtrx matrix put /col-1 {0 setgray} bind def /col0 {0.000 0.000 0.000 srgb} bind def /col1 {0.000 0.000 1.000 srgb} bind def /col2 {0.000 1.000 0.000 srgb} bind def /col3 {0.000 1.000 1.000 srgb} bind def /col4 {1.000 0.000 0.000 srgb} bind def /col5 {1.000 0.000 1.000 srgb} bind def /col6 {1.000 1.000 0.000 srgb} bind def /col7 {1.000 1.000 1.000 srgb} bind def /col8 {0.000 0.000 0.560 srgb} bind def /col9 {0.000 0.000 0.690 srgb} bind def /col10 {0.000 0.000 0.820 srgb} bind def /col11 {0.530 0.810 1.000 srgb} bind def /col12 {0.000 0.560 0.000 srgb} bind def /col13 {0.000 0.690 0.000 srgb} bind def /col14 {0.000 0.820 0.000 srgb} bind def /col15 {0.000 0.560 0.560 srgb} bind def /col16 {0.000 0.690 0.690 srgb} bind def /col17 {0.000 0.820 0.820 srgb} bind def /col18 {0.560 0.000 0.000 srgb} bind def /col19 {0.690 0.000 0.000 srgb} bind def /col20 {0.820 0.000 0.000 srgb} bind def /col21 {0.560 0.000 0.560 srgb} bind def /col22 {0.690 0.000 0.690 srgb} bind def /col23 {0.820 0.000 0.820 srgb} bind def /col24 {0.500 0.190 0.000 srgb} bind def /col25 {0.630 0.250 0.000 srgb} bind def /col26 {0.750 0.380 0.000 srgb} bind def /col27 {1.000 0.500 0.500 srgb} bind def /col28 {1.000 0.630 0.630 srgb} bind def /col29 {1.000 0.750 0.750 srgb} bind def /col30 {1.000 0.880 0.880 srgb} bind def /col31 {1.000 0.840 0.000 srgb} bind def end /cp {closepath} bind def /ef {eofill} bind def /gr {grestore} bind def /gs {gsave} bind def /sa {save} bind def /rs {restore} bind def /l {lineto} bind def /m {moveto} bind def /rm {rmoveto} bind def /n {newpath} bind def /s {stroke} bind def /sh {show} bind def /slc {setlinecap} bind def /slj {setlinejoin} bind def /slw {setlinewidth} bind def /srgb {setrgbcolor} bind def /rot {rotate} bind def /sc {scale} bind def /sd {setdash} bind def /ff {findfont} bind def /sf {setfont} bind def /scf {scalefont} bind def /sw {stringwidth} bind def /tr {translate} bind def /tnt {dup dup currentrgbcolor 4 -2 roll dup 1 exch sub 3 -1 roll mul add 4 -2 roll dup 1 exch sub 3 -1 roll mul add 4 -2 roll dup 1 exch sub 3 -1 roll mul add srgb} bind def /shd {dup dup currentrgbcolor 4 -2 roll mul 4 -2 roll mul 4 -2 roll mul srgb} bind def /$F2psBegin {$F2psDict begin /$F2psEnteredState save def} def /$F2psEnd {$F2psEnteredState restore end} def /pageheader { save newpath 0 98 moveto 0 0 lineto 98 0 lineto 98 98 lineto closepath clip newpath -11.0 102.4 translate 1 -1 scale $F2psBegin 10 setmiterlimit 0 slj 0 slc 0.06299 0.06299 sc } bind def /pagefooter { $F2psEnd restore } bind def %%EndProlog pageheader % % Fig objects follow % % % here starts figure with depth 50 % Polyline 0 slj 0 slc 30.000 slw n 1485 1395 m 1683 657 l 1143 117 l 405 315 l 207 1053 l 747 1593 l cp gs col0 s gr % here ends figure; 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pagefooter showpage %%Trailer %EOF spatstat/vignettes/fv.Rnw0000644000176200001440000021335314475230742015247 0ustar liggesusers\documentclass[10pt]{article} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage[bottom=0.5cm, right=1.5cm, left=1.5cm, top=1.5cm]{geometry} % \VignetteIndexEntry{Guide to Function Objects in Spatstat} % $Revision: 1.3 $ $Date: 2023/08/16 02:16:49 $ \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\link}[1]{#1} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \newcommand{\fv}{\texttt{"fv"}} \newcommand{\env}{\texttt{"envelope"}} \newcommand{\rat}{\texttt{"rat"}} \newcommand{\obj}[1]{object of class {#1}} \newcommand{\objs}[1]{objects of class {#1}} \newcommand{\objsfvenv}{\objs\fv{} and \env} \newcommand{\fun}[1]{\texttt{#1}} \newcommand{\class}[1]{\texttt{"{#1}"}} \newcommand{\Kfun}{$K$-function} \newcommand{\Lfun}{$L$-function} \newcommand{\pois}[1]{{#1}_{\mbox{\scriptsize pois}}} \newcommand{\isoest}[1]{\widehat{#1}_{\mbox{\scriptsize iso}}} \newcommand{\figref}[1]{Figure~\ref{#1}} \newcommand{\secref}[1]{Section~\ref{#1}} \newcommand{\eqref}[1]{(\ref{#1})} \newcommand{\editedToHere}{% \newpage % \hrule % % \begin{Large} % \begin{center} % NOT EDITED BEYOND HERE % \end{center} % \end{Large} % % \hrule % } \begin{document} \bibliographystyle{plain} <>= library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) setmargins <- function(...) { options(SweaveHooks=list(fig=function() par(mar=c(...)+0.1))) } @ <>= options(SweaveHooks=list(fig=function() par(mar=c(5,4,2,4)+0.1))) options(width=100) @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.5\textwidth} \title{A guide to function objects (class \fv\ and \env) in \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{{\small Draft of \today}} \maketitle \thispagestyle{empty} \begin{abstract} This vignette explains how to use and manipulate \objsfvenv. \framebox{\textbf{This vignette is under development!}} \end{abstract} \setcounter{tocdepth}{1} \tableofcontents \newpage \section{Introduction} \subsection{Functional summary statistics} An \obj\fv\ (`function value table') is a convenient way of storing several different estimates of the same function. It is common practice to summarise a spatial point pattern dataset using a summary function, such as Ripley's \Kfun\ $K(r)$, rather than a single numerical summary value. Typically, an empirical estimate of the function, obtained from the data, will be compared with the `theoretical' version of the function that would be expected if the point pattern was completely random. There may be several different empirical estimates of the function, based on different estimation techniques, and we also want to compare these estimates with one another. The \spst{} family of packages makes it very easy to compute and handle multiple versions of a summary function. Taking the Finnish Pines data \texttt{finpines} as an example, we can compute and plot estimates of Ripley's \Kfun\ by typing <<>>= K <- Kest(finpines) @ <>= plot(K) @ The plot shows several curves, which represent the different empirical estimates of the \Kfun\ (namely the isotropic correction $\widehat K_{\mbox{\scriptsize iso}}(r)$, translation correction $\widehat K_{\mbox{\scriptsize trans}}(r)$, and border correction $\widehat K_{\mbox{\scriptsize bord}}(r)$) and also the theoretical value $K_{\mbox{\scriptsize pois}}(r)$ that would be expected if the point pattern was completely random. All these functions are plotted against the distance argument $r$. The object \texttt{K} belongs to class \fv{} (``function value table''). It is a data frame (that is, it also belongs to the class \class{data.frame}) with attributes giving extra information such as the recommended way of plotting the function. One column of the data frame contains evenly spaced values of the distance argument $r$, while the other columns contain estimates of the value of the function, or the theoretical value of the function under CSR, corresponding to these distance values. More information is given by the print method \texttt{print.fv:} <<>>= K @ This indicates that the columns in the data frame are named \texttt{r}, \texttt{theo}, \texttt{border}, \texttt{trans}, and \texttt{iso}, and explains their contents. For example, the column \texttt{iso} contains estimates of the \Kfun{} using the isotropic edge correction. This column is labelled in the plot by the \R\ expression \texttt{hat(K)[iso](r)} which is rendered as the mathematical notation $\widehat K_{\mbox{\scriptsize iso}}(r)$. The function argument in an \class{fv} object is usually, but not always, called \texttt{r}. (Counterexamples include \fun{transect.im} which returns an \fv\ object with function argument \texttt{t}, and \fun{roc} which returns an \fv\ object with function argument \texttt{p}.) The command \texttt{plot(K)} is dispatched to the method \texttt{plot.fv} to generate the graphic shown above. The plot method uses the auxiliary information contained in \texttt{K} to attach meaningful labels to the graphic. Stripping off the auxiliary information we can inspect the data frame itself: <<>>= head(as.data.frame(K)) @ This vignette explains how to plot, manipulate and create objects of class \fv. \subsection{Simulation envelopes} Simulation envelopes of summary functions are often used to assess statistical significance in early stages of analysis. The \spst{} command \texttt{envelope} generates simulation envelopes of a summary function: <>= E <- envelope(finpines, Kest, nsim=39) @ <>= plot(E) @ In this example, the command \verb!E <- envelope(finpines, Kest, nsim=39)! generates 39 simulated point patterns according to a completely random process, computes the estimated \Kfun{} for each simulated pattern, and finds the simulation envelopes by identifying the pointwise minimum and maximum of the 39 simulated functions. The result \texttt{E} is again an \obj\fv, but additionally belongs to the class \env, and contains additional information about how the envelopes were computed. In the resulting plot, generated by the method \texttt{plot.envelope}, the region between the upper and lower simulation envelopes is filled in grey shading. The solid black line is the estimated \Kfun{} for the original \texttt{finpines} dataset, and the dashed red line is the theoretical \Kfun{} for a completely random pattern. There is a lot of auxiliary information, displayed by \texttt{print.envelope}: <<>>= E @ This vignette also explains how to plot, manipulate and create \objs\env. Since envelope objects also belong to class \fv, the vignette first focuses on the capabilities of class \fv. \subsection{Why bother?} \label{S:whybother} Any self-respecting programmer would regard it as a trivial task to organise data in a data frame and plot each column of data as a curve in a graph. Although the task is trivial, it can be time-consuming, it is prone to error, and it can take many attempts to get it exactly right. The authors of \spst\ developed the class \fv\ to make this job easier. The class \fv{} is designed to \begin{itemize} \item support \emph{multiple versions of a function}, such as the different estimates of the \Kfun{} obtained using different edge corrections, the theoretical version of the \Kfun{} for a completely random process, the upper and lower simulation envelopes of the \Kfun, and so on. \item do the \emph{``book-keeping''} about the different versions of the function, such as the names of the different columns. \item perform automatic \emph{plotting} of the function, handling all the details of layout and labelling, including generating the mathematical labels for each curve. \item support \emph{calculations} that will be applied automatically to all the versions of the function. \item support \emph{conversion} to other data types in base \R, such as data frames and functions. \end{itemize} For example, Besag's $L$ function is defined as $L(r) = \sqrt{K(r)/\pi}$. Since we have already computed the \Kfun{} in the example above, we can compute and plot the $L$-function just by typing <<>>= L <- sqrt(K/pi) @ <>= plot(L) @ Several kinds of magic have happened here: \begin{itemize} \item The expression \texttt{sqrt(K/pi)}, where \texttt{K} is an \obj\fv, has been evaluated automatically by calculating $\sqrt{K(r)/\pi}$ for each of the versions of the function stored in \texttt{K}; \item The internal data in the object \texttt{K}, which provide mathematical labels for each version of the \Kfun, have been modified according to the algebraic operation that was just performed; \item The result has been saved as a new \obj\fv{} named \texttt{L}; \item The \texttt{plot} method has correctly displayed each version of the modified function using the modified mathematical labels, both on the vertical axis and in the legend box; \item The \texttt{plot} method has \textbf{automatically computed the position of the legend box} to prevent it from overlapping the plotted curves; \item The unit of length for the function argument has been correctly saved in the object \texttt{L} and correctly reported on the horizontal axis label. \end{itemize} The class \env{} extends the class \fv{} to handle additional information about how the envelopes were computed. The code supporting the class \env{} performs many of the ``trivial'' but error-prone calculations involving envelopes. An \obj\env{} can also contain the simulated data (the point patterns and/or the summary functions) that were used to compute the envelopes, which makes it possible to re-use the simulated data to compute a different version of the envelope. \newpage \section{Plotting} \label{S:plot.fv} \subsection{Default plot} If \texttt{f} is an object of class \class{fv}, the command \texttt{plot(f)} is dispatched to the method \fun{plot.fv}. The default behaviour of \texttt{plot(f)} is to generate a plot containing several curves, each representing a different version of the same target function, plotted against the distance argument $r$. <>= plot(Gest(finpines)) @ <>= aa <- plot(Gest(finpines)) @ Here \texttt{Gest} computes estimates of the nearest-neighbour distance distribution function $G(r)$. The plot shows three empirical estimates of $G(r)$ for the \texttt{finpines} dataset, together with the `theoretical' curve $\pois G(r)$ expected for a completely random pattern, all plotted against the distance argument $r$. The legend indicates the meaning of each curve. The main title identifies the object in \R\ that was plotted. The return value from \fun{plot.fv} is a data frame containing more detailed information about the meaning of the curves. For the plot generated above, the return value is <>= aa <- plot(Gest(finpines)) aa @ <>= aa @ Here \texttt{lty} and \texttt{col} are the graphics parameters controlling the line type and line colour, and \texttt{label} is the mathematical notation for each edge-corrected estimate, in the syntax recognised by \R{} graphics functions. The plot generated by \texttt{plot.fv} uses the base \R\ graphics system (not \texttt{lattice} or \texttt{ggplot}). and is affected by graphics parameters specified by \texttt{par()}. \subsection{Modifying parameters of the default plot} The default plot can easily be modified: \begin{description} \item[margin space:] To change the amount of white space around the plot, use \texttt{par('mar')}. \item[main title:] use \texttt{main=""} to suppress the main title. \item[legend:] Set \texttt{legend=FALSE} to suppress the legend. Use the argument \texttt{legendargs} to modify the legend. The legend position is automatically computed to avoid overlap with the plotted curves, but this can be overridden by \texttt{legendpos}. \item[range of values:] Use \texttt{xlim} and \texttt{ylim} to specify the ranges of values on the $x$ and $y$ axes. \textbf{See the note below about the ``recommended range''.} Use \texttt{ylim.covers} to specify a numerical value or values that must be covered by the $y$ axis. For example, \texttt{ylim.covers=0} means that the $y$ axis will always include the origin. \end{description} For further information, see \texttt{help(plot.fv)}. \subsection{Recommended range and recommended columns} The default plot of an \fv\ object does not necessarily display all the data that is contained in the object: \begin{description} \item[shorter range of distances:] the range of values of the distance argument $r$ displayed in the default plot may be shorter than the range of values actually contained in the data frame. \item[not all columns of data:] the plot may not display all the columns of data contained in the data frame. \end{description} This happens because an \obj\fv\ contains ``recommendations'' about the range of distances that should be displayed, and about the columns of data that should be shown. These recommendations are based on standard statistical practice. The recommendations are followed when the default plot is generated, unless they are specifically overridden. Consider this example: <<>>= G <- Gest(finpines) G @ The printout shows the range of values of \texttt{r} that are present in the table as the `\texttt{available range}'. It also gives a `\texttt{recommended range}' which is generally shorter than the available range. \emph{The default plot of the object will only show the function values over the recommended range} and not over the full range of values available. This is done so that the interesting detail is clearly visible in the default plot. Values outside the recommended range may be unreliable due to increased variance or bias, depending on the edge correction. To prevent this behaviour and use the full range of function values available, set \texttt{clip.xlim=FALSE} in the plot command. Alternatively, specify the desired range of \texttt{r} values using the argument \texttt{xlim} in the plot command. The printout also says that the default plot formula is \verb! . ~ r ! where ``\verb!.!'' stands for \texttt{"km", "rs", "han", "theo"}. This means that the default plot will display only the columns named \texttt{"km", "rs", "han"} and \texttt{"theo"} and will \textbf{not} display the columns named \texttt{"hazard"} and \texttt{"theohaz"} which are mentioned in the printout. This is consistent with the graphic shown above. In this example, the column named \texttt{"hazard"} is an estimate of the \emph{hazard rate} $h(r) = G'(r)/(1-G(r))$ of the nearest neighbour distance function, rather than an estimate of $G(r)$ itself. The column named \texttt{"theohaz"} is the corresponding theoretical value of the hazard rate, expected if the point pattern is completely random. It makes sense that the hazard rate $h(r)$ and distribution function $G(r)$ should not normally be plotted together. Therefore when \texttt{Gest} is executed, it designates \texttt{"km", "rs", "han", "theo"} as the ``recommended columns'' that should be displayed by default, and it stores this information in the resulting object \texttt{G}. When \texttt{plot(G)} is executed, \texttt{plot.fv} uses this information to determine which columns are to be plotted. \subsection{Plot specified by a formula} \label{S:plot.formula} Different kinds of plots can be specified using a \texttt{formula} as the second argument to \texttt{plot.fv}. The left side of the formula represents what variables will be plotted on the vertical ($y$) axis, and the right side determines the variable on the horizontal ($x$) axis. For example, in the object \texttt{K <- Kest(finpines)}, the column named \texttt{iso} contains the values of the isotropic correction estimate. To plot the isotropic correction estimate against $r$, simply do <>= plot(K, iso ~ r) @ In \fun{plot.fv}, both sides of the plot formula are interpreted as mathematical expressions, so that operators like `\verb!+!', `\verb!-!', `\verb!*!', `\verb!/!' have their usual meaning in arithmetic. The right-hand side of the formula can be any expression that, when evaluated, yields a numeric vector, and the left-hand side is any expression that evaluates to a vector or matrix of compatible dimensions. If the left-hand side of the formula, when evaluated, yields a matrix, then each column of that matrix is plotted against the specified $x$ variable as a separate curve. In particular the left-hand side of the formula may invoke the function \fun{cbind} to indicate that several different curves should be plotted. For example, to plot only the isotropic correction estimator and the theoretical curve: <>= plot(K, cbind(iso, theo) ~ r) @ Notice that, in this example, \texttt{plot.fv} is clever enough to recognise that \texttt{iso} and \texttt{theo} are both versions of the \Kfun\ $K(r)$, and to decide that the appropriate label for the vertical axis is just $K(r)$. The plot formula may also involve the names of constants like \texttt{pi}, standard functions like \texttt{sqrt}, and some special abbreviations listed in Table~\ref{tab:fvnames}. \begin{table}[!h] \begin{tabular}{ll} \verb!.x! & argument of function \\ \verb!.y! & best estimate of function \\ \verb!.! & all recommended estimates of function \\ \verb!.a! & all columns of function values \\ \verb!.s! & upper and lower limits of shading \end{tabular} \caption{ Recognised abbreviations for columns of an \class{fv} object. } \label{tab:fvnames} \end{table} The symbol \verb!.x! represents the function argument, usually \texttt{"r"}. The symbol \verb!.y! represents one of the columns of function values which has been designated as the `best' estimate, for use by some other commands in \spst. The symbol `\verb!.!' represents the `recommended' estimates. The default plotting formula is \verb!. ~ .x! indicating that each of the recommended estimates will be plotted against the function argument. The formula \verb!.y ~ .x! means that the best estimate of the function will be plotted against the function argument. To expand these abbreviations for a particular \fv\ object, use the function \texttt{fvnames}. <<>>= fvnames(K, ".y") fvnames(K, ".") @ A plot formula can be used to specify a transformation that should be applied to the function values before they are displayed. For example, to subtract the theoretical Poisson value from each of the function estimates: <>= plot(K, . - theo ~ r) @ Alternatively one could plot the function estimates \emph{against} the Poisson value: <>= plot(K, . ~ theo) @ This plot has some theoretical support. In the discussion of Ripley's paper, Cox \cite{cox77discuss} proposed that $\widehat K(r)$ should be plotted against $r^2$, which is almost equivalent. We can follow Cox's recommendation exactly: <>= plot(K, . ~ r^2) @ The mathematical labels for the plot axes, and for the individual curves, are constructed automatically by \spst\ from the plot formula. If the plot formula involves the names of external variables, these will be rendered in Greek where possible. For example, to plot the average number of trees surrounding a typical tree in the Swedish Pines data, <>= lambda <- intensity(swedishpines) plot(K, lambda * . ~ r) @ Here we use the name \texttt{lambda} so that it will be rendered as the Greek letter $\lambda$ in the graphics: the $y$-axis will be labelled $\lambda K(r)$. \section{Calculating with an \fv\ object} This section explains how to do calculations involving a single \obj\fv. The next section covers calculations involving several \objs\fv. \subsection{Arithmetic and mathematical operators} Arithmetic and mathematical operations on an \obj\fv\ can be performed by simply writing the arithmetic expression involving the name of the object. The following are valid: <>= K <- Kest(cells) K/pi sqrt(K/pi) @ These inline calculations are performed by the operators \texttt{Ops.fv} and \texttt{Math.fv}. The operation is applied to each column of \emph{function values}; the function argument \texttt{r} will not be affected. The result is another \obj\fv\ with the same number of columns, with the same column names, but with appropriately adjusted auxiliary information. The expression can involve a command which returns an \obj\fv: <>= sqrt(Kest(cells)/pi) @ The auxiliary information contained in the resulting object will be slightly less elegant in this case. These arithmetic and mathematical operations are applied only to the \emph{recommended} columns of function values identified by \texttt{fvnames(, ".")}. \subsection{Other vectorised operations} Functions such as \texttt{pmax} and \texttt{cumsum} apply to vector data, but are not recognised as arithmetic or mathematical operators, so they are not covered by \texttt{Ops.fv} and \texttt{Math.fv}. For expressions involving \texttt{pmax} and \texttt{cumsum} (or indeed any algebraic expression whatsoever), use the command \texttt{eval.fv} to perform the calculation simultaneously for each column of function values: <>= Kpos <- eval.fv(pmax(0, K)) @ The result \texttt{Kpos} is another \obj\fv\ in which the function values are all non-negative. The first argument of \texttt{eval.fv} should be an expression involving the \textbf{name} of the \obj\fv. By default, the calculation is only applied to the \emph{recommended} columns of function values identified by \texttt{fvnames(, ".")}. This may be overridden by setting \texttt{dotonly=FALSE} in the call to \texttt{eval.fv}. The computations of \texttt{Ops.fv} and \texttt{Math.fv} are implemented using \texttt{eval.fv} but there may be slight differences in the handling of the auxiliary information. \subsection{Calculations involving specific columns} \label{p:with.fv} To manipulate or combine one or more columns of data in an \class{fv} object, it is typically easiest to use \fun{with.fv}, a method for the generic \fun{with}. This behaves in a very similar way to \texttt{with.data.frame}. For example: <<>>= Kr <- Kest(redwood) z <- with(Kr, iso - theo) x <- with(Kr, r) @ The results \texttt{x} and \texttt{z} are numeric vectors, where \texttt{x} contains the values of the distance argument $r$, and \texttt{z} contains the difference between the columns \texttt{iso} (isotropic correction estimate) and \texttt{theo} (theoretical value for CSR) for the \Kfun{} estimate of the redwood seedlings data. For this to work, we have to know that \texttt{Kr} contains columns named \texttt{r}, \texttt{iso} and \texttt{theo}. Printing the object will reveal this information, as would typing \texttt{names(Kr)} or \texttt{colnames(Kr)}. The general syntax is \texttt{with(X, expr)} where \texttt{X} is an \class{fv} object and \texttt{expr} can be any expression involving the names of columns of \texttt{X}. The expression can include functions, so long as they are capable of operating on numeric vectors. The expression can also involve the abbreviations listed in Table~\ref{tab:fvnames}: <<>>= Kcen <- with(Kr, . - theo) @ subtracts the `theoretical' value from all the available edge correction estimates. The result \texttt{Kcen} is another \class{fv} object. You can also get a result which is a vector or single number: <<>>= with(Kr, max(abs(iso-theo))) @ \subsection{Extracting data} An \obj\fv\ is essentially a data frame with additional attributes. It contains the values of the desired function (such as $K(r)$) at a finely spaced grid of values of the function argument $r$. The data frame can be extracted (and the additional attributes removed) using \texttt{as.data.frame.fv}: <<>>= df <- as.data.frame(K) @ A single column of values can be extracted using the \verb!$! operator in the usual way: \verb!K$iso! %$ would extract a vector containing the isotropic correction estimates of $K(r)$. The subset extraction operator `\verb![!' has a method %] for \class{fv} objects. This always returns another \class{fv} object, so it will refuse to remove the column containing values of the function argument \texttt{r}, for example. To override this refusal, convert the object to a data frame using \fun{as.data.frame} and then use `\verb![!': % ] the result will be a data frame or a vector. Commands designed for data frames often work for \class{fv} objects as well. The functions \texttt{head} and \texttt{tail} extract the top (first few rows) and bottom (last few rows) of a data frame. They also work on \class{fv} objects: the result is a new \class{fv} object containing the function values for a short interval of $r$ values at the beginning or end of the range. The function \texttt{subset} selects designated subsets of a data frame using an elegant syntax and this also works on \class{fv} objects. To restrict \texttt{K} to the range $r \le 0.1$ and remove the border correction, <<>>= Ko <- subset(K, r < 0.1, select= -border) @ \subsection{Converting to a true function} An \obj\fv\ is meant to represent a function, but it contains only sample values of the function at a grid of values of the function argument. The table of function values can also be converted to a true function in the \R{} language using \fun{as.function}. This makes it easy to evaluate the function at any desired distance $r$. <<>>= Ks <- Kest(swedishpines) kfun <- as.function(Ks) kfun(9) @ By default, the result \texttt{kfun} is a function in \R, with a single argument \texttt{r} (or whatever the original function argument was called). The new function accepts numeric values or numeric vectors of distance values, and returns the values of the `best' estimate of the function, interpolated linearly between entries in the table. If one of the other function estimates is required, use the argument \texttt{value} to \fun{as.function} to select it. Several estimates can be chosen: <<>>= kf <- as.function(Ks, value=".") kf(9) kf(9, "trans") @ kfun(0.1) @ \subsection{Special operations} \label{S:manip.fv} An \class{fv} object can be manipulated using the operations listed in Table~\ref{tab:fvmethods}. \begin{table}[!h] \begin{tabular}[c]{ll} \texttt{f} & print a description \\ \texttt{print(f)} & print a description \\ \texttt{plot(f)} & plot the function estimates \\ \texttt{as.data.frame(f)} & strip extra information (returns a data frame) \\ \verb!f$iso! & extract column named \texttt{iso} (returns a numeric vector) \\ \verb!f[i,j]! & extract subset (returns an \class{fv} object) \\ \verb!subset(f, ...)! & extract subset (returns an \class{fv} object) \\ \texttt{with(f, expr)} & perform calculations with columns of data frame\\ \texttt{eval.fv(expr)} & perform calculations with several \class{fv} objects \\ \texttt{bind.fv(f, d)} & combine an \class{fv} object \texttt{f} and data frame \texttt{d} \\ \texttt{min(f)}, \texttt{max(f)}, \texttt{range(f)} & range of function values \\ \texttt{Smooth(f)} & apply smoothing to function values \\ \texttt{deriv(f)} & derivative of function\\ \texttt{stieltjes(g,f)} & compute Stieltjes integral with respect to \texttt{f} \\ \texttt{as.function(f)} & convert to a function \end{tabular} \caption{Operations for manipulating an \class{fv} object \code{f}.} \label{tab:fvmethods} \end{table} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Calculating with several \fv\ objects} \subsection{Arithmetic and mathematical operators} Arithmetic and mathematical operations involving several \objs\fv\ can be performed by simply writing the arithmetic expression involving the objects: <>= Kcel <- Kest(cells) Kred <- Kest(redwood) Kdif <- Kcel - Kred @ These inline calculations are performed by the operators \texttt{Ops.fv} and \texttt{Math.fv}. The operation is applied to each column of \emph{function values}; the function argument \texttt{r} will not be affected. The result is another \obj\fv\ with the same number of columns, with the same column names, but with appropriately adjusted auxiliary information. The \fv\ objects should be `compatible' in the sense that they have the same column names, and the same vector of $r$ values. However, \texttt{eval.fv} will attempt to reconcile incompatible objects. (The \spst\ generic function \fun{compatible} determines whether two or more objects are compatible, and the generic function \fun{harmonise} makes them compatible, if possible.) The expression can involve sub-expressions which return \objs\fv: <>= Kest(cells) - Kest(redwood) @ The auxiliary information contained in the resulting object will be slightly less elegant in this case. \subsection{Other vectorised operations} For expressions involving \texttt{pmax} and \texttt{cumsum} (or indeed any algebraic expression whatsoever), use the command \texttt{eval.fv} to perform the calculation simultaneously for each column of function values. <>= Kcel <- Kest(cells) Kred <- Kest(redwood) Kmax <- eval.fv(pmax(Kcel, Kred)) @ The result \texttt{Kmax} is another \obj\fv. The first argument of \texttt{eval.fv} should be an expression involving the \textbf{names} of the \objs\fv. By default, the calculation is only applied to the \emph{recommended} columns of function values identified by \texttt{fvnames(x, ".")} where \texttt{x} is the \obj\fv. This may be overridden by setting \texttt{dotonly=FALSE} in the call to \texttt{eval.fv}. The expression is not permitted to contain sub-expressions that evaluate to \objs\fv. However, you can use the argument \texttt{envir} to supply such sub-expressions: <>= Kmax <- eval.fm(pmax(Kcel, Kred), envir=list(Kcel=Kest(cells), Kred=Kest(redwood))) @ The computations of \texttt{Ops.fv} and \texttt{Math.fv} are implemented using \texttt{eval.fv} but there may be slight differences in the handling of the auxiliary information. \subsection{Combining objects} Several \class{fv} objects can be combined using the operations listed in Table~\ref{tab:fvmethods.multi}. \begin{table}[!h] \begin{tabular}[c]{ll} \texttt{eval.fv(expr)} & perform calculations with several \class{fv} objects \\ \verb!cbind(f1, f2, ...)! & combine \class{fv} objects \texttt{f1, f2, ...} \\ \texttt{bind.fv(f, d)} & combine an \class{fv} object \texttt{f} and data frame \texttt{d} \\ \verb!collapse.fv(f1, f2, ...)! & combine several redundant \class{fv} objects \\ \verb!compatible(f1, f2, ...)! & check whether \class{fv} objects are compatible \\ \verb!harmonise(f1, f2, ...)! & make \class{fv} objects compatible \end{tabular} \caption{Operations for manipulating several \class{fv} objects \code{f1}, \code{f2}.} \label{tab:fvmethods.multi} \end{table} Use \code{\link{cbind.fv}} to combine several \code{"fv"} objects. Use \code{\link{bind.fv}} to glue additional columns onto an existing \code{"fv"} object. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Creating \fv\ objects from raw data} This section explains how to create \objs\fv\ from raw numerical data. This would be useful if you are implementing a completely new kind of summary function. Subsection~\ref{S:creator} explains how to create an \obj\fv\ by providing the numerical data and the required auxiliary information. Section~\ref{S:as.fv} describes an easier way to convert a data frame (or similar object) to an \obj\fv\ without specifying the auxiliary information, using default rules for the auxiliary information. Section~\ref{S:compileK} describes special tools \texttt{compileK, compilepcf, compileCDF} for creating an \obj\fv\ from a numeric vector of distance values, using the rules that apply to the \Kfun, or the pair correlation function, or the nearest-neighbour distance distribution function. \subsection{The creator function \fun{fv}} \label{S:creator} \subsubsection{The creator function} The low-level function \code{fv} is used to create an object of class \code{"fv"} from raw numerical data. It has the following syntax: \begin{verbatim} fv(x, argu = "r", ylab = NULL, valu, fmla = NULL, alim = NULL, labl = names(x), desc = NULL, unitname = NULL, fname = NULL, yexp = ylab) \end{verbatim} The arguments are as follows: \begin{itemize} \item \code{x} contains the numerical data. It should be a data frame, in which one column gives the values of the function argument for which the function has been evaluated, and at least one other column contains the corresponding values of the function. These other columns typically give the values of different versions or estimates of the same function, for example, different estimates of the \Kfun{} obtained using different edge corrections. However they may also contain the values of related functions such as the derivative or hazard rate. \item \code{argu} specifies the name of the column of \code{x} that contains the values of the function argument (typically \code{argu="r"} but this is not compulsory). \item \code{valu} specifies the name of another column that contains the `recommended' estimate of the function. It will be used to provide function values in those situations where a single column of data is required. For example, \code{envelope} computes its simulation envelopes using the recommended value of the summary function. \item \code{fmla} specifies the default plotting behaviour. It should be a formula, or a string that can be converted to a formula. Variables in the formula are names of columns of \code{x}. See \code{plot.fv} for the interpretation of this formula. \item \code{alim} specifies the recommended range of the function argument. This is used in situations where statistical theory or statistical practice indicates that the computed estimates of the function are not trustworthy outside a certain range of values of the function argument. By default, \code{plot.fv} will restrict the plot to this range. \item \code{fname} is a character string (or a vector of 2 character strings) giving the name of the function itself. For example, the \Kfun{} would have \code{fname="K"}, while the inhomogeneous \Kfun\ has \code{fname=c("K", "inhom")}. \item \code{ylab} is a mathematical expression for the function value, used when labelling an axis of the plot, or when printing a description of the function. It should be an \R{} language object. For example the \Kfun's mathematical name $K(r)$ is rendered by \code{ylab=quote(K(r))}. \item \code{yexp} is another mathematical expression for the function value. If \code{yexp} is present, then \code{ylab} will be used only for printing, and \code{yexp} will be used for annotating axes in a plot. (Otherwise \code{yexp} defaults to \code{ylab}). \item \code{labl} is a character vector specifying plot labels for each column of \code{x}. These labels will appear on the plot axes (in non-default plots), legends and printed output. Entries in \code{labl} may contain the string \code{"\%s"} which will be replaced by \code{fname} when plotted or printed. For example the border-corrected estimate of the \Kfun{} has label \code{"\%s[bord](r)"} which becomes \code{"K[bord](r)"} when it is used in \texttt{plot.fv} or \texttt{print.fv}. \item \code{desc} is a character vector containing intelligible explanations of each column of \code{x}. Entries in \code{desc} may contain the string \code{"\%s"} which will be replaced by \code{ylab}. For example the border correction estimate of the \Kfun{} has description \code{"border correction estimate of \%s"}. This will be replaced by \code{"border correction estimate of K(r)"} when it is used in \texttt{print.fv}. \item \code{unitname} is the name of the unit of length for the \underline{function argument}. Typically the function argument \code{"r"} represents distance between points. The distance values are typically expressed in terms of a distance unit, such as metres or feet. This unit will be printed on the horizontal axis. The argument \code{unitname} is an object of class \class{unitname}, or \code{NULL} representing dimensionless values. \end{itemize} \subsubsection{Syntax for \texttt{ylab} and \texttt{yexp}} Mathematical symbols and notation are supported in \R\ base graphics. The labels on the axes of a graph, in the body of the graph, and in graph legends, can all include mathematical notation. The notation has to be encoded as an \R\ language expression. The decoding is slightly idiosyncratic, and this affects the programming of the class \fv. The arguments \code{ylab} and \code{yexp} are mathematical expressions for the function value: \texttt{ylab} is used when printing a description of the function, and \texttt{yexp} is used when labelling an axis. Usually \texttt{ylab} and \texttt{yexp} are the same. For example the \Kfun's mathematical name $K(r)$ is rendered by \code{ylab=quote(K(r))} and \code{yexp=ylab}. An example where they are different is the multitype \Kfun\ $K_{1,2}(r)$ where we set \code{ylab=quote(Kcross[1,2](r))} and \code{yexp=quote(Kcross[list(1,2)](r))} to get the most satisfactory behaviour. A useful programming tip is to use \code{substitute} instead of \code{quote} to insert values of variables into an expression, e.g. \code{substitute(Kcross[i,j](r), list(i=42,j=97))} yields the same as \code{quote(Kcross[42, 97](r))}.) \subsubsection{Syntax for \texttt{labl}} The argument \texttt{labl} is a character vector specifying plot labels for each column of \code{x}. These labels will appear on the plot axes (in non-default plots), legends and printed output. Entries in \code{labl} may contain the string \code{"\%s"} which will be replaced by \code{fname} when plotted or printed. For example the border-corrected estimate of the \Kfun{} has label \code{"\%s[bord](r)"} which becomes \code{"K[bord](r)"} when it is used in \texttt{plot.fv} or \texttt{print.fv}. This mechanism allows the code to adjust the labels when the object is changed --- for example, to produce the correct labels in \code{plot(sqrt(K/pi))} as shown in Section~\ref{S:whybother}. Things become more complicated if \texttt{fname} is a character vector of length 2. In that case the appropriate expression for the border-corrected estimate is \verb!"{hat(%s)[%s]^{bord}}(r)"! which becomes \verb!{hat(K)[inhom]^{bord}}(r)! when it is used in \texttt{plot.fv} or \texttt{print.fv}. We strongly recommend using the function \fun{makefvlabel} to create the appropriate labels. Its syntax is: \begin{verbatim} makefvlabel(op=NULL, accent=NULL, fname, sub=NULL, argname="r") \end{verbatim} where the arguments are character strings: \begin{description} \item[op] is a prefix or operator such as \code{"var"} (rarely used); \item[accent] is an accent that should be applied to the main function symbol, usually \texttt{"hat"} for empirical estimates; \item[fname] is the name of the function (usually a single letter or a character vector of length 2); \item[sub] is an optional subscript, typically used to discriminate between different estimates of the function, such as different edge corrections; \item[argname] is the name of the function argument. \end{description} Examples: <<>>= makefvlabel(NULL, NULL, "K", "pois") makefvlabel(NULL, "hat", "K", "bord") makefvlabel(NULL, "hat", c("K", "inhom"), "bord") makefvlabel("var", "hat", c("K", "inhom"), "bord") @ \subsubsection{Syntax for \texttt{desc}} Each entry of \texttt{desc} is a single character string. It may contain a \underline{single} instance of \code{"\%s"}, which will be replaced by the function name when required. \subsection{Conversion function \fun{as.fv}} \label{S:as.fv} The generic function \texttt{as.fv} converts other kinds of data to an \obj\fv. The methods \fun{as.fv.matrix} and \fun{as.fv.data.frame} provide a lazy way to convert a table of function data to an \obj\fv. The auxiliary information is determined by applying default rules. Other methods apply to classes of objects which intrinsically contain an \obj\fv, and they simply extract the \fv\ object. For example, a fitted model of class \class{kppm} contains the summary function (either the $K$ function or the pair correlation function) that was used to fit the model; so the method \fun{as.fun.kppm} simply extracts this summary function. \subsection{compileK, compilepcf, compileCDF} \label{S:compileK} A shortcut is provided for programmers wishing to implement a summary function that is similar to Ripley's $K$ function, the pair correlation function $g$, the empty space function $F$ or the nearest-neighbour distance distribution function $G$. \subsubsection{$K$ functions and pair correlation functions} Programmers who wish to implement a summary function similar to Ripley's $K$ function or the pair correlation function can use the commands \texttt{compileK} or \texttt{compilepcf}. These low-level functions construct estimates of the $K$ function or pair correlation function, or any similar functions, given only the matrix of pairwise distances and optional weights associated with these distances. These functions are useful for code development and for teaching, because they perform a common task, and do the housekeeping required to make an object of class \fv\ that represents the estimated function. However, they are not very efficient. The basic syntax of \texttt{compileK} and \texttt{compilepcf} is: <>= compileK(D, r, weights = NULL, denom = 1, ...) compilepcf(D, r, weights = NULL, denom = 1, ...) @ where \begin{itemize} \item \texttt{D} is a square matrix giving the distances between all pairs of points; \item \texttt{r} is a vector of distance values, equally spaced, at which the summary function should be calculated; \item \texttt{weights} is an optional matrix of numerical weights for the pairwise distances; \item \texttt{denom} is the denominator for the estimator. It may be a single number, or a numeric vector with the same length as \texttt{r}. \end{itemize} The command \texttt{compileK} calculates the weighted estimate of the $K$ function, \[ K(r) = \frac{1}{v(r)} \sum_i \sum_{j \neq i} w_{i,j} \; 1\{ d_{i,j} \le r\} \] and \texttt{compilepcf} calculates the weighted estimate of the pair correlation function, \[ g(r) = \frac{1}{v(r)} \sum_i \sum_{j \neq i} w_{i,j}\; \kappa ( d_{i,j} - r) \] where $d_{i,j}$ is the distance between spatial points $i$ and $j$, with corresponding weight $w_{i,j}$, and $v(r)$ is the specified denominator. Here $\kappa$ is a fixed-bandwidth smoothing kernel. For a point pattern in two dimensions, the usual denominator $v(r)$ is constant for the $K$ function, and proportional to $r$ for the pair correlation function: <<>>= X <- japanesepines D <- pairdist(X) Wt <- edge.Ripley(X, D) lambda <- intensity(X) a <- (npoints(X)-1) * lambda r <- seq(0, 0.25, by=0.01) K <- compileK(D=D, r=r, weights=Wt, denom=a) g <- compilepcf(D=D, r=r, weights=Wt, denom= a * 2 * pi * r) @ The result of \texttt{compileK} or \texttt{compilepcf} can then be edited (as explained in the next section) to change the function name and other information as desired. \subsubsection{Cumulative distribution functions} Programmers wishing to implement a summary function which is a cumulative distribution function, similar to the functions \texttt{Gest} or \texttt{Fest}, can use the command \texttt{compileCDF}. The basic syntax of \texttt{compileCDF} is: <>= compileCDF(D, B, r, ..., han.denom = NULL) @ where \begin{itemize} \item \texttt{D} is a numeric vector of observed distances (such as the distance from each data point to its nearest neighbour); \item \texttt{B} is a numeric vector of censoring distances (such as the distance from each data point to the boundary of the window); \item \texttt{r} is a vector of distance values, equally spaced, at which the summary function should be calculated; \item \texttt{han.denom} is the denominator for the Hanisch estimator. It is usually a numeric vector with the same length as \texttt{r}. \end{itemize} An example for the nearest-neighbour distance distribution function $G(r)$: <<>>= X <- japanesepines D <- nndist(X) B <- bdist.points(X) r <- seq(0, 1, by=0.01) h <- eroded.areas(Window(X), r) G <- compileCDF(D=D, B=B, r=r, han.denom=h) ## give it a better name G <- rebadge.fv(G, new.fname="G", new.ylab=quote(G(r))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Editing the auxiliary information in \fv\ objects} The ``auxiliary information'' in an \obj\fv\ consists of the function name, a mathematical expression for the function, mathematical expressions for each version of the function contained in a column of data, the choice of which columns will be plotted by default, and other information. A programmer will often wish to create an \fv\ object first, perhaps using some existing code, and then edit the auxiliary information. The safe way to edit the auxiliary information is to \textbf{use the internal functions in \spst} which support the \fv\ class: \begin{itemize} \item \texttt{rebadge.fv} is a low-level function which allows the user to change any of the entries in the auxiliary information as desired. \item \texttt{rebadge.as.crossfun} and \texttt{rebadge.as.dotfun} are wrappers for \texttt{rebadge.fv} which change the auxiliary information into the form expected for a cross-type or dot-type summary function. \item \texttt{fvlabels} extracts the mathematical code for each version of the summary function, and \verb!fvlabels<-! changes the codes. \item \texttt{makefvlabel} creates suitable mathematical code for a version of the summary function. \item The functions \texttt{fvnames} and \verb!fvnames<-! manage the definition of the abbreviations listed in Table~\ref{tab:fvnames}. \item The methods \texttt{formula.fv} and \verb!formula<-.fv! manage the default plotting formula. \item \verb!names<-.fv! changes the names of the columns in the \fv\ object, and adjusts the internal data accordingly. \item \texttt{tweak.fv.entry} is a very low-level function that changes the auxiliary information about one of the columns of data. \item \texttt{prefixfv} is another wrapper for \texttt{rebadge.fv} that adds a prefix to the name of the function. \end{itemize} \subsection{Low-level editing} \texttt{rebadge.fv} is a low-level function which allows the user to change any of the entries in the auxiliary information as desired. It has syntax <>= rebadge.fv(x, new.ylab, new.fname, tags, new.desc, new.labl, new.yexp=new.ylab, new.dotnames, new.preferred, new.formula, new.tags) @ where \texttt{x} is the \obj\fv. The arguments \texttt{new.fname}, \texttt{new.ylab} and \texttt{new.yexp} (if present) specify new values for the function name \texttt{fname} and the mathematical expressions for the function, \texttt{ylab} and \texttt{yexp}, described in Section~\ref{S:creator}. The argument \texttt{new.dotnames} specifies a new value for the selection of columns that are plotted by default. This is a character vector of column names of \texttt{x} and is associated with the abbreviation ``\verb!.!'' in Table~\ref{tab:fvnames}. The argument \texttt{new.preferred} specifies a new value for the choice of column that is designated the ``preferred'' column and is used in calculations which require a single column of data, such as simulation envelopes. This is a single character string which must be a column name of \texttt{x} and is associated with the abbreviation ``\verb!.y!'' in Table~\ref{tab:fvnames}. The argument \texttt{new.formula} specifies a new default plotting formula for the summary function. The argument \texttt{new.desc} specifies new values for the string descriptions of the individual columns, replacing the argument \texttt{desc} described in Section~\ref{S:creator}. It should be a character vector with one entry for every column of \texttt{x} (or see below). The argument \texttt{new.labl} specifies new values for the mathematical labels of the individual columns, replacing the argument \texttt{desc} described in Section~\ref{S:creator}. It should be a character vector with one entry for every column of \texttt{x} (or see below). The argument \texttt{tags} can be used to select some of the columns of data so that only the auxiliary data for the selected columns will be changed. It should be a character vector with entries which match the names of columns of \texttt{x}. In that case, \texttt{new.desc} and \texttt{new.labl} should have the same length as \texttt{tags}, and they will be taken as replacement values for the selected columns only. The optional argument \texttt{new.tags} changes the names of the columns of \texttt{x} (or the columns selected by \texttt{tags}) to new values. \subsection{Changing information about one column} The method \verb!names<-.fv! changes the names of the columns in the \fv\ object, and adjusts the internal data accordingly. The function \texttt{tweak.fv.entry} is a very low-level function that changes the auxiliary information about one of the columns of data. It has syntax <>= tweak.fv.entry(x, current.tag, new.labl=NULL, new.desc=NULL, new.tag=NULL) @ where \texttt{current.tag} is the current name of the column for which the information should be changed, \texttt{new.labl} is the new mathematical label for the column, \texttt{new.desc} is the new text description of the column, and \texttt{new.tag} is the new name of the column. All these arguments are single strings or \texttt{NULL}. \subsection{Special idioms} A few functions are available for performing special idioms. The function \texttt{prefixfv} is a wrapper for \texttt{rebadge.fv} that adds a prefix to the name of the function, and to all the relevant auxiliary information. It has syntax <>= prefixfv(x, tagprefix="", descprefix="", lablprefix=tagprefix, whichtags=fvnames(x, "*")) @ where \texttt{tagprefix}, \texttt{descprefix} and \texttt{lablprefix} are strings that should be added to the beginning of the column name, the text description, and the mathematical expression for each column of data. The argument \texttt{whichtags} specifies which columns of data should be changed; the default is to change all columns. The function \texttt{rebadge.as.crossfun} changes the auxiliary information into the form expected for a bivariate, cross-type summary function, analogous to the bivariate $K$-function $K_{i,j}(r)$ between two types of points labelled $i$ and $j$ that is computed by the \spst\ function \texttt{Kcross}. It has syntax <>= rebadge.as.crossfun(x, main, sub=NULL, i, j) @ where \texttt{main} is the main part of the function name, \texttt{sub} is the subscript part of the function name, and \texttt{i} and \texttt{j} are the type labels. For example <>= rebadge.as.crossfun(x, "L", i="A", j="B") @ would create a function $L_{A,B}(r)$, and <>= rebadge.as.crossfun(x, "L", "inhom", "A", "B") @ would create a function $L_{\mbox{\scriptsize inhom},A,B}(r)$. Similarly the function \texttt{rebadge.as.dotfun} changes the auxiliary information into the form expected for a ``one type-to-any type'' summary function, analogous to the function $K_{i \bullet}(r)$ that is computed by the \spst\ function \texttt{Kdot}. It has syntax <>= rebadge.as.dotfun(x, main, sub=NULL, i) @ \subsection{Handling mathematical labels} The auxiliary information in an \fv\ object includes mathematical labels for the different versions of the function, which are displayed by \texttt{plot.fv}. The function \texttt{fvlabels} extracts the mathematical code for each version of the summary function from the \fv\ object, and \verb!fvlabels<-! changes the codes. The mathematical codes are strings which must be recognisable to the \texttt{plotmath} code in the \R\ base graphics system which is somewhat idiosyncratic. The strings may also (and usually do) include the substring \verb!%s! (appearing once or twice) which will be replaced by the function name. For example, if the function name is \texttt{"K"} and the label is \verb!"hat(%s)[iso](r)"! this will be parsed as \verb!hat(K)[iso](r)! which is rendered as $\widehat K_{\mbox{\scriptsize iso}}(r)$. The function \texttt{makefvlabel} creates suitable mathematical code for a version of the summary function. Programmers are strongly advised to use \texttt{makefvlabel}. \subsection{Changing default behaviour} The default behaviour for plotting an \fv\ object depends on its default \texttt{plot formula} and typically on its \texttt{dot names}. The default plot formula is printed when the object is printed, and can be extracted using \texttt{formula.fv}. <<>>= K <- Kest(cells) formula(K) @ The interpretation of the plot formula is explained in Section~\ref{S:plot.formula}. In the example above, the left hand side of the formula uses the abbreviation ``\verb!.!'' which stands for ``the default list of columns to be plotted''. This abbreviation can be expanded using \texttt{fvnames}: <<>>= fvnames(K, ".") @ which indicates that the columns named \texttt{"iso"}, \texttt{"trans"}, \texttt{"border"} and \texttt{"theo"} will be plotted. The choice of ``dot names'' can be changed using \verb!fvnames<-!: <<>>= fvnames(K, ".") <- c("iso", "theo") @ In general the functions \texttt{fvnames} and \verb!fvnames<-! manage the definition of all the abbreviations listed in Table~\ref{tab:fvnames}. \section{Pooling several function estimates} \subsection{Pooling} ``Pooling'' or combining several datasets into a single dataset is a common statistical procedure. If we are only interested in a summary statistic of the data, then in some special circumstances, the summary statistic of the pooled dataset can be calculated from the summary statistics of the original, separate datasets. For example, if we have a set of $n_1$ observations with sample mean $m_1$, and another set of $n_2$ observations with sample mean $m_2$, then the sample mean of the pooled set of $n_1+n_2$ observations has sample mean $(n_1 m_1 + n_2 m_2)/(n_1+n_2)$, a weighted average of the sample means of the original datasets. This procedure is loosely called ``pooling'' the sample mean. If we have two point pattern datasets, observed in different windows, we can ``pool'' the patterns by simply treating them as a single point pattern observed in the combined window. If we pool two point pattern datasets, the estimated $K$-function of the pooled pattern can be calculated from the estimated $K$-functions $K_1(r)$ and $K_2(r)$ of the original point patterns, if we know the number of points in each of the two original patterns. That is, Ripley's $K$-function can be ``pooled''. The summary functions used in spatial statistics can be pooled, provided they are able to be expressed as a ratio $f(r) = A(r)/B$ or $f(r) = A(r)/B(r)$ where $A(r)$ is the ``numerator'' and $B$ or $B(r)$ is the ``denominator''. The pooled estimate is the ratio of the sum of numerators divided by the sum of denominators. For details, see section 16.8.1 of \cite{baddrubaturn15}. \subsection{Pooling summary functions} The generic function \texttt{pool} performs pooling of summary statistics (including summary functions like the $K$-function). In order for this to work correctly, we must know the numerator and denominator for each of the individual summary statistics or summary functions. For this purpose there is a special class \class{rat} (for ``ratio object''). An \obj\rat\ contains two attributes named \texttt{"numerator"} and \texttt{"denominator"} which contain the numerator and denominator of the ratio. For many of the summary functions provided in \spst, if we set the argument \texttt{ratio=TRUE}, the numerator and denominator will be calculated separately and saved in the resulting object, which will belong to the class \class{rat} (``ratio object'') as well as \fv. <<>>= class(Kest(cells)) class(Kest(cells, ratio=TRUE)) @ This capability is currently available for the functions \texttt{compileK}, \texttt{compilepcf}, \texttt{Finhom}, \texttt{Gcross.inhom}, \texttt{Gdot.inhom}, \texttt{Ginhom}, \texttt{GmultiInhom}, \texttt{Jcross.inhom}, \texttt{Jdot.inhom}, \texttt{Jinhom}, \texttt{Jmulti.inhom}, \texttt{K3est}, \texttt{Kcross}, \texttt{Kdot}, \texttt{Kest}, \texttt{Kinhom}, \texttt{Kmulti}, \texttt{Ksector}, \texttt{linearKinhom}, \texttt{linearK}, \texttt{linearpcfinhom}, \texttt{linearpcf}, \texttt{nnorient}, \texttt{pairorient}, \texttt{pcfcross}, \texttt{pcfdot}, \texttt{pcfmulti}, \texttt{pcf.ppp} and \texttt{Tstat}. The method \texttt{pool.rat} will pool several objects which all belong to the classes \class{fv} and \class{rat}: <<>>= X1 <- runifpoint(50) X2 <- runifpoint(50) K1 <- Kest(X1, ratio=TRUE) K2 <- Kest(X2, ratio=TRUE) K <- pool(K1, K2) @ <<>>= Xlist <- runifpoint(50, nsim=6) Klist <- lapply(Xlist, Kest, ratio=TRUE) K <- do.call(pool, Klist) @ There is also a fallback method \texttt{pool.fv} which is used when some of the objects do not contain ratio information. This method effectively pretends that all the objects have the same denominator. \subsection{Low level utilities} Programmers wishing to implement a summary function with ratio information can use the following low-level utilities: \begin{itemize} \item \texttt{ratfv} is the creator function, analogous to \texttt{fv}. Its syntax is <>= ratfv(df, numer, denom, ..., ratio=TRUE) @ where \texttt{df} is a data frame, \texttt{numer} and \texttt{denom} are \objs\fv, and additional arguments \verb!...! are passed to \texttt{fv}. It is sufficient to specify either \texttt{df} or \texttt{numer}, in addition to \texttt{denom}. \item \texttt{bind.ratfv} glues extra columns onto an existing \obj\fv and \class{rat}. \item \texttt{conform.ratfv} forces the auxiliary information in the numerator and denominator of an \obj\fv and \class{rat} to agree with the auxiliary information of the main object. \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Structure of \objs\fv} This section explains the information contained in \objs\fv. \subsection{Advice} We strongly discourage the user from unpacking the internal contents of \objs\fv{} and manipulating the contents directly. Instead, we recommend using the functions that are available in \spst{} for handling these objects. Although it is easy to extract the internal data contained in an object in \R, the structure of \objs\fv\ is idiosyncratic, and the internal format is variable. Looking at one example of an \obj\fv\ will not tell you how it all works. This is because there are many cases to handle, and many quirks in the formatting of algebraic expressions in \R. Using the functions provided in \spst\ is also more efficient than extracting data yourself, because it avoids creating multiple copies of the data. Most of all, \textbf{do not change the internal contents of \objs\fv}. This can easily violate the internal format and cause errors. Use the functions supplied for handling these objects. \subsection{Objects of class \fv} Objects of class \fv\ are returned by many commands in the \spst\ packages. Usually these objects are obtained by analysing a spatial point pattern dataset. There are also functions to create such objects from raw data. An \obj\fv{} is essentially a data frame with additional attributes containing auxiliary information. \subsubsection*{Data frame structure} The first column of the data frame contains values of the function argument. These values are arranged in increasing order, are usually evenly-spaced, and usually start from zero. The first column usually (but not always) has the column name \texttt{r}. Subsequent columns of the data frame contain the values of different versions of the summary function, corresponding to the values of the function argument. These columns may have any column names. These versions of the function may be referred to by their column names when plotting and manipulating the object. <<>>= G <- Gest(finpines) df <- as.data.frame(G) head(df) @ In this example, the object \texttt{G} contains estimates of the nearest-neighbour distance distribution function $G(r)$ for the \texttt{finpines} dataset. For the distance value $r = $ \Sexpr{round(df[6, "r"], 9)} metres, the estimate of $G(r)$ using the \texttt{han} method is \Sexpr{round(df[6, "han"],8)}. Columns of data can be extracted using the data frame structure. To extract the sequence of \texttt{r} values, use \verb!df$r! or \verb!G$r! or \verb!df[, "r"]!. To extract the corresponding values of \texttt{han}, use \verb!df$han! or \verb!G$han! or \verb!df[, "han"]!. \subsubsection*{Auxiliary information} In the example above, to find out what the column \texttt{han} means, we need the auxiliary information stored in the object \texttt{G}. This can be printed out directly in readable form: <<>>= G @ Thus, \texttt{han} refers to the estimate of $G(r)$ using Hanisch's method. The auxiliary information is stored in attributes of the object. The full list of attributes is as follows: \begin{tabular}{lll} \texttt{argu} & character(1) & Name of function argument (usually \texttt{"r"}) \\ \texttt{valu} & character(1) & Name of preferred function value \\ \texttt{ylab} & language & Mathematical expression for function (for vertical axis of plot) \\ \texttt{yexp} & language & Mathematical expression for function (in algebra) \\ \texttt{fmla} & character(1) & Default plotting formula \\ \texttt{alim} & numeric(2) & Recommended range of function argument \\ \texttt{labl} & character($m$) & Mathematical labels for each column\\ \texttt{desc} & character($m$) & Text descriptions of each column\\ \texttt{units} & unitname & Unit of length (for function argument) \\ \texttt{fname} & character(1 or 2) & Symbol for function only \\ \texttt{dotnames} & character($k \le m$) & Column names of all recommended versions \\ \texttt{shade} & character(0 or 2) & Column names of limits of grey shading\\ \end{tabular} \code{argu} is the name of the column of the data frame that contains the values of the function argument (typically \code{argu="r"} but this is not compulsory). \code{valu} specifies the name of another column that contains the `recommended' estimate of the function. It will be used to provide function values in those situations where a single column of data is required. For example, \code{envelope} computes its simulation envelopes using the recommended value of the summary function. \code{fmla} specifies the default plotting behaviour, as explained in Section~\ref{S:plot.fv}. It is a character string that can be converted to a \texttt{formula} in the \R\ language. \code{alim} specifies the recommended range of the function argument. It is a numeric vector of length 2. This is used in situations where statistical theory or statistical practice indicates that the computed estimates of the function are not trustworthy outside a certain range of values of the function argument. By default, \code{plot.fv} will restrict the plot to this range. \code{fname} gives the name of the function itself. For example, the \Kfun{} would have \code{fname="K"}. It is either a character string, or a vector of two character strings, where the second element is interpreted as a subscript. For example, the inhomogeneous \Kfun{} computed by \code{Kinhom} has \code{fname=c("K", "inhom")}. \code{ylab} is a mathematical expression for the function value, used when printing a description of the function. It is an \R{} language object. For example the \Kfun's mathematical name $K(r)$ is rendered by \code{ylab=quote(K(r))}. \code{yexp} is another mathematical expression for the function value, used for annotating axes in a plot. \code{labl} is a character vector specifying plot labels for each column of the data frame. These labels will appear on the plot axes (in non-default plots), legends and printed output. Entries in \code{labl} may contain the string \code{"\%s"} which will be replaced by \code{fname}. \code{desc} is a character vector containing intelligible explanations of each column of the data frame. Entries in \code{desc} may contain the string \code{"\%s"} which will be replaced by \code{ylab}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Structure of \objs\env} This section explains the information contained in \objs\env. \subsection{The \texttt{envelope} command} The \spst\ function \fun{envelope} performs the calculations required for envelopes. It computes the summary function for a point pattern dataset, generates simulated point patterns, computes the summary functions for the simulated patterns, and computes the envelopes of these summary functions. <>= E <- envelope(swp, Kest, nsim=39, fix.n=TRUE) @ The result is an object of class \class{envelope} and \class{fv} which can be printed and plotted and manipulated using the tools for \class{fv} objects, and by additional tools provided for \class{envelope} objects. The print method gives a lot of detail: <<>>= E @ \subsection{Re-using envelope data} The method \texttt{envelope.envelope} allows new \fun{envelope} commands to be applied to a previously computed \class{envelope} object, provided it contains the necessary data. In the original call to \fun{envelope}, if the argument \texttt{savepatterns=TRUE} was given, the resulting \class{envelope} object contains all the simulated point patterns. Alternatively if the argument \texttt{savefuns=TRUE} was given, the resulting object contains the individual summary functions for each of the simulated patterns. This information is not saved, by default, for efficiency's sake. Envelopes created with \texttt{savepatterns=TRUE} allow any kind of new envelopes to be computed using the same simulated point patterns: <>= E1 <- envelope(redwood, Kest, savepatterns=TRUE) E2 <- envelope(E1, Gest, global=TRUE, transform=expression(fisher(.))) @ Envelopes created with \texttt{savefuns=TRUE} allow the user to switch between pointwise and global envelopes of the same summary function, to apply different transformations of the summary function, and to change some parameters: <>= A1 <- envelope(redwood, Kest, nsim=39, savefuns=TRUE) A2 <- envelope(A1, global=TRUE, nsim=19, transform=expression(sqrt(./pi))) @ \subsection{Pooling several envelopes} It is also possible to combine the simulation data from several envelope objects and to compute envelopes based on the combined data. This is done using \fun{pool.envelope}, a method for the \spst\ generic \fun{pool}. The envelopes must be compatible, in that they are envelopes for the same function, and were computed using the same options. The individual summary functions must have been saved. <>= E1 <- envelope(cells, Kest, nsim=10, savefuns=TRUE) E2 <- envelope(cells, Kest, nsim=20, savefuns=TRUE) E <- pool(E1, E2) @ \subsection{Structure of envelope objects} An \obj\env{} is an \obj\fv{} with additional auxiliary information: \begin{itemize} \item the names of two of the columns of function values, designated as the upper and lower simulation envelopes of the function, saved in \texttt{attr(, "shade")} and retrievable as \texttt{fvnames(, .s)} \item details of how the envelopes were computed, saved in \texttt{attr(, "einfo")} \item optionally, the simulated point patterns used to compute the envelopes, saved in \texttt{attr(, "simpatterns")} \item optionally, the simulated summary functions (the summary functions computed for the simulated point patterns) used to compute the envelopes, saved in \texttt{attr(, "simfuns")} \end{itemize} Objects of class \env\ inherit the class \fv, so they can be manipulated using methods for class \fv, but there are extra methods for the special class \env. \subsection{The \texttt{einfo} list} Additional attribute \texttt{einfo} is a list of: \begin{tabular}{lll} \texttt{call} & character(1) & original function call \\ \texttt{Yname} & character(1) & name of original dataset \\ \texttt{valname} & character(1) & column name of function values used\\ \texttt{csr} & logical(1) & \texttt{TRUE} if simulations based on CSR \\ \texttt{csr.theo} & logical (1) & see below\\ \texttt{use.theory} & logical (1) & see below\\ \texttt{pois} & logical(1) & \texttt{TRUE} if simulations are Poisson process\\ \texttt{simtype} & character(1) & Type of simulation (see below) \\ \texttt{constraints} & character(1) & Additional information (see below) \\ \texttt{nrank} & integer(1) & Rank of envelopes \\ \texttt{nsim} & integer(1) & Number of simulations for envelope \\ \texttt{Nsim} & integer(1) & Total number of simulations\\ \texttt{global} & logical(1) & \texttt{TRUE} if global envelopes\\ \texttt{ginterval} & numeric(0 or 2) & Domain of function argument for global envelopes \\ \texttt{dual} & logical(1) & \texttt{TRUE} if two sets of simulations performed\\ \texttt{nsim2} & integer(1) & Number of simulations in second set \\ \texttt{VARIANCE} & logical(1) & \texttt{TRUE} if limits are based on standard deviation \\ \texttt{nSD} & numeric(1) & Number of standard deviations defining limits \\ \texttt{alternative} & character(1) & \texttt{two.sided}, \texttt{less} or \texttt{greater} \\ \texttt{scale} & \texttt{NULL} or function & Scaling function for function argument \\ \texttt{clamp} & logical(1) & \texttt{TRUE} if one-sided deviations must be positive \\ \texttt{use.weights} & logical(1) & \texttt{TRUE} if sample mean is weighted\\ \texttt{do.pwrong} & logical(1) & \texttt{TRUE} if ``wrong $p$-value'' should be calculated \\ \texttt{gaveup} & logical(1) & \texttt{TRUE} if simulations terminated early \end{tabular} \begin{thebibliography}{1} \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \bibitem{besa77d} J.E. Besag. \newblock Contribution to the discussion of the paper by Ripley (1977). \newblock \emph{Journal of the Royal Statistical Society, Series B} \textbf{39} (1977) 193--195. \bibitem{cox77discuss} D.R. Cox. \newblock Contribution to the discussion of the paper by Ripley (1977). \newblock \emph{Journal of the Royal Statistical Society, Series B} \textbf{39} (1977) 206. \end{thebibliography} \end{document} spatstat/NEWS0000644000176200001440000134661514517140766012647 0ustar liggesusers CHANGES IN spatstat VERSION 3.0-7 OVERVIEW o New vignette on function objects (class 'fv' and 'envelope') o Vignette on shapefiles temporarily removed. o Updated cross-references to sub-packages. o Minor changes to package information. SIGNIFICANT USER-VISIBLE CHANGES o Vignette 'fv' New vignette which explains how to manipulate and plot objects of class 'fv' and 'envelope'. o Vignette 'shapefiles' This vignette has been temporarily removed because of its dependence on the defunct packages 'sp' and 'maptools'. It will be rewritten and reinstated soon. CHANGES IN spatstat VERSION 3.0-6 OVERVIEW o Updated cross-references to sub-packages. o Package documentation corrected and updated. CHANGES IN spatstat VERSION 3.0-5 OVERVIEW o Package documentation repaired (latex package issues). CHANGES IN spatstat VERSION 3.0-4 OVERVIEW o Package version information updated. o Minor omissions corrected in vignettes. o Package documentation corrected and updated. CHANGES IN spatstat VERSION 3.0-3 OVERVIEW o Package version information updated. o Minor errors corrected in vignettes. CHANGES IN spatstat VERSION 3.0-2 OVERVIEW o Internal tweaks. CHANGES IN spatstat VERSION 3.0-1 OVERVIEW o Package nicknames are temporarily suppressed. CHANGES IN spatstat VERSION 3.0-0 OVERVIEW o Further split of the spatstat family of packages. o All package versions have been updated to 3.0-x. SIGNIFICANT USER-VISIBLE CHANGES o Package structure The package 'spatstat.core' has been split into two packages called 'spatstat.explore' (for exploratory data analysis) and 'spatstat.model' (for modelling and formal inference). o Package dependence 'spatstat' now depends on the new packages 'spatstat.explore' and 'spatstat.model' which have replaced the old package 'spatstat.core'. o spatstat.family Packages 'spatstat.explore' and 'spatstat.model' have been added and 'spatstat.core' has been deleted from the list of packages. o bugfixes, latest.news Updated to cover the new sub-packages 'spatstat.explore' and 'spatstat.model'. CHANGES IN spatstat VERSION 2.3-4 OVERVIEW o Minor bug fixes and improvements. BUG FIXES o latest.news Did not include news from the new package 'spatstat.random'. Fixed. o spatstat.family Omitted the new package 'spatstat.random'. Fixed. CHANGES IN spatstat VERSION 2.3-3 OVERVIEW o Minor internal changes. CHANGES IN spatstat VERSION 2.3-1 OVERVIEW o spatstat depends on the package 'spatstat.random' SIGNIFICANT USER-VISIBLE CHANGES o package dependence spatstat now depends on the new package 'spatstat.random'. CHANGES IN spatstat VERSION 2.3-0 OVERVIEW o Minor tweaks to package information and documentation o The NEWS file for package='spatstat' now contains only the news for the sub-package 'spatstat'. Other changes in the spatstat family of packages are reported in the NEWS file for the relevant sub-package. o 'latest.news' now collects news from all packages in the spatstat family o 'bugfixes' now collects bug fixes from all packages in the spatstat family o New function 'spatstat.family' NEW FUNCTIONS o spatstat.family Return the names of all packages in the spatstat family. SIGNIFICANT USER-VISIBLE CHANGES o latest.news Now gives the latest news for all packages in the spatstat family. o bugfixes Now lists bugs reported for all packages in the spatstat family. CHANGES IN spatstat VERSION 2.2-0 OVERVIEW o Minor tweaks to package information CHANGES IN spatstat VERSION 2.1-0 OVERVIEW o Minor changes to package structure. CHANGES IN spatstat VERSION 2.0-1 OVERVIEW o Minor changes to satisfy CRAN checks. CHANGES IN spatstat VERSION 2.0-0 OVERVIEW o We thank Corey Anderson, Michael Chirico, Andy Craig, Marcelino de la Cruz, Tilman Davies, Pavel Fibich, Kurt Hornik, Gopalan Nair, Yonatan Rosen and Rasmus Waagepetersen for contributions. o spatstat has been divided into 7 sub-packages (spatstat.utils, spatstat.data, spatstat.sparse, spatstat.geom, spatstat.core, spatstat.linnet and spatstat). o spatstat now depends on R version 3.5.0 or later. o Version nickname: "Caution: contains small parts" SIGNIFICANT USER-VISIBLE CHANGES o package structure The original 'spatstat' package has been divided into 7 sub-packages (spatstat.utils, spatstat.data, spatstat.sparse, spatstat.geom, spatstat.core, spatstat.linnet and spatstat). The remaining 'spatstat' package requires all the other sub-packages. Your existing code scripts should still work with minimal changes. o sessionInfo Output now lists packages that are imported but not loaded. o sessionLibs Package names are now sorted alphabetically. CHANGES IN spatstat VERSION 1.64-1 OVERVIEW o Important bug fix in vcov.ppm o Relative risk estimation may include case weights. o We thank Ian Buller, Brian Ripley, Maximilian Vogtland and Maximilian Hesselbarth for contributions. o Nickname: 'Help you I can, yes!' SIGNIFICANT USER-VISIBLE CHANGES o rshift.ppp, rshift.splitppp New argument 'nsim'. o relrisk.ppp New argument 'weights'. o density.splitppp New argument 'weights'. BUG FIXES o vcov.ppm Variances were sometimes overestimated for Gibbs models. That is, entries of the Fisher information matrix were underestimated, because some contributions due to interaction were omitted (due to a coding error). Fixed. o density.ppp Crashed when se=TRUE if there were multiple columns of 'weights'. Fixed. o rbind.hyperframe Crashed unless all arguments had the same number of rows. (Spotted by Maximilian Vogtland). Fixed. CHANGES IN spatstat VERSION 1.64-0 OVERVIEW o We thank Robert Aue, Tilman Davies, Greg McSwiggan, Tyler Rudolph and Rasmus Plenge Waagepetersen for contributions. o Interactive graphics functions have been removed to a separate package. o spatstat no longer needs the packages 'tcltk' and 'rpanel'. o The suggested package 'maptools' should be version 0.9-9 or later. o Important bug fix in density.ppp. o Add new vertices to a linear network. o Relative risk estimation on a network. o Leave-one-out density estimation on a network. o Improvements and extensions to linear networks code. o Improvements to 'nndist' methods. o Function lengths.psp has been renamed lengths_psp. o Bug fixes related to mppm. o Stability improvements. o Version nickname: 'Susana Distancia' NEW FUNCTIONS o relrisk.lpp Method for 'relrisk' for point patterns on a linear network. o bw.relrisklpp Bandwidth selection for relative risk on a network. o densityfun.lpp Method for 'densityfun' for point patterns on a linear network. o addVertices Add new vertices to a network, at locations outside the existing network. o lengths_psp This is the new name of the function 'lengths.psp', which had to be changed because of a conflict with the generic 'lengths'. o densityEqualSplit The equal-split algorithm for kernel density estimation on a network is now visible as a separate function. o densityHeat The heat-equation algorithm for kernel density estimation on a network is now visible as a separate function. It has also been extended to computing leave-one-out density estimates at the data points. o hotrod Compute the heat kernel kappa(u,v) on a one-dimensional line segment. o heatkernelapprox Calculate an approximation to the value of the heat kernel on a network evaluated at the source point, kappa(u,u). SIGNIFICANT USER-VISIBLE CHANGES o nndist.pp3, nndist.ppx, nndist.lpp These functions now recognise the argument 'by' allowing computation of the nearest distance to each group of points. o pairdist.lpp, crossdist.lpp These functions can now handle large networks, using the sparse representation. o density.lpp, densityQuick.lpp Infinite bandwidth (sigma=Inf) is now permitted, and results in a density estimate that is constant over all locations. o as.linnet.psp The resulting network now has an attribute 'camefrom' indicating the provenance of each line segment in the network. o as.linnet.linnet New argument 'maxsize'. o repairNetwork Increased capability of detecting and repairing inconsistencies. o joinVertices New argument 'marks'. o insertVertices Marks attached to the lines of the network are now retained. o as.lpp Accepts more data formats. o iplot, iplot.ppp, iplot.layered, iplot.linnet, iplot.default These interactive plotting functions have been removed from spatstat into a new package 'spatstat.gui' o istat This interactive analysis function has been removed from spatstat into a new package 'spatstat.gui' o crossdist.lpp New argument 'check'. o lengths.psp This function will soon be Deprecated, in favour of the new name 'lengths_psp' o density.lpp Formal arguments changed. No effect on usage. o integral.linim Now handles complex-valued functions. o transect.im New argument 'nsample'. o bw.lppl Accelerated when distance="path". o collapse.fv Recognises the abbreviations used by fvnames() BUG FIXES o density.ppp Edge correction factors were calculated incorrectly when the window was not a rectangle, causing a negative bias in the estimated intensity. [Spotted by Tilman Davies.] Bug introduced in spatstat 1.57-0, october 2018. Fixed. o mppm Internal data were malformed if the interaction was Hardcore() or MultiHard() or a hybrid involving these interactions. This caused various errors when the fitted model was used. Fixed. o mppm Ignored the arguments 'nd' and 'eps' controlling the quadrature scheme. Fixed. o "[.linnet", "[.lpp" In X[W] where W is a window, if a vertex of the network lay exactly on the boundary of W, an edge of length zero was created. Fixed. o valid.ppm Crashed sometimes when applied to the result of subfits(). Fixed. o as.im.densityfun Crashed if argument W was missing. Fixed. o as.linnet.linnet This code could crash the R session, when sparse=FALSE, if there was insufficient memory available to create the matrix of distances between all pairs of network vertices. Fixed. o Summary.linim A spurious warning was generated when the operation any() or all() was applied to a logical-valued image on a network. Fixed. o "[<-.linim" Crashed if the assignment would have replaced some existing NA values. Fixed. CHANGES IN spatstat VERSION 1.63-3 OVERVIEW o Minor changes for compatibility with future versions of R o Minor improvements o Version nickname: "Wet paint" SIGNIFICANT USER-VISIBLE CHANGES o plot.ppp The coordinate axes will be plotted if axes=TRUE. Axis labels xlab, ylab will be plotted if ann=TRUE. CHANGES IN spatstat VERSION 1.63-2 OVERVIEW o Minor changes to satisfy the package checker. o Version nickname: "I'm sorry Dave, I'm afraid I can't do that" CHANGES IN spatstat VERSION 1.63-1 OVERVIEW o Enhancement to plot.linim o Fill missing pixel values by taking the nearest defined pixel value. o Minor improvements and bug fixes. o spatstat now requires spatstat.utils version 1.17-0. o Version nickname: 'Made from recycled electrons' NEW FUNCTIONS o is.linim Tests whether an object belongs to class 'linim'. o nearestValue Given a pixel image on a subset of a rectangle, extend the image to the entire rectangle, using the nearest well-defined pixel value. SIGNIFICANT USER-VISIBLE CHANGES o markconnect Runs faster and handles much larger datasets. o markvario Runs faster and handles much larger datasets. o markcorr, markconnect, markvario The 'weights' can now be an expression to be evaluated, or a function, or a pixel image, as well as a numeric vector. o Smooth.ppp The 'weights' can now be an expression to be evaluated, or a function, or a pixel image, as well as a numeric vector. o rpoislpp If 'lambda' is a list of 'linim' or 'linfun' objects, then the argument L can be omitted. o plot.linim New argument 'fatten' improves visual appearance when style="colour". o plot.im, plot.owin The coordinate axes will be plotted if axes=TRUE. Axis labels xlab, ylab will be plotted if ann=TRUE. o bugfixes If sincedate="all" or sinceversion="all", then all bugs will be listed. o Jfox New argument 'warn.trim' makes it possible to suppress repeated warnings. o requireversion New argument 'fatal' BUG FIXES o predict.rhohat, simulate.rhohat When applied to a 'rhohat' object computed from linear network data (lpp or lppm), there was a warning about the lengths of vectors, and the results were incorrect. Fixed. o predict.rhohat, simulate.rhohat Crashed when applied to a 'rhohat' object computed from *multitype* linear network data (multitype lpp or lppm). Fixed. o Jfox envelope() commands using the summary function 'Jfox' crashed sometimes with a message about illegal spacing of 'r' values. Fixed. o leverage, influence, dfbetas Crashed when applied to an 'ippm' object in which the irregular components of the score, but not the Hessian, were provided by symbolic differentiation. Fixed. o parres Crashed in rare circumstances, when the data did not contain enough useable values to perform smoothing. Fixed. CHANGES IN spatstat VERSION 1.63-0 OVERVIEW o Compute minimum or maximum nearest-neighbour distance between each pair of types in a multitype point pattern. o Important bug fix in simulations of the multitype hard core process. o Numerous improvements and bug fixes. o Deprecated functions have been removed. o Version nickname: "Trees in space" SIGNIFICANT USER-VISIBLE CHANGES o minnndist, maxnndist New argument 'by' makes it possible to find the minimum or maximum nearest neighbour distance between each pair of possible types in a multitype pattern. o beachcolours If 'sealevel' lies outside 'srange', then 'srange' will be extended to include it (without a warning). o split<-.ppp The default for argument 'un' in 'split<-.ppp' now agrees with the default for the same argument in 'split.ppp'. o lineardisc New argument 'add'. Default plotting behaviour has changed. o rmh, rmh.default The printed output of the debugger (invoked by snoop=TRUE) has been improved. o plot.owin New argument 'use.polypath' controls how to plot a filled polygon when it has holes. o plot.profilepl This function has now been documented, and the graphics improved. o erode.owin, dilate.owin These deprecated functions have now been deleted (replaced by erosion.owin and dilation.owin) o delaunay.distance, delaunay.network These deprecated functions have now been deleted (replaced by delaunayDistance, delaunayNetwork) o dirichlet.edges, dirichlet.network, dirichlet.vertices, dirichlet.weights These deprecated functions have now been deleted (replaced by dirichletEdges, dirichletNetwork, dirichletVertices, dirichletWeights) BUG FIXES o rmh, simulate.ppm, MultiHard Simulated realisations of the multitype hard core model were completely incorrect (the interaction was effectively removed, changing the model into a Poisson process). Fixed. o kppm, AIC For kppm models fitted with method='clik2', the resulting value of logLik() was equal to 1/2 of the correct value. This would have affected model comparison using AIC, and model selection using step(). Fixed. o hyperframe Did not correctly handle date-time values (columns of class 'Date', etc). Fixed o rlpp The resulting pattern was unmarked even when it should have been multitype. Fixed. o idw Estimates were zero if 'se=TRUE' and 'power != 2' and 'at="pixels"'. Fixed. o model.matrix.mppm Sometimes returned a matrix with the wrong number of rows. Fixed. o nncross.ppp Format of output was incorrect if X was an empty pattern. Fixed. o rmh, rmh.default For a marked point process, the debugger did not display the marks. (The rmh debugger is invoked by calling rmh with snoop=TRUE). Fixed. o pairs.im, pairs.linim The argument 'labels' was sometimes ignored. Fixed. o as.im.data.frame Results were incorrect for factor-valued data. Fixed. o relrisk.ppp Crashed if there were more than 2 types of points and method = "leastsquares" or "weightedleastsquares". Fixed. o as.im.nnfun Crashed when applied to a function generated by nnfun.psp. Fixed. o diagnose.ppm Crashed for some models with an error message from 'beachcolours'. Fixed. o predict.rho2hat Crashed if one of the original covariates was a function rather than an image. Fixed. o lineardisc Crashed in some graphics environments. Fixed. o lineardisc Crashed if the network segments had marks. Fixed. o rmh Crashed for multitype models if 'nsave' was specified. Fixed. o vcov.mppm, simulate.mppm Crashed for multitype models with a hardcore interaction component. Fixed. o effectfun Crashed if 'covname' was not the name of a covariate appearing in the model and was not one of the reserved names 'x', 'y', 'marks'. Fixed. CHANGES IN spatstat VERSION 1.62-2 OVERVIEW o Urgent bug fixes and workarounds. o Version nickname: "Shape-shifting lizard" SIGNIFICANT USER-VISIBLE CHANGES o colourmap Argument 'col' is now permitted to have length 1, representing a colour map in which all values are mapped to the same colour. o lut Argument 'outputs' is now permitted to have length 1, representing a lookup table in which all inputs are mapped to the same output value. BUG FIXES o envelope Crashed sometimes, with a message about unrecognised arguments, when applied to a summary function created by the user. Fixed. CHANGES IN spatstat VERSION 1.62-1 OVERVIEW o We thank Mohammad Ghorbani, Ute Hahn, Abdollah Jalilian, Nestor Luambua, Greg McSwiggan, Annie Mollie and Jakob Gulddahl Rasmussen for contributions. o spatstat now requires spatstat.utils version 1.15-0 and goftest version 1.2-2. o Nearest Neighbour Index function can now return mark values. o Important fix in Ripley isotropic correction. o Index of repulsion strength for determinantal point process models. o Nearest neighbours between two point patterns in any number of dimensions. o More options for handling bad simulation outcomes in envelope(). o Bandwidth selectors warn about extreme values of bandwidth. o Tessellations on a linear network can now have marks. o New functions for simulating point processes on a linear network. o More functions for manipulating tessellations on a linear network. o mppm accepts case weights. o Bug fixes and minor improvements. o Nickname: 'An update which will live in infamy' NEW FUNCTIONS o repul Repulsiveness index for a determinantal point process model. o reach.kppm Reach (interaction distance) for a Cox or cluster point process model. o summary.dppm, print.summary.dppm Summary method for determinantal point process models. o nncross.ppx Nearest neighbours between two point patterns in any number of dimensions. o uniquemap.matrix Method for uniquemap for matrices. o repairNetwork Detect and repair inconsistencies in internal data in a linnet or lpp object. o marks<-.lintess, unmark.lintess Assign marks to the tiles of a tessellation on a linear network. o marks.lintess Extract the marks of the tiles of a tessellation on a linear network. o tilenames.lintess Extract the names of the tiles in a tessellation on a linear network o tilenames<-.lintess Change the names of the tiles in a tessellation on a linear network o nobjects.lintess Count the number of tiles in a tessellation on a linear network o as.data.frame.lintess Convert a tessellation on a linear network into a data frame. o rcelllpp Simulate the cell point process on a linear network o rSwitzerlpp Simulate the Switzer-type point process on a linear network o intersect.lintess Form the intersection of two tessellations on a linear network o chop.linnet Divide a linear network into tiles using infinite lines SIGNIFICANT USER-VISIBLE CHANGES o lintess New argument 'marks' Tessellations can now have marks. o tilenames, tilenames<- These functions are now generic, with methods for 'tess' and 'lintess' o mppm New argument 'weights' specifies case weights for each row of data. o unstack.lintess Now handles marks. o plot.lintess Modified to display the marks attached to the tiles. Changed options: style=c("colour", "width", "image"). o as.linfun.lintess The default function values are the marks. o print.lintess, summary.lintess, print.summary.lintess Output now includes information about marks. o nnfun.ppp, nnfun.psp, nnfun.lpp New argument 'value' specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. o envelope.ppp, envelope.ppm, envelope.kppm, envelope.pp3 New arguments 'rejectNA' and 'silent'. o envelope.lpp, envelope.lppm New arguments 'maxnerr', 'rejectNA' and 'silent'. o plot.psp New argument 'col' gives control over the colour map representing the values of marks attached to the segments. o plot.im Some warnings are suppressed when do.plot=FALSE. o plot.linim New explicit argument 'box' determines whether to plot a bounding box. Default is now FALSE in all cases. o Kest, Kinhom, pcf, pcfinhom, edge.Ripley Calculation of isotropic edge correction for polygonal windows has changed slightly. Results are believed to be more accurate. Computation has been accelerated by about 20 percent in typical cases. o bw.diggle, bw.ppl, bw.lppl, bw.pcf, bw.CvL, bw.voronoi A warning is issued if the optimal value of the cross-validation criterion occurs at an endpoint of the search interval. New argument 'warn'. o mad.test, dclf.test, dg.test, bits.test Function values which are infinite, NA or NaN are now ignored in the calculation (with a warning) instead of causing an error. Warning messages are more detailed. o rmhcontrol, rmh The parameter 'nsave' can now be a vector of integers. o diagnose.ppm Accelerated, when type="inverse", for models without a hard core. o uniquemap.data.frame Accelerated for some cases. o vcov.ppm, vcov.mppm New argument 'nacoef.action' specifies what to do if some of the fitted coefficients are NA, NaN or Inf. o Lest, Linhom, Ldot, Lcross, Ldot.inhom, Lcross.inhom These summary functions now have an explicit argument 'correction'. Behaviour is unchanged. o bugfixes Arguments sinceversion="book" or sincedate="book" are interpreted to give all bugs reported after publication of the spatstat book. o cbind.hyperframe, rbind.hyperframe The result now retains the row.names of the original arguments. o print.summary.owin More information is printed. o append.psp Arguments may be NULL. o as.psp Now permits a data frame of marks to have only one column, instead of coercing it to a vector. BUG FIXES o as.linnet.psp Sometimes produced a network with duplicated segments. [Such objects can be repaired using 'repairNetwork'.] Fixed. o edge.Ripley, Kest, Kinhom Isotropic correction weights for polygonal windows were sometimes incorrect for small radius 'r' if the polygon contained many small segments [spotted by Annie Mollie] or if the polygon was very long and thin [spotted by Nestor Luambua]. Problem arose in spatstat 1.60-0. Fixed. o lppm Did not correctly handle the case where the left-hand side of the formula is the name of an entry in the 'data' argument. Fixed. o plot.lpp Did not correctly handle the argument 'which.marks'. Fixed. o plot.im Did not correctly handle the argument 'ribargs$at'. Fixed. o density.lpp Sometimes requested a larger value of 'iterMax' but ignored it. Fixed. o [.linnet, [.lpp Crashed if x contained inconsistent internal data (when index 'j' was a window, and snip=TRUE). Fixed. o plot.linim Crashed if the pixel values were complex numbers. Fixed. o plot.linfun Crashed if the function values were complex numbers. Fixed. o integral.linim, mean.linim Crashed if the image had logical values. Fixed. o Re, Im, Arg, Mod, Conj For pixel images on a linear network (class 'linim') these operations crashed if the pixel values were complex numbers. o studpermu.test Crashed if the hyperframe was very large. Fixed. o studpermu.test Crashed in some cases when the simulated functions were incompatible with each other, due to the use of different edge corrections. Fixed. o vcov.ppm, print.ppm, summary.ppm Crashed in some cases if the fitted coefficients were NA. Fixed. o quantess.owin, quantess.ppp, quantess.im Crashed in some cases because the tile labels were not unique. Fixed. o plot.pp3 Did not correctly handle graphical arguments ('col', 'pch', 'cex') if they were vectors [Spotted by Abdollah Jalilian]. Fixed. o shift.linnet Generated a spurious warning. Fixed. o density.lpp, lixellate Crashed in some cases when a data point was exactly at a vertex. [Spotted by Jakob Gulddahl Rasmussen.] Fixed. o plot.linim Crashed (when style="width") if any pixel values were NaN. Fixed. o Fest, Jest, Jdot, Jcross, Hest, Iest, rectcontact envelope() commands using one of these summary functions crashed sometimes with a message about illegal spacing of 'r' values. Fixed. o plot.linnet, plot.psp Ignored argument 'col' when style="width". Fixed. o rshift.psp Crashed if X had a data frame of marks. [Spotted by Ute Hahn.] Fixed. o Kscaled Crashed if Ripley's isotropic edge correction was selected but the translation edge correction was not. Fixed. CHANGES IN spatstat VERSION 1.61-0 OVERVIEW o We thank Jordan Brown, Tilman Davies and Greg McSwiggan for contributions. o Fast kernel estimation on a linear network using 2D kernels. o Nonparametric maximum likelihood estimation of 'rho'. o Extension of Scott's rule for bandwidth selection. o Cross-validated bandwidth selection on a linear network. o More support for character-valued images. o Random thinning of clumps. o Bug fixes and minor improvements. o Nickname: 'Puppy zoomies' NEW FUNCTIONS o densityQuick.lpp Fast kernel estimator of point process intensity on a network using 2D smoothing kernel. o bw.scott.iso Isotropic version of Scott's rule (for point patterns in any dimension). o data.lppm Extract the original data point pattern from a fitted model of class 'lppm'. o rthinclumps Divide a spatial region into clumps and randomly delete some of them. o dimnames.hyperframe, dimnames<-.hyperframe Methods for extracting and changing the 'dimnames' of a hyperframe. SIGNIFICANT USER-VISIBLE CHANGES o rhohat Estimation by nonparametric maximum likelihood is now supported, assuming the intensity is a monotone function of the covariate. New options: smoother="increasing" and smoother="decreasing". o density.lpp New argument 'distance' offers a choice of different kernel methods. o bw.scott Now handles point patterns of any dimension. New arguments 'isotropic' and 'd'. o bw.ppl New argument 'shortcut' allows faster computation. Additional arguments '...' are now passed to density.ppp. o [<-.im New argument 'drop' controls behaviour when indices are missing as in 'x[] <- value' o mppm Now supports 'self-starting' interactions. o as.im New argument 'stringsAsFactors' is recognised by many methods. It enables the creation of character-string-valued images. o plot.im Axes are now prevented from extending outside the image rectangle. o plot.im New argument 'zap'. o blur New argument 'kernel'. o Smooth.im New argument 'kernel'. o quadrat.test New argument 'df.est'. o edge.Ripley Numerical stability has been improved on some platforms. Results may have changed in marginal cases (e.g. where the circle of radius r centred at X is tangent to an edge of the polygonal window). o rownames, rownames<-, colnames, colnames<- These operations now work for hyperframes. o quadrat.test Improved labelling of test when argument 'CR' is given. o plot.pppmatching This existing function now has a help file. New argument 'adjust'. o solist, is.sob, lintess Objects of class 'lintess' are now recognised as 2D spatial objects for the purposes of solist() and is.sob(). o as.linfun.lintess Functions created by as.linfun.lintess() now generate better output when the function is printed. BUG FIXES o densityVoronoi.lpp Did not correctly handle patterns containing duplicated points. Fixed. o quadrat.test Argument 'CR' was ignored when method="MonteCarlo". Fixed. o localKcross.inhom, localLcross.inhom Argument lambdaX was ignored. Fixed. o "[.linim" Factor values were erroneously converted to integers, in some cases. Fixed. o "[<-.hyperframe" Did not handle some classes of objects correctly. Fixed. o effectfun In a multitype point process model, effectfun() ignored any user-specified value of 'marks'. Fixed. o as.linim.linfun Additional arguments (other than spatial coordinates) were ignored. Fixed. o plot.solist Display output was mangled if one entry in the list was an object that would normally generate multiple panels of graphics, such as an 'lpp' or 'tess' object with multiple columns of marks, or a marked 'msr', 'leverage.ppm' or 'influence.ppm' object. Fixed. o plot.lpp Return value was mangled when x had multiple columns of marks. Fixed. o colourtable Crashed in some cases when 'breaks' was given. Fixed. o rLGCP Crashed if 'win' was not a rectangle and 'mu' was not a constant. Fixed. o intersect.tess Crashed if 'Y' was a window object and 'keepmarks=TRUE'. Fixed. o envelope.lppm Crashed if argument 'simulate' was given. Fixed. o unstack.solist Did not correctly handle objects of class 'lintess'. o unstack.solist Did not correctly handle objects of class 'tess' if they had multiple columns of marks. o plot.pppmatching Issued spurious warnings about unrecognised plot arguments. Fixed. o plot.lintess Issued spurious warnings about unrecognised plot arguments. Fixed. o shift.lpp, rotate.lpp Issued spurious warnings if argument 'origin' was given. Fixed. CHANGES IN spatstat VERSION 1.60-1 OVERVIEW o Version number incremented for administrative reasons. CHANGES IN spatstat VERSION 1.60-0 OVERVIEW o We thank Ottmar Cronie, Tilman Davies, Andrew Hardegen, Tom Lawrence, Robin Milne, Mehdi Moradi, Gopalan Nair, Tim Pollington and Suman Rakshit for contributions. o Random thinning and random labelling of spatial patterns extended to different types of pattern. o Confidence intervals for multitype K functions. o Envelopes for balanced two-stage test o Accelerated some code. o Minor bug fixes and improvements. o Package built under R 3.6.0. o Version nickname: 'Swinging Sixties' NEW FUNCTIONS o bits.envelope Global simulation envelope corresponding to bits.test, the balanced independent two-stage Monte Carlo test. o extrapolate.psp Extrapolate line segments to obtain infinite lines. o uniquemap Map duplicate points to unique representatives. Generic with methods for ppp, lpp, ppx o uniquemap.data.frame Map duplicate rows to unique representatives o localKcross, localLcross, localKdot, localLdot, localKcross.inhom, localLcross.inhom Multitype local K functions. SIGNIFICANT USER-VISIBLE CHANGES o lohboot Now works for multitype K functions Kcross, Kdot, Lcross, Ldot, Kcross.inhom, Lcross.inhom. o Kinhom, pcfinhom Leave-one-out calculation is implemented when 'lambda' is a fitted model of class 'dppm'. o Kcross.inhom, Kdot.inhom, Lcross.inhom, Ldot.inhom Leave-one-out calculation is implemented when 'lambdaX' is a fitted model of class 'dppm'. o rthin, rlabel These functions now work on point patterns of all classes (ppp, lpp, pp3, ppx) and line segment patterns (psp). o bw.abram New argument 'smoother' determines how the pilot estimate is computed. Formal arguments rearranged. o plot.im New argument 'riblab'. o rlabel New arguments 'nsim' and 'drop'. o localK, localKinhom New argument 'rmax'. o rLGCP Accelerated. o anyDuplicated.ppp Accelerated. o duplicated.ppp Accelerated, in most cases. o simulate.kppm Accelerated, for LGCP models. o predict.ppm Accelerated, for models fitted with method="VBlogi" o print.rmhmodel Output improved. BUG FIXES o plot.linim, plot.linfun Not all the entries of 'leg.args' were passed to text.default. Fixed. o densityVoronoi.ppp Did not correctly handle patterns containing duplicated points. Fixed. o markcorr The argument 'correction="none"' did not generate any results. [Spotted by Tim Pollington.] Fixed. o names<-.fv Did not adjust the plotting formula, so that a subsequent call to plot.fv would complain about missing variables. Fixed. o im.apply Crashed if 'FUN' returned factor values. Fixed. o stienenSet Crashed if the data contained duplicated points. Fixed. o predict.ppm, effectfun Crashed in some cases, with the message 'ncol(x)=nrow(v) is not TRUE'. Fixed. o parres Crashed in some cases, with the message 'logical subscript too long'. Fixed. o dclf.test, mad.test, dg.test, bits.test Crashed in some cases if the summary function values were infinite or NA. Fixed. CHANGES IN spatstat VERSION 1.59-0 OVERVIEW o We thank Lucia Cobo Sanchez, Tilman Davies, Maximilian Hesselbarth, Kassel Hingee, Mehdi Moradi, Suman Rakshit, Jan Sulavik and Luke Yates for contributions. o Extensions to adaptive intensity estimators. o 'Dartboard' tessellation using polar coordinates. o Performance improvements. o Minor improvements and bug fixes. o Version nickname: "J'ai omis les oeufs de caille" NEW FUNCTIONS o polartess Tessellation using polar coordinates. o bw.abram Variable bandwidths for adaptive smoothing, using Abramson's Rule. o densityAdaptiveKernel Calculates adaptive estimate of point process intensity using variable-bandwidth kernel estimation. o densityVoronoi, densityVoronoi.ppp Calculates adaptive estimate of point process intensity using tessellation methods. This is an extension of the old function adaptive.density. o densityVoronoi.lpp Voronoi or smoothed Voronoi estimator of intensity for point pattern on a linear network. o coords.quad New method for 'coords', to extract the coordinates of the points in a quadrature scheme. o lineartileindex Low-level function to classify points on a linear network according to which tile of a tessellation they fall inside. SIGNIFICANT USER-VISIBLE CHANGES o latest.news Now prints news documentation for the current major version, by default. New argument 'major'. o quantess The covariate Z can now be "rad" or "ang" representing polar coordinates. New argument 'origin' specifies the origin of polar coordinates. New argument 'eps' controls accuracy of calculation. o envelope The argument 'simulate' can now be a function (such as 'rlabel'). The function will be applied repeatedly to the original data Y, and should yield point patterns of the same kind. o adaptive.density This function can now perform adaptive estimation by two methods: either tessellation-based methods or variable-bandwidth kernel estimation. The calculations are performed by either 'densityVoronoi' or 'densityAdaptiveKernel'. o densityVoronoi This performs the calculations of the old function 'adaptive.density'. New argument 'fixed' specifies the subsampling. New argument 'method' allows a choice between the original algorithm and the recently described 'smoothed Voronoi' estimator. Default value of 'f' changed to 'f=1'. o pcf.ppp Now accepts correction="none". o pairorient Default edge corrections now include 'bord.modif'. o funxy Functions of class 'funxy' can now be applied to quadrature schemes. o lohboot Computation accelerated when the window is a rectangle. o nncorr, nnmean, nnvario New argument 'na.action'. o pp3 New argument 'marks'. o clusterfit New argument 'verbose'. o beachcolours, beachcolourmap Improved positioning of the yellow colour band. o linearK, linearpcf, linearKdot, linearKcross, linearpcfcross, linearpcfdot Computation accelerated for networks which are not connected. o as.linnet.psp Computation accelerated. o as.linfun.lintess Computation accelerated. o selfcut.psp Computation accelerated. The result now has an attribute "camefrom" indicating the provenance of each segment in the result. o bw.stoyan The rule has been modified so that, if the pattern is empty, it is now treated as if it contained 1 point, so that a finite bandwidth value is returned. o rebound.owin Now preserves unitnames of the objects. o rescale.owin, rescale.ppp, rescale.psp The geometrical type of the window is now preserved in all cases (previously if the window was polygonal but was equivalent to a rectangle, the rescaled window was a rectangle). o shift.im, shift.owin, shift.ppp, shift.psp More options for the argument 'origin'. o nnwhich.ppp, nnwhich.default Accelerated, in the case k > 1. o is.subset.owin Improved robustness against numerical error. o plot.im Improved behaviour when the pixel values are almost constant. o Finhom, Ginhom, Jinhom A warning is issued if bias is likely to occur because of undersmoothing. New arguments 'warn.bias' and 'savelambda'. o plot.colourmap Now handles a colourmap defined on an interval of length zero. o ewcdf Computation accelerated. New arguments 'normalise' and 'adjust'. The result does not inherit class 'ecdf' if normalise=FALSE. o spatialcdf Computation accelerated. The result does not inherit class 'ecdf' if normalise=FALSE. o effectfun New argument 'nvalues'. o parres The argument 'covariate' may be omitted if the model involves only one covariate. o alltypes If 'envelope=TRUE' and the envelope computation reaches the maximum permitted number of errors (maxnerr) in evaluating the summary function for the simulated patterns, then instead of triggering a fatal error, the envelope limits will be set to NA. o simulate.kppm Additional arguments '...' are now passed to the function that performs the simulation. BUG FIXES o spatialcdf The argument 'weights' was ignored in some cases. Fixed. o ppp Points inside the window were erroneously rejected as lying outside the window, if the window was a polygon equivalent to a rectangle with sides longer than 10^6 units. Fixed. o inside.owin All results were FALSE if the window was a polygon equivalent to a rectangle with sides longer than 10^6 units. Fixed. o sumouter Result was incorrect (all entries were zero) if 'w' was missing and 'y' was given. Fixed. o extractbranch.lpp Point pattern coordinates were sometimes erroneously set to NA. Fixed. o beachcolours, beachcolourmap The number of colours was not always equal to 'ncolours'. [Spotted by Tilman Davies.] Fixed. o is.subset.owin Sometimes gave the wrong answer for polygonal windows due to numerical rounding error. Fixed. o update.kppm Crashed if the updating information was a point pattern and the original model call did not include a formula. Spotted by Luke Yates. Fixed. o incircle, inpoint Crashed if the window was extremely thin. Fixed. o effectfun Crashed in some obscure cases. Fixed. o closepairs.pp3 Crashed if distinct=FALSE and what="all". Fixed. o update.ippm Crashed if the model was fitted using method="logi". Fixed. o plot.msr Crashed sometimes if x was multitype and multiplot=FALSE. Fixed. o anova.mppm Crashed if applied to a single model, unless the current environment was the global environment. Fixed. o lurking.mppm If 'covariate' was a list of images, the code crashed sometimes with message 'Fisher information is singular'. Fixed. o im Crashed if 'mat' was a 1-dimensional table (class 'table'). Fixed. o dirichlet Crashed if the pattern was empty or contained only 1 point. Fixed. o rjitter Crashed if the pattern contained 0 or 1 points and the argument 'radius' was not specified. Fixed. o quantess.owin Crashed if Z was a function(x,y). Fixed. o quadrat.test The p-value was NA if one of the observed counts was zero, for the Cressie-Read tests with CR not equal to 1 or -1. Fixed. o quadratcount.ppp Sometimes issued an incorrect warning that data points were outside the tessellation, when 'tess' was a tessellation represented by a pixel image. Fixed. o as.linim.linfun Factor-valued functions were converted to integer-valued images. Spotted by Suman Rakshit. Fixed. o plot.linfun Did not display factor-valued functions correctly. Spotted by Suman Rakshit. Fixed. o dclf.test, mad.test Crashed, in rare cases, when applied to an 'envelope' object. Spotted by Jan Sulavik. Fixed. o plot.spatialcdf, plot.ewcdf A horizontal line was plotted at height 1, even if the CDF was unnormalised. Fixed. o plot.tess The names of the tiles were sometimes re-ordered in the legend. Fixed. o rThomas, rMatClust, rCauchy, rVarGamma If the simulation window was not a rectangle, the attribute 'Lambda' was a numeric vector, rather than a pixel image. Fixed. CHANGES IN spatstat VERSION 1.58-2 OVERVIEW o Venn diagram tessellation o Internal bug fixes. NEW FUNCTIONS o venn.tess Venn diagram tessellation. CHANGES IN spatstat VERSION 1.58-1 OVERVIEW o Bug fixes. o Version nickname: "Compliment Sandwich" BUG FIXES o lpp Did not correctly detect some situations where the input data are invalid. Fixed. o lurking.ppp, lurking.ppm Did not correctly detect some situations where the input data are invalid. Fixed. CHANGES IN spatstat VERSION 1.58-0 OVERVIEW o We thank Andrew Bevan, Hamidreza Heydarian and Andrew P Webster for contributions. o Mark-mark scatter plot. o Standard error calculation for inverse-distance weighting o Minor improvements and extensions. o Version nickname: 'Drop Bear' NEW FUNCTIONS o markmarkscatter Mark-mark scatterplot SIGNIFICANT USER-VISIBLE CHANGES o idw Standard errors can now be calculated by setting 'se=TRUE'. o im.apply Computation accelerated, especially when NA's are absent. New arguments 'fun.handles.na' and 'check'. o kppm, dppm, clusterfit Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments 'q,p,rmax,rmin' now take precedence over entries with the same names in the list 'ctrl'. o rotmean Improved algorithm stability o summary.kppm Prints more information about algorithm convergence. o closepairs.pp3, crosspairs.pp3 Argument 'what' can take the value "ijd" o plot.onearrow Graphical parameters, specified when the object was created, are now taken as the defaults for graphical parameters to the plot. BUG FIXES o rotmean When result="im" the resulting image did not have the same dimensions as the input. [Spotted by Hamidreza Heydarian.] Fixed. o rotmean If argument 'origin' was given, and if result="im" was specified, the resulting image was wrongly displaced. Fixed. o runifpointx Result was mangled when n=0 or n=1. Fixed. CHANGES IN spatstat VERSION 1.57-1 OVERVIEW o Bug fixes. o Version nickname: 'Cartoon Physics' BUG FIXES o multiplicity.default Some entries of the result were incorrectly set to 1. Fixed. o anova.ppm Crashed if adjust=TRUE and the models were fitted with use.gam=TRUE. Fixed. CHANGES IN spatstat VERSION 1.57-0 OVERVIEW o We thank Corey Anderson, Ryan Arellano, Hadrien Commenges, Ottmar Cronie, Tilman Davies, Maximilian Hesselbarth, Kassel Hingee, Tomas Lazauskas, Marie-Colette van Lieshout, Rasmus Waagepetersen and 'daitakahashi' for contributions. o Cronie-van Lieshout bandwidth selection. o Smoothing functions handle non-Gaussian kernels. o Infinite smoothing bandwidths permitted. o Positive confidence limits for rhohat. o Improved bivariate interpolation. o subset() method for line segment patterns. o Important bug fixes in rthin and density.ppp o Minor bug fixes and improvements. o Version nickname: 'Zombie apocalypse' NEW FUNCTIONS o bw.CvL Cronie-Van Lieshout bandwidth selection for density estimation. [Contributed by Ottmar Cronie and Marie-Colette van Lieshout.] o subset.psp Method for 'subset' for line segment patterns. SIGNIFICANT USER-VISIBLE CHANGES o densityfun.ppp, Smoothfun.ppp, Smooth.ppp These commands now handle non-Gaussian kernels. o density.ppp, relrisk.ppp, Smooth.ppp, densityfun.ppp, Smoothfun.ppp Argument 'sigma' can be infinite. o interp.im New argument 'bilinear' specifies the choice of interpolation rule. o rhohat Methods for rhohat have a new argument 'positiveCI' specifying whether confidence limits should be positive. o plot.colourmap New argument 'increasing' specifies whether the colours are displayed in order left-to-right/bottom-to-top. Changed default behaviour for discrete colour maps when vertical=FALSE. o split.ppx Argument 'f' can be a logical vector. o relrisk.ppp If se=TRUE and at="pixels", the result belongs to class 'solist'. o imcov, setcov, convolve.im The name of the unit of length is preserved. o density.ppp Slightly accelerated for non-Gaussian kernels. o bw.scott The two bandwidth values in the result now have names ('sigma.x' and 'sigma.y'). o pairdist.default Now checks whether the data are valid 2-dimensional coordinates. o pixellate.ppp New argument 'savemap' o rtemper New argument 'track'. Code runs slightly faster. o eval.im, eval.linim New argument 'warn'. o Kres, Kcom, Gcom, psstG If any of the calculated weights for the summary function are infinite or NA, they are reset to zero, with a warning, instead of a fatal error. BUG FIXES o rthin If P was close to 1, the result was sometimes an empty point pattern when it should have been identical to X. [Spotted by Maximilian Hesselbarth.] Fixed. o density.ppp Standard error calculations were incorrect when sigma was a single numeric value. The output was equal to 'sqrt(sigma)' times the correct answer. Fixed. o density.ppp Result was incorrect for non-Gaussian kernels when at="points" and leaveoneout=FALSE. Fixed. o density.ppp Did not pass additional arguments "..." to a user-supplied kernel function, in some cases. Fixed. o as.im.function, as.im.funxy If the function values were factor values and the window was not a rectangle, the result was an image with all pixel values NA. [Spotted by Corey Anderson.] Fixed. o plot.funxy If the function values were factor values and the window was not a rectangle, the plot was empty. [Spotted by Corey Anderson.] Fixed. o nnorient Crashed if the border correction did not retain any data points. [Spotted by Tomas Lazauskas.] Fixed. o linim Crashed in some cases with a message about unitnames. Fixed. o density.lpp Default value of 'dx' was sometimes incorrect. Fixed. o rMatClust, rThomas, rCauchy, rVarGamma Issued a spurious warning about bandwidth selection when saveLambda=TRUE. Fixed. o density.ppp Issued a spurious warning about bandwidth selection when 'kernel' was a user-supplied function. Fixed. o clusterfield.function Issued a spurious warning about bandwidth selection. Fixed. o relrisk.ppp Issued a spurious warning if the argument 'case' or 'control' was given, for a bivariate point pattern. Fixed. o superimpose.ppp If 'W' was a character string or function, the behaviour was not exactly as described in the help file. Fixed. o plot.psp If the marks were factor values, the colour map was displayed upside down. Fixed. o eval.fv If one of the 'fv' objects included ratio information (class 'rat') then this was erroneously retained, in some cases. Fixed. o linearKcross Crashed (with a message about a missing value of 'correction') if there were no pairs of points to count. Fixed. o envelope.lpp Crashed (randomly) when fix.n=TRUE and fix.marks=TRUE. Fixed. CHANGES IN spatstat VERSION 1.56-1 OVERVIEW o We thank Agustin Lobo for contributions. o Improvements to infrastructure. o Bug fixes. o Version nickname: "Invisible Friend" NEW FUNCTIONS o as.im.expression New method for 'as.im' for expressions. o flipxy.tess Method for 'flipxy' for tessellations. SIGNIFICANT USER-VISIBLE CHANGES o sdr This is now a generic function, with a method for class 'ppp'. o pointsOnLines The result now has an attribute named "map" which maps each point to its parent line segment. o summary.lpp Improved output. o intersect.owin Argument 'fatal' now defaults to FALSE. o quadrature schemes (class "quad" and "logiquad") Improved print and summary methods. BUG FIXES o cut.lpp Crashed if the marks were a data frame or hyperframe. Fixed. o summary.lpp, print.summary.lpp Output was garbled if the marks were a data frame or hyperframe. Fixed. o integral.linim Crashed if the function had NA values. Fixed. o Tstat Crashed if ratio=TRUE. Fixed. o intersect.owin Ignored argument 'fatal' in some cases. [Spotted by Agustin Lobo.] Fixed. o plot.tess Crashed if do.col=TRUE and 'values' was a factor. Fixed. o pcf.ppp Crashed if 'domain' was given and ratio=TRUE. Fixed. o "[<-.sparse3Darray" Crashed if 'value' was one-dimensional and the indices i, j, k specified a two-dimensional subset of x. Fixed. o plot.quad Crashed if tiles=TRUE for a quadrature scheme created by quadscheme(method="dirichlet", exact=TRUE). Fixed. o bugtable Crashed if there were no bugs! Fixed. o sparse array code An array bounds violation (segmentation fault) could occur. Fixed. o internal code Numerous internal bugs have been fixed. CHANGES IN spatstat VERSION 1.56-0 OVERVIEW o We thank Sebastian Meyer and Suman Rakshit for contributions. o Kernel estimate of intensity as a function(x,y) o Extract discrete and continuous components of a measure. o Improvements and extensions to leverage and influence code. o Plot a line segment pattern using line widths. o Find connected components of each tile in a tessellation. o Geometrical operations on 'distfun' objects. o Join vertices in a linear network. o Distance map and contact distribution for rectangular structuring element. o An infinite number of infinitesimal bugs has been detected and fixed. o Version nickname: "Bondi Tram" NEW FUNCTIONS o densityfun, densityfun.ppp Compute a kernel estimate of intensity of a point pattern and return it as a function of spatial location. o as.im.densityfun Convert function(x,y) to a pixel image. o measureDiscrete, measureContinuous Extract the discrete and continuous components of a measure. o connected.tess Find connected components of each tile in a tessellation and make a new tessellation composed of these pieces. o dffit.ppm Effect change diagnostic DFFIT for spatial point process models. o shift.distfun, rotate.distfun, reflect.distfun, flipxy.distfun, affine.distfun, scalardilate.distfun Methods for geometrical operations on 'distfun' objects. o rescale.distfun Change the unit of length in a 'distfun' object. o plot.indicfun Plot method for indicator functions created by as.function.owin. o Smooth.leverage.ppm, Smooth.influence.ppm Smooth a leverage function or an influence measure. o integral.leverage.ppm, integral.influence.ppm Compute the integral of a leverage function or an influence measure. o mean.leverage.ppm Compute the mean value of a leverage function. o rectdistmap Distance map using rectangular metric. o rectcontact Contact distribution function using rectangular structuring element. o joinVertices Join specified vertices in a linear network. SIGNIFICANT USER-VISIBLE CHANGES o plot.psp Segments can be plotted with widths proportional to their mark values. New argument 'style'. o msr Infinite and NA values are now detected (if check=TRUE) and are reset to zero, with a warning. o leverage.ppm, influence.ppm, dfbetas.ppm Faster computation in some cases. o as.im.nnfun, as.im.smoothfun New argument 'approx' chooses between a fast, approximate algorithm and a slow, exact algorithm. o cdf.test More jittering is applied when jitter=TRUE. Warnings about tied values should not occur any more. o plot.im Improved behaviour when all pixel values are NA. o plot.tess Now generates a separate plot panel for each column of marks, if do.col=TRUE. New argument 'multiplot'. o pcfinhom Now handles correction='good' o solist New argument '.NameBase' o runifpointOnLines, rpoisppOnLines New argument 'drop' o plot.studpermutest This existing function now has a help file. o bdist.points Accelerated, for polygonal windows (thanks to Sebastian Meyer). o linnet When argument 'edges' is specified, the code now checks whether any edges are duplicated. BUG FIXES o kernel.moment Result was incorrect for kernel="cosine" and kernel="optcosine". Fixed. o "[.msr" Format was mangled if the subset contained exactly one quadrature point. Fixed. o tess If a list of tiles was given, and the tiles were pixel images or masks, their pixel resolutions were ignored, and reset to the default 128x128. Fixed. o plot.linim Ignored argument 'legend' when style="colour". [Spotted by Suman Rakshit.] Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm Crashed sometimes if the model was fitted by method="logi". Fixed. o Smooth.ppp Crashed with some combinations of the arguments varcov and weights when X had several columns of marks. Fixed. o plot.tess Crashed sometimes when do.col=TRUE and values=NULL. Fixed. o dilation.ppp Crashed if polygonal=FALSE. o plot.tess Ignored the marks of a tessellation defined by a pixel image. Fixed. o predict.ppm Crashed when computing a confidence interval or prediction interval if 'window' was a tessellation. Fixed. o tiles names(tiles(x)) was not always identical to tilenames(x). Fixed. o model.images.lppm Crashed in many cases. Fixed. o model.images.lppm Names or dimnames were missing in some cases. Fixed. o nncross.ppp Result had the wrong format if npoints(X) = 0 and length(what) = 1. (Spotted by Sebastian Meyer). Fixed. o plot.colourmap Crashed if the colourmap was defined on an interval of dates or times. Fixed. o StraussHard, leverage.ppm, influence.ppm, dfbetas.ppm Leverage and influence calculations generated an error when applied to models fitted with the StraussHard interaction. Fixed. o "[.ppp" Crashed if there were several columns of marks, some of which were factors, and drop=TRUE. Fixed. CHANGES IN spatstat VERSION 1.55-1 OVERVIEW o We thank Jens Astrom, Ines Moncada, Mehdi Moradi and Nicholas Read for contributions. o More support for tessellations. o Improved support for linear networks. o Fixed longstanding bug in leverage and influence diagnostics. o Minor improvements and bug fixes. o Version nickname: "Gamble Responsibly" NEW FUNCTIONS o summary.ssf Summary method for a spatially sampled function (class 'ssf'). o unstack.tess Given a tessellation with multiple columns of marks, take the columns one at a time, and return a list of tessellations, each carrying only one of the original columns of marks. SIGNIFICANT USER-VISIBLE CHANGES o plot.tess This plot method can now fill each tile with a different colour. New arguments 'do.col', 'values', 'col' and 'ribargs'. Old argument 'col' has been renamed 'border' for consistency. o integral.linim, integral.linfun Argument 'domain' can now be a tessellation. o integral.ssf Argument 'domain' can now be a tessellation. o as.owin.default Now accepts a structure with entries named 'xmin,ymin,xmax,ymax' in any order. This handles objects of class 'bbox' in the 'sf' package. o as.owin.default Now detects objects of class "SpatialPolygons" and issues a more helpful error message. o pseudoR2.ppm, pseudoR2.lppm The null model now includes any offset terms, by default. [Suggested by Jens Astrom.] New argument 'keepoffset'. o closepairs.ppp New argument 'periodic' o fitted.ppm New argument 'ignore.hardcore'. o predict.ppm New argument 'ignore.hardcore'. o leverage.ppm, influence.ppm, dfbetas.ppm Computation has been vastly accelerated for models with Geyer interaction fitted using isotropic or translation edge corrections. o leverage.ppm, influence.ppm, dfbetas.ppm Virtually all models and edge corrections are now supported, using a "brute force" algorithm. This can be slow in some cases. o cdf.test Monte Carlo test runs faster. o summary.distfun, summary.funxy Pixel resolution can now be controlled. o persp.funxy Improved z-axis label. o plot.ppp Improved placement of symbol legend when argument 'symap' is given. o plot.msr Changed the default rule for bandwidth for smoothing the density. BUG FIXES o nnmark, as.im.ssf if marks(X) was a matrix rather than a data frame, the results were completely incorrect (and had completely wrong format). Fixed. o predict.mppm If the model included random effects, and if the library 'MASS' was not loaded, the predictions were on the log scale (i.e. they were logarithms of the correct values). [Spotted by Nicholas Read.] Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm Calculations were slightly incorrect for models with a hard core. Fixed. o leverage.ppm The mean leverage value (shown as a contour line in plot.leverage.ppm) was slightly incorrect for Gibbs models. Fixed. o Ops.msr If the input data contained an auxiliary pixel image of the density component of the measure (attribute "smoothdensity") this image was not updated; it was copied to the output unchanged. Plots of the resulting measure were incorrect, but calculations with the measure were correct. Fixed. o integral.msr If the result was a matrix, it was the transpose of the correct answer. Fixed. o "[.linim" The result sometimes had the wrong class. Fixed. o "[.linnet" In calculating L[W] where W is a window, the code ignored segments of L that crossed W without having a vertex in W. Fixed. o nnmap Crashed if W = NULL. Fixed. o density.lpp, nncross.lpp Crashed sometimes with an obscure message about "z$which". [Spotted by Ines Moncada.] Fixed. o as.im.distfun Crashed, for the distfun of a point pattern, if approx=FALSE. Fixed. o as.solist Crashed when x was a 'layered' object. Fixed. o linnet Crashed in some trivial cases where there were no points or lines. Fixed. CHANGES IN spatstat VERSION 1.55-0 OVERVIEW o We thank 'AdriMaz' and Nicholas Read for contributions. o Lurking variable plot for models fitted to several point patterns. o Improvements to code for class 'mppm'. o Improvements to leverage and influence diagnostics. o Improved summary information for funxy and distfun objects. o Bug fixes and improvements. o Removed old warnings and deprecated functions. o Nickname: "Stunned Mullet" NEW FUNCTIONS o contour.leverage.ppm Method for 'contour' for leverage functions of class 'leverage.ppm' o lurking New generic function for lurking variable plots. o lurking.ppp, lurking.ppm These are equivalent to the original function 'lurking'. They are now methods for the new generic 'lurking'. o lurking.mppm New method for class 'mppm' Lurking variable plot for models fitted to several point patterns. o print.lurk Prints information about the object returned by the function 'lurking' representing a lurking variable plot. o model.matrix.mppm Method for 'model.matrix' for models of class 'mppm'. o test.crossing.psp, test.selfcrossing.psp Previously undocumented functions for testing whether segments cross. SIGNIFICANT USER-VISIBLE CHANGES o predict.ppm Now recognises the arguments 'dimyx' and 'eps' for specifying the resolution of the grid of prediction points. o leverage.ppm, dfbetas.ppm Increased the default resolution of the pixel images. Spatial resolution can now be controlled by the arguments 'dimyx', 'eps'. o ppmInfluence The result now belongs to class 'ppmInfluence', for which there are methods for 'leverage', 'influence', 'dfbetas' which extract the desired component. o plot.leverage.ppm New argument 'what'. o persp.leverage.ppm New arguments 'zlab' and 'what'. o as.im.leverage.ppm New argument 'what'. o summary.funxy, summary.distfun Printed information now includes a summary of the function values. o lurking.ppm Accelerated. o "[.psp" Accelerated. o clf.test, conspire, bounding.box, ksmooth.ppp, mpl, superimposePSP, eval.hyper, smooth.fv, smooth.ppp, smooth.msr, rtoro, plot.kstest These deprecated functions have now been removed. o bermantest This deprecated function has now been removed. Use berman.test instead. o kstest This deprecated function has now been removed. Use cdf.test instead. o plot.ppp A very old warning, about the interpretation of the mark scale as the circle diameter, is no longer printed. BUG FIXES o nnmap, nnmark Values were incorrect if the resulting pixel image had unequal numbers of rows and columns. Fixed. o vcov.mppm Format was incorrect (rows/columns were omitted) in some cases. Fixed. o model.matrix.ppm, model.frame.ppm Values were sometimes incorrect when applied to the result of subfits(). To be precise, if 'fit' was an mppm object fitted to a hyperframe that included 'design covariates' (covariates that take a constant value in each row of the hyperframe), and if 'futs <- subfits(fit)', then model.matrix(futs[[i]]) gave incorrect values in the columns corresponding to the design covariates. Fixed. o model.matrix.ppm The attribute 'assign' was omitted, in some cases. Fixed. o simulate.dppm, simulate.detpointprocfamily In dimensions higher than 2, the result was shifted so that it was centred at the origin. Fixed. o Smooth.ppp Crashed if geometric=TRUE and there were several columns of marks. Fixed. o simulate.dppm, simulate.detpointprocfamily Crashed if nsim > 1 and the spatial dimension was not equal to 2. Fixed. o plot.leverage.ppm Contour line was annotated, which was not intended. Fixed. o leverage.ppm The leverage function was oversmoothed, when the model was fitted with method="logi". Fixed. CHANGES IN spatstat VERSION 1.54-0 OVERVIEW o We thank Rochelle Constantine, Lily Kozmian-Ledward, Ian Renner and Leigh Torres for contributions. o New dataset 'cetaceans'. o Gamma correction for colour maps and image plots. o Class 'units' has been renamed 'unitname' to avoid package collision. o Bug fix in leverage code o Tighter bounding box for psp, lpp, linnet objects. o Improved layout in plot.solist o Tools to increase colour saturation. o Connected components of a 3D point pattern. o Accelerated computations on linear networks. o Accelerated simulation of determinantal point processes. o Improved printing of 3D point patterns. o Minor corrections to handling of unitnames. o Nickname: 'Vacuous Mission Statement' NEW DATASETS o cetaceans Nine replicates of a marine survey in New Zealand, consisting of recorded sightings of dolphins, whales and other species. Generously contributed by Lily Kozmian-Ledward, Rochelle Constantine and Leigh Torres. NEW FUNCTIONS o to.saturated Convert a colour value to the corresponding fully-saturated colour. o intensity.psp Compute the average total length of segments per unit area. o boundingbox.psp Bounding box for line segment patterns. This produces a tighter bounding box than the previous default behaviour. o boundingbox.lpp Bounding box for point patterns on a linear network. This produces a tighter bounding box than the previous default behaviour. o boundingbox.linnet Bounding box for a linear network. This produces a tighter bounding box than the previous default behaviour. o "Frame<-.default" New default method for assigning bounding frame to a spatial object. o connected.pp3 Connected components of a 3D point pattern. o colouroutputs, "colouroutputs<-" Extract or assign colour values in a colour map. (Documented a previously-existing function) SIGNIFICANT USER-VISIBLE CHANGES o plot.im New argument 'gamma' supports gamma correction of colour maps. New argument 'ncolours' specifies the default number of colours. o colourmap, lut New argument 'gamma' supports gamma correction of colour maps. o plot.solist, plot.anylist New argument 'panel.vpad' controls vertical space for panel title when equal.scales=FALSE. o class 'units' The class 'units' has been renamed 'unitname' to avoid a clash with other packages. o unitname The generic function 'unitname' now returns an object of class 'unitname'. o print.units, summary.units, print.summary.units, as.character.units, compatible.units These methods are now called print.unitname, summary.unitname, print.summary.unitname, as.character.unitname and compatible.unitname. o as.units This function has been renamed 'as.unitname' and now returns an object of class 'unitname'. o rescale.units This method has been renamed 'rescale.unitname' and now returns an object of class 'unitname'. o profilepl New argument 'fast' controls the use of shorcuts. o reload.or.compute New argument 'force'. o pixellate.ppp, pixellate.owin, pixellate.psp New argument 'DivideByPixelArea'. o density.psp New argument 'at' determines locations where the density is evaluated. o as.solist as.solist(x) always returns an object of class 'solist', removing any additional classes. o lineardirichlet Accelerated. o integral.linim Accelerated. o "[.ppp", "[.lpp", "[.psp" In the expression X[W] where W is a window, if X has a unitname but W does not, the result now inherits the unitname of X. o distfun.ppp New argument 'undef'. o print.pp3 More informative output when x is marked. BUG FIXES o leverage.ppm, influence.ppm, dfbetas.ppm Calculations were slightly incorrect for models fitted using the border correction. Fixed. o integral.linim Gave incorrect value in some extreme cases (where many network segments were shorter than one pixel width). Fixed. o update.kppm Did not function correctly when several additional arguments were given. Fixed. o plot.solist Panel titles were cut off, when equal.scales=FALSE (the default). Fixed. o intersection.owin, union.owin, setminus.owin The result sometimes did not inherit the correct 'unitname'. Fixed. CHANGES IN spatstat VERSION 1.53-2 OVERVIEW o We thank Christophe Biscio and Rasmus Waagepetersen for contributions. o Correction to 'lohboot' o Improvements to ppm and update.ppm o Bug fixes and minor improvements. o Nickname: "Quantum Entanglement" NEW FUNCTIONS o fitin.profilepl Extract the fitted interaction from a model fitted by profile likelihood. SIGNIFICANT USER-VISIBLE CHANGES o lohboot Algorithm has been corrected and extended thanks to Christophe Biscio and Rasmus Waagepetersen. New arguments 'block', 'basicboot', 'Vcorrection'. o ppm.ppp, ppm.quad New argument 'clipwin' o update.ppm For the case 'update(model, X)' where X is a point pattern, if the window of X is different from the original window, then the model is re-fitted from scratch (i.e. use.internal=FALSE). o plot.leverage.ppm A contour line showing the average value of leverage is now drawn on the colour ribbon, as well as on the main image. New argument 'args.contour'. BUG FIXES o lohboot Implementation was completely incorrect. [Spotted and fixed by Christophe Biscio and Rasmus Waagepetersen.] Fixed. o update.ppm Did not always work correctly with formulae that included 'polynom()' terms. Fixed. CHANGES IN spatstat VERSION 1.53-1 OVERVIEW o We thank Suman Rakshit for contributions. o Bug fix in plot.linim o Nickname: "Drongo" BUG FIXES o plot.linim Colour map was mangled if log=TRUE. Fixed. CHANGES IN spatstat VERSION 1.53-0 OVERVIEW o We thank Tilman Davies and Mehdi Moradi for contributions. o Numerous bug fixes for linear networks code. o spatstat now requires the sub-package 'spatstat.data' which contains the datasets. o Minor enhancements and bug fixes. o Nickname: "Tinfoil Hat" NEW FUNCTIONS o "[<-.linim" Subset assignment method for pixel images on a linear network. o nnfromvertex Given a point pattern on a linear network, find the nearest data point from each vertex of the network. o tile.lengths Calculate the length of each tile in a tessellation on a network. o text.ppp, text.lpp, text.psp Methods for 'text' for spatial patterns. SIGNIFICANT USER-VISIBLE CHANGES o datasets All datasets installed in 'spatstat' have now been moved into the sub-package 'spatstat.data'. This should not have any effect on normal use. The 'spatstat.data' package is automatically loaded when spatstat is loaded, and the datasets are lazy-loaded so that they are available in the usual way. To list all datasets you now need to type 'data(package="spatstat.data")' o nbfires This dataset now includes information about the different land and sea borders of New Brunswick. o rhohat New argument 'subset' allows computation for a subset of the data. o predict.lppm Argument 'locations' can now be an 'lpp' object. o ewcdf Argument 'weights' can now be NULL. o plot.msr New arguments 'equal.markscale' and 'equal.ribbon'. o plot.im The number of tick marks in the colour ribbon can now be controlled using the argument 'nint' in 'ribargs'. o plot.symbolmap New argument 'nsymbols' controls the number of symbols plotted. o square Handles a common error in the format of the arguments. o [.linim More robust against artefacts. o [.linnet More robust against artefacts when the subset index is a pixel mask. o linim The image Z is now automatically restricted to the network. New argument 'restrict'. o plot.linim When style="width", negative values are plotted in red (by default). New argument 'negative.args' controls this. o plot.linim New argument 'zlim' specifies the range of values to be mapped. o Summary.linim Recognises the argument 'finite' so that range(x, finite=TRUE) works for a linim object x. o identify.psp Improved placement of labels. Arguments can be passed to text.default to control the plotting of labels. o as.polygonal Accelerated when w is a pixel mask. o density.lpp Accelerated in the default case. o Kinhom Stops gracefully if 'lambda' contains any zero values. o print.linim Prints more information. BUG FIXES o with.msr The value of 'atommass' was incorrect, due to a coding error. Fixed. o [.linim Internal data was sometimes corrupted. Fixed. o as.linim The result had incorrect internal format when Window(X) was a mask and one of the arguments 'eps', 'dimyx', 'xy' was present. Fixed. o as.im.im If W was a rectangle or polygonal window, the pixel resolution of the result was determined by the spatstat defaults, rather than being determined by the image argument X. This was contrary to the rule advertised in help(as.im). Fixed. o density.lpp In the 'slow' case (kernel not Gaussian, or continuous=FALSE), occasionally a pixel could incorrectly be assigned the value 1. [Spotted by Mehdi Moradi.] Fixed. o "[.solist" Ignored the "..." arguments in some cases. Fixed. o density.lpp Ignored the resolution arguments 'eps', 'dimyx' in the default case. Fixed. o plot.msr Plotted the panel titles on top of each other, if how="contour". Fixed. o contour.im Plotted the title text at the wrong place when add=TRUE and show.all=TRUE. Fixed. o predict.lppm Crashed if 'locations' was an 'lpp' object. Fixed. o plot.ppp Crashed if the window had height 0 and width 0 and the pattern had several columns of marks. Fixed. o plot.solist Crashed if all panels had windows of height 0 and width 0. Fixed. o linearK, linearKinhom, linearpcf, linearpcfinhom Crashed if the linear network was disconnected and one component of the network contained fewer than 2 points. Fixed. o integral.linim Crashed in some cases. Fixed. o "[.linim" Crashed in some cases. Fixed. CHANGES IN spatstat VERSION 1.52-1 OVERVIEW o Bug fix to satisfy the development version of R. o Nickname: "Apophenia" SIGNIFICANT USER-VISIBLE CHANGES o Ops.imlist Improved the 'names' of the result. BUG FIXES o bw.smoothppp Crashes in R-devel. Fixed. CHANGES IN spatstat VERSION 1.52-0 OVERVIEW o We thank Nicholas Read, Abdollah Jalilian, Suman Rakshit, Dominic Schuhmacher and Rasmus Waagepetersen for contributions. o Important bug fixes. o Now handles disconnected linear networks. o Effect function is now available for all types of fitted model. o A model can be fitted or re-fitted to a sub-region of data. o More support for measures. o 'Pool' operations improved. o Geometric-mean smoothing. o Changed algorithm defaults in ippm. o Version nickname: "Rudimentary Lathe" NEW FUNCTIONS o as.data.frame.envelope Extract function data from an envelope object, including the functions for the simulated data ('simfuns') if they were saved. o is.connected, is.connected.default, is.connected.linnet Determines whether a spatial object consists of one topologically connected piece, or several pieces. o is.connected.ppp Determines whether a point pattern is connected after all pairs of points closer than distance R are joined. o hist.funxy Histogram of values of a spatial function. o model.matrix.ippm Method for 'model.matrix' which allows computation of regular and irregular score components. o harmonise.msr Convert several measures (objects of class 'msr') to a common quadrature scheme. SIGNIFICANT USER-VISIBLE CHANGES o Smooth.ppp New argument 'geometric' supports geometric-mean smoothing. o Kinhom New argument 'ratio'. o linearKinhom, linearpcfinhom Changed default behaviour when 'lambda' is a fitted model. New arguments 'update' and 'leaveoneout'. o linearK, linearKinhom, linearpcf, linearpcfinhom, compilepcf Ratio calculations are now supported. New argument 'ratio'. o effectfun Now works for 'ppm', 'kppm', 'lppm', 'dppm', 'rppm' and 'profilepl' objects. o ppm, kppm The argument 'subset' can now be a window (class 'owin') specifying the subset of data to which the model should be fitted. o fitted.lppm New argument 'leaveoneout' allows leave-one-out computation of fitted value. o pool.rat New arguments 'relabel' and 'variance'. o density.lpp The return value is a pixel image of class 'linim' in all cases. o plot.linim, plot.linfun A scale bar is now plotted when style="width". New argument 'legend'. o ippm Default values for the parameters of the optimisation algorithm (nlm.args) have changed. o ippm The internal format of the result has been extended slightly. o bind.fv New argument 'clip'. o as.im.distfun New argument 'approx' specifies the choice of algorithm. o "[.psp" New argument 'fragments' specifies whether to keep fragments of line segments that are cut by the new window, or only to retain segments that lie entirely inside the window. o predict.rhohat New argument 'what' determines which value should be calculated: the function estimate, the upper/lower confidence limits, or the standard error. o pool.fv New arguments 'relabel' and 'variance' o pool.rat New argument 'weights'. o plot.msr New argument 'massthresh'. o Ops.msr Calculations like A+B can now be performed even when the measures A and B are not defined on the same quadrature scheme. o density.ppp New argument 'verbose'. o bw.pcf New argument 'verbose'. o hist.im New argument 'xname'. o [.leverage.ppm New argument 'update'. o [.layered Additional arguments '...' are now passed to other methods. o logLik.ppm The warning about pseudolikelihood ('log likelihood not available') is given only once, and is not repeated in subsequent calls, within a spatstat session. o kppm Refuses to fit a log-Gaussian Cox model with anisotropic covariance. o plot.linim, plot.linfun The return value has a different format. Arguments have been renamed and reorganised. o density.lpp New argument 'old'. o ippm Accelerated. o Smooth.ppp Now exits gracefully if any mark values are NA, NaN or Inf. o timeTaken Now exits gracefully if there is no timing information. o nbfires The unit of length for the coordinates is now specified in this dataset. BUG FIXES o bw.pcf Results were totally incorrect due to a typo. [Spotted by Abdollah Jalilian and Rasmus Waagepetersen.] Fixed. o predict.rho2hat Results were incorrect for a rho2hat object computed from a point pattern. Fixed. o density.ppp If the smoothing bandwidth was very small (e.g.\ smaller than pixel width), results were inaccurate if the default resolution was used, and completely wrong if another resolution was specified. [Spotted by Dominic Schuhmacher.] Fixed. o linearK, linearKinhom, linearpcf, linearpcfinhom, linearKcross, linearKdot, linearpcfcross, linearpcfdot, linearKcross.inhom, linearKdot.inhom, linearpcfcross.inhom, linearpcfdot.inhom Crashed if the network was disconnected. Fixed. o crossdist.lpp Crashed if the network was disconnected. Fixed. o countends Crashed if the network was disconnected. Fixed. o model.images.ppm Crashed for models fitted using 'covfunargs'. Fixed. o model.matrix.ppm Crashed for models fitted using 'covfunargs', if argument 'Q' was given. Fixed. o polynom Expansion of some polynomials caused an error message about 'invalid model formula'. Fixed. o plot.ppp The argument 'type="n"' did not suppress plotting of the legend, for marked point patterns. Fixed. o plot.psp Ignored 'show.all' when 'add=TRUE'. Fixed. o intensity.ppm Result had incorrect 'names' attribute in some cases. Fixed. o marks<-.ppx The assignment marks(X) <- a, where 'a' is a single atomic value, caused an error if 'X' contained zero points. Fixed o model.depends Crashed when applied to regression models fitted by 'gam', or point process models fitted by 'ppm' with 'use.gam=TRUE'. Fixed. o pool.fv Crashed sometimes, if the arguments did not have the same set of column names. Fixed. o pool.rat Crashed with an error message from 'fmt' if there were more than 20 objects to be pooled. Fixed. o linearK The 'theo' column was missing if npoints(X) < 2 and correction="Ang". Fixed. o model.matrix.ppm Result was malformed if the model was fitted with 'use.gam=TRUE'. Fixed. o effectfun Crashed if 'covname' was omitted, if the model was fitted with 'use.gam=TRUE'. Fixed. o nncross.lpp Result had incorrect format if Y was empty, in some cases. Fixed. o linearKinhom Plot label for y axis was incorrect. [Spotted by Suman Rakshit.] Fixed. o plot.solist If the entries were 'linim' objects, they were plotted using image() so arguments like 'style="w"' were ignored. Fixed. o as.ppp.data.frame Crashed if X was an object of class 'tbl_df' from the dplyr package. Fixed. o plot.lpp Crashed if there were multiple columns of marks. Fixed. CHANGES IN spatstat VERSION 1.51-0 OVERVIEW o We thank Greg McSwiggan, Mehdi Moradi and Tammy L Silva for contributions. o New fast algorithm for kernel smoothing on a linear network. o Leverage and influence diagnostics extended to Poisson/Gibbs models fitted by logistic composite likelihood. o Two-stage Monte Carlo test. o Dirichlet/Voronoi tessellation on a linear network. o Thinning of point patterns on a linear network. o More support for functions and tessellations on a linear network. o Improvements and bug fixes. o Version nickname: 'Poetic Licence' NEW FUNCTIONS o bits.test: Balanced Independent Two-Stage Monte Carlo test, an improvement on the Dao-Genton test. o lineardirichlet Computes the Dirichlet-Voronoi tessellation associated with a point pattern on a linear network. o domain.lintess, domain.linfun Extract the linear network from a 'lintess' or 'linfun' object. o summary.lintess Summary of a tessellation on a linear network. o clicklpp Interactively add points on a linear network. o envelopeArray Generate an array of envelopes using a function that returns 'fasp' objects. SIGNIFICANT USER-VISIBLE CHANGES o density.lpp New fast algorithm (up to 1000 times faster) for the default case where kernel="gaussian" and continuous=TRUE. Generously contributed by Greg McSwiggan. o leverage.ppm, influence.ppm, dfbetas.ppm These methods now work for models that were fitted by logistic composite likelihood (method='logi'). o rthin Argument X can now be a point pattern on a linear network (class 'lpp'). o fitted.ppm New option: type = "link" o update.kppm New argument 'evaluate'. o integral.linfun New argument 'delta' controls step length of approximation to integral. o as.linim.default New argument 'delta' controls spacing of sample points in internal data. o as.linfun.lintess New argument 'values' specifies the function value for each tile. New argument 'navalue'. BUG FIXES o leverage.ppm, influence.ppm, dfbetas.ppm Results for Gibbs models were incorrect due to a mathematical error. (Results for Poisson models were correct). Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm, ppmInfluence Calculations were incorrect for a Geyer model fitted using an edge correction other than "border" or "none". Fixed. o step, kppm, update.kppm 'step' did not work for kppm objects in some cases due to a scoping problem in update.kppm. Fixed. o improve.kppm Crashed if the window was not a rectangle. Fixed. o pcf.ppp, pcfinhom Crashed if kernel="epa" rather than "epanechnikov". Fixed. o alltypes Crashed if envelope=TRUE and reuse=FALSE. Fixed. o pairdist.lpp, nndist.lpp, nnwhich.lpp, nncross.lpp Crashed if the network was disconnected. Fixed. o as.im.linim, as.linim.linim Additional arguments such as 'eps' and 'dimyx' were ignored. Fixed. o as.im.default Arguments 'eps and 'xy' were ignored if X was a single numeric value. Fixed. o 'timed' class Printing of these objects did not work in some locales. Fixed. o runifpoint Ignored 'drop' argument if the window was a rectangle. Fixed. CHANGES IN spatstat VERSION 1.50-0 OVERVIEW o We thank Richard Cotton, Adrian Heyner, Abdollah Jalilian, Dominic Schuhmacher and Rasmus Waagepetersen for contributions. o spatstat now 'Imports' the package 'spatstat.utils'. o Bandwidth selection for pair correlation function. o Improvements and bug fixes. o Version nickname: 'Bunyip Aristocracy' NEW PACKAGE STRUCTURE o spatstat is being split into several sub-packages, to satisfy the requirements of CRAN. This should not affect the user: existing code will continue to work in the same way. Currently there are two sub-packages, called 'spatstat.utils' and 'spatstat'. Typing 'library(spatstat)' will load the familiar 'spatstat' package which can be used as before, and will silently import the 'spatstat.utils' package. The 'spatstat.utils' package contains utility functions that were originally written for 'spatstat': they were undocumented internal functions in 'spatstat', but are now documented and accessible in a separate package because they may be useful for other purposes. To access these functions, you need to type 'library(spatstat.utils)'. NEW FUNCTIONS o bw.pcf Bandwidth selection for pair correlation function. Original code contributed by Abdollah Jalilian and Rasmus Waagepetersen. o grow.box3 Expand a three-dimensional box. SIGNIFICANT USER-VISIBLE CHANGES o as.owin Now refuses to convert a 'box3' to a two-dimensional window. o pixellate.ppp If the pattern is empty, the result is an integer-valued image (by default) for consistency with the results for non-empty patterns. o ppp If the coordinate vectors x and y contain NA, NaN or infinite values, these points are deleted with a warning, instead of causing a fatal error. o ppm Argument 'interaction' can now be a function that makes an interaction, such as Poisson, Hardcore, MultiHard. o pcf, pcfinhom New argument 'close' for advanced use. o runifpointx, rpoisppx New argument 'drop'. o shapley, ponderosa In these installed datasets, the functions shapley.extra$plotit and ponderosa.extra$plotit have changed slightly (to accommodate the dependence on the package spatstat.utils). o kppm Improved printed output. BUG FIXES o rMaternI, rMaternII If 'win' was a three-dimensional box of class 'box3', the result was a two-dimensional point pattern. [Spotted by Adrian Heyner.] Fixed. o rmhmodel.ppm, simulate.ppm Crashed when applied to a fitted Lennard-Jones model. [Spotted by Dominic Schuhmacher.] Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm Crashed when applied to some hard-core models. Fixed. o "[.ppx" The format of the result was slightly malformed if exactly one point was selected. Fixed. o unmark.lpp, marks<-.lpp The result had class c("lpp", "lpp", "ppx") instead of c("lpp", "ppx"). Fixed. CHANGES IN spatstat VERSION 1.49-0 OVERVIEW o We thank Tilman Davies, Kassel Hingee, Abdollah Jalilian, Brian Ripley and Dominic Schuhmacher for contributions. o spatstat now 'Suggests' the package 'fftwtools'. o Operations on signed measures. o Operations on lists of pixel images. o Improved pixellation of point patterns. o Stieltjes integral extended. o Subset operators extended. o Greatly accelerated 'rmh' when using 'nsave' o Some computations accelerated. o Size of namespace reduced, for efficiency. o Bug fixes. o Version nickname: 'So-Called Software' NEW DEPENDENCIES o fftwtools spatstat now 'Suggests' the package 'fftwtools'. This package provides a very fast implementation of the Fast Fourier Transform, leading to much faster computation in the spatstat functions 'density.ppp', 'relrisk.ppp', 'convolve.im', 'blur', 'scan.test' and many other functions. The 'fftwtools' package requires the external software library 'fftw'. We strongly recommend installing this library if possible. NEW FUNCTIONS o hexagon, regularpolygon Create regular polygons. o Ops.msr Arithmetic operations for measures. o Math.imlist, Ops.imlist, Summary.imlist, Complex.imlist Arithmetic operations for lists of pixel images. o measurePositive, measureNegative, measureVariation, totalVariation Positive and negative parts of a measure, and variation of a measure. o as.function.owin Convert a spatial window to a function (x,y), the indicator function. o as.function.ssf Convert an object of class 'ssf' to a function(x,y) o as.function.leverage.ppm Convert an object of class 'leverage.ppm' to a function(x,y) SIGNIFICANT USER-VISIBLE CHANGES o stieltjes Argument 'M' can be a stepfun object (such as an empirical CDF). o quantile.ewcdf The function is now normalised to the range [0,1] before the quantiles are computed. This can be suppressed by setting normalise=FALSE. o pixellate.ppp New arguments 'fractional' and 'preserve' for more accurate discretisation. o "[.layered" Subset index i can now be an 'owin' object. o "[.solist" Subset index i can now be an 'owin' object. o plot.solist, plot.imlist, plot.anylist Result is now an (invisible) list containing the result from executing the plot of each panel. o ppp New argument 'checkdup'. o Summary.im Argument 'na.rm' is no longer ignored. o cdf.test The methods for classes ppp, ppm, lpp, lppm, slrm have a new argument 'interpolate'. o as.solist The argument x can now be a spatial object; as.solist(cells) is the same as solist(cells). o bw.diggle, bw.ppl, bw.relrisk, bw.smoothppp These functions now extract and store the name of the unit of length from the point pattern dataset. When the bandwidth selection criterion is plotted, the name of the unit of length is shown on the x-axis. o polynom This function now has a help file. o rmhcontrol New parameter 'pstage' determines when to generate random proposal points. o rmh Accelerated, in the case where multiple patterns are saved using 'nsave'. o bdist.pixels Accelerated for polygonal windows. New argument 'method'. o spatstat namespace The namespace of the spatstat package has been shortened (by internally registering the native routines) which should make the package run faster. o sum.im, range.im, max.im, min.im These functions have been removed, as they are now subsumed in Summary.im. BUG FIXES o plot.msr If one of 'nrows' or 'ncols' was specified, but not both, an obscure error occurred. Fixed. o plot.solist, plot.imlist, plot.anylist Crashed if 'nrows' and 'ncols' were given values implying that some rows or columns would not contain any plots. Fixed. o as.ppp.lpp Crashed if there was more than one column of marks. Fixed. o has.close.pp3 Results were incorrect, or a crash occurred, when argument 'Y' was given. Fixed. o rmpoispp If 'lambda' was a list of images, 'names(lambda)' was ignored, rather than serving as the default value of 'types'. Fixed. o bugfixes Output was garbled, in rare cases. Fixed. o kppm Result was malformed when clusters="VarGamma" and method="clik2". Spotted by Abdollah Jalilian. Fixed. o QQversion Plotting labels were malformed. Fixed. CHANGES IN spatstat VERSION 1.48-0 OVERVIEW o We thank Kim Colyvas, Yongtao Guan, Gopalan Nair, Nader Najari, Suman Rakshit, Ian Renner and Hangsheng Wang for contributions. o Sufficient Dimension Reduction for point processes. o Alternating Gibbs Sampler for point process simulation. o Intensity approximation for area-interaction and Geyer models. o New class of spatially sampled functions. o ROC and AUC extended to other types of point patterns and models. o More support for linear networks. o More support for infinite straight lines. o Simulation of 'rhohat' objects. o Kernel smoothing accelerated. o Methods for 'head' and 'tail' for spatial patterns. o More low-level functionality. o Improvements and bug fixes. o spatstat now has more than 1000 help files. o Nickname: 'Model Prisoner' NEW CLASSES o ssf Class of spatially sampled functions. NEW FUNCTIONS o sdr, dimhat Sufficient Dimension Reduction for point processes. Matlab code contributed by Yongtao Guan, translated by Suman Rakshit. o rags, ragsAreaInter, ragsMultiHard Alternating Gibbs Sampler for point processes. o psib Sibling probability (index of clustering strength in a cluster process). o bugfixes List all bug fixes in recent versions of a package. o roc.kppm, roc.lppm, roc.lpp Methods for 'roc' (receiver operating characteristic curve) for fitted models of class 'kppm' and 'lppm' and point patterns of class 'lpp' o auc.kppm, auc.lppm, auc.lpp Methods for 'auc' (area under the ROC curve) for fitted models of class 'kppm' and 'lppm' and point patterns of class 'lpp' o rlpp Random points on a linear network with a specified probability density. o cut.lpp Method for 'cut' for point patterns on a linear network. o crossing.linnet Find crossing points between a linear network and another set of lines. o ssf Create a spatially sampled function o print.ssf, plot.ssf, contour.ssf, image.ssf Display a spatially sampled function o as.im.ssf, as.ppp.ssf, marks.ssf, marks<-.ssf, unmark.ssf, [.ssf, with.ssf Manipulate data in a spatially sampled function o Smooth.ssf Smooth a spatially sampled function o integral.ssf Approximate integral of spatially sampled function o simulate.rhohat Generate a Poisson random point pattern with intensity that is a function of a covariate, given by a 'rhohat' object. o head.ppp, head.ppx, head.psp, head.tess, tail.ppp, tail.ppx, tail.psp, tail.tess Methods for 'head' and 'tail' for spatial patterns. o as.data.frame.tess Convert a tessellation to a data frame. o timeTaken Extract the timing data from a 'timed' object or objects. o rotate.infline, shift.infline, reflect.infline, flipxy.infline Geometrical transformations for infinite straight lines. o whichhalfplane Determine which side of an infinite line a point lies on. o points.lpp Method for 'points' for point patterns on a linear network. o pairs.linim Pairs plot for images on a linear network. o has.close Faster way to check whether a point has a close neighbour. o closetriples Low-level function to find all close triples of points. o matrixpower, matrixsqrt, matrixinvsqrt Raise a matrix to any power. SIGNIFICANT USER-VISIBLE CHANGES o intensity.ppm Intensity approximation is now available for the Geyer saturation process and the area-interaction process (results of research with Gopalan Nair). o envelope.lpp, envelope.lppm New arguments 'fix.n' and 'fix.marks' allow envelopes to be computed using simulations conditional on the observed number of points. o "[.im" The subset index "i" can now be a linear network (object of class 'linnet'). The result of "x[i, drop=FALSE]" is then a pixel image of class 'linim'. o cut.ppp Argument z can be "x" or "y" indicating one of the spatial coordinates. o rThomas, rMatClust, rCauchy, rVarGamma, rPoissonCluster, rNeymanScott New argument 'saveparents'. o lintess Argument 'df' can be missing or NULL, resulting in a tesellation with only one tile. o lpp X can be missing or NULL, resulting in an empty point pattern. o plot.lintess Improved plot method, with more options. o rpoisline Also returns information about the original infinite random lines. o density.ppp, Smooth.ppp Accelerated. o density.psp New argument 'method' controls the method of computation. New faster option 'method="FFT"' o nndist.lpp Accelerated. BUG FIXES o F3est Estimates of F(r) for the largest value of r were wildly incorrect. Fixed. o clip.infline Results were incorrect unless the midpoint of the window was the coordinate origin. Fixed. o integral.linim Results were inaccurate if many of the segment lengths were shorter than the width of a pixel. Fixed. o predict.lppm Bizarre error messages about 'class too long' or 'names too long' occurred if the model was multitype. Fixed. o superimpose Point patterns containing 0 points were ignored when determining the list of possible marks. Fixed. o chop.tess Vertical lines were not handled correctly with pixellated tessellations. Fixed. o timed Argument 'timetaken' was ignored. Fixed. o ppm Crashed if method="logi" and the 'covariates' were a data frame. [Spotted by Kim Colyvas and Ian Renner.] Fixed. o rpoislpp, runiflpp Crashed if nsim > 1. Fixed. o rpoisline Crashed if zero lines were generated. Fixed. o model.frame.ppm Crashed if the original model was fitted to a data frame of covariates and there were NA's amongst the covariate values. [Spotted by Kim Colyvas.] Fixed. o any, all When applied to pixel images (objects of class 'im') the result was sometimes NA when a finite value should have been returned. Fixed. o predict.rhohat When the original data were on a linear network, the result of predict.rhohat did not belong to the correct class 'linim'. Fixed. CHANGES IN spatstat VERSION 1.47-0 OVERVIEW o We thank Marcel Austenfeld, Guy Bayegnak, Tilman Davies, Cenk Icos, Jorge Mateu, Frederico Mestre, Mehdi Moradi, Virginia Morera Pujol, Suman Rakshit and Sven Wagner for contributions. o Non-Gaussian smoothing kernels. o Important bug fix in linearK, linearpcf o Changed internal format of linnet and lpp objects. o Faster computation in linear networks. o Bias correction techniques. o Bounding circle of a spatial object. o Minkowski sum also applicable to point patterns and line segment patterns. o Option to plot marked points as arrows. o Kernel smoothing accelerated. o Workaround for bug in some graphics drivers affecting image orientation. o Bug fixes and improvements. o Version nickname: 'Responsible Gambler' NEW FUNCTIONS o anyNA.im Method for 'anyNA' for pixel images. o bc Bias correction (Newton-Raphson) for fitted model parameters. See also 'rex'. o boundingcircle, boundingcentre Find the smallest circle enclosing a window or point pattern. o "[.linim" Subset operator for pixel images on a linear network. o mean.linim, median.linim, quantile.linim The mean, median, or quantiles of pixel values in a pixel image on a linear network. o rex Richardson extrapolation for numerical integrals and statistical model parameter estimates. o weighted.median, weighted.quantile Median or quantile of numerical data with associated weights. SIGNIFICANT USER-VISIBLE CHANGES o linear networks The internal format of a 'linnet' (linear network) object has been changed. Existing datasets of class 'linnet' and 'lpp' are still supported. However, computation will be faster if they are converted to the new format. To convert a linnet object L to the new format, use L <- as.linnet(L). To convert an lpp object X to the new format, use X <- as.lpp(X). o density.ppp, Smooth.ppp New argument 'kernel' allows the user to specify the smoothing kernel. o density.ppp, Smooth.ppp Argument 'weights' can now be a pixel image. o MinkowskiSum, %(+)% Now accepts arguments which are point patterns or line segment patterns as well as windows. o plot.im New argument 'workaround' to avoid a bug in some device drivers that causes the image to be displayed in the wrong spatial orientation. [Thanks to Marcel Austenfeld for drawing attention to this.] o sumouter New argument 'y' allows computation of asymmetric outer products. o linearKinhom, linearpcfinhom New argument 'normpower'. o rmh.default, rmh.ppm New arguments 'nsim', 'saveinfo'. o symbolmap, plot.ppp, plot.lpp New option: shape="arrows" o rcellnumber New argument 'mu'. o lengths.psp New argument 'squared'. o plot.linfun Now passes arguments to the function being plotted. o as.linnet.psp If the line segment pattern has marks, then the resulting linear network also carries these marks in the $lines component. o summary.owin, summary.im The fraction of frame area that is occupied by the window/image is now reported. o density.ppp, Smooth.ppp Computation accelerated by about 15% in the case where at='points' and kernel='gaussian'. o linearK, linearpcf Accelerated by about 40%. o pixellate.ppp Accelerated in the case where weights are given o density.ppp Accelerated in the cases where weights are given or 'diggle=TRUE' o dilation.ppp Improved geometrical accuracy. Now accepts arguments to control resolution of polygonal approximation. o discs New argument 'npoly'. Accelerated in some cases. o plot.pp3 New arguments 'box.front', 'box.back' control plotting of the box. o grow.rectangle New argument 'fraction'. o nnfun.lpp New argument 'k'. o bw.ppl New argument 'sigma'. o lppm New argument 'random' controls placement of dummy points. o rhohat.lpp New argument 'random' controls placement of dummy points. o quadrat.test.ppm Accelerated in the case where the original window is a rectangle. o kppm, mincontrast, cauchy.estpcf, lgcp.estpcf, matclust.estpcf, thomas.estpcf, vargamma.estpcf A warning about infinite values of the summary function no longer occurs when the default settings are used. o circumradius This function is now deprecated, in favour of 'boundingradius' o print.quad More information is printed. BUG FIXES o linearK, linearpcf, and relatives: These functions were sometimes greatly underestimated when the network had segments shorter than 10 coordinate units. [Bug introduced in spatstat 1.44-0, december 2015.] Fixed. o integral.linim, integral.linfun Results were slightly inaccurate because of a bias in the distribution of sample points. [Bug introduced in spatstat 1.41-0, february 2015.] Fixed. o intensity.ppm Result was incorrect for Gibbs models if the model was *exactly* equivalent to a Poisson process (i.e. if all interaction coefficients were exactly zero). [Bug introduced in spatstat 1.28-1, june 2012.] Fixed. o rSSI Sometimes terminated prematurely. [Spotted by Frederico Mestre.] Fixed. o perspPoints Crashed if the image Z contained NA (i.e. if Z was only defined on a subset of the bounding frame). Spotted by Guy Bayegnak. Fixed. o plot.ppp, plot.lpp Crashed if the argument 'shape' was given. Fixed. o plot.kppm Crashed if the model was not fitted by minimum contrast. Fixed. o superimpose Crashed if the argument was a 'solist' containing line segment patterns. Fixed. o Jest Crashed sometimes, depending on the shape of the observation window. [Spotted by Cenk Icos.] Fixed. o plot.studpermutest Crashed when the summary statistic was a multitype pair correlation function or multitype K function. [Spotted by Sven Wagner.] Fixed. o pool.anylist Crashed with a message about buffer size, if the list was longer than about 100 items. Fixed. o diagnose.ppm, plot.diagppm Crashed in some cases when cumulative=FALSE. Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm Crashed sometimes with a message about wrong replacement length. [Spotted by Virginia Morera Pujol.] Fixed. o as.linnet.psp Crashed with marked segment patterns, if any segments were very short. [Spotted by Suman Rakshit.] Fixed. o stieltjes Returned NA if some values of f were not finite. Fixed. o plot.symbolmap If a new plot window was initialised, it was sometimes too small to contain the geometric figures (circles, squares etc) in the symbol map. Fixed. o plot.ppp, plot.im Ignored xlim, ylim. Fixed. o rhohat.lpp Ignored nd, eps. Fixed. o nnfun.lpp Print method gave incorrect information about the point pattern. Fixed. o "[.fv" The default plot formula was not updated. Fixed. o fitted.ppm The result was sometimes a 1-dimensional array rather than a numeric vector. Fixed. CHANGES IN spatstat VERSION 1.46-1 OVERVIEW o Important bug fix. o Version nickname: 'Spoiler Alert' BUG FIXES o density.ppp, Smooth.ppp The results of density(X, at="points") and Smooth(X, at="points") were incorrect in some cases. The contribution from the left-most data point (the point with the smallest x coordinate) was omitted. [Bug introduced in spatstat 1.26-0, April 2012.] Fixed. CHANGES IN spatstat VERSION 1.46-0 OVERVIEW o We thank Corey Anderson and Sebastian Meyer for contributions. o spatstat now depends on R 3.3.0 or later. o Improvements to inhomogeneous multitype K and L functions. o Variance approximation for pair correlation function. o Leverage and influence for multitype point process models. o Functions for extracting components of vector-valued objects. o Important bug fix in Smooth.ppp o Minor improvements and bug fixes. o Version nickname: 'Multidimensional Toothbrush' NEW FUNCTIONS o split.msr Decompose a measure into parts. o unstack.msr Decompose a vector-valued measure into its component measures. o unstack.ppp, unstack.psp, unstack.lpp Given a spatial pattern with several columns of marks, separate the columns and return a list of spatial patterns, each having only one column of marks. o kernel.squint Integral of squared kernel, for the kernels used in density estimation. SIGNIFICANT USER-VISIBLE CHANGES o Kcross.inhom, Kdot.inhom, Kmulti.inhom, Ldot.inhom, Lcross.inhom These functions now allow intensity values to be given by a fitted point process model. New arguments 'update', 'leaveoneout', 'lambdaX'. o diagnose.ppm Infinite values of 'rbord' are now ignored and treated as zero. This ensures that diagnose.ppm has a sensible default when the fitted model has infinite reach. o pcf.ppp Now calculates an analytic approximation to the variance of the estimate of the pair correlation function (when var.approx=TRUE). Now returns the smoothing bandwidth used, as an attribute of the result. o plot.ppp When 'clipwin' is given, any parts of the boundary of the window of x that lie inside 'clipwin' will also be plotted. o plot.msr Now handles multitype measures. New argument 'multiplot'. o plot.anylist If a list entry x[[i]] belongs to class 'anylist', it will be expanded so that each entry x[[i]][[j]] will be plotted as a separate panel. o influence.ppm, leverage.ppm These can now be applied to multitype point process models and the results can be plotted. o plot.influence.ppm, plot.leverage.ppm New argument 'multiplot'. o plot.anylist, plot.solist, plot.listof New arguments panel.begin.args, panel.end.args o influence.ppm, leverage.ppm, dfbetas.ppm For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. BUG FIXES o Smooth.ppp Results were incorrect when at='points' and leaveoneout=FALSE. [Bug introduced in spatstat 1.20-5, October 2010.] Fixed. o funxy Did not correctly handle one-line functions: the resulting objects evaluated the wrong function in some cases. [Spotted by Sebastian Meyer. Bug introduced in spatstat 1.45-0] Fixed. o mppm Did not recognise the variable 'marks' in a formula. Fixed. o Smooth.ppp, bw.smoothppp Crashed if X had two columns of marks and one column was constant. [Bug introduced in spatstat 1.38-0, October 2014] Fixed. o Smooth.ppp Results for 'at="points"' were garbled, for some values of 'sigma', if X had more than one column of marks. [Bug introduced in spatstat 1.38-0, October 2014] Fixed. o plot.layered Crashed if one layer was a point pattern with several columns of marks. Fixed. o plot.ppm Sometimes gave a spurious warning about a singular matrix. Fixed. o setminus.owin Gave wrong or strange answer if the correct answer was empty. Fixed. o parameters.dppm Crashed, due to a typo. Fixed. o progressreport Crashed if n = 1. Fixed. CHANGES IN spatstat VERSION 1.45-2 OVERVIEW o We thank Ottmar Cronie, Virginia Morera Pujol, Sven Wagner and Marie-Colette van Lieshout for contributions. o Recursive-partition point process models. o Minkowski sum, morphological dilation and erosion with any shape. o Important bug fix in spatial CDF tests. o More bug fixes for replicated patterns. o Simulate a model fitted to replicated point patterns. o Inhomogeneous multitype F and G functions. o Summary functions recognise correction="all" o Leverage and influence code handles bigger datasets. o More support for pixel images. o Improved progress reports. o New dataset 'redwood3' o spatstat now Depends on the package 'rpart' o Version nickname: 'Caretaker Mode' NEW DATASETS o redwood3 A more accurate version of the 'redwood' data. NEW FUNCTIONS o as.im.data.frame Build a pixel image from a data frame of coordinates and pixel values. o covering Cover a window using discs of a given radius. o dilationAny, erosionAny, %(-)% Morphological dilation and erosion by any shape. o FmultiInhom, GmultiInhom Inhomogeneous multitype/marked versions of the summary functions Fest, Gest. o kernel.moment Moment or incomplete moment of smoothing kernel. o MinkowskiSum, %(+)% Minkowski sum of two windows: A %(+)% B, or MinkowskiSum(A,B) o nobjects New generic function for counting the number of 'things' in a dataset. There are methods for ppp, ppx, psp, tess. o parameters.interact, parameters.fii Extract parameters from interpoint interactions. [These existing functions are now documented.] o ppmInfluence Calculate leverage.ppm, influence.ppm and dfbetas.ppm efficiently. o rppm, plot.rppm, predict.rppm, prune.rppm Recursive-partition point process models o simulate.mppm Simulate a point process model fitted to replicated point patterns. o update.interact Update the parameters of an interpoint interaction. [This existing function is now documented.] o where.max, where.min Find the spatial location(s) where a pixel image achieves its maximum or minimum value. SIGNIFICANT USER-VISIBLE CHANGES o cdf.test.mppm Now handles Gibbs models. Now recognises covariate="x" or "y". o leverage.ppm, influence.ppm, dfbetas.ppm For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. o plot.im Now handles complex-valued images. o connected.im Now handles a logical-valued image properly. o qqplot.ppm Argument 'expr' can now be a list of point patterns, or an envelope object containing a list of point patterns. o as.layered Default method now handles a (vanilla) list of spatial objects. o summary functions The argument 'correction="all"' is now recognised: it selects all the available options. This applies to Fest, F3est, Gest, Gcross, Gdot, Gmulti, G3est, Gfox, Gcom, Gres, Hest, Jest, Jmulti, Jcross, Jdot, Jfox, Kest, Kinhom, Kmulti, Kcross, Kdot, Kcom, Kres, Kmulti.inhom, Kcross.inhom, Kdot.inhom, Kscaled, Ksector, Kmark, K3est, Lscaled, markcorr, markcrosscorr, nnorient, pairorient, pcfinhom, pcfcross.inhom, pcfcross, pcf, Tstat. o clarkevans The argument 'correction="all"' is now recognised: it selects all the available options. [This is also the default.] o predict.mppm The argument 'type="all"' is now recognised: it selects all the available options. [This is also the default.] o plot.kppm The argument 'what="all"' is now recognised: it selects all the available options. [This is also the default.] o connected.im, connected.owin Arguments '...' now determine pixel resolution. o anova.mppm New argument 'fine' o as.owin.data.frame New argument 'step' o discs Now accepts a single numeric value for 'radii'. o plot.ppp, plot.profilepl, plot.quadratcount, plot.quadrattest, plot.tess Now recognise graphics parameters for text, such as 'family' and 'srt' o as.function.tess New argument 'values' specifies the function values. o cdf.test Calculations are more robust against numerical rounding effects. o progressreport Behaviour improved. New arguments 'tick', 'showtime'. o simulate.ppm New argument 'verbose' o compileK, compilepcf These internal functions are now documented. BUG FIXES o cdf.test.ppm Calculation of p-values was incorrect for Gibbs models: 1-p was computed instead of p. [Spotted by Sven Wagner.] Fixed. o subfits The interaction coefficients of the submodels were incorrect for Gibbs models with a multitype interaction (MultiStrauss, etc). [Spotted by Sven Wagner.] Fixed. o subfits Crashed when a Gibbs model included factor-valued spatial covariates and not all levels of the factor were present in each row of the data. [Spotted by Sven Wagner.] Fixed. o subfits For Gibbs models with a multitype interaction (MultiStrauss, etc), computation of the conditional intensity caused an error. [Spotted by Sven Wagner.] Fixed. o diagnose.ppm Crashed if what="smooth", when the original window was a rectangle. [Spotted by Virginia Morera Pujol.] Fixed. o mppm The x and y coordinates were not permitted in the random-effects formula 'random'. [Spotted by Sven Wagner.] Fixed. o vcov.ppm The result had no 'dimnames', if the model was fitted using method="ho". Fixed. CHANGES IN spatstat VERSION 1.45-1 OVERVIEW o This version was never released. CHANGES IN spatstat VERSION 1.45-0 OVERVIEW o We thank Monsuru Adepeju, Mario D'Antuono, Markus Herrmann, Paul Hewson, Kassel Hingee, Greg McSwiggan, Suman Rakshit and Sven Wagner for contributions. o Important bug fix in leverage/influence diagnostics for Gibbs models. o Numerous bug fixes in code for replicated point patterns. o Surgery on linear networks. o Tessellations on a linear network. o Laslett's Transform. o Colour maps for point patterns with continuous marks are easier to define. o Pair correlation function estimates can be pooled. o Stipulate a particular version of a package. o Fixed namespace problems arising when spatstat is not loaded. o Bug fixes and performance improvements. o spatstat now contains 100,000 lines of R code. o Version nickname: 'One Lakh' NEW FUNCTIONS o laslett Laslett's Transform. [Thanks to Kassel Hingee] o lintess Tessellation on a linear network. o divide.linnet Divide a linear network into pieces demarcated by a point pattern. o insertVertices Insert new vertices in a linear network. o thinNetwork Remove vertices and/or segments from a linear network etc o connected.linnet Find connected components of a linear network. o nvertices, nvertices.linnet, nvertices.owin Count the number of vertices in a linear network or vertices of the boundary of a window. o as.data.frame.linim, as.data.frame.linfun Extract a data frame of spatial locations and function values from an object of class 'linim' or 'linfun'. o as.linfun, as.linfun.linim, as.linfun.lintess Convert other kinds of data to a 'linfun' object. o requireversion Require a particular version of a package (for use in stand-alone R scripts). SIGNIFICANT USER-VISIBLE CHANGES o [.linnet, [.lpp New argument 'snip' determines what to do with segments of the network that cross the boundary of the window. Default behaviour has changed. o pcfinhom Default behaviour is changed when 'lambda' is a fitted model. The default is now to re-fit the model to the data before computing pcf. New arguments 'update' and 'leaveoneout' control this. o envelope methods New argument 'funYargs' contains arguments to the summary function when applied to the data pattern only. o plot.ppp, plot.lpp For a point pattern with continuous marks ('real numbers') the colour arguments 'cols', 'fg', 'bg' can now be vectors of colour values, and will be used to determine the default colour map for the marks. o symbolmap Now accepts a vector of colour values for the arguments 'col', 'cols', 'fg', 'bg' if argument 'range' is given. o closepairs.ppp, closepairs.pp3 New arguments 'distinct' and 'neat' allow more options. o closepairs.ppp, closepairs.pp3 Argument 'ordered' has been replaced by 'twice' (but 'ordered' is still accepted, with a warning). o closepairs.ppp, closepairs.pp3 Performance improved (computation time and memory requirements reduced.) This should improve the performance of many functions in spatstat. o Geyer The saturation parameter 'sat' can now be less than 1. o lpp, as.lpp These functions now handle the case where 'seg' and 'tp' are given but 'x' and 'y' are missing. o linnet If the argument 'edges' is given, then this argument now determines the ordering of the sequence of line segments. For example, the i-th row of 'edges' specifies the i-th line segment in as.psp(L). o funxy, distfun The functions created by funxy and distfun have arguments (x,y). The user may now give a ppp or lpp object for the argument 'x', instead of giving two coordinate vectors 'x' and 'y'. o crossing.psp New argument 'details' gives more information about the intersections between the segments. o subset.ppp, subset.lpp, subset.pp3, subset.ppx The argument 'subset' can now be any argument acceptable to the "[" method. o density.lpp New argument 'weights'. o pcf.ppp New argument 'ratio' allows several estimates of pcf to be pooled. o summary.ppm New argument 'fine' selects the algorithm for variance estimation. o texturemap Argument 'textures' can be missing or NULL. o plot.lpp New argument 'show.network' o linnet New argument 'warn' o mppm Performs more checks for consistency of the input data. o mppm New arguments 'gcontrol' and 'reltol.pql' control the fitting algorithm. o edge.Trans New argument 'gW' for efficiency. o pool.fv The default plot of the pooled function no longer includes the variance curves. o clickpoly The polygon is now drawn progressively as the user clicks new vertices. o Kest Accelerated computation (for translation and rigid corrections) when window is an irregular shape. o vcov.ppm, leverage.ppm, influence.ppm, dfbetas.ppm Performance slightly improved, for Gibbs models. o Internal code Performance slightly improved. o Fest, Hest Additional checks for errors in input data. BUGS o leverage.ppm, influence.ppm, parres.ppm, addvar.ppm Calculations were completely incorrect for Gibbs models, due to a coding error. Fixed. o update.kppm If the call to 'update' did not include a formula argument or a point pattern argument, then all arguments were ignored. Example: update(fit, improve.type="quasi") was identical to 'fit'. Fixed. o diagnose.ppm When applied to a model obtained from subfits(), in the default case (oldstyle=FALSE) the variance calculations were incorrect. Consequently the dotted lines representing significance bands were incorrect. An error or warning about negative variances occurred sometimes. However, calculations with oldstyle=TRUE were correct. The default has now been changed to oldstyle=TRUE for such models. o [.lpp The local coordinate 'seg' was completely incorrect, when 'i' was a window. Fixed. o leverage.ppm, influence.ppm, parres.ppm, addvar.ppm Crashed for Gibbs models in which the coefficient vector had length 1, such as the stationary Hardcore model. Fixed. o subfits Crashed if the model included factor-valued spatial covariates. [Spotted by Sven Wagner] Fixed. o subfits If the model included factor-valued spatial covariates, and if not all levels of the factor were present in each row of the data, the resulting objects were malformed and caused errors in other code. [Spotted by Sven Wagner] Fixed. o subfits Crashed with some random-effects formulas. [Spotted by Sven Wagner] Fixed. o improve.kppm An error message about a missing object 'gminus1' occurred when vcov=TRUE, fast.vcov=FALSE and type="clik1" or "wclik1". Fixed. o plot.profilepl Failed with a message about a missing object 'finite'. Fixed. o selfcut.psp Gave an error if marks(A) was a vector rather than a data frame. [Spotted by Paul Hewson.] Fixed. o suffstat Gave an error for point process models with Geyer interaction. Fixed. o nncross.lpp, distfun.lpp Crashed with obscure errors if Y consisted of a single point. Fixed. o scan.test, scanmeasure Crashed sometimes with an error message from 'grow.mask'. Fixed. o dppm Crashed sometimes with a message that the point pattern could not be found. [Scoping bug.] Fixed. o mppm, profilepl Crashed, with a message about 'SpatstatVersion', if the 'spatstat' package was neither loaded nor attached. [Spotted by Markus Herrmann.] Fixed. o qqplot.ppm Crashed sometimes when applied to a model obtained from subfits(). Fixed. o anova.mppm Crashed sometimes with a message about mismatched coefficients. [Spotted by Sven Wagner.] Fixed. o anova.mppm Crashed sometimes with a message about unrecognised option 'type="score"'. [Spotted by Sven Wagner.] Fixed. o split.ppx Crashed if 'f' was not a factor. Fixed. o idw The result was a pixel image defined in the rectangle Frame(X) instead of Window(X). Fixed. o ppm Parameter estimates were slightly inaccurate when the model included the Geyer() interaction and the "isotropic" edge correction was used. Fixed. o [.ppx Crashed if the number of points selected was less than 2. Fixed. o linnet Crashed if there were no line segments at all. Fixed. o kppm, improve.kppm Crashed if the model was stationary and improve.type != "none". Fixed. o as.linim.default Did not correctly handle factor-valued data. Fixed. o texturemap Crashed if no graphical arguments were specified. Fixed. o vcov.mppm Ignored "..." arguments. Fixed. o Kest If ratio=TRUE and correction=c('border', 'none') the result did not contain ratio information. Fixed. o plot.ppp, plot.lpp Arguments 'chars' and 'cols' were ignored in some cases. Fixed. o ppm Ignored argument 'emend'. Fixed. o plot.dppm Gave warnings about unrecognised argument 'objectname'. Fixed. o overlap.owin Sometimes returned a very small negative value, when the correct answer was 0. Fixed. CHANGES IN spatstat VERSION 1.44-1 OVERVIEW o We thank Brian Ripley for contributions. o Urgent bug fix. o More support for replicated point patterns. o More support for tessellations. o Version nickname: 'Gift Horse' NEW FUNCTIONS o as.function.tess Convert a tessellation to a function(x,y). The function value indicates which tile of the tessellation contains the point (x,y). o tileindex Determine which tile of a tessellation contains a given point (x,y). o persp.leverage.ppm Method for persp plots for objects of class leverage.ppm o AIC.mppm, extractAIC.mppm AIC for point process models fitted to replicated point patterns. o nobs.mppm, terms.mppm, getCall.mppm Methods for point process models fitted to replicated point patterns. SIGNIFICANT USER-VISIBLE CHANGES o anova.mppm Now handles Gibbs models, and performs the adjusted composite likelihood ratio test. o update, step These functions now work for models of class 'mppm'. o textureplot Argument x can now be something acceptable to as.im o logLik.mppm New argument 'warn'. BUGS o nncross.lpp, nnwhich.lpp, distfun.lpp Caused a segmentation fault. [Spotted by Brian Ripley.] Fixed. o anova.ppm If a single 'object' was given, and the object was a Gibbs model, then 'adjust' was effectively set to FALSE. Fixed. CHANGES IN spatstat VERSION 1.44-0 OVERVIEW o We thank Jonas Geldmann, Andrew Hardegen, Kassel Hingee, Tom Lawrence, Robin Milne, Gopalan Nair, Suman Rakshit, Peijian Shi and Rasmus Waagepetersen for contributions. o More support for multidimensional point patterns and point processes. o More options for envelopes and related Monte Carlo tests. o More support for model comparison. o k-th nearest neighbours on a linear network. o Penttinen process can be simulated (by Metropolis-Hastings or CFTP). o Calculate the predicted variance of number of points. o Convexifying operation for sets. o Subdivide a linear network. o Accelerated algorithms for linear networks. o Quadrat counting accelerated, in some cases. o Version nickname: 'The Sound of One Hand Typing' NEW FUNCTIONS o rPenttinen Simulate the Penttinen process using perfect simulation. o varcount Given a point process model, compute the predicted variance of the number of points falling in a window. o inside.boxx Test whether multidimensional points lie inside a specified multidimensional box. o lixellate Divide each segment of a linear network into smaller segments. o nsegments.linnet, nsegments.lpp Count the number of line segments in a linear network. o grow.boxx Expand a multidimensional box. o deviance.ppm, deviance.lppm Deviance for a fitted point process model. o pseudoR2 Pseudo-R-squared for a fitted point process model. o tiles.empty Checks whether each tile of a tessellation is empty or nonempty. o summary.linim Summary for a pixel image on a linear network. SIGNIFICANT USER-VISIBLE CHANGES o rMaternI, rMaternII These functions can now generate random patterns in three dimensions and higher dimensions, when the argument 'win' is of class 'box3' or 'boxx'. o "[.ppx" The subset index 'i' may now be a spatial domain of class 'boxx' or 'box3'. o rmh.ppm, rmhmodel.ppm, simulate.ppm A model fitted using the 'Penttinen' interaction can now be simulated. o rmh.default, rmhmodel.default These functions now recognise cif='penttinen' for the Penttinen interaction. o envelope New argument 'clamp' gives greater control over one-sided envelopes. o dclf.test, mad.test, dclf.progress, mad.progress, dclf.sigtrace, mad.sigtrace New argument 'clamp' determines the test statistic for one-sided tests. o dclf.progress, mad.progress, dclf.sigtrace, mad.sigtrace, mctest.progress, mctest.sigtrace, dg.progress, dg.sigtrace New argument 'rmin' determines the left endpoint of the test interval. o dclf.test, mad.test, dg.test, dg.progress, dg.sigtrace, dg.envelope (also accepted by dclf.progress, mad.progress, dclf.sigtrace, mad.sigtrace) New argument 'leaveout' specifies how to calculate the deviation between the observed summary function and nominal reference value. o envelope New argument 'funargs' o Hest Argument X can now be a pixel image with logical values. New argument 'W'. [Based on code by Kassel Hingee.] o nncross.lpp, distfun.lpp New argument 'k' allows calculation of k-th nearest neighbour. Computation accelerated. o logLik.ppm New argument 'absolute'. o plot.kppm New arguments 'pause' and 'xname'. o tess Argument 'window' is ignored when xgrid, ygrid are given. o as.polygonal Can now repair errors in polygon data, if repair=TRUE. o rStrauss, rHardcore, rStraussHard, rDiggleGratton, rDGS, rPenttinen New argument 'drop'. o Kest.fft Now has '...' arguments allowing control of spatial resolution. o lppm Computation accelerated. o quadratcount.ppp Computation accelerated in some cases. o dg.test Computation accelerated. BUGS o runifpointx, rpoisppx Crashed if nsim > 1. Fixed. o triangulate.owin Results were incorrect in some special cases. Fixed. o quadrat.test, clarkevans.test In rare cases, the computed Monte Carlo p-value could have been greater than 1. This could have occurred only when nsim was an even number and when the correct p-value was equal to 1. Fixed. o linearmarkequal Result was a data frame instead of an 'fv' object. Fixed. o point-in-polygon test The function inside.owin could take a very long time to check whether points are inside a polygonal window, if the coordinates were very large numbers. This was due to numerical overflow. (Fixed??) o as.fv.kppm Crashed if the model was not fitted by minimum contrast. Fixed. o plot.fv Crashed in some obscure cases. Fixed. o collapse.fv Did not allow 'same=NULL'. Fixed. o dclf.progress, mad.progress, dg.progress, dclf.sigtrace, mad.sigtrace, dg.sigtrace The results could not be re-plotted using a plot formula, because the internal data were slightly corrupted. Fixed. o Kest.fft Result was incorrectly normalised. Fixed. o crosspairs If X and Y were identical point patterns, the result was not necessarily symmetric (on some machines) due to numerical artifacts. Fixed. o plot.fv Lines were not correctly clipped to the plot region when 'ylim' was given. Fixed. o pool.envelope The 'scale' argument was not handled correctly. Fixed. CHANGES IN spatstat VERSION 1.43-0 OVERVIEW o We thank Leanne Bischof, Christophe Biscio, Belarmain Fandohan, Andrew Hardegen, Frederic Lavancier, Tom Lawrence, Martin Maechler, Greg McSwiggan, Robin Milne, Gopalan Nair, Tuomas Rajala, Suman Rakshit, Ben Ramage, Francois Semecurbe and Ida-Maria Sintorn for contributions. o spatstat now depends on the package 'nlme'. o spatstat now depends on R 3.2.2 or later. o Simulation algorithms have been accelerated; simulation outcomes are *not* identical to those obtained from previous versions of spatstat. o Determinantal point process models. o Random-effects and mixed-effects models for replicated patterns. o Dao-Genton test, and corresponding simulation envelopes. o Simulated annealing and simulated tempering. o spatstat colour tools now handle transparent colours. o Improvements to "[" and subset() methods o Extensions to kernel smoothing on a linear network. o Support for one-dimensional smoothing kernels. o Bug fix in Metropolis-Hastings simulation. o Mark correlation function may include weights. o Cross-correlation version of the mark correlation function. o Variance calculations for replicated patterns. o Penttinen pairwise interaction model. o Contour plots with colours determined by a colour map. o New dataset: Australian states and territories. o More support for multi-dimensional point patterns. o Minor improvements and bug fixes. o Version nickname: "Mixed Effects" NEW DATASET o austates The states and large mainland territories of Australia represented as polygonal regions forming a tessellation. NEW FUNCTIONS o dppm Fit a determinantal point process model to point pattern data. o fitted.dppm, predict.dppm, intensity.dppm Predict a fitted dppm object. o logLik.dppm, AIC.dppm, extractAIC.dppm, nobs.dppm Likelihood and AIC for determinantal point process models (enabling the use of 'step') o coef.dppm, formula.dppm, print.dppm, terms.dppm, labels.dppm, model.frame.dppm, model.matrix.dppm, model.images.dppm, is.stationary.dppm, reach.dppm, unitname.dppm, unitname<-.dppm, Window.dppm Various methods for dppm objects. o parameters.dppm Extract meaningful list of model parameters o objsurf.dppm Objective function surface of a dppm object o residuals.dppm Residual measure for a dppm object. o dppBessel, dppCauchy, dppGauss, dppMatern, dppPowerExp Determinantal Point Process models. o update.dppmodel Set parameter values in a dpp model. o is.stationary.dppmodel, print.dppmodel, reach.dppmodel, valid.dppmodel Basic information about a dpp model o rdpp, simulate.dppmodel Simulation of a dpp model. o intensity.dppmodel, Kmodel.dppmodel, pcfmodel.dppmodel Moments of a dpp model o dim.dppmodel, dppapproxkernel, dppapproxpcf, dppeigen, dppfamily, dppkernel, dppparbounds, dppspecdenrange, dppspecden Helper functions for dpp models. o dclf.sigtrace, mad.sigtrace, mctest.sigtrace Significance trace of Monte Carlo test o dg.test Dao-Genton adjusted Monte Carlo goodness-of-fit test. o dg.envelope Simulation envelopes corresponding to Dao-Genton test. o dg.sigtrace Significance trace for Dao-Genton test o dg.progress Progress plot for Dao-Genton test o markcrosscorr Mark cross-correlation function for point patterns with several columns of marks o fixef.mppm, ranef.mppm Extract fixed effects and random effects from a point process model fitted to replicated point patterns. o rtemper Simulated annealing or simulated tempering. o to.opaque, to.transparent Change transparency value in colours o rgb2hsva Convert RGB to HSV data, like rgb2hsv, but preserving transparency. o superimpose.ppplist, superimpose.splitppp New methods for 'superimpose' for lists of point patterns. o dkernel, pkernel, qkernel, rkernel Probability density, cumulative probability, quantiles and random generation from distributions used in basic one-dimensional kernel smoothing. o kernel.factor Auxiliary calculations for one-dimensional kernel smoothing. o PPversion, QQversion Transformation of a summary function to its P-P or Q-Q counterpart. o spatdim Spatial dimension of any object in the spatstat package. o as.boxx Convert data to a multi-dimensional box. o intensity.ppx Method for 'intensity' for multi-dimensional space-time point patterns. o fourierbasis Evaluate Fourier basis functions in any number of dimensions. o valid New generic function, with methods valid.ppm, valid.lppm, valid.dppmodel o emend, emend.ppm, emend.lppm New generic function with methods for ppm and lppm. emend.ppm is equivalent to project.ppm o Penttinen New pairwise interaction model. o quantile.density Calculates quantiles from kernel density estimates. o CDF.density Calculates cumulative distribution function from kernel density estimates. SIGNIFICANT USER-VISIBLE CHANGES o simulation Several basic simulation algorithms have been accelerated. Consequently, simulation outcomes are not identical to those obtained with previous versions of spatstat, even when the same random seed is used. To ensure compatibility with previous versions of spatstat, revert to the slower code by setting spatstat.options(fastthin=FALSE, fastpois=FALSE). o mppm Now handles models with a random effect component. New argument 'random' is a formula specifying the random effect. o vcov.mppm Now handles models with Gibbs interactions. o [.ppp New argument 'clip' determines whether the window is clipped. o [.ppp The previously-unused argument 'drop' now determines whether to remove unused levels of a factor. o [.pp3, [.lpp, [.ppx, subset.ppp, subset.pp3, subset.lpp, subset.ppx These methods now have an argument 'drop' which determines whether to remove unused levels of a factor. o density.lpp Now supports both the 'equal-split continuous' and 'equal-split discontinuous' smoothers. New argument 'continuous' determines the choice of smoother. o envelope New argument 'scale' allows global envelopes to have width proportional to a specified function of r, rather than constant width. o dclf.test, mad.test, dclf.progress, mad.progress, mctest.progress New argument 'scale' allows summary function values to be rescaled before the comparison is performed. o dclf.test, mad.test New argument 'interpolate' supports interpolation of p-value. o dclf.progress, mad.progress, mctest.progress New argument 'interpolate' supports interpolation of critical value of test. o simulate.ppm New argument 'w' controls the window of the simulated patterns. o default.rmhcontrol, default.rmhexpand New argument 'w'. o markcorr New argument 'weights' allows computation of the weighted version of the mark correlation function. o density.lpp New argument 'kernel' specifies the smoothing kernel. Any of the standard one-dimensional smoothing kernels can be used. o contour.im New argument 'col' specifies the colour of the contour lines. If 'col' is a colour map, then the contours are drawn in different colours. o plot.ppp The default colour for the points is now a transparent grey, if this is supported by the plot device. o rgbim, hsvim New argument 'A' controls the alpha (transparency) channel. o rgb2hex, col2hex, paletteindex, is.colour, samecolour, complementarycolour, is.grey, to.grey These colour tools now handle transparent colours. o rgb2hex New argument 'maxColorValue' o to.grey New argument 'transparent'. o progressreport New argument 'state' New option: style="tk" o rLGCP This function no longer requires the package 'RandomFields' to be loaded explicitly. o kppm Fitting a model with clusters="LGCP" no longer requires the package 'RandomFields' to be loaded explicitly. o rpoispp Accelerated, when 'lambda' is a pixel image. o rthin Accelerated, when 'P' is a single number. o spatstat.options New options 'fastthin' and 'fastpois' enable fast simulation algorithms. Set these options to FALSE to reproduce results obtained with previous versions of spatstat. o split.ppp The splitting variable 'f' can now be a logical vector. o collapse.fv This is now treated as a method for the 'nlme' generic 'collapse'. Its syntax has been adjusted slightly. o diagnose.ppm, plot.diagppm New arguments col.neg, col.smooth control the colour maps. o valid.ppm This is now a method for the generic function 'valid'. o ppm.ppp, ppm.quad New argument 'emend', equivalent to 'project'. o "[<-.im" Accepts an array for 'value'. o as.im.function New argument 'strict'. o bw.ppl New argument 'weights'. o plot.mppm New argument 'se'. o dclf.test, mad.test Formal arguments 'use.theo' and 'internal' have been removed. o predict.kppm, residuals.kppm Now issues a warning when the calculation ignores the cluster/Cox component and treats the model as if it were Poisson. (This currently happens in predict.kppm when se=TRUE or interval != "none", and in residuals.kppm when type != "raw"). BUG FIXES o lpp Crashed if X was a 4-column matrix. Fixed. o plot.fv Crashed with some graphics devices, if legend=TRUE. Fixed. o effectfun Crashed if 'covname' was missing. Fixed. o rVarGamma, rMatClust, rThomas, rCauchy, rNeymanScott Crashed if 'kappa' was a function or image instead of a single number. [Spotted by Ben Ramage.] Fixed. o plot.mppm Crashed with a message about "figure margins too large" unless the argument se=FALSE was given explicitly. Fixed. o opening.owin, closing.owin Crashed sometimes, with a message about a rectangle not containing a window. Fixed. o persp.im Crashed if all pixel values were equal to zero (unless zlim was given). Fixed. o predict.ppm Crashed sometimes if the model was fitted with use.gam=TRUE. o as.linim.linfun Generated an error ('L must be a linear network') if extra arguments were given. o as.function.fv Generated an error when executed in the 'covr' package. Fixed. o rmh, simulate.ppm Results were incorrect for inhomogeneous multitype models simulated with fixall=TRUE (i.e. prescribing a fixed number of points of each type) if the model was segregated (i.e. if different types of points had different first order trend). Fixed. o dclf.progress, mad.progress Ignored the argument 'alternative'. Fixed. o $<-.hyperframe, [<-.hyperframe Result was garbled if 'value' was a hyperframe with one column. o rmh.ppm Argument 'w' was ignored in some cases. Fixed. o Hest There was an artefact at r=0 when conditional=TRUE. Fixed. o [.msr The result of M[W] where W is a window was a measure with window W, instead of intersect.owin(W, Window(M)). Fixed. o pool.envelope Did not always respect the value of 'use.theory'. Fixed. o envelope, pool.envelope If 'ginterval' was given, the results were in a slightly incorrect format. Fixed. o pool.envelope Did not check for compatible values of 'ginterval'. Fixed. CHANGES IN spatstat VERSION 1.42-2 OVERVIEW o We thank Bob Klaver and Harold-Jeffrey Ship for contributions. o Improvements to simulation of Neyman-Scott processes. o Improvements to fitting of Neyman-Scott models. o Extended functionality for pixel images. o Fitted intensity on linear network o Triangulation of windows. o Corrected an edge correction. o Bug fixes and performance improvements. o Nickname: 'Barking at Balloons' NEW FUNCTIONS o triangulate.owin Decompose a spatial window into triangles. o fitted.lppm Fitted intensity values for a point process on a linear network. SIGNIFICANT USER-VISIBLE CHANGES o rThomas, rMatClust, rCauchy, rVarGamma When the model is approximately Poisson, it is simulated using rpoispp. This avoids computations which would require huge amounts of memory. New argument 'poisthresh' controls this behaviour. o update.kppm Now handles additional arguments in any order, with or without names. Changed arguments. Improved behaviour. o kppm, clusterfit New argument 'algorithm' specifies the choice of optimisation algorithm. o kppm Left hand side of formula can now involve entries in the list 'data'. o rotmean New argument 'padzero'. Default behaviour has changed. o rose.default New argument 'weights'. o rose New arguments 'start' and 'clockwise' specify the convention for measuring and plotting angles. o padimage New argument 'W' allows an image to be padded out to fill any window. o union.owin Improved behaviour when there are more than 2 windows. o clusterset Improved behaviour. o affine.owin Allows transformation matrix to be singular, if the window is polygonal. BUG FIXES o spatstat spatstat could not be installed on some 64-bit VM systems because of an apparent bug in R. Fixed. o rThomas, rMatClust, rCauchy, rVarGamma Large values of the scale parameter could cause the algorithm to freeze or require huge amounts of memory. Fixed. o pcf, pcfinhom Crashed if the point pattern was empty. Fixed. o plot.fv Gave an error message if all 'y' values were equal, when legend=TRUE. Fixed. o rose.default Display was incorrect when unit="radian". Fixed. o Kest Ohser-Stoyan rigid motion correction (correction='rigid') was calculated incorrectly at large distances. Fixed. o summary.im Issued a warning about numerical overflow in some cases. [Spotted by Bob Klaver.] Fixed. o plot.im Sometimes warned that 'box' is not a graphical parameter. Fixed. CHANGES IN spatstat VERSION 1.42-1 OVERVIEW o We thank Andrew Hardegen, Tom Lawrence, Robin Milne, Suman Rakshit, and Brian Ripley for contributions. o Urgent bug fix. o More robust simulation of cluster processes. o Slightly accelerated. o Version nickname: 'Vogon Poetry' NEW FUNCTIONS o boundingbox.solist Method for boundingbox for lists of spatial objects. SIGNIFICANT USER-VISIBLE CHANGES o rThomas, rMatClust, rCauchy, rVarGamma, rNeymanScott New faster algorithm which is more robust against extreme values of the parameters. o rNeymanScott New argument 'nonempty' controls choice of algorithm. o solist, as.solist Accelerated. o as.list.hyperframe Accelerated. BUG FIXES o residuals.mppm Brought some computers to a grinding halt, due to the bug in solist(). Fixed. o solist, as.solist In rare cases, the format was corrupted, or the algorithm never terminated. Fixed. CHANGES IN spatstat VERSION 1.42-0 OVERVIEW o We thank Anders Bilgrau, Ute Hahn, Jack Hywood, Tuomas Rajala, Cody Schank, Olivia Semboli and Ben Taylor for contributions. o Version nickname: 'Life, The Universe and Everything' o Permutation test for difference between groups of point patterns. o Variational Bayes estimation for point process models. o Score test in anova.ppm o ROC curve, and discrimination index AUC, for fitted models. o Interactive text editor for spatial datasets. o Tools for analysing data on a tree. o Kernel density/intensity estimation on a linear network. o Random pixel noise. o Improved behaviour of polygon geometry operations. o Improved support for cluster and Cox models. o Improved basic support for owin objects. o Improved support for tessellations. o More hierarchical Gibbs interactions. o Modifications to Kest. o summary method for Cox and cluster models. o class 'listof' is almost completely replaced by 'anylist' and 'solist'. o Improvements and bug fixes. o spatstat now depends on R version 3.2.0 or later. NEW FUNCTIONS o studpermu.test Studentised permutation test for difference between groups of point patterns. Generously contributed by Ute Hahn. o AIC.kppm, extractAIC.kppm, logLik.kppm, nobs.kppm Methods for computing AIC for fitted Cox and cluster models. o transmat Convert pixel arrays between different display conventions. o roc Receiver Operating Characteristic curve. o auc Discrimination index AUC (area under the ROC curve) o edit.ppp, edit.psp, edit.im Interactive text editor works for spatial datasets. o edit.hyperframe Interactive text editor works for hyperframes. o parameters Extract all parameters from a fitted model. o density.lpp Kernel estimation of point process intensity on a linear network. o extractbranch, deletebranch, treeprune, treebranchlabels, begins Tools for analysing data on a tree. o rnoise Random pixel noise. o as.data.frame.owin Convert a window to a data frame. o harmonise.owin Convert several binary mask windows to a common pixel grid. o copyExampleFiles Copy the raw data files from an installed dataset to a chosen folder, for use in a practice exercise. o density.ppplist Method for 'density' for lists of point patterns. o inradius Radius of largest circle inside a window. o mergeLevels Merge different levels of a factor. o relevel.im, relevel.ppp, relevel.ppx Change the reference level of a factor. o simulate.profilepl simulation method for models fitted by profile maximum pseudolikelihood. o predict.rho2hat Prediction method for class rho2hat o with.msr Evaluate (an expression involving) components of a measure. o summary.kppm, print.summary.kppm, coef.summary.kppm Methods for 'summary' and 'coef(summary(..))' for Cox and cluster models. o as.im.funxy Method for as.im for class funxy. o shift.linim, scalardilate.linim, affine.linim Geometrical transformations for 'linim' objects. o Smooth.solist Smooth method for a list of spatial objects. o unitname.tess, unitname<-.tess Tessellations now keep track of the name of the unit of length. o dirichletAreas Faster algorithm for tile.areas(dirichlet(X)). o identify.lpp Method for 'identify' for point patterns on a linear network. o HierStraussHard, HierHard Hierarchical interactions for Gibbs models. o delaunayDistance, delaunayNetwork, dirichletEdges, dirichletNetwork, dirichletVertices, dirichletWeights These functions will replace delaunay.distance, delaunay.network, dirichlet.edges, dirichlet.network, dirichlet.vertices and dirichlet.weights respectively. The latter are now 'deprecated'. SIGNIFICANT USER-VISIBLE CHANGES o ppm Now supports Variational Bayes fitting method. o kppm 'AIC' and 'step' now work for kppm objects fitted using maximum Palm likelihood. o kppm The default for the weight function 'weightfun' has been changed, for better performance. o envelope envelope methods now have argument 'use.theory' specifying whether to use the 'theoretical' value of the summary function when constructing simultaneous envelopes. o anova.ppm Now performs the Score Test, for Poisson models only, if argument test="Rao" or test="score". o Kest New argument 'rmax' controls maximum value of argument 'r' o diagnose.ppm Now computes and prints the null standard deviation of the smoothed Pearson residual field, when appropriate. o nncorr, nnmean, nnvario New argument 'k' specifies k-th nearest neighbour. o quadrat.test.ppp, quadrat.test.quadratcount New argument 'lambda' supports a test of the Poisson process with given intensity 'lambda'. o clickpoly, clickbox These functions now handle graphical arguments to polygon() when drawing the resulting polygon or rectangle. o owin, as.owin, as.mask owin(mask=D) or as.owin(D) or as.mask(D) will produce a binary mask window if D is a data frame with two columns of (x,y) coordinates or a data frame with three columns containing (x,y,logical). o as.owin.data.frame W can now be a data frame with only two columns, giving the spatial coordinates of the pixels that are inside the window. o rose Tick marks now have labels showing the angle (in degrees or radians). o distcdf New argument 'regularise' determines whether values at short distances will be smoothed to avoid discretisation artefacts. o rpoislinetess Return value now has an attribute 'lines' giving the realisation of the Poisson line process. o intersect.owin, union.owin, setminus.owin New argument 'p' controls resolution of polygon clipping algorithm. o intersect.owin, union.owin Arguments may be lists of windows, of class 'solist'. Formal arguments A and B have been removed. o superimpose Now handles lists of point patterns (objects of class 'ppplist' or 'splitppp') o density.ppp New argument 'positive' allows the user to stipulate that density values must be positive (avoiding numerical errors which occasionally produce small negative values). o adaptive.density Now accepts f = 0 (uniform intensity estimate) and f = 1 (Voronoi intensity estimate) as well as 0 < f < 1. Algorithm accelerated. o rSSI Can now generate inhomogeneous patterns. o effectfun Now works for 'kppm' and 'lppm' objects as well. o integral.im, integral.msr Argument 'domain' can now be a tessellation; the integral over each tile of the tessellation is returned. o allstats, compareFit, markcorr, split.ppx, by.ppp Result is now of class 'anylist'. o by.im, density.splitppp, idw, model.images, nnmark, pixellate.ppp, predict.lppm, predict.ppm, quadratcount.splitppp, quadratresample, relrisk, Smooth.msr, split.im, tiles Result is now of class 'solist'. o split.ppp New argument 'reduce'. Result now inherits class 'ppplist' and 'solist', as well as 'splitppp' o rLGCP New argument 'nsim' allows multiple patterns to be generated. o alltypes New argument 'reuse' determines whether all simulation envelopes are based on the same set of simulated patterns, or on independent sets. o rpoispp, runifpoint New argument 'ex' makes it possible to generate a random pattern similar to an example point pattern. o effectfun Argument 'covname' is not needed if the model has only one covariate. o quadratcount Argument 'tess' can now be anything acceptable to as.tess. o tess New argument 'unitname' specifies the name of the unit of length. If it is missing, unitname information will be extracted from the other data. o intersect.tess, chop.tess, quadrats Results of these functions now have the same 'unitname' as their input. o persp.im, nnclean, plot.qqppm, plot.bw.optim These plotting functions now obey spatstat.options('monochrome') o lurking Now returns an object of class 'lurk' which has a plot method. Two-standard-deviation limits are now plotted using grey shading. o marktable New argument 'N' for studying the N nearest neighbours. New argument 'collapse' for manipulating the contingency table. o harmonise.fv Now discards columns with names which do not match. o eval.fv New argument 'equiv' can be used to declare that two columns with different names in different objects are equivalent. o quantile.ewcdf New argument 'type' controls the type of quantile. o plot.imlist New argument 'plotcommand' specifies how to plot each image. o persp.im The lower extent of the apron can now be controlled by 'zlim'. o quadscheme Argument 'method' is partially matched. o Kdot, Ldot New argument 'from' is an alternative to 'i'. o Kcross, Lcross New arguments 'from' and 'to' are alternatives to 'i' and 'j' respectively. o varblock Changed the ordering (and therefore default colours/styles) of curves in the plot, to match other functions like lohboot. o bw.diggle New argument 'nr' controls accuracy. o textureplot Now accepts a pixel image, a tessellation, or anything acceptable to as.tess. o textureplot Line spacing in legend now matches line spacing in main display. o [.tess Subset index can now be a window. o plot.tess Can now plot a text label in each tile. o plot.tess New argument 'do.plot'. o MultiHard, MultiStrauss, MultiStraussHard, HierStrauss Printed output of fitted model now respects spatstat.options('terse'). o print.ppm Reduced redundancy in output in some cases. o print.msr Responds better to spatstat.options('terse'). o print.ppm, print.fii, print.interact Irregular parameters are now printed to the number of significant figures specified by options("digits"). o square New argument 'unitname'. o plot.fv Return value is now invisible. o delaunay.distance, delaunay.network, dirichlet.edges, dirichlet.network, dirichlet.vertices These functions are now 'deprecated', and will be replaced by delaunayDistance, delaunayNetwork, dirichletEdges, dirichletNetwork and dirichletVertices respectively. o data(residualspaper) In the real datasets (Fig1 and Fig11), the name of the unit of length has now been recorded. o rLGCP This function now requires the package 'RandomFields' to be loaded explicitly by library(RandomFields) or require(RandomFields), unless model="exp". o iplot, istat These functions now require the package 'rpanel' to be loaded explicitly by library(rpanel) or require(rpanel). o ppm, quadscheme Improved calculation of Dirichlet weights. o countends New argument 'toler' controls numerical errors o diagnose.ppm Improved handling of additional graphics arguments. o pcf3est Mathematical labels changed. o plot.hyperframe Default margin spacing has been increased. BUG FIXES o Kinhom, Linhom The value of 'theo' was erroneously rescaled by a small amount, when renormalise=TRUE (the default). Fixed. o Kmark Values were erroneously rescaled. Fixed. o union.owin Strange results were sometimes obtained when taking the union of more than two windows. Fixed. o rpoispp3 Implementation was incorrect for nsim > 1. (Results may have been incorrect.) Spotted by Jack Hywood. Fixed. o as.owin.data.frame Crashed if the window was not connected. Fixed. o Frame<- Crashed when applied to a binary mask. Fixed. o rho2hat Crashed if cov1="x" and cov2="y". Fixed. o as.mask Crashed sometimes when only the argument 'xy' was given. Fixed. o ppm Crashed (rarely) when method='ho' if the simulated pattern was empty. Fixed. o istat, iplot Crashed in recent versions of rpanel. Fixed. o convexhull Crashed if applied to a 'psp' object. Fixed. o plot.ppm Crashed with message about 'variable lengths differ'. Fixed. o plot.solist Crashed when applied to a list of point patterns if some patterns had more than one column of marks. Fixed. o Smooth.ppp Crashed if applied to a point pattern with several columns of marks if some of the columns were factors. Fixed. o runifpoint3, rpoispp3 Crashed if nsim > 1. Spotted by Jack Hywood. Fixed. o hist.im Crashed if argument 'freq' was given. Fixed. o MultiStraussHard Generated misleading error messages (e.g. 'model is invalid') when arguments 'iradii' and 'hradii' did not have the same pattern of NA's. Fixed. o plot.solist Figures were sometimes aligned incorrectly when the argument 'panel.args' was given. Fixed. o scaletointerval Results sometimes fell slightly outside the desired interval due to numerical error. Fixed. o plot.solist Behaved incorrectly when plotcommand='persp'. Fixed. o "[.hyperframe" Sometimes returned an 'anylist' when it should have returned a 'solist'. Fixed. o plot.im Did not plot surrounding frame box when ribbon=FALSE. Fixed. o envelope The functions stored when savefuns=TRUE did not inherit the correct name for the unit of length. Fixed. o print.ppm, print.fii, print.interact Layout was misaligned. Fixed. o plot.plotppm Paused for input when it was not appropriate. Fixed. o plot.fv On png devices, the legend box was drawn with a white background, obscuring the main plot. Fixed. o plot.owin, plot.ppp, plot.im There was unnecessary extra space above the main title. Fixed. o plot.rho2hat Colour map ribbon was drawn but not annotated. Fixed. o density.splitppp, density.ppplist Format was out of order if se=TRUE. Fixed. o MultiStraussHard project.ppm sometimes yielded a model that was still invalid. Fixed. CHANGES IN spatstat VERSION 1.41-1 OVERVIEW o This is identical to the major release 1.41-0 except for minor bug fixes. The change log for 1.41-0 is repeated here with minor modifications. o Version nickname: 'Ides of March' o We thank Ahmed El-Gabbas, Ute Hahn, Aruna Jammalamadaka, Ian Renner, Brian Ripley, Torben Tvedebrink and Sasha Voss for contributions. o Fixed a bug causing a segmentation fault. o Standard errors for kernel estimates of intensity. o Test for segregation. o Tessellations may now have marks. o Nested splitting. o More support for cluster models. Reorganised parametrisation. o Sparse data representation of linear networks. o More support for data on a linear network. o New datasets: 'spiders' and 'dendrite'. o Improvements and bug fixes. o spatstat no longer uses Fortran. o spatstat no longer depends on the package 'scatterplot3d'. o spatstat now imports (rather than 'suggests') the Matrix package. NEW DATASETS o dendrite Dendritic spines on the dendrite network of a neuron. A point pattern on a linear network. Generously contributed by Aruna Jammalamadaka. o spiders Spider webs on the mortar lines of a brick wall. A point pattern on a linear network. Generously contributed by Sasha Voss. NEW FUNCTIONS o segregation.test Test of spatial segregation of types in a multitype point pattern. o clusterfield, clusterkernel Compute the cluster kernel (offspring density) of a cluster process model, or compute the cluster field generated by superimposing copies of the cluster kernel at specified locations. o clusterradius Compute the radius of the support of the offspring density of a cluster process model. o as.linnet.psp Convert a line segment pattern to a linear network by guessing the connectivity using a distance threshold. o iplot.linnet, iplot.lpp Methods for interactive plotting 'iplot' for objects of class lpp and linnet. o Mathematical operations are now supported for pixel images on a linear network. See help(Math.linim) o dirichlet.network, delaunay.network The linear networks formed by the Dirichlet tessellation and Delaunay triangulation. o dirichlet.edges The edges of the Dirichlet tessellation. o selfcut.psp Cut line segments where they cross each other. o vertices.linnet Extract the vertices (nodes) of the linear network. o vertexdegree Compute the degree of each vertex in a linear network. o pixellate.linnet Pixellate a linear network. o subset.hyperframe 'subset' method for class 'hyperframe'. o head.hyperframe, tail.hyperframe 'head' and 'tail' methods for hyperframes. o clickdist Measures the distance between two spatial locations clicked by the user. o solapply, anylapply wrappers for 'lapply' which return a list of class 'solist' or 'anylist'. o Kmark Weighted K-function. Identical to 'markcorrint' and will eventually replace it. o marks.tess, marks<-.tess, unmark.tess: Extract or change the marks associated with the tiles of a tessellation. o quantess Quantile tessellation: divide space into pieces which contain equal amounts of 'stuff'. o nestsplit Nested split o integral New generic function for integrating functions, with methods for 'im', 'msr', 'linim' and 'linfun'. o selfcut.psp Cut line segments where they cross each other o as.function.im Convert a pixel image to a function(x,y). o as.linnet.linim Extract the linear network from a 'linim' object. o pool.fv, pool.anylist New methods for 'pool' o Window.linnet Extract the two-dimensional window containing a linear network. SIGNIFICANT USER-VISIBLE CHANGES o linnet, lpp A linear network can now be built in 'sparse matrix' form which requires much less memory. o chicago The Chicago street crimes data are now stored in 'sparse matrix' form. To convert them to non-sparse form, use as.lpp(chicago, sparse=FALSE) o kppm The parametrisation of cluster models has been reorganised. The scale parameter is now always called 'scale'. Results should be backward-compatible. o cauchy.estK, cauchy.estpcf, matclust.estK, matclust.estpcf, thomas.estK, thomas.estpcf, vargamma.estK, vargamma.estpcf The parametrisation of cluster models has been reorganised. The scale parameter is now always called 'scale'. o plot.kppm Also plots the cluster kernel. o density.ppp New argument 'se' allows calculation of standard errors as well. o plot.pp3 Now produces a genuine perspective view. New arguments control the eye position for the perspective view. o Emark, Vmark These functions can now work with multiple columns of marks. o pixellate.psp Can now count the number of segments that intersect each pixel, instead of the total length of intersection. o linfun If g = linfun(f, L), the function f will always be called as f(x,y,seg,tp, ...) It is no longer expected to handle the case where 'seg' and 'tp' are absent. The resulting function g can now be called as g(X) where X is an lpp object, or as g(x,y) or g(x,y,seg,tp) where x,y,seg,tp are coordinates. o tess New argument 'marks' allows marks to be associated with tiles. o anova.lppm Outdated argument 'override' has been removed. o split<-.ppp Preserves the original ordering of the data, if possible. o MultiHard, MultiStrauss, MultiStraussHard, HierStrauss Zero values in the interaction radii are now treated as NA. Improved handling of missing arguments. Printed output now respects options('width') o linearKinhom, linearKcross.inhom, linearKdot.inhom, linearpcfinhom, linearpcfcross.inhom, linearpcfdot.inhom If the intensity argument lambda, lambdaI, lambdaJ, lambdadot is a fitted point process model, the model is first updated by re-fitting it to the data, before computing the fitted intensity. o solutionset The expression will be evaluated using pixel arithmetic (Math.im) if it cannot be evaluated using eval.im. o to.grey Now uses better weights for the R, G, B channels. o rVarGamma Accelerated. o summary.mppm, print.mppm These functions now respect options('width') and spatstat.options('terse'). o print.quadrattest Now respects options('width') and spatstat.options('terse'). o print.pp3 Now respects options('width') o print.lpp Now respects options('width') and options('digits'). o print.owin, print.im, print.summary.owin, print.summary.im Now respect options('width'). o nnmean Now yields a vector, instead of a 1-column matrix, when there is only a single column of marks. o pairdist.psp, crossdist.psp, nndist.psp The option 'method="Fortran"' is no longer supported. The default is 'method="C"'. o [.hyperframe: When a row of data is extracted with drop=TRUE, the result belongs to class 'anylist'. o installation of spatstat A Fortran compiler is no longer needed to compile spatstat from source. o hyperframe class The internal structure of hyperframes has changed slightly: columns of objects are now stored and returned as lists of class 'anylist' or 'solist'. There should be no change in behaviour. o datasets Internal format of the datasets bdspots, bei, clmfires, demohyper, flu, gorillas, heather, Kovesi, murchison, osteo, pyramidal, waterstriders has changed slightly to use the classes 'anylist' and 'solist'. There should be no change in behaviour. o K3est New argument 'ratio'. o spatstat.options New option 'par.points3d' sets default arguments for plot.pp3. o diagnose.ppm New arguments 'xlab', 'ylab', 'rlab' determine the labels in the 4-panel plot, and new argument 'outer' controls their position. The confusing default value for 'compute.sd' has been changed. o iplot.layered New argument 'visible' controls which layers are initially visible. o plot.lpp New argument 'show.window' controls whether to plot the containing window. o textstring Any number of spatial locations (x,y) can be specified, with a corresponding vector of text strings. o plot.hyperframe New argument 'mar' o plot.linnet New argument 'do.plot' o summary.hyperframe Improved output. o eval.linim Improved scoping rules. o pixellate.owin Accelerated. o summary.linnet Now prints more information, and respects options('digits'). o rmpoispp, rmpoint The vector of possible types of points will default to the 'names' vector of the argument 'lambda', 'n', or 'f' where appropriate. o rpoislpp Argument 'L' can be omitted when lambda is a 'linim' or 'linfun' o simulate.ppm, simulate.kppm, simulate.lppm, simulate.slrm New argument 'drop': if nsim = 1 and drop=TRUE, the result is a point pattern rather than a list containing one point pattern. o runifdisc, runifpoint, rpoint, rpoispp, rmpoint, rmpoispp, rMaternI, rMaternII, rSSI, rPoissonCluster, rGaussPoisson, rstrat, rsyst, rcell, rthin, rNeymanScott, rMatClust, rThomas, rCauchy, rVarGamma, rpoispp3, runifpoint3 New argument 'drop': if nsim = 1 and drop=TRUE, the result is a point pattern rather than a list containing one point pattern. o spatstat.options New option 'units.paren' controls the type of parenthesis enclosing the explanatory text about the unit of length, in print.ppm, plot.fv, etc. o closepairs, crosspairs New option: what="ijd" returns only the indices i, j and the distance d o rCauchy, rMatClust, rNeymanScott, rPoissonCluster, rThomas, rVarGamma Argument names have changed. BUG FIXES o sumouter A segmentation fault could occur if any data were NA. Fixed. o simulate.kppm Simulation failed for log-Gaussian Cox processes (in simulate.kppm only) with an error message from the RandomFields package. Fixed. o ppm, predict.ppm, profilepl Crashed sometimes with message "interaction evaluator did not return a matrix". Fixed. o lppm step() did not work correctly on 'lppm' objects. Fixed. o quadscheme If quadscheme() was called explicitly, with the stipulated number of tiles exceeding the number of dummy points given, then the quadrature weights were sometimes vastly inflated - total quadrature weight was much larger than window area. Spotted by Ian Renner. Fixed. o predict.rhohat Result was incorrect for data on a non-rectangular window (and a warning was issued about incorrect vector length). Fixed. o Math.im Unary operators did not work (e.g."-x") Fixed. o density.ppp Crashed when at="points" if the dataset had exactly 1 point. Fixed. o rSSI Crashed if nsim > 1. Fixed. o influence.ppm, leverage.ppm, dfbetas.ppm Crashed or issued a warning if any quadrature points had conditional intensity zero under the model (negative infinite values of the sufficient statistic). Fixed. o clickppp, clickpoly Did not work correctly in the RStudio display device. Fixed. o Iest Ignored the arguments 'r' and 'eps'. Fixed. o markvario Result was garbled, when X had more than one column of marks. Fixed. o rMatClust, rVarGamma, rCauchy, rNeymanScott Result was a list, but not a 'solist', when nsim > 1. Fixed. o print.mppm, summary.mppm, subfits Crashed if a Poisson interaction was implied but not given explicitly. Fixed. o Kest Crashed if ratio=TRUE and the window was a rectangle. Fixed. o anova.ppm Crashed sometimes with message 'models were not all fitted to the same size of dataset'. (This occurred if there were quadrature points with conditional intensity equal to zero in some models but not in all models.) Fixed. o vcov.kppm Occasionally ran out of memory. Fixed. o as.linim.linfun Erroneously converted the pixel values to numeric values. Fixed. o as.owin.layered Ignored layers with zero area. Fixed. o plot.ppm Paused the plot between frames even when there was only one frame. Fixed. o plot.layered Did not allocate space for legends of 'lpp' objects. Fixed. o plot.lpp Ignored symbolmap arguments like 'cex' and confused the arguments 'col' and 'cols'. Fixed. o plot.diagppm Ignored add=TRUE in some cases. Fixed. o iplot.layered Did not handle 'diagramobj' objects correctly. Fixed. o plot.yardstick Changed arguments. CHANGES IN spatstat VERSION 1.41-0 OVERVIEW o We thank Ahmed El-Gabbas, Ute Hahn, Aruna Jammalamadaka, Ian Renner, Brian Ripley, Torben Tvedebrink and Sasha Voss for contributions. o Fixed a bug causing a segmentation fault. o Standard errors for kernel estimates of intensity. o Test for segregation. o Tessellations may now have marks. o Nested splitting. o More support for cluster models. Reorganised parametrisation. o Sparse data representation of linear networks. o More support for data on a linear network. o New datasets: 'spiders' and 'dendrite'. o Improvements and bug fixes. o spatstat no longer uses Fortran. o spatstat no longer depends on the package 'scatterplot3d'. o spatstat now imports (rather than 'suggests') the Matrix package. o Nickname: 'Team Australia' NEW DATASETS o dendrite Dendritic spines on the dendrite network of a neuron. A point pattern on a linear network. Generously contributed by Aruna Jammalamadaka. o spiders Spider webs on the mortar lines of a brick wall. A point pattern on a linear network. Generously contributed by Sasha Voss. NEW FUNCTIONS o segregation.test Test of spatial segregation of types in a multitype point pattern. o clusterfield, clusterkernel Compute the cluster kernel (offspring density) of a cluster process model, or compute the cluster field generated by superimposing copies of the cluster kernel at specified locations. o clusterradius Compute the radius of the support of the offspring density of a cluster process model. o as.linnet.psp Convert a line segment pattern to a linear network by guessing the connectivity using a distance threshold. o iplot.linnet, iplot.lpp Methods for interactive plotting 'iplot' for objects of class lpp and linnet. o Mathematical operations are now supported for pixel images on a linear network. See help(Math.linim) o dirichlet.network, delaunay.network The linear networks formed by the Dirichlet tessellation and Delaunay triangulation. o dirichlet.edges The edges of the Dirichlet tessellation. o selfcut.psp Cut line segments where they cross each other. o vertices.linnet Extract the vertices (nodes) of the linear network. o vertexdegree Compute the degree of each vertex in a linear network. o pixellate.linnet Pixellate a linear network. o subset.hyperframe 'subset' method for class 'hyperframe'. o head.hyperframe, tail.hyperframe 'head' and 'tail' methods for hyperframes. o clickdist Measures the distance between two spatial locations clicked by the user. o solapply, anylapply wrappers for 'lapply' which return a list of class 'solist' or 'anylist'. o Kmark Weighted K-function. Identical to 'markcorrint' and will eventually replace it. o marks.tess, marks<-.tess, unmark.tess: Extract or change the marks associated with the tiles of a tessellation. o quantess Quantile tessellation: divide space into pieces which contain equal amounts of 'stuff'. o nestsplit Nested split o integral New generic function for integrating functions, with methods for 'im', 'msr', 'linim' and 'linfun'. o selfcut.psp Cut line segments where they cross each other o as.function.im Convert a pixel image to a function(x,y). o as.linnet.linim Extract the linear network from a 'linim' object. o pool.fv, pool.anylist New methods for 'pool' o Window.linnet Extract the two-dimensional window containing a linear network. SIGNIFICANT USER-VISIBLE CHANGES o linnet, lpp A linear network can now be built in 'sparse matrix' form which requires much less memory. o chicago The Chicago street crimes data are now stored in 'sparse matrix' form. To convert them to non-sparse form, use as.lpp(chicago, sparse=FALSE) o kppm The parametrisation of cluster models has been reorganised. The scale parameter is now always called 'scale'. Results should be backward-compatible. o cauchy.estK, cauchy.estpcf, matclust.estK, matclust.estpcf, thomas.estK, thomas.estpcf, vargamma.estK, vargamma.estpcf The parametrisation of cluster models has been reorganised. The scale parameter is now always called 'scale'. o plot.kppm Also plots the cluster kernel. o density.ppp New argument 'se' allows calculation of standard errors as well. o plot.pp3 Now produces a genuine perspective view. New arguments control the eye position for the perspective view. o Emark, Vmark These functions can now work with multiple columns of marks. o pixellate.psp Can now count the number of segments that intersect each pixel, instead of the total length of intersection. o linfun If g = linfun(f, L), the function f will always be called as f(x,y,seg,tp, ...) It is no longer expected to handle the case where 'seg' and 'tp' are absent. The resulting function g can now be called as g(X) where X is an lpp object, or as g(x,y) or g(x,y,seg,tp) where x,y,seg,tp are coordinates. o tess New argument 'marks' allows marks to be associated with tiles. o anova.lppm Outdated argument 'override' has been removed. o split<-.ppp Preserves the original ordering of the data, if possible. o MultiHard, MultiStrauss, MultiStraussHard, HierStrauss Zero values in the interaction radii are now treated as NA. Improved handling of missing arguments. Printed output now respects options('width') o linearKinhom, linearKcross.inhom, linearKdot.inhom, linearpcfinhom, linearpcfcross.inhom, linearpcfdot.inhom If the intensity argument lambda, lambdaI, lambdaJ, lambdadot is a fitted point process model, the model is first updated by re-fitting it to the data, before computing the fitted intensity. o solutionset The expression will be evaluated using pixel arithmetic (Math.im) if it cannot be evaluated using eval.im. o to.grey Now uses better weights for the R, G, B channels. o rVarGamma Accelerated. o summary.mppm, print.mppm These functions now respect options('width') and spatstat.options('terse'). o print.quadrattest Now respects options('width') and spatstat.options('terse'). o print.pp3 Now respects options('width') o print.lpp Now respects options('width') and options('digits'). o print.owin, print.im, print.summary.owin, print.summary.im Now respect options('width'). o nnmean Now yields a vector, instead of a 1-column matrix, when there is only a single column of marks. o pairdist.psp, crossdist.psp, nndist.psp The option 'method="Fortran"' is no longer supported. The default is 'method="C"'. o [.hyperframe: When a row of data is extracted with drop=TRUE, the result belongs to class 'anylist'. o installation of spatstat A Fortran compiler is no longer needed to compile spatstat from source. o hyperframe class The internal structure of hyperframes has changed slightly: columns of objects are now stored and returned as lists of class 'anylist' or 'solist'. There should be no change in behaviour. o datasets Internal format of the datasets bdspots, bei, clmfires, demohyper, flu, gorillas, heather, Kovesi, murchison, osteo, pyramidal, waterstriders has changed slightly to use the classes 'anylist' and 'solist'. There should be no change in behaviour. o K3est New argument 'ratio'. o spatstat.options New option 'par.points3d' sets default arguments for plot.pp3. o diagnose.ppm New arguments 'xlab', 'ylab', 'rlab' determine the labels in the 4-panel plot, and new argument 'outer' controls their position. The confusing default value for 'compute.sd' has been changed. o iplot.layered New argument 'visible' controls which layers are initially visible. o plot.lpp New argument 'show.window' controls whether to plot the containing window. o textstring Any number of spatial locations (x,y) can be specified, with a corresponding vector of text strings. o plot.hyperframe New argument 'mar' o plot.linnet New argument 'do.plot' o summary.hyperframe Improved output. o eval.linim Improved scoping rules. o pixellate.owin Accelerated. o summary.linnet Now prints more information, and respects options('digits'). o rmpoispp, rmpoint The vector of possible types of points will default to the 'names' vector of the argument 'lambda', 'n', or 'f' where appropriate. o rpoislpp Argument 'L' can be omitted when lambda is a 'linim' or 'linfun' o simulate.ppm, simulate.kppm, simulate.lppm, simulate.slrm New argument 'drop': if nsim = 1 and drop=TRUE, the result is a point pattern rather than a list containing one point pattern. o runifdisc, runifpoint, rpoint, rpoispp, rmpoint, rmpoispp, rMaternI, rMaternII, rSSI, rPoissonCluster, rGaussPoisson, rstrat, rsyst, rcell, rthin, rNeymanScott, rMatClust, rThomas, rCauchy, rVarGamma New argument 'drop' o spatstat.options New option 'units.paren' controls the type of parenthesis enclosing the explanatory text about the unit of length, in print.ppm, plot.fv, etc. o closepairs, crosspairs New option: what="ijd" returns only the indices i, j and the distance d o rCauchy, rMatClust, rNeymanScott, rPoissonCluster, rThomas, rVarGamma Argument names have changed. BUG FIXES o sumouter A segmentation fault could occur if any data were NA. Fixed. o simulate.kppm Simulation failed for log-Gaussian Cox processes (in simulate.kppm only) with an error message from the RandomFields package. Fixed. o ppm, predict.ppm, profilepl Crashed sometimes with message "interaction evaluator did not return a matrix". Fixed. o lppm step() did not work correctly on 'lppm' objects. Fixed. o quadscheme If quadscheme() was called explicitly, with the stipulated number of tiles exceeding the number of dummy points given, then the quadrature weights were sometimes vastly inflated - total quadrature weight was much larger than window area. Spotted by Ian Renner. Fixed. o predict.rhohat Result was incorrect for data on a non-rectangular window (and a warning was issued about incorrect vector length). Fixed. o Math.im Unary operators did not work (e.g."-x") Fixed. o density.ppp Crashed when at="points" if the dataset had exactly 1 point. Fixed. o rSSI Crashed if nsim > 1. Fixed. o influence.ppm, leverage.ppm, dfbetas.ppm Crashed or issued a warning if any quadrature points had conditional intensity zero under the model (negative infinite values of the sufficient statistic). Fixed. o clickppp, clickpoly Did not work correctly in the RStudio display device. Fixed. o Iest Ignored the arguments 'r' and 'eps'. Fixed. o markvario Result was garbled, when X had more than one column of marks. Fixed. o rMatClust, rVarGamma, rCauchy, rNeymanScott Result was a list, but not a 'solist', when nsim > 1. Fixed. o print.mppm, summary.mppm, subfits Crashed if a Poisson interaction was implied but not given explicitly. Fixed. o Kest Crashed if ratio=TRUE and the window was a rectangle. Fixed. o anova.ppm Crashed sometimes with message 'models were not all fitted to the same size of dataset'. (This occurred if there were quadrature points with conditional intensity equal to zero in some models but not in all models.) Fixed. o vcov.kppm Occasionally ran out of memory. Fixed. o as.linim.linfun Erroneously converted the pixel values to numeric values. Fixed. o as.owin.layered Ignored layers with zero area. Fixed. o plot.ppm Paused the plot between frames even when there was only one frame. Fixed. o plot.layered Did not allocate space for legends of 'lpp' objects. Fixed. o plot.lpp Ignored symbolmap arguments like 'cex' and confused the arguments 'col' and 'cols'. Fixed. o plot.diagppm Ignored add=TRUE in some cases. Fixed. o iplot.layered Did not handle 'diagramobj' objects correctly. Fixed. o plot.yardstick Changed arguments. CHANGES IN spatstat VERSION 1.40-0 OVERVIEW o We thank Markus Herrmann, Peter Kovesi, Andrew Lister, Enrique Miranda, Tuomas Rajala, Brian Ripley, Dominic Schuhmacher and Maxime Woringer for contributions. o Important bug fixes. o Mathematical operators now apply to images. o Parametric estimates of relative risk from fitted point process models. o Standard errors for relative risk (parametric and non-parametric). o Kernel smoothing and rose diagrams for angular data. o Perceptually uniform colour maps. o Hierarchical interactions for multitype patterns. o Hard core parameters in all interactions no longer need to be specified and will be estimated from data. o Improvements to analysis of deviance and model selection. o New datasets. o New vignette, summarising all datasets installed with spatstat. o Tests and diagnostics now include a Monte Carlo option. o Faster checking of large datasets. o Faster simulations. o Code for drawing diagrams (arrows, scale bars). o Version nickname: 'Do The Maths' NEW DATASETS o bdspots Breakdown spots on microelectronic capacitor electrodes. Generously contributed by Prof Enrique Miranda. o Kovesi Colour maps with perceptually uniform contrast. Generously contributed by Peter Kovesi. NEW FUNCTIONS o Mathematical operations are now supported for images. For example: alpha <- atan(bei.extra$grad) * 180/pi See help(Math.im) o relrisk.ppm Spatially-varying probabilities of different types of points predicted by a fitted point process model. o circdensity Kernel density estimate for angular data o rose Rose diagram (rose of directions) for angular data o nnorient Nearest neighbour orientation distribution. o AIC.ppm Calculate AIC of a Gibbs model using Takeuchi's rule. o interp.colours Interpolate a sequence of colour values. o anyDuplicated.ppp, anyDuplicated.ppx Fast replacements for any(duplicated(x)) for point patterns. o textstring, onearrow, yardstick Objects representing a text string, an arrow, or a scale bar, for use in drawing spatial diagrams. o plot.imlist, image.imlist, contour.imlist Methods for the new class 'imlist' o [<-.layered, [[<-.layered More support for class 'layered' SIGNIFICANT USER-VISIBLE CHANGES o (vignettes) New vignette 'datasets' summarises all the datasets installed with the spatstat package. o relrisk The function relrisk is now generic, with methods for ppp and ppm. New argument 'relative' specifies whether to calculate the relative risk or the absolute probability of each type of point. New argument 'se' specifies whether to calculate standard errors. o plot.im The default colour map for plotting images, specified by spatstat.options('image.colfun'), has been changed to a perceptually uniform map. o DiggleGratton, Fiksel, MultiHard, MultiStraussHard The hard core distance parameters in these models can now be omitted by the user, and will be estimated automatically from data (by the 'self-starting' feature of interactions). This was already true of Hardcore and StraussHard. o Hybrid Hybrid models now apply the 'self-starting' feature to each component model. o anova.ppm Can now reconcile models fitted using different dummy points, different values of 'rbord', different values of 'use.gam', etc. o profilepl New argument 'aic' makes it possible to optimise the parameters by minimising AIC. o profilepl No longer requires values for parameters which are 'optional' (such as the hard core distance). o rmh, simulate.ppm, rmh.ppm, rmh.default The Metropolis-Hastings algorithm now starts by deleting any points in the initial state that are 'illegal' (i.e. whose conditional intensity is equal to zero). This ensures that the result of rmh never contains illegal points. o runifpoint, rpoispp, rStrauss, rHardcore, rStraussHard, rDiggleGratton, rDGS, runifdisc, rpoint, rMaternI, rMaternII, rSSI, rPoissonCluster, rGaussPoisson, rstrat, rsyst, rcell, rthin, rjitter, rNeymanScott, rMatClust, rThomas, rCauchy, rVarGamma, rmpoint, rmpoispp, runifpointOnLines, rpoisppOnLines, runiflpp, rpoislpp, runifpointx, rpoisppx, runifpoint3, rpoispp3 These random point pattern generators now have an argument 'nsim' specifying the number of simulated realisations to be generated. o pairorient New argument 'cumulative'. New algorithm to compute kernel estimate of probability density. Default behaviour changed. Argument 'units' has been renamed 'unit' for consistency. Labels and descriptions of columns have been corrected. o predict.ppm New syntax (backward-compatible). New argument 'se' replaces option 'type="se"'. Old argument 'total' is deprecated: use 'window' and set 'type="count"'. o cdf.test The methods for class 'ppm' and 'lppm' now handle Gibbs models and perform a Monte Carlo test in this case. o lurking, diagnose.ppm Lurking variable plot can now include simulation envelopes. o rmh.ppm New argument 'w' determines the window in which the simulated pattern is generated. o ppp Accelerated. o Gcom, Gres When conditional=TRUE and restrict=TRUE, the Hanisch estimate was not calculated exactly as described in Appendix E.1 of Baddeley, Rubak and Moller (2011). The intensity was estimated on the full window rather than the eroded window. Fixed. o step, drop1, add1, extractAIC The AIC of a Gibbs model is now calculated using Takeuchi's rule for the degrees of freedom. o model.matrix.ppm, model.matrix.kppm New argument 'Q' allows prediction at any desired locations. o vcov.ppm New argument 'fine' gives more control over computation. o predict.ppm For multitype models, when the result is a list of images, the names of list entries are now identical to the mark levels (e.g. "hickory" instead of "markhickory") o print.slrm Output now respects options('width') o image.listof New argument 'ribmar' controls margin space around the ribbon when equal.ribbon=TRUE. o integral.im New argument 'domain' specifies the domain of integration. o plot.fasp New argument 'transpose' allows rows and columns to be exchanged. o plot.im The list 'ribargs' can now include the parameter 'labels'. o rmh, rpoint, rpoispp, rmpoint, rmpoispp Accelerated, for inhomogeneous processes. o stienen Now recognises the parameter 'lwd'. o suffstat Accelerated (also affects ppm with method='ho'). o Poisson, AreaInter, BadGey, Concom, DiggleGatesStibbard, DiggleGratton, Fiksel, Geyer, Hardcore, Hybrid, LennardJones, MultiHard, MultiStrauss, MultiStraussHard, OrdThresh, Ord, PairPiece, Pairwise, SatPiece, Saturated, Softcore, Strauss, StraussHard, Triplets These functions can now be printed (by typing the function name) to give a sensible description of the required syntax. o fitin A plot of the fitted interpoint interaction of a point process model e.g. plot(fitin(ppm(swedishpines ~ 1, Strauss(9)))) now shows the unit of length on the x-axis. o fitin Plots of the fitted interpoint interaction are now possible for some higher-order interactions such as Geyer and AreaInter. o anova.ppm New argument 'warn' to suppress warnings. o rmhmodel.ppm Argument 'win' renamed 'w' for consistency with other functions. o print.ppm Printed output for the fitted regular parameters now respects options('digits'). o print.ppm, print.summary.ppm Output now respects options('width') and spatstat.options('terse') o print.ppm By default, standard errors are not printed for a model fitted with method="logi" (due to computational load) o plot.profilepl Now recognises 'lty', 'lwd', 'col' etc o vesicles, gorillas Some of the raw data files for these datasets are also installed in spatstat for demonstration and training purposes. BUG FIXES o rmh, rmh.ppm, rmh.default, simulate.ppm The result of simulating a model with a hard core did not necessarily respect the hard core constraint, and simulation of a model with strong inhibition did not necessarily converge. This only happened if the first order trend was large, the starting state (n.start or x.start) was not given, and the number of iterations (nrep) was not very large. It occurred because of a poor choice for the default starting state. Bug was present since about 2010. Fixed. o markcorrint Results were completely incorrect. Bug introduced in spatstat 1.39-0, october 2014. Fixed. o Kinhom Ignored argument 'reciplambda2' in some cases. Bug introduced in spatstat 1.39-0, october 2014. Fixed. o relrisk When at="pixels", a small fraction of pixel values were sometimes wildly inaccurate, due to numerical errors. This affected the range of values in the result, and therefore the appearance of plots. Fixed. o model.images Crashed if the model was multitype. Fixed. o profilepl Crashed in some cases when the interaction was multitype. [Spotted by Andrew Lister.] Fixed. o profilepl Crashed if the model involved covariates that were not given in a 'data' argument. Fixed. o envelope.ppm Crashed if global=TRUE and savefuns=TRUE. Fixed. o setminus.owin Crashed if the result was empty and the input was polygonal. Fixed. o predict.ppm Crashed sometimes when type="cif" and ngrid was very large. Fixed. o pixelquad If X was a multitype point pattern, the result was mangled. Fixed. o relrisk Did not accept a character string value for the argument 'case'. Fixed. o intensity.ppm Format of result was incorrect for ppm(Y ~ 1) where Y is multitype. Fixed. o $<-.hyperframe Columns containing character values were converted to factors. Fixed. o clickppp Sometimes filled the window in solid black colour.. Fixed. o plot.psp Ignored 'show.all' in some cases. Fixed. o plot.ppp Warned about NA values amongst the marks, even if there were no NA's in the column(s) of marks selected by the argument 'which.marks'. Fixed. o stienen Did not suppress the border circles when border=FALSE. Fixed. CHANGES IN spatstat VERSION 1.39-1 OVERVIEW o Urgent bug fix. o We thank Suman Rakshit and Brian Ripley for contributions. BUG FIXES o bdry.mask, convexhull In R-devel only, these functions could return an empty window, causing errors in other packages. [Spotted by Brian Ripley.] Fixed. o project2segment An error occurred if any line segments had length zero. [Spotted by Suman Rakshit.] Fixed. CHANGES IN spatstat VERSION 1.39-0 OVERVIEW o We thank Shane Frank, Shaaban Ghalandarayeshi, Ute Hahn, Mahdieh Khanmohammadi, Nicoletta Nava, Jens Randel Nyengaard, Sebastian Schutte, Rasmus Waagepetersen and Carl G. Witthoft for contributions. o ANOVA extended to Gibbs models. o Improved algorithm for locally-scaled K-function. o Leave-one-out calculation of fitted values in ppm objects. o New dataset: presynaptic vesicles. o Geometrical operations with windows and images. o More edge corrections for K-function. o Improved handling and plotting of 'fv' objects. o Utilities for perspective views of surfaces. o New classes 'anylist', 'solist' will ultimately replace 'listof'. o Bug fixes. o Version nickname: 'Smoke and Mirrors' NEW DATASETS o vesicles Synaptic vesicles (includes raw data files for training purposes) NEW CLASSES o anylist List of objects. (A replacement for 'listof') o solist List of two-dimensional spatial objects. (A replacement for some uses of 'listof') NEW FUNCTIONS o perspPoints, perspLines, perspSegments, perspContour Draw points and lines on a surface, as seen in perspective view. o hopskel.test Hopkins-Skellam test of CSR o project2set For each data point in a point pattern X, find the nearest spatial location in a given window W. o stienen, stienenset Stienen diagrams o dirichlet.vertices Vertices of the Dirichlet tessellation o discs Union of discs. Given a point pattern dataset recording the locations and diameters of objects, find the region covered by the objects. o increment.fv Increments of a summary function: g(x) = f(x+h)-f(x-h). o rotmean Rotational average of pixel values in an image o fardist Distance from each pixel/data point to farthest part of window boundary o circumradius.owin Circumradius of a window o rmax.Trans, rmax.Ripley Compute the maximum distance 'r' for which the translation edge correction and isotropic edge correction are valid. o is.grey Determines whether a colour value is a shade of grey. o harmonise Convert several objects of the same class to a common format. (New generic function with existing methods harmonise.im and harmonise.fv) o area New generic function, with methods for 'owin' and 'default'. o Fhazard Hazard rate of empty space function o anylist, as.anylist, [.anylist, [<-.anylist, print.anylist, summary.anylist Support for new class 'anylist' o solist, as.solist, [.solist, [<-.solist, print.solist, summary.solist Support for new class 'solist' o plot.anylist, plot.solist Plot methods for the classes 'anylist' and 'solist' (Currently identical to 'plot.listof') SIGNIFICANT USER-VISIBLE CHANGES o anova.ppm Now applies to Gibbs models as well as Poisson models, using adjusted composite likelihood ratio test statistic. o persp.im If visible=TRUE, the algorithm will also calculate which pixels of x are visible in the perspective view. This is useful for drawing points or lines on a perspective surface. o Kscaled Improved algorithm [thanks to Ute Hahn.] New arguments 'renormalise' and 'normpower' allow renormalisation of intensity, similar to Kinhom. o Kest New option: correction="rigid" computes the rigid motion correction. o pairwise interactions Fitted parameters and other calculations for pairwise interaction models DiggleGatesStibbard, DiggleGratton, Fiksel, Geyer, Strauss may change slightly due to a change in handling numerical rounding effects. o eval.fv Functions no longer need to have exactly the same sequence of 'r' values. They will now be made compatible using 'harmonise.fv'. o fitted.ppm New argument 'leaveoneout' allows leave-one-out calculation of fitted intensity at original data points. o Kinhom, Linhom New argument 'leaveoneout' specifies whether the leave-one-out rule should be applied when calculating the fitted intensities. o crosspaircounts Results may change slightly due to a change in handling numerical rounding effects. o Fest, Gest New argument 'domain' supports bootstrap methods. o plot.fv New argument 'mathfont' determines the font (e.g. plain, italic, bold) for mathematical expressions on the axes and in the legend. Defaults to italic. o scanpp Upgraded to handle multiple columns of mark data. o circumradius The function 'circumradius' is now generic, with methods for the classes 'owin' and 'linnet'. o edge.Trans New argument 'give.rmax' o fvnames, plot.fv The symbol '.a' is now recognised. It stands for 'all function values'. o as.function.fv Argument 'extrapolate' can have length 1 or 2. o varblock New argument 'confidence' determines the confidence level. o $<-.fv This can now be used to add an extra column to an 'fv' object (previously it refused). o minnndist, maxnndist New argument 'positive'. If TRUE, coincident points are ignored: the nearest-neighbour distance of a point is the distance to the nearest point that does not coincide with the current point. o plot.fv Improved handling of 'shade' argument. o Kmeasure Now passes '...' arguments to as.mask() o Ksector Now allows start < 0. New arguments 'units' and 'domain'. o pairorient New arguments 'units' and 'domain'. o eroded.areas New argument 'subset' o disc New argument 'delta' o plot.plotppm New argument 'pppargs' o harmonise.fv, harmonise.im These are now methods for the new generic 'harmonise' o Fest, Gest These functions now also compute the theoretical value of hazard for a Poisson process, if correction = "km". o with.fv Improved mathematical labels. o Gfox, Jfox Improved mathematical labels. o area.owin This function is now a method for the new generic 'area' o edges Default for argument 'check' changed to FALSE. BUG FIXES o varblock Calculations were incorrect if more than one column of edge corrections was computed. [Bug introduced in spatstat 1.21-1, november 2010.] Fixed. o varblock Crashed if one of the quadrats contained no data points. Fixed. o lohboot Interval was calculated wrongly when global=TRUE and fun="Lest" or "Linhom". Fixed. o nnmark Crashed when at="points" if there was only a single column of marks. [Spotted by Shane Frank.] Fixed. o plot.msr Some elements of the plot were omitted or cut off. Fixed. o plot.msr Did not work with 'equal.scales=TRUE'. Fixed. o plot.msr, augment.msr Crashed if every data point was duplicated. Fixed. o as.im.owin Crashed if X was a 1 x 1 pixel array. Fixed. o owin Coordinates of polygon data were altered slightly when fix=TRUE. [Spotted by Carl Witthoft.] Fixed. o objects of class 'fv' Assigning a new value to names(x) or colnames(x) or dimnames(x) would cause the internal data format to become corrupted. Fixed. o to.grey, complementarycolour Did not work properly on 'colourmap' objects. Fixed. o Kest Ignored argument 'var.approx' if the window was a rectangle. Fixed. o rmh.ppm, rmhmodel.ppm Ignored the argument 'new.coef'. [Spotted by Sebastian Schutte] Fixed. o as.function.fv The meanings of 'extrapolate=TRUE' and 'extrapolate=FALSE' were swapped. Fixed. o varblock Handled the case 'fun=Lest' incorrectly. Fixed. o [.fv Sometimes garbled the internal data format, causing plot.fv to crash. Fixed. o range.fv Sometimes returned NA even when na.rm=TRUE. Fixed. o Fest Argument 'eps' was not interpreted correctly. Fixed. o plot.fv Argument 'lwd' was not passed to legend() o flipxy.owin Sometimes deleted the name of the unit of length. Fixed. CHANGES IN spatstat VERSION 1.38-1 OVERVIEW o We thank Ute Hahn and Xavier Raynaud for contributions. o Urgent Bug Fixes. o Nickname: 'Le Hardi' NEW FUNCTIONS o "[<-.fv", "$<-.fv" Subset replacement methods for 'fv' objects. SIGNIFICANT USER-VISIBLE CHANGES o clarkevans.test Simulations are now performed with a fixed number of points. o plot.owin, plot.ppp, plot.psp, plot.im The default size of the outer margin of white space has been reduced. o dclf.test Improved information in printed output. BUG FIXES o update.ppm Results were incorrect in several cases. [Spotted by Xavier Raynaud.] Bug introduced in spatstat 1.38-0. Fixed. o Kinhom, Linhom Calculations were incorrect if 'lambda' was a fitted point process model. [Spotted by Xavier Raynaud.] Bug introduced in spatstat 1.38-0. Fixed. o envelope.envelope Ignored the arguments 'global' and 'VARIANCE'. Fixed. o fv objects If 'f' was an object of class 'fv', then an assignment like f$name <- NULL mangled the internal format of the object 'f', leading to errors in print.fv and plot.fv. [Spotted by Ute Hahn.] Fixed. o split.ppp split(X, A) where A is a rectangular tessellation, produced errors if the window of 'A' did not include the window of 'X'. [Spotted by Ute Hahn.] Fixed. o names<-.hyperframe Mangled the internal format. [Spotted by Ute Hahn.] Fixed. o plot.fv y axis label was incorrect in some cases when the 'fv' object had only a single column of function values. [Spotted by Ute Hahn.] Fixed. CHANGES IN spatstat VERSION 1.38-0 OVERVIEW o We thank Malissa Baddeley, Colin Beale, Oscar Garcia, Daniel Esser, David Ford, Eric Gilleland, Andrew Hardegen, Philipp Hunziker, Abdollah Jalilian, Tom Lawrence, Lore De Middeleer, Robin Milne, Mike Porter, Suman Rakshit, Pablo Ramon, Jason Rudokas, Christopher Ryan, Dominic Schuhmacher, Medha Uppala and Rasmus Waagepetersen for contributions. o spatstat now Requires the package 'goftest' and Suggests the package 'Matrix'. o New dataset: 'sporophores' o Palm likelihood method for fitting cluster processes and Cox processes. o Quasi-likelihood and weighted composite likelihood methods for estimating trend in cluster processes and Cox processes. o Further extensions to model formulas in ppm and kppm. o Faster variance calculations for ppm objects. o One-sided tests and one-sided envelopes of summary functions. o Cramer-Von Mises and Anderson-Darling tests of spatial distribution. o Cressie-Read test statistic in quadrat counting tests. o Spatial cumulative distribution functions. o Faster algorithm for point pattern matching. o Improvements to plots. o Increased support for envelopes. o New generic functions 'Window', 'Frame' and 'domain'. o Directional K-function and directional distribution. o Raster calculations accelerated. o Summary functions accelerated. o Many improvements and bug fixes. o Version nickname: 'Wicked Plot' NEW DATASETS o sporophores Spatial pattern of three species of mycorrhizal fungi around a tree. [Contributed by E. David Ford.] NEW FUNCTIONS o improve.kppm Re-estimate the trend in a kppm (cluster or Cox) model using quasi-likelihood or weighted first-order composite likelihood. [Contributed by Abdollah Jalilian and Rasmus Waagepetersen.] o Window, Window<- Generic functions to extract and change the window of a spatial object in two dimensions. Methods for ppp, psp, im, and many others. o Frame, Frame<- Generic functions to extract and change the containing rectangle ('frame') of a spatial object in two dimensions. o domain Generic function to extract the spatial domain of a spatial object in any number of dimensions. o Ksector Directional version of the K-function. o pairorient Point pair orientation distribution. o spatialcdf Compute the spatial cumulative distribution of a spatial covariate, optionally using spatially-varying weights. o cdf.test [Supersedes 'kstest'.] Test of goodness-of-fit of a Poisson point process model. The observed and predicted distributions of the values of a spatial covariate are compared using either the Kolmogorov-Smirnov, Cramer-Von Mises or Anderson-Darling test. o berman.test Replaces 'bermantest'. o harmonise.fv Make several functions compatible. o simulate.lppm Simulate a fitted point process model on a linear network. o subset.ppp, subset.lpp, subset.pp3, subset.ppx Methods for 'subset', for point patterns. o closepairs.pp3, crosspairs.pp3 Low-level functions to find all close pairs of points in three dimensions o volume.linnet Method for the generic 'volume'. Returns the length of the linear network. o padimage Pad the border of a pixel image. o as.layered Convert spatial data to a layered object. o panel.contour, panel.image, panel.histogram Panel functions for 'pairs' plots. o range.fv, min.fv, max.fv Range, minimum and maximum of function values. SIGNIFICANT USER-VISIBLE CHANGES o ppm.formula The left hand side of the formula can now be the name of an object in the list 'data', or an expression involving such objects. o ppm The right hand side of the formula can now include the symbol '.' representing all covariates in the list 'data'. o ppm New argument 'subset' makes it possible to fit the model in a subset of the spatial domain defined by an expression. o kppm New option: method="palm", will fit the model by maximising Palm likelihood. o pppdist Substantially accelerated. New argument 'auction' controls choice of algorithm. o rhohat New arguments 'weights' and 'horvitz' for weighted calculations. o persp.im Surface heights and colours can now be controlled by different images. Option to draw a grey apron around the sides of the perspective plot. Return value has a new attribute 'expand'. o plot.listof New arguments 'halign' and 'valign' give improved control over the alignment of panels. o plot.listof If all components of the list are objects of class 'fv' representing functions, then if equal.scales=TRUE, these functions will all be plotted with the same axes (i.e. the same xlim and the same ylim). o envelope The argument 'transform' is now processed by 'with.fv' giving more options, such as 'transform=expression(. - r)' o envelope, dclf.test, mad.test One-sided tests and one-sided envelopes can now be produced, by specifying the argument 'alternative'. o dclf.test, mad.test A pointwise test at fixed distance 'r' can now be performed by setting rinterval = c(r,r). o envelope New arguments 'fix.n' and 'fix.marks' for envelope.ppp and envelope.ppm make it easy to generate simulated patterns conditional on the total number of points, or on the number of points of each type. o quadrat.test Can now calculate the Cressie-Read test statistic instead of the Pearson X2 statistic. o Kres, Gres, Kcom, Gcom, psst, psstA, psstG New argument 'model' makes it easier to generate simulation envelopes of the residual summary functions. o layered, plot.layered The layer plot arguments can include the argument '.plot' specifying a function to perform the plotting instead of the generic 'plot'. o deriv.fv New arguments make it possible to differentiate a periodic function. o ppm Argument 'data' or 'covariates' can now include entries which are not spatial covariates, provided they do not appear in the model formula. o closepairs, crosspairs These functions are now generic, with methods for 'ppp' and 'pp3' o rLGCP Updated to conform to new usage of RandomFields package. Argument syntax has changed. Now allows control over pixel resolution. o bw.diggle New arguments 'correction' and 'hmax' for controlling the calculation. o predict.lppm New argument 'new.coef' for computing predictions with a different vector of model coefficients. o predict.ppm If 'locations' is a pixel image, its pixels determine the spatial locations for prediction. o cut.ppp Argument 'z' can now be a window. o split.ppp Argument 'f' can now be a window. o print.ppm, summary.ppm, coef.summary.ppm The table of parameter estimates, standard errors and confidence intervals now also includes the value of the (signed square root) Wald test statistic. o plot.im Now automatically detects problems in some Windows graphics displays and tries to avoid them. o plot.im The position of axis tick marks alongside the colour ribbon can now be controlled using the parameter 'at' in the argument 'ribargs'. o plot.ppp Can now plot numeric marks using characters chosen by 'pch' or 'chars' with size determined by mark value. o plot.ppp New argument 'meansize' for controlling mark scale. o hsvim, rgbim New argument 'autoscale' causes automatic scaling of colour channel values. o plot.ppp If type='n', a legend is now displayed when x is a marked point pattern. o whist Accelerated by a factor of 5. o Fest, Jest Accelerated by a factor of 2 to 3. o fryplot Accelerated. Now displays a legend if the point pattern is marked. Now handles numerical marks nicely. New argument 'axes'. o frypoints Accelerated. New arguments 'to', 'from' and 'dmax'. o duplicated.ppp New option: rule = 'unmark' o rjitter Argument 'radius' now has a default. o Smooth.msr New argument 'drop' o LambertW Now handles NA and infinite values. o update.ppm Now handles formulae with a left-hand side. o raster.x, raster.y, raster.xy These functions can now handle images, as well as masks. o Smooth.ppp If the mark values are exactly constant, the resulting smoothed values are now exactly constant. o eval.im, eval.fv, eval.fasp Argument 'envir' can now be a list, instead of an environment. o plot.ppp The printout (of the resulting symbol map object) now displays the numerical value of the mark scale. o with.fv Improved mathematical labels. o plot.fv Improved mathematical labels on x axis. o ppm Improved error messages. o vcov.ppm Computations greatly accelerated for Hybrid interactions and for Area-interaction models. o vcov.kppm Computations greatly accelerated (when fast=TRUE) o interp.im Argument 'x' can now be a point pattern. o pool.envelope Improved handling of text information. o miplot Improved layout. o print.summary.ppp Improved layout. Now respects spatstat.options('terse') o print.profilepl Improved layout. Now respects spatstat.options('terse') o anova.ppm Now respects spatstat.options('terse') o print.fv, print.envelope Now respect spatstat.options('terse') and options('width') o summary.envelope Now respects options('width') o kstest, bermantest These functions will soon be Deprecated. They are retained only for backward compatibility. BUG FIXES o vcov.ppm Sometimes gave wrong answers for Poisson models fitted by method='logi'. Fixed. o unnormdensity If weights were missing, the density was normalised, contrary to the documentation. Fixed. o logLik.ppm, anova.ppm, AIC For models fitted by 'ippm', the number of degrees of freedom was incorrect. Fixed. o im.apply Pixels outside the window were not assigned the value NA as they should. Fixed. o pixellate.owin Crashed, unpredictably, if the pixel raster had unequal numbers of rows and columns. [Spotted by Rasmus Waagepetersen.] Fixed. o vcov.ppm Crashed for pairwise interaction models fitted by method="logi". Fixed. o predict.ppm Crashed for models fitted by method="logi" if the model included external covariates. Fixed. o predict.ppm Crashed if the argument 'covariates' or 'data' in the original call to 'ppm' included entries that were not spatial covariates. [These entries were ignored by ppm but caused predict.ppm to crash.] Fixed. o simulate.kppm, rNeymanScott, rThomas, rMatClust Crashed randomly when simulating an inhomogeneous model. [Spotted by Philipp Hunziker.] Fixed. o bw.diggle In some extreme cases, generated an error message about `NaN values in Foreign function call.' [Spotted by Colin Beale.] Fixed. o textureplot Crashed if 'spacing' was too large. Fixed. o superimpose.psp Crashed if the result was empty. Fixed. o istat Crashed with an error message about 'vars'. Fixed. o dirichlet, delaunay, delaunay.distance Crashed in rare cases due to a problem in package 'deldir'. [Spotted by Pierre Legendre.] Fixed. o rgbim, hsvim Crashed if any argument was constant. Fixed. o scaletointerval Crashed if x was constant. Fixed. o linnet, [.linnet Crashed if the result contained only a single vertex. [Spotted by Daniel Esser.] Fixed. o plot.fv If some of the function values were NA, they were replaced by fictitious values (by linearly interpolating). Fixed. o crossdist.ppp Ignored argument 'squared' if periodic=FALSE. [Spotted by Mike Porter.] Fixed. o marks<-.ppp Ignored argument 'drop'. [Spotted by Oscar Garcia.] Fixed. o update.ppm Sometimes did not respect the argument 'use.internal'. Fixed. o plot.rhohat Did not respect the argument 'limitsonly'. Fixed. o contour.im Argument 'axes' defaulted to TRUE, but FALSE was intended. Fixed. o print.hyperframe, as.data.frame.hyperframe Column names were mangled if the hyperframe had a single row. Fixed. o as.psp.data.frame Generated a warning about partially-matched names in a data frame. [Spotted by Eric Gilleland.] Fixed. o plot.leverage.ppm Generated a warning from 'contour.default' if the leverage function was constant. Fixed. o plot.diagppm Issued warnings about unrecognised graphics parameters. Fixed. o update.symbolmap Discarded information about the range of input values. Fixed. o plot.fv Label for y axis was garbled, if argument 'shade' was given. Fixed. o plot.ppp The legend was sometimes plotted when it should not have been (e.g. when add=TRUE). Fixed. o plot.listof, plot.im In an array of plots, containing both images and other spatial objects, the titles of the panels were not correctly aligned. Fixed. o plot.tess, plot.quadratcount Ignored arguments like 'cex.main'. Fixed. o iplot Navigation buttons (Left, Right, Up, Down, Zoom In, Zoom Out) did not immediately refresh the plot. Fixed. o iplot.layered Reported an error 'invalid argument type' if all layers were deselected. Fixed. CHANGES IN spatstat VERSION 1.37-0 OVERVIEW o Ege Rubak is now a joint author of spatstat. o We thank Peter Forbes, Tom Lawrence and Mikko Vihtakari for contributions. o Spatstat now exceeds 100,000 lines of code. o New syntax for point process models (ppm, kppm, lppm) equivalent to syntax of lm, glm, ... o Covariates in ppm and kppm can now be tessellations. o Confidence intervals and prediction intervals for fitted models. o Quasirandom point patterns and sequences. o Plots using texture fill. o Support for mappings from data to graphical symbols and textures. o Automatic re-fitting of model in Ginhom, Kinhom, Finhom, Jinhom. o Support for Mixed Poisson distribution. o Interpretation of mark scale parameters has changed in plot.ppp o Syntax of multitype interactions (eg MultiStrauss) has changed. o Bug fix in Metropolis-Hastings simulation of 'StraussHard' models o Changed default behaviour of perfect simulation algorithms. o Improvements to layout of text output. o Version nickname: 'Model Prisoner' NEW CLASSES o symbolmap An object of class 'symbolmap' represents a mapping from data to graphical symbols o texturemap An object of class 'texturemap' represents a mapping from data to graphical textures. NEW FUNCTIONS o split.hyperframe, split<-.hyperframe methods for split and split<- for hyperframes. o dmixpois, pmixpois, qmixpois, rmixpois (log-)normal mixture of Poisson distributions. o vdCorput, Halton, Hammersley, rQuasi quasirandom sequences and quasirandom point patterns. o Smoothfun create a function(x,y) equivalent to the result of Smooth.ppp o minnndist, maxnndist Faster ways to compute min(nndist(X)), max(nndist(X)) o add.texture Draw a simple texture inside a specified region. o textureplot Display a factor-valued pixel image using texture fill. o texturemap Create a texture map o plot.texturemap Plot a texture map in the style of a legend o symbolmap Create a symbol map o update.symbolmap Modify a symbol map o invoke.symbolmap Apply symbol map to data values, and plot them o plot.symbolmap Plot the symbol map in the style of a legend o as.owin.boxx Converts a 'boxx' to an 'owin' if possible. o ellipse Create an elliptical window. o clickbox Interactively specify a rectangle, by point-and-click on a graphics device. o complementarycolour Compute the complementary colour value of a given colour value, or the complementary colour map of a given colour map. o gauss.hermite Gauss-Hermite quadrature approximation to the expectation of any function of a normally-distributed random variable. o boundingbox Generic function, replaces bounding.box o edges Extract boundary edges of a window. Replaces and extends 'as.psp.owin' o pixelcentres Extract centres of pixels as a point pattern. SIGNIFICANT USER-VISIBLE CHANGES o ppm, kppm, lppm NEW SYNTAX FOR POINT PROCESS MODELS The model-fitting functions 'ppm', 'kppm' and 'lppm' now accept a syntax similar to 'lm' or 'glm', for example ppm(X ~ Z), but still accept the older syntax ppm(X, ~Z). To support both kinds of syntax, the functions 'ppm' and 'kppm' are now generic, with methods for the classes 'formula', 'ppp' and 'quad'. The 'formula' method handles a syntax like ppm(X ~ Z) while the 'ppp' method handles the old syntax ppm(X, ~Z). Similarly 'lppm' is generic with methods for 'formula' and 'lpp'. o ppm, kppm, lppm Covariates appearing in the model formula can be objects which exist in the R session, instead of always having to be elements of the list `covariates'. o ppm.formula, kppm.formula, lppm.formula Formulae involving polynom() are now expanded, symbolically, so that polynom(x, 3) becomes x + I(x^2) + I(x^3) and polynom(x,y,2) becomes x + y + I(x^2) + I(x*y) + I(y^2). This neatens the model output, and also makes it possible for anova() and step() to add or delete single terms in the polynomial. o predict.ppm New argument 'interval' allows confidence intervals or prediction intervals to be calculated. o predict.ppm New argument 'total' allows for prediction of the total number of points in a specified region. o plot.ppp, plot.lpp For marked point patterns, a legend is automatically added to the plot, by default. Arguments have changed: new arguments include parameters of the legend, and an optional symbol map. Result has changed: it is now an object of class 'symbolmap'. o plot.ppp, plot.lpp Interpretation of the parameters 'markscale' and 'maxsize' has changed. The size of a circle in the plot is now defined as the circle's diameter instead of its radius. (Size of a square is measured, as before, by its side length). o parres Now handles the case where the fitted model is not separable but its restriction to the given 'subregion' is separable. o envelope Now issues a warning if the usage of envelope() appears to be `invalid' in the sense that the simulated patterns and the data pattern have not been treated equally. o Kinhom, Finhom, Ginhom, Jinhom New argument 'update'. If 'lambda' is a fitted model (class ppm or kppm) and update=TRUE, the model is re-fitted to the data pattern, before the intensities are computed. o rDiggleGratton, rDGS, rHardcore, rStrauss, rStraussHard By default the point pattern is now generated on a larger window, and trimmed to the original window. New argument expand=TRUE. o MultiStrauss, MultiHard, MultiStraussHard The syntax of these functions has changed. The new code should still accept the old syntax. o rhohat rhohat.ppp and rhohat.quad have new argument 'baseline' o ippm Algorithm improved. Argument syntax changed. o default.dummy, quadscheme Dummy points can now be generated by a quasirandom sequence. o plot.owin The window can now be filled with one of 8 different textures. Arguments changed. o ppm, kppm Covariates in the model can now be tessellations. o [.im New argument 'tight' allows the resulting image to be trimmed to the smallest possible rectangle. o [.psp, rlinegrid, rpoisline These functions now handle binary mask windows. o rotate The user can specify the centre of rotation. o rescale rescale() and all its methods now have argument 'unitname' which can be used to change the name of the unit of length. o anova.ppm Output format has been improved. Number of columns of result has changed. o print.ppp, print.summary.ppp, print.owin, print.summary.owin, print.im, print.summary.im, print.fv, print.msr, print.profilepl These functions now avoid over-running the text margin (i.e. they respect options('width') where possible). o layerplotargs<- Now handles any spatial object, converting it to a 'layered' object. o effectfun Improved display in case se.fit=TRUE. o scaletointerval New argument 'xrange' o contour.im New argument 'show.all'. Default value of 'axes' changed to FALSE. o identify.ppp Now handles multivariate marks. o plot.listof Improved layout. New arguments 'hsep', 'vsep'. Argument 'mar.panel' may have length 1, 2 or 4. o plot.splitppp This function is no longer identical to plot.listof. Instead it is a much simpler function which just calls plot.listof with equal.scales=TRUE. o anova.ppm Output is neater. o plot.layered New argument 'do.plot' o plot.psp New argument 'do.plot' o as.psp.owin New argument 'window' o plot.im, contour.im, textureplot New argument 'clipwin' o plot.ppp New argument 'clipwin' o plot.msr New argument 'how' allows density to be plotted as image and/or contour o diagnose.ppm, plot.diagppm More options for 'plot.neg' o plot.leverage.ppm, plot.influence.ppm, plot.msr Argument 'clipwin' can now be used to restrict the display to a subset of the full data. o [.hyperframe, [<-.hyperframe, $.hyperframe, $<-.hyperframe These functions are now documented. o leverage.ppm, influence.ppm, dfbetas.ppm Resulting objects are now smaller (in memory size). o print.ppm Now indicates whether the irregular parameters 'covfunargs' were optimised (by profilepl or ippm) or whether they were simply provided by the user. o plot.ppp A point pattern with numerical marks can now be plotted as filled dots with colours determined by the marks, by setting pch=21 and bg= o colourmap Now handles dates and date-time values (of class 'Date' or 'POSIXt'). o plot.ppp, print.ppp, summary.ppp Improved handling of dates and date-time values (of class 'Date' or 'POSIXt') in the marks of a point pattern. o cut.im Now refuses to handle images whose pixel values are factor, logical or character. o centroid.owin New argument 'as.ppp' o superimpose Improved default names for columns of marks. o Softcore() Improved printout. o kppm, lgcp.estpcf, lgcp.estK Adjusted to new structure of RandomFields package. No change in syntax. o data(murchison) This dataset now belongs to class 'listof' so that it can be plotted directly. o data(clmfires) The format of the covariate data has changed. The objects 'clmcov100' and 'clmcov200' are now elements of a list 'clmfires.extra'. o bounding.box This function is now Deprecated; it has been replaced by the generic boundingbox(). o as.psp.owin This function is now Deprecated; it has been replaced and extended by the function edges(). o plot.kstest Changed defaults so that the two curves are distinguishable. o with.fv Improved mathematical labels. BUG FIXES o intensity.quadratcount Values were incorrect for a rectangular tessellation (the matrix of intensities was transposed). Fixed. o rmh, simulate.ppm Simulation of the Strauss-hard core model (StraussHard) was incorrect (intensity of the simulated process was about 15% too low). Bug introduced in spatstat 1.31-0 (January 2013). o intensity.quadratcount Crashed for a rectangular tessellation with only a single row or column. Fixed. o model.images.ppm Crashed sometimes if the argument W was given. Fixed. o eval.im Crashed when applied to images with only a single row or column. Fixed. o ppp, marks<-.ppp If the marks were a vector of dates, they were erroneously converted to numbers. Fixed. o ippm Crashed if the model formula included an offset term that was not a function. Fixed. o leverage.ppm Crashed sometimes when the model had irregular parameters ('covfunargs'). Fixed. o residuals.ppm Crashed sometimes when type='score'. Fixed. o scaletointerval Did not handle dates and date-time values correctly. Fixed. o rbind.hyperframe, as.list.hyperframe Gave incorrect results for hyperframes with 1 row. Fixed. o Kinhom Did not renormalise the result (even when renormalise=TRUE), in some cases. Spotted by Peter Forbes. Fixed. o disc If mask=TRUE the disc was erroneously clipped to the square [-1,1] x [-1,1]. Fixed. o plot.fv Sometimes shaded the wrong half of the graph when the 'shade' coordinates were infinite. Fixed. o print.ppm Gave an error message if the coefficient vector had length zero. Fixed. o vcov.ppm Gave an error message if the coefficient vector had length zero. Fixed. o plot.distfun, as.im.distfun These functions effectively ignored the argument 'invert' in the original call to distfun. Fixed. o plot.msr Ignored certain additional arguments such as 'pch'. Fixed. o cut.im Crashed if the image had 1 row or 1 column of pixels. Fixed. o iplot.ppp Crashed with message about missing object 'vals'. Fixed. o effectfun Demanded a value for every covariate supplied in the original call to ppm, even for covariates which were not used in the model. Fixed. o plot.listof, plot.hyperframe When plotting 3D point patterns (class pp3), these functions issued warnings about 'add' being an unrecognised graphics argument. Fixed. CHANGES IN spatstat VERSION 1.36-0 OVERVIEW o We thank Sebastian Meyer, Kevin Ummer, Jean-Francois Coeurjolly, Ege Rubak, Rasmus Waagepetersen, Oscar Garcia and Sourav Das for contributions. o Important change to package dependencies. o Geometrical inconsistencies in polygons are now repaired automatically. o Improved quadrature schemes and reduced bias in ppm. o New vignette 'Summary of Recent Changes to Spatstat'. o Approximation to K function and pcf for Gibbs models. o Counterpart of 'apply' for lists of images. o Hexagonal grids and tessellations. o Extensions to scan test and Allard-Fraley cluster set estimator. o Change the parameters of a fitted model before simulating it. o Accelerated Kest, Kinhom for rectangular windows. o Extensions and improvements to plotting functions. o Improvements to labelling of 'fv' objects. o New demo of summary functions. o More methods for 'intensity'. o Version nickname: 'Intense Scrutiny' NEW FUNCTIONS o Kmodel.ppm, pcfmodel.ppm Compute approximation to K-function or pair correlation function of a Gibbs point process model. o im.apply Apply a function to corresponding pixel values in several images. o hexgrid, hextess Create a hexagonal grid of points, or a tessellation of hexagonal tiles o shift.tess, rotate.tess, reflect.tess, scalardilate.tess, affine.tess Apply a geometrical transformation to a tessellation. o quantile.ewcdf Extract quantiles from a weighted cumulative distribution function. o scanLRTS Evaluate the spatially-varying test statistic for the scan test. o pcfmulti General multitype pair correlation function o intensity.splitppp Estimate intensity in each component of a split point pattern. o intensity.quadratcount Use quadrat counts to estimate intensity in each quadrat. o as.owin.quadratcount, as.owin.quadrattest Extract the spatial window in which quadrat counts were performed. o reload.or.compute Utility function for R scripts: either reload results from file, or compute them. o to.grey Convert colour to greyscale. o Smooth.im Method for Smooth() for pixel images. Currently identical to blur(). o demo(sumfun) Demonstration of nonparametric summary functions in spatstat. SIGNIFICANT USER-VISIBLE CHANGES o Package Dependencies spatstat now "Imports" (rather than "Depends" on) the libraries mgcv, deldir, abind, tensor, polyclip. This means that these libraries are not accessible to the user unless the user explicitly loads them by typing 'library(mgcv)' and so on. o owin, as.owin Polygon data are no longer subjected to strict checks on geometrical validity (self-crossing points, overlaps etc.) Instead, polygon geometry is automatically repaired. o ppm The default quadrature scheme for a point pattern has been improved (in the case of a non-rectangular window) to remove a possible source of bias. o Performance various parts of spatstat now run slightly faster. o scan.test Now handles multiple values of circle radius 'r'. o plot.scan.test, as.im.scan.test These functions can now give the optimal value of circle radius 'r'. o pcfcross, pcfdot Algorithms have been reimplemented using a single-pass kernel smoother and now run much faster. Bandwidth selection rule improved. o plot.listof, plot.splitppp Default behaviour has changed: panels are now plotted on different scales. o plot.listof, plot.splitppp When 'equal.scales=TRUE' the panels are plotted on exactly equal scales and are exactly aligned (under certain conditions). o ppp, marks.ppp, marks<-.ppp New argument 'drop' determines whether a data frame with a single column will be converted to a vector. o simulate.ppm, rmh.ppm, rmhmodel.ppm New argument 'new.coef' allows the user to change the parameters of a fitted model, before it is simulated. o logLik.ppm New argument 'new.coef' allows the user to evaluate the loglikelihood for a different value of the parameter. o clusterset The argument 'result' has been renamed 'what'. It is now possible to give multiple values to 'what' so that both types of result can be computed together. o residuals.ppm Argument 'coefs' has been renamed 'new.coef' for consistency with fitted.ppm etc. o residuals.ppm If drop=TRUE the window associated with the residuals is now taken to be the domain of integration of the composite likelihood. o intensity.ppp Now has argument 'weights' o density.ppp, Smooth.ppp, markmean, markvar, intensity.ppp Argument 'weights' can now be an 'expression'. o pcf New argument 'domain' causes the computation to be restricted to a subset of the window. o nnclean The result now has attributes which give the fitted parameter values, information about the fitting procedure, and the histogram bar heights. o nnclean Extra arguments are now passed to hist.default. o plot.tess For a tessellation represented by a pixel image, plot.tess no longer treats the pixel labels as palette colours. o relrisk New argument 'case' allows the user to specify which mark value corresponds to the cases in a case-control dataset. o Kinhom Now accepts correction="good" o spatstat.options New option ('monochrome') controls whether plots generated by spatstat will be displayed in colour or in greyscale. This will eventually be applied to all plot commands in spatstat. o plot.im, persp.im, contour.im, plot.owin, plot.psp, plot.fv, plot.fasp These functions now obey spatstat.options('monochrome') o plot.ppp, plot.owin, plot.im, plot.psp, plot.tess, plot.layered New universal argument 'show.all' determines what happens when a plot is added to an existing plot. If show.all = TRUE then everything is plotted, including the main title and colour ribbon. o plot.ppp New argument 'show.window' o plot.im New arguments 'add' and 'do.plot'. More arguments recognised by 'ribargs' o plot.layered New arguments 'add', 'main' Better argument handling. o plot.fv Improved handling of argument 'shade' o layered, layerplotargs, plot.layered The plotting argument can now be a list of length 1, which will be replicated to the correct length. o varblock Ugly legends have been repaired. o quad.ppm New argument 'clip' o edge.Trans New arguments 'dx', 'dy' o disc Argument 'centre' can be in various formats. o affine, shift Argument 'vec' can be in various formats. o Geyer, BadGey A warning is no longer issued when the parameter 'sat' is fractional. o adaptive.density Now has argument 'verbose' o Smooth.ppp 'sigma' is now a formal argument of Smooth.ppp o plot.quadratcount, plot.quadrattest These functions have now been documented. o Summary functions and envelopes Improved mathematical labels in plots. o Kest Accelerated, in the case of a rectangular window. o Kscaled Argument 'lambda' can now be a fitted model (class ppm) o print.fv Improved layout. o plot.bermantest Improved graphics. o which.max.im This function is now deprecated. which.max.im(x) is superseded by im.apply(x, which.max) o smooth.ppp, smooth.fv, smooth.msr These functions are now deprecated, in favour of 'Smooth' with a capital 'S' BUG FIXES o bw.ppl Crashed if the point pattern had multiple points at the same location. Fixed. o quantile Crashed when applied to the result of 'ewcdf'. Fixed. o marks<-.ppp Crashed with a message about 'unrecognised format' if the current or replacement values of marks were date/time values (belonging to class 'Date' or 'POSIXt'). Fixed. o plot.im Crashed in case log=TRUE if the window was not a rectangle. Fixed. o vcov.ppm Crashed sometimes for models with a hard core term (Hardcore, StraussHard, MultiHard or MultiStrauss interactions). Spotted by Rasmus Waagepetersen. Fixed. o multiplicity.data.frame Results were incorrect and included NA's (spotted by Sebastian Meyer). Fixed. o markvar Values were incorrect. Fixed. o Smooth.ppp Ignored argument 'diggle'. Fixed. o rotate.im, affine.im Factor-valued images were not handled correctly. Fixed. o shift.layered If argument 'origin' was used, different layers were shifted by different amounts. Fixed. o tile.areas Sometimes returned a list instead of a numeric vector. Fixed. o print.ppp If the marks were date/time values (belonging to class 'Date' or 'POSIXt'), print.ppp reported that they were double precision numbers. Fixed. o plot.layered Graphics were mangled if the argument 'add=FALSE' was given explicitly. Fixed. o Smooth.ppp The argument 'sigma' was only recognised if it was explicitly named. For example in 'Smooth(X, 5)' the '5' was ignored. Fixed. o clusterset The bounding frame of the result was smaller than the original bounding frame of the point pattern dataset, when result="domain" and exact=TRUE. Fixed. o plot.im Ignored argument 'col' if it was a 'function(n)'. Fixed. o Kinhom Ignored argument 'correction' if there were more than 1000 points. Fixed. o [.fv Mangled the plot label for the y axis. Fixed. o cbind.fv Mangled the plot label for the y axis. Fixed. o plot.envelope Main title was always 'x'. Fixed. o print.ppp Ran over the right margin. Fixed. o union.owin, intersect.owin, setminus.owin Sometimes deleted the name of the unit of length. Fixed. CHANGES IN spatstat VERSION 1.35-0 OVERVIEW o We thank Melanie Bell, Leanne Bischof, Ida-Maria Sintorn, Ege Rubak, Martin Hazelton, Oscar Garcia, Rasmus Waagepetersen, Abdollah Jalilian and Jens Oehlschlaegel for contributions. o Support for analysing replicated spatial point patterns. o New vignette on analysing replicated spatial point patterns. o Objective function surface plots. o Estimator of point process intensity using nearest neighbour distances. o Improved estimator of pair correlation function. o Four new datasets. o Simple point-and-click interface functions for general use. o More support for fv objects. o More support for ppx objects. o Extensions to nearest neighbour functions. o Morphological operations accelerated. o Bug fix to pair correlation functions. o Bug fix to k-th nearest neighbour distances o Version nickname: 'Multiple Personality' NEW CLASSES o mppm An object of class 'mppm' represents a Gibbs point process model fitted to several point pattern datasets. The point patterns may be treated as independent replicates of the same point process, or as the responses in an experimental design, so that the model may depend on covariates associated with the design. Methods for this class include print, plot, predict, anova and so on. o objsurf An object of class 'objsurf' contains values of the likelihood or objective function in a neighbourhood of the maximum. o simplepanel An object of class 'simplepanel' represents a spatial arrangement of buttons that respond to mouse clicks, supporting a simple, robust graphical interface. NEW FUNCTIONS o mppm Fit a Gibbs model to several point patterns. The point pattern data may be organised as a designed experiment and the model may depend on covariates associated with the design. o anova.mppm Analysis of Deviance for models of class mppm o coef.mppm Extract fitted coefficients from a model of class mppm o fitted.mppm Fitted intensity or conditional intensity for a model of class mppm o kstest.mppm Kolmogorov-Smirnov test of goodness-of-fit for a model of class mppm o logLik.mppm log likelihood or log pseudolikelihood for a model of class mppm o plot.mppm Plot the fitted intensity or conditional intensity of a model of class mppm o predict.mppm Compute the fitted intensity or conditional intensity of a model of class mppm o quadrat.test Quadrat counting test of goodness-of-fit for a model of class mppm o residuals.mppm Point process residuals for a model of class mppm o subfits Extract point process models for each individual point pattern dataset, from a model of class mppm o vcov.mppm Variance-covariance matrix for a model of class mppm o integral.msr Integral of a measure. o objsurf For a model fitted by optimising an objective function, this command computes the objective function in a neighbourhood of the optimal value. o contour.objsurf, image.objsurf, persp.objsurf, plot.objsurf Plot an 'objsurf' object. o fvnames Define groups of columns in a function value table, for use in plot.fv, etc o multiplicity New generic function for which multiplicity.ppp is a method. o unique.ppx, duplicated.ppx, multiplicity.ppx Methods for unique(), duplicated() and multiplicity() for 'ppx' objects. These also work for 'pp3' and 'lpp' objects. o closepairs, crosspairs, closepaircounts, crosspaircounts Low-level functions for finding all close pairs of points o nndensity Estimate point process intensity using k-th nearest neighbour distances o simplepanel, run.simplepanel Support for a simple point-and-click interface for general use. NEW DATASETS o pyramidal Diggle-Lange-Benes data on pyramidal neurons in cingulate cortex. 31 point patterns divided into 3 groups. o waterstriders Nummelin-Penttinen waterstriders data. Three independent replicates of a point pattern formed by insects. o simba Simulated data example for mppm. Two groups of point patterns with different interpoint interactions. o demohyper Simulated data example for mppm. Point patterns and pixel image covariates, in two groups with different regression coefficients. SIGNIFICANT USER-VISIBLE CHANGES o plot.hyperframe The argument 'e' now has a different format. Instead of plot(h, plot(XYZ)) one must now type plot(h, quote(plot(XYZ))) This is necessary in order to avoid problems with 'S4 method dispatch'. o pcf.ppp, pcfinhom New argument 'divisor' enables better performance of the estimator of pair correlation function for distances close to zero. o applynbd The arguments N, R and criterion may now be specified together. o markstat The arguments N and R may now be specified together. o ppx New argument 'simplify' allows the result to be converted to an object of class 'ppp' or 'pp3' if appropriate. o as.function.fv Now allows multiple columns to be interpolated o multiplicity.ppp This function is now a method for the generic 'multiplicity'. It has also been accelerated. o nnfun.ppp, distfun.ppp New argument 'k' allows these functions to compute k-th nearest neighbours. o rVarGamma, kppm, vargamma.estK, vargamma.estpcf New argument 'nu.pcf' provides an alternative way to specify the kernel shape in the VarGamma model, instead of the existing argument 'nu.ker'. Function calls that use the ambiguous argument name 'nu' will no longer be accepted. o nnmap Image is now clipped to the original window. o dilation, erosion, opening, closing Polygonal computations greatly accelerated. o plot.colourmap Improved appearance and increased options, for discrete colourmaps. o plot.msr Improved appearance o plot.ppp, plot.owin An `empty' plot can now be generated by setting type="n" o nndist.ppp, nnwhich.ppp, nncross.ppp Column names of the result are now more informative. BUG FIXES o nncross.ppp Results were completely incorrect when k > 1. Spotted by Jens Oehschlaegel. Bug was introduced in spatstat 1.34-1. Fixed. o rVarGamma Simulations were incorrect; they were generated using the wrong value of the parameter 'nu.ker'. Spotted by Rasmus Waagepetersen and Abdollah Jalilian. Bug was always present. Fixed. o pair correlation functions (pcf.ppp, pcfdot, pcfcross, pcfinhom, ...) The result had a negative bias at the maximum 'r' value, because contributions to the pcf estimate from interpoint distances greater than max(r) were mistakenly omitted. Spotted by Rasmus Waagepetersen and Abdollah Jalilian. Bug was always present. Fixed. o demo(spatstat) This demonstration script had some unwanted side-effects, such as rescaling the coordinates of standard datasets 'bramblecanes', 'amacrine' and 'demopat', which caused the demonstration to crash when it was repeated several times, and caused errors in demo(data). Fixed. o rmh Visual debugger crashed sometimes with message 'XI not found'. Fixed. o predict.ppm Crashed if the model was fitted using 'covfunargs'. Fixed. o bounding.box Crashed if one of the arguments was NULL. Fixed. o multiplicity.ppp Did not handle data frames of marks. Fixed. CHANGES IN spatstat VERSION 1.34-1 OVERVIEW o We thank Kurt Hornik, Ted Rosenbaum, Ege Rubak and Achim Zeileis for contributions. o Important bug fix. SIGNIFICANT USER-VISIBLE CHANGES o as.box3 Now accepts objects of class 'ppx' or 'boxx'. o crossdist.ppp, crossdist.pp3, crossdist.default New argument 'squared' allows the squared distances to be computed (saving computation time in some applications) BUG FIXES o union.owin, is.subset.owin, dilation.owin Results were sometimes completely wrong for polygons with holes. Spotted by Ted Rosenbaum. Fixed. o psstA, areaLoss Crashed in some cases, with error message 'Number of items to replace is not a multiple of replacement length'. Spotted by Achim Zeileis. Fixed. CHANGES IN spatstat VERSION 1.34-0 OVERVIEW o We thank Andrew Bevan, Ege Rubak, Aruna Jammalamadaka, Greg McSwiggan, Jeff Marcus, Jose M Blanco Moreno, and Brian Ripley for contributions. o spatstat and all its dependencies are now Free Open Source. o spatstat does not require the package 'gpclib' any more. o spatstat now depends on the packages 'tensor', 'abind' and 'polyclip' o polygon clipping is now enabled always. o Substantially more support for point patterns on linear networks. o Faster computations for pairwise interaction models. o Bug fixes in nearest neighbour calculations. o Bug fix in leverage and influence diagnostics. o Version nickname: "Window Cleaner" o spatstat now requires R version 3.0.2 or later NEW FUNCTIONS o as.lpp Convert data to a point pattern on a linear network. o distfun.lpp Distance function for point pattern on a linear network. o eval.linim Evaluate expression involving pixel images on a linear network. o linearKcross, linearKdot, linearKcross.inhom, linearKdot.inhom Multitype K functions for point patterns on a linear network o linearmarkconnect, linearmarkequal Mark connection function and mark equality function for multitype point patterns on a linear network o linearpcfcross, linearpcfdot, linearpcfcross.inhom, linearpcfdot.inhom Multitype pair correlation functions for point patterns on a linear network o linfun New class of functions defined on a linear network o nndist.lpp, nnwhich.lpp, nncross.lpp Methods for nndist, nnwhich, nncross for point patterns on a linear network o nnfun.lpp Method for nnfun for point patterns on a linear network o vcov.lppm Variance-covariance matrix for parameter estimates of a fitted point process model on a linear network. o bilinearform Computes a bilinear form o tilenames, tilenames<- Extract or change the names of tiles in a tessellation. SIGNIFICANT USER-VISIBLE CHANGES o package dependencies Previous versions of spatstat used the package 'gpclib' to perform geometrical calculations on polygons. Spatstat now uses the package 'polyclip' for polygon calculations instead. o free open-source licence The restrictive licence conditions of 'gpclib' no longer apply to users of spatstat. Spatstat and all its dependencies are now covered by a free open-source licence. o polygon clipping In previous versions of spatstat, geometrical calculations on polygons could be performed 'exactly' using gpclib or 'approximately' using pixel discretisation. Polygon calculations are now always performed 'exactly'. o intersect.owin, union.owin, setminus.owin If A and B are polygons, the result is a polygon. o erosion, dilation, opening, closing If the original set is a polygon, the result is a polygon. o intersect.tess, dirichlet The tiles of the resulting tessellation are polygons if the input was polygonal. o plot.owin Polygons with holes can now be plotted with filled colours on any device. o lppm New arguments 'eps' and 'nd' control the quadrature scheme. o pairwise interaction Gibbs models Many calculations for these models have been accelerated. BUG FIXES o nncross.pp3 Values were completely incorrect in some cases. Usually accompanied by a warning about NA values. (Spotted by Andrew Bevan.) Fixed. o nnmap, nnmark A small proportion of pixels had incorrect values. [These were the pixels lying on the boundary of a Dirichlet cell.] Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm Results were incorrect for non-Poisson processes. Fixed. o distcdf Results were incorrect in some cases when W was a window and V was a point pattern. Fixed. o Kcross, Kdot, pcfcross, pcfdot Results were incorrect in some rare cases. Fixed. o as.fv.kppm Erroneously returned a NULL value. Fixed. o vcov.ppm For point process models fitted with method = 'logi', sometimes crashed with error "object 'fit' not found". (Spotted by Ege Rubak). Fixed. o vcov.ppm For multitype point process models, sometimes crashed with error "argument 'par' is missing". Fixed. o plot.im Crashed if some of the pixel values were infinite. Fixed. o owin owin(poly=..) crashed if there were NA's in the polygon coordinates. Spotted by Jeff Marcus. Fixed. o plot.fv Crashed, giving an incomprehensible error, if the plot formula contained a number with a decimal point. Fixed. o alltypes Crashed if envelopes=TRUE and global=TRUE, with error message 'csr.theo not found'. Spotted by Jose M Blanco Moreno. Fixed. o chop.tess, rMosaicField Format of result was garbled in some cases. Fixed. o vcov.ppm Sometimes gave an irrelevant warning "parallel option not available". Fixed. CHANGES IN spatstat VERSION 1.33-0 OVERVIEW o We thank Kurt Hornik and Brian Ripley for advice. o The package namespace has been modified. o Numerous internal changes. o Likelihood cross-validation for smoothing bandwidth. o More flexible models of intensity in cluster/Cox processes. o New generic function for smoothing. o Version nickname: 'Titanic Deckchair' NEW FUNCTIONS o bw.ppl Likelihood cross-validation technique for bandwidth selection in kernel smoothing. o is.lppm, is.kppm, is.slrm Tests whether an object is of class 'lppm', 'kppm' or 'slrm' o Smooth New generic function for spatial smoothing. o Smooth.ppp, Smooth.fv, Smooth.msr Methods for Smooth (identical to smooth.ppp, smooth.fv, smooth.msr respectively) o fitted.kppm Method for 'fitted' for cluster/Cox models SIGNIFICANT USER-VISIBLE CHANGES o namespace The namespace of the spatstat package has been changed. o internal functions Some undocumented internal functions are no longer visible, as they are no longer exported in the namespace. These functions can still be accessed using the form spatstat:::functionname. Functions that are not visible are not guaranteed to exist or to remain the same in future. o methods For some generic functions defined in the spatstat package, it is possible that R may fail to find one of the methods for the generic. This is a temporary problem due to a restriction on the size of the namespace in R 3.0.1. It will be fixed in future versions of R and spatstat. It only applies to methods for a generic which is a spatstat function (such as nndist) and does not apply to methods for generics defined elsewhere (such as density). In the meantime, if this problem should occur, it can be avoided by calling the method explicitly, in the form spatstat:::genericname.classname. o speed The package should run slightly faster overall, due to the improvement of the namespace, and changes to internal code. o envelope New argument 'envir.simul' determines the environment in which to evaluate the expression 'simulate'. o kppm More flexible models of the intensity, and greater control over the intensity fitting procedure, are now possible using the arguments 'covfunargs', 'use.gam', 'nd', 'eps' passed to ppm. Also the argument 'X' may now be a quadrature scheme. o distcdf Arguments W and V can now be point patterns. o Kest New option: correction = "good" selects the best edge correction that can be computed in reasonable time. o bw.diggle Accelerated. o predict.ppm Calculation of standard error has been accelerated. o smooth.ppp, smooth.fv, smooth.msr These functions will soon be 'Deprecated' in favour of the methods Smooth.ppp, Smooth.fv, Smooth.msr respectively. o stratrand, overlap.owin, update.slrm, edge.Trans, edge.Ripley These already-existing functions are now documented. BUG FIXES o kppm, matclust.estpcf, pcfmodel The pair correlation function of the Matern Cluster Process was evaluated incorrectly at distances close to 0. This could have affected the fitted parameters in matclust.estpcf() or kppm(clusters="MatClust"). Fixed. o anova.ppm Would cause an error in future versions of R when 'anova.glm' is removed from the namespace. Fixed. CHANGES IN spatstat VERSION 1.32-0 OVERVIEW o We thank Ege Rubak for major contributions. o Thanks also to Patrick Donnelly, Andrew Hardegen, Tom Lawrence, Robin Milne, Gopalan Nair and Sean O'Riordan. o New 'logistic likelihood' method for fitting Gibbs models. o Substantial acceleration of several functions including profile maximum pseudolikelihood and variance calculations for Gibbs models. o Nearest neighbours for point patterns in 3D o Nearest-neighbour interpolation in 2D o New 'progress plots' o Hard core thresholds can be estimated automatically. o More support for colour maps o More support for 'fv' objects o Spatstat now has version nicknames. The current version is "Logistical Nightmare". o Minor improvements and bug fixes. NEW FUNCTIONS o nncross.pp3 Method for 'nncross' for point patterns in 3D o nnmark Mark of nearest neighbour - can be used for interpolation o dclf.progress, mad.progress Progress plots (envelope representations) for the DCLF and MAD tests. o deriv.fv Numerical differentiation for 'fv' objects. o interp.colourmap Smooth interpolation of colour map objects - makes it easy to build colour maps with gradual changes in colour o tweak.colourmap Change individual colour values in a colour map object o beachcolourmap Colour scheme appropriate for `altitudes' (signed numerical values) o as.fv Convert various kinds of data to an 'fv' object o quadscheme.logi Generates quadrature schemes for the logistic method of ppm. o beginner Introduction for beginners. SIGNIFICANT USER-VISIBLE CHANGES o ppm New option: method = "logi" Fits a Gibbs model by the newly developed 'logistic likelihood' method which is often faster and more accurate than maximum pseudolikelihood. Code contributed by Ege Rubak. o profilepl Greatly accelerated, especially for area-interaction models. o vcov.ppm Greatly accelerated for higher-order interaction models. o smooth.ppp Now handles bandwidths equal to zero (by invoking 'nnmark') o Hardcore, StraussHard The hard core distance 'hc' can now be omitted; it will be estimated from data. o plot.ppp Now behaves differently if there are multiple columns of marks. Each column of marks is plotted, in a series of separate plots arranged side-by-side. o plot.im Argument 'col' can now be a function o lohboot Now computes confidence intervals for L-functions as well (fun="Lest" or fun="Linhom") o dclf.test, mad.test The argument X can now be an object produced by a previous call to dclf.test or mad. o plot.fv Labelling of plots has been improved in some cases. o smooth.fv Further options added. o density.ppp The argument 'weights' can now be a matrix. o smooth.ppp Accelerated, when there are several columns of marks. o density.ppp Accelerated slightly. o simulate.ppm, simulate.kppm The total computation time is also returned. o simulate.kppm Now catches errors (such as 'insufficient memory'). o latest.news, licence.polygons Can now be executed by typing the name of the function without parentheses. o latest.news The text is now displayed one page at a time. BUG FIXES o Hest, Gfox, Jfox The 'raw' estimate was not computed correctly (or at least it was not the raw estimate described in the help files). Spotted by Tom Lawrence. Fixed. o edges2vees Format of result was incorrect if there were fewer than 3 edges. Fixed. o Jfox The theoretical value (corresponding to independence between X and Y) was erroneously given as 0 instead of 1. Spotted by Patrick Donnelly. Fixed. o ppm, quadscheme, default.dummy If the grid spacing parameter 'eps' was specified, the quadrature scheme was sometimes slightly incorrect (missing a few dummy points near the window boundary). Fixed. o print.timed Matrices were printed incorrectly. Fixed. CHANGES IN spatstat VERSION 1.31-3 OVERVIEW o spatstat now 'Suggests' the package 'tensor' o Code slightly accelerated. o More support for pooling of envelopes. o Bug fixes. NEW FUNCTIONS o nnmap Given a point pattern, finds the k-th nearest point in the pattern from each pixel in a raster. o coef.fii, coef.summary.fii Extract the interaction coefficients of a fitted interpoint interaction o edges2vees Low-level function for finding triples in a graph. SIGNIFICANT USER-VISIBLE CHANGES o predict.ppm New argument 'correction' allows choice of edge correction when calculating the conditional intensity. o pool.envelope New arguments 'savefuns' and 'savepatterns'. o pool.envelope Envelopes generated with VARIANCE=TRUE can now be pooled. o pool.envelope The plot settings of the input data are now respected. o Numerous functions have been slightly accelerated. BUG FIXES o predict.ppm Calculation of the conditional intensity omitted the edge correction if correction='translate' or correction='periodic'. Fixed. o shift.lpp, rotate.lpp, scalardilate.lpp, affine.lpp, shift.linnet, rotate.linnet, scalardilate.linnet, affine.linnet The enclosing window was not correctly transformed. Fixed. o rHardcore, rStraussHard, rDiggleGratton, rDGS The return value was invisible. Fixed. o ppm In rare cases the results obtained with forcefit=FALSE and forcefit=TRUE were different, due to numerical rounding effects. Fixed. CHANGES IN spatstat VERSION 1.31-2 OVERVIEW o We thank Robin Corria Anslie, Julian Gilbey, Kiran Marchikanti, Ege Rubak and Thordis Linda Thorarinsdottir for contributions. o spatstat now depends on R 3.0.0 o More support for linear networks o More functionality for nearest neighbours o Bug fix in fitting Geyer model o Performance improvements and bug fixes NEW FUNCTIONS o affine.lpp, shift.lpp, rotate.lpp, rescale.lpp, scalardilate.lpp Geometrical transformations for point patterns on a linear network o affine.linnet, shift.linnet, rotate.linnet, rescale.linnet, scalardilate.linnet Geometrical transformations for linear networks o [.linnet Subset operator for linear networks o timed Records the computation time taken SIGNIFICANT USER-VISIBLE CHANGES o nncross nncross.ppp can now find the k-th nearest neighbours, for any k. o nndist, nnwhich New argument 'by' makes it possible to find nearest neighbours belonging to specified subsets in a point pattern, for example, the nearest neighbour of each type in a multitype point pattern. o [.fv Now handles the argument 'drop'. o with.fv Argument 'drop' replaced by new argument 'fun' (with different interpretation). o [.lpp Subset index may now be a window (class 'owin') o Kest Options correction='border' and correction='none' now run about 4 times faster, thanks to Julian Gilbey. o density.ppp Numerical underflow no longer occurs when sigma is very small and 'at="points"'. A warning is no longer issued. Thanks to Robin Corria Anslie. o crossing.psp New argument 'fatal' allows the user to handle empty intersections o union.owin It is now guaranteed that if A is a subset of B, then union.owin(A,B)=B. o plot.colourmap Now passes arguments to axis() to control the plot. Appearance of plot improved. o image.listof Now passes arguments to plot.colourmap() if equal.ribbon=TRUE. o kppm Accelerated (especially for large datasets). o plot.envelope plot.envelope is now equivalent to plot.fv and is essentially redundant. o rThomas, rMatClust, rNeymanScott Improved explanations in help files. o All functions Many functions have been slightly accelerated. BUG FIXES o ppm Results were incorrect for the Geyer saturation model with a non-integer value of the saturation parameter 'sat'. Spotted by Thordis Linda Thorarinsdottir. Bug introduced in spatstat 1.20-0, July 2010. Fixed. o ppm Fitting a stationary Poisson process using a nonzero value of 'rbord', as in "ppm(X, rbord=R)" with R > 0, gave incorrect results. Fixed. o predict.slrm Crashed with message 'longer object length is not a multiple of shorter object length' if the original data window was not a rectangle. Fixed. o iplot Main title was sometimes incorrect. Fixed. o plot.layered Ignored argument 'main' in some cases. Fixed. o plot.listof, image.listof Crashed sometimes with a message 'figure margins too large' when equal.ribbon=TRUE. Fixed. o print.ppx Crashed if the object contained local coordinates. Fixed. o transect.im Crashed if the transect lay partially outside the image domain. Fixed. o rthin Crashed if X was empty. Fixed. o max.im, min.im, range.im Ignored additional arguments after the first argument. Fixed. o update.lppm Updated object did not remember the name of the original dataset. Fixed. o envelope Grey shading disappeared from plots of envelope objects when the envelopes were transformed using eval.fv or eval.fasp. Fixed. CHANGES IN spatstat VERSION 1.31-1 OVERVIEW o We thank Marcelino de la Cruz, Daniel Esser, Jason Goldstick, Abdollah Jalilian, Ege Rubak and Fabrice Vinatier for contributions. o Nonparametric estimation and tests for point patterns in a linear network. o More support for 'layered' objects. o Find clumps in a point pattern. o Connected component interaction model. o Improvements to interactive plots. o Visual debugger for Metropolis-Hastings algorithm. o Bug fix in Metropolis-Hastings simulation of Geyer process. o Faster Metropolis-Hastings simulation. o Faster computation of 'envelope', 'fv' and 'fasp' objects. o Improvements and bug fixes. NEW FUNCTIONS o connected.ppp Find clumps in a point pattern. o kstest.lpp, kstest.lppm The spatial Kolmogorov-Smirnov test can now be applied to point patterns on a linear network (class 'lpp') and point processes on a linear network (class 'lppm'). o bermantest.lpp, bermantest.lppm Berman's Z1 and Z2 tests can now be applied to point patterns on a linear network (class 'lpp') and point processes on a linear network (class 'lppm'). o rhohat.lpp, rhohat.lppm Nonparametric estimation of the dependence of a point pattern on a spatial covariate: 'rhohat' now applies to objects of class 'lpp' and 'lppm'. o intensity.lpp Empirical intensity of a point pattern on a linear network. o as.function.rhohat Converts a 'rhohat' object to a function, with extrapolation beyond the endpoints. o [.layered Subset operator for layered objects. o shift, rotate, affine, rescale, reflect, flipxy, scalardilate These geometrical transformations now work for 'layered' objects. o iplot.layered Interactive plotting for 'layered' objects. o as.owin.layered Method for as.owin for layered objects. o [.owin Subset operator for windows, equivalent to intersect.owin. o rcellnumber Generates random integers for the Baddeley-Silverman counterexample. o is.lpp Tests whether an object is a point pattern on a linear network. o is.stationary.lppm, is.poisson.lppm New methods for is.stationary and is.poisson for class 'lppm' o sessionLibs Print library names and version numbers (for use in Sweave scripts) SIGNIFICANT USER-VISIBLE CHANGES o iplot iplot is now generic, with methods for 'ppp', 'layered' and 'default'. iplot methods now support zoom and pan navigation. o rmh.default New argument 'snoop' allows the user to activate a visual debugger for the Metropolis-Hastings algorithm. o connected connected() is now generic, with methods for 'im', 'owin' and 'ppp'. o alltypes Now works for lpp objects o rlabel Now works for lpp, pp3, ppx objects o plot.kstest Can now perform P-P and Q-Q plots as well. o plot.fasp New argument 'samey' controls whether all panels have the same y limits. o plot.fasp Changed default value of 'samex'. o Objects of class 'envelope', 'fv' and 'fasp' Reduced computation time and storage required for these objects. o pcfmodel.kppm Improved calculation. o plot.fv Improved collision-avoidance algorithm (for avoiding overlaps between curves and legend) o ppm Improved error handling o envelope All methods for 'envelope' now handle fun=NULL o setminus.owin Better handling of the case where both arguments are rectangles. o rmh Simulation has been further accelerated. o lppm Accelerated. o vcov.ppm Accelerated. o marktable Accelerated. o Triplets() interaction Accelerated. o alltypes Accelerated when envelope=TRUE. BUG FIXES o rmh Simulation of the Geyer saturation process was incorrect. [Bug introduced in previous version, spatstat 1.31-0.] Fixed. o rmh Simulation of the Geyer saturation process was incorrectly initialised, so that the results of a short run (i.e. small value of 'nrep') were incorrect, while long runs were correct. [Bug introduced in spatstat 1.17-0, october 2009.] Fixed. o ppm Objects fitted with use.gam=TRUE caused fatal errors in various functions including print, summary, vcov and model.frame. Spotted by Jason Goldstick. Fixed. o lpp, runiflpp, rpoislpp Empty point patterns caused an error. Fixed. o rmh.default Crashed for hybrid models, with message 'Attempt to apply non-function'. Spotted by Ege Rubak. Fixed. o relrisk Crashed when 'at="points"' for a multitype pattern with more than 2 types. Spotted by Marcelino de la Cruz. Fixed. o erosion.owin, dilation.psp, border Ignored the arguments "..." in some cases (namely when the window was polygonal and 'gpclib' was disabled). Fixed. o rsyst, rcell Did not correctly handle the argument 'dx'. Spotted by Fabrice Vinatier. Fixed. o correction="trans" Various functions such as Kest no longer recognised 'correction = "trans"'. Fixed. o istat Crashed with an error message about envelopes. Fixed. o summary.ppm, print.ppm p-values which were exactly equal to zero were reported as NA. Fixed. o [.im Crashed if the intersection consisted of a single row or column of pixels. Fixed. o plot.im Sometimes incorrectly displayed an image consisting of a single row or column of pixels. Fixed. o plot.layered The plot region was determined by the first layer, so that objects in subsequent layers could sometimes fall outside the plot region. Fixed. o transect.im If the arguments 'from' and 'to' were numeric vectors of length 2, the result was garbled. Fixed. o Inhomogeneous K functions and pair correlation functions [Kinhom, pcfinhom, Kcross.inhom, Kdot.inhom, pcfcross.inhom, etc.] These functions reported an error 'lambda is not a vector' if the intensity argument lambda was computed using density(, at="points"). Fixed. o rlabel Did not accept a point pattern with a hyperframe of marks. Fixed. o alltypes Crashed when envelope=TRUE if the summary function 'fun' did not have default values for the marks i and j. Fixed. o Kres, Gres, psst, psstA Ignored the unit of length. Fixed. CHANGES IN spatstat VERSION 1.31-0 OVERVIEW o We thank Frederic Lavancier and Ege Rubak for contributions. o Major bug fix in simulation of area-interaction process. o Metropolis-Hastings simulations accelerated. o Rounding of spatial coordinates o clmfires dataset corrected. o Bug fixes and minor improvements. NEW FUNCTIONS o round.ppp Round the spatial coordinates of a point pattern to a specified number of decimal places. o rounding Determine whether a dataset has been rounded. SIGNIFICANT USER-VISIBLE CHANGES o rmh Simulation of the following models has been accelerated: areaint, dgs, diggra, fiksel, geyer, hardcore, lennard, multihard, strauss, straush, straussm, strausshm. o rmh The transition history of the simulation (which is saved if 'track=TRUE') now also contains the value of the Hastings ratio for each proposal. o clmfires The clmfires dataset has been modified to remove errors and inconsistencies. o plot.linim Appearance of the plot has been improved, when style='width'. o summary.ppm Now reports whether the spatial coordinates have been rounded. o dclf.test, mad.test The range of distance values ('rinterval') used in the test is now printed in the test output, and is saved as an attribute. BUG FIXES o rmh Simulation of the Area-Interaction model was completely incorrect. Spotted by Frederic Lavancier. The bug was introduced in spatstat version 1.23-6 or later. Fixed. o dclf.test The test statistic was incorrectly scaled (by a few percent). This did not affect the p-value of the test. Fixed. o ppx If argument 'coord.type' was missing, various errors occurred: a crash may have occurred, or the results may have depended on the storage type of the data. Spotted by Ege Rubak. Fixed. o plot.ppx Crashed for 1-dimensional point patterns. Spotted by Ege Rubak. Fixed. CHANGES IN spatstat VERSION 1.30-0 OVERVIEW o We thank Jorge Mateu, Andrew Bevan, Olivier Flores, Marie-Colette van Lieshout, Nicolas Picard and Ege Rubak for contributions. o The spatstat manual now exceeds 1000 pages. o Hybrids of point process models. o Five new datasets o Second order composite likelihood method for kppm. o Inhomogeneous F, G and J functions. o Delaunay graph distance o Fixed serious bug in 'lppm' for marked patterns. o bug fix in some calculations for Geyer model o Improvements to linear networks code o Pixel images can now be displayed with a logarithmic colour map. o spatstat now formally 'Depends' on the R core package 'grDevices' o miscellaneous improvements and bug fixes NEW DATASETS o clmfires Forest fires in Castilla-La Mancha o gordon People sitting on the grass in Gordon Square, London o hyytiala Mixed forest in Hyytiala, Finland (marked by species) o paracou Kimboto trees in Paracou, French Guiana (marked as adult/juvenile) o waka Trees in Waka national park (marked with diameters) NEW FUNCTIONS o Hybrid The hybrid of several point process interactions [Joint research with Jorge Mateu and Andrew Bevan] o is.hybrid Recognise a hybrid interaction or hybrid point process model. o Finhom, Ginhom, Jinhom Inhomogeneous versions of the F, G and J functions [Thanks to Marie-Colette van Lieshout] o delaunay.distance Graph distance in the Delaunay triangulation. o distcdf Cumulative distribution function of the distance between two independent random points in a given window. o bw.frac Bandwidth selection based on window geometry o shortside.owin, sidelengths.owin Side lengths of (enclosing rectangle of) a window SIGNIFICANT USER-VISIBLE CHANGES o ppm Can now fit models with 'hybrid' interactions [Joint research with Jorge Mateu and Andrew Bevan] o kppm Now has the option of fitting models using Guan's (2006) second order composite likelihood. o envelope.lpp Now handles multitype point patterns. o envelope.envelope New argument 'transform' allows the user to apply a transformation to previously-computed summary functions. o runifpointOnLines, rpoisppOnLines, runiflpp, rpoislpp Can now generate multitype point patterns. o rmhmodel, rmh, simulate.ppm Now handle point process models with 'hybrid' interactions. o kppm Accelerated, and more reliable, due to better choice of starting values in the optimisation procedure. o kppm The internal format of kppm objects has changed. o minimum contrast estimation Error messages from the optimising function 'optim' are now trapped and handled. o rhohat This command is now generic, with methods for ppp, quad, and ppm. o raster.x, raster.y, raster.xy These functions have a new argument 'drop' o summary.ppm Improved behaviour when the model covariates are a data frame. o progressreport Output improved. o second order summary functions (Kest, Lest, Kinhom, pcf.ppp, Kdot, Kcross, Ldot etc etc) These functions now accept correction="translation" as an alternative to correction = "translate", for consistency. o plot.im New argument 'log' allows colour map to be equally spaced on a log scale. o as.owin.ppm, as.owin.kppm New argument 'from' allows the user to extract the spatial window of the point data (from="points") or the covariate images (from="covariates") o dclf.test, mad.test The rule for handling tied values of the test statistic has been changed. The tied values are now randomly ordered to obtain a randomised integer rank. o with.fv New argument 'enclos' allows evaluation in other environments BUG FIXES o lppm For multitype patterns, the fitted model was completely incorrect due to an error in constructing the quadrature scheme. Fixed. o Geyer For point process models with the 'Geyer' interaction, vcov.ppm() and suffstat() sometimes gave incorrect answers. [Spotted by Ege Rubak.] Fixed. o as.im.im Did not correctly handle factor-valued images if one of the arguments 'dimyx', 'eps', 'xy' was given. Fixed. o envelope.lppm Crashed if the model was multitype. Fixed. o lpp Did not handle empty patterns. Fixed. o density.ppp If 'sigma' was a bandwidth selection function such as bw.scott() which returned a numeric vector of length 2, a warning message was issued, and the smoothing bandwidth was erroneously taken to be the first element of the vector. Fixed. o Fest, Jcross, Jdot, Jmulti If these functions were computed using correction = 'rs', plotting them would sometimes give an error, with the message "no finite x/y limits". Fixed. o pcfmodel.kppm For models with clusters="VarGamma" the value of the pcf at distance r=0 was given as NaN. Fixed. o vcov.ppm Result was incorrect in rare cases, due to numerical rounding effects. Fixed. o rLGCP, simulate.kppm For models fitted to point patterns in an irregular window, simulation sometimes failed, with a message that the image 'mu' did not cover the simulation window. (Spotted by George Limitsios.) Fixed. o rLGCP, simulate.kppm Crashed sometimes with an error about unequal x and y steps (from 'GaussRF'). Fixed. CHANGES IN spatstat VERSION 1.29-0 OVERVIEW o We thank Colin Beale, Li Haitao, Frederic Lavancier, Erika Mudrak and Ege Rubak for contributions. o random sequential packing o Allard-Fraley estimator o method for pooling several quadrat tests o better control over dummy points in ppm o more support for data on a linear network o nearest neighbour map o changes to subsetting of images o improvements and bug fixes NEW FUNCTIONS o clusterset Allard-Fraley estimator of high-density features in a point pattern o pool.quadrattest Pool several quadrat tests o nnfun Nearest-neighbour map of a point pattern or a line segment pattern o as.ppm Converts various kinds of objects to ppm o crossdist.lpp Shortest-path distances between pairs of points in a linear network o nobs.lppm Method for 'nobs' for lppm objects. o as.linim Converts various kinds of objects to 'linim' o model.images.slrm Method for model.images for slrm objects o rotate.im Rotate a pixel image SIGNIFICANT USER-VISIBLE CHANGES o "[.im" and "[<-.im" New argument 'j' allows any type of matrix indexing to be used. o "[.im" Default behaviour changed in the case of a rectangular subset. New argument 'rescue' can be set to TRUE to reinstate previous behaviour. o rSSI Performs 'Random Sequential Packing' if n=Inf. o ppm New argument 'eps' determines the spacing between dummy points. (also works for related functions quadscheme, default.dummy, ...) o fitted.ppm, predict.ppm Argument 'new.coef' specifies a vector of parameter values to replace the fitted coefficients of the model. o lppm Stepwise model selection using step() now works for lppm objects. o vcov.slrm Can now calculate correlation matrix or Fisher information matrix as well as variance-covariance matrix. o eval.fv Improved behaviour when plotted. o "[.fv" Improved behaviour when plotted. o lohboot When the result is plotted, the confidence limits are now shaded. o lohboot New argument 'global' allows global (simultaneous) confidence bands instead of pointwise confidence intervals. o vcov.ppm Accelerated by 30% in some cases. o quadrat.test.splitppp The result is now a single object of class 'quadrattest' o progressreport Improved output (also affects many functions which print progress reports) o Full redwood data (redwoodfull) Plot function redwoodfull.extra$plotit has been slightly improved. o nncross This function is now generic, with methods for 'ppp' and 'default'. o distfun The internal format of objects of class 'distfun' has been changed. o duplicated.ppp, unique.ppp New argument 'rule' allows behaviour to be consistent with package 'deldir' BUG FIXES o bdist.tiles Values were incorrect in some cases due to numerical error. (Spotted by Erika Mudrak.) Fixed. o vcov.ppm, suffstat These functions sometimes gave incorrect values for marked point process models. Fixed. o simulate.ppm, predict.ppm Did not correctly handle the 'window' argument. (Spotted by Li Haitao). Fixed. o smooth.ppp, markmean If sigma was very small, strange values were produced, due to numerical underflow. (Spotted by Colin Beale). Fixed. o MultiHard, MultiStrauss, MultiStraussHard Crashed if the data point pattern was empty. (Spotted by Ege Rubak). Fixed. o vcov.ppm Crashed sporadically, with multitype interactions. (Spotted by Ege Rubak). Fixed. o rStrauss, rHardcore, rStraussHard, rDiggleGratton, rDGS If the simulated pattern was empty, these functions would either crash, or return a pattern containing 1 point. (Spotted by Frederic Lavancier). Fixed. o model.matrix.slrm Crashed if the model was fitted using split pixels. Fixed. o residuals.ppm, diagnose.ppm Did not always correctly handle models that included offset terms. Fixed. o project.ppm When a model was projected by project.ppm or by ppm(project=TRUE), the edge corrections used the projected models were sometimes different from the edge corrections in the original model, so that the projected and unprojected models were not comparable. Fixed. o plot.listof, plot.splitppp Crashed sometimes due to a scoping problem. Fixed. o dclf.test, mad.test Crashed if any of the function values were infinite or NaN. Fixed. o psstA Default plot did not show the horizontal line at y=0 corresponding to a perfect fit. Fixed. o vcov.ppm names attribute was spelt incorrectly in some cases. Fixed. CHANGES IN spatstat VERSION 1.28-2 OVERVIEW o We thank Thomas Bendtsen, Ya-Mei Chang, Daniel Esser, Robert John-Chandran, Ege Rubak and Yong Song for contributions. o New code for Partial Residual Plots and Added Variable Plots. o maximum profile pseudolikelihood computations vastly accelerated. o New dataset: cells in gastric mucosa o now possible to capture every k-th state of Metropolis-Hastings algorithm. o size of 'ppm' objects reduced. o scope of 'intensity.ppm' extended. o quadrat.test can now perform Monte Carlo tests and one/two-sided tests o improvements to 'plot.fv' o improvement to 'rescale' o some datasets reorganised. o numerous bug fixes NEW DATASET o mucosa Cells in gastric mucosa Kindly contributed by Dr Thomas Bendtsen NEW FUNCTIONS o parres Partial residual plots for spatial point process models. A diagnostic for the form of a covariate effect. o addvar Added variable plots for spatial point process models. A diagnostic for the existence of a covariate effect. SIGNIFICANT USER-VISIBLE CHANGES o profilepl Accelerated (typically by a factor of 5). o rmh, rmhcontrol It is now possible to save every k-th iteration of the Metropolis-Hastings algorithm. The arguments 'nsave' and 'nburn' may be given to rmh or to rmhcontrol. They specify that the point pattern will be saved every 'nsave' iterations, after an initial burn-in of 'nburn' iterations. o simulate.ppm New argument 'singlerun' determines whether the simulated patterns are generated using independent runs of the Metropolis-Hastings algorithm or are obtained by performing one long run of the algorithm and saving every k-th iteration. o exactMPLEstrauss New argument 'project' determines whether the parameter gamma is constrained to lie in [0,1]. o intensity.ppm Now works for stationary point process models with the interactions DiggleGratton, DiggleGatesStibbard, Fiksel, PairPiece and Softcore. o plot.fv Improved algorithm for avoiding collisions between graphics and legend. o plot.fv New argument 'log' allows plotting on logarithmic axes. o envelope Can now calculate an estimate of the true significance level of the "wrong" test (which declares the observed summary function to be significant if it lies outside the pointwise critical boundary anywhere). Controlled by new argument 'do.pwrong'. o quadrat.test New argument 'alternative' allows choice of alternative hypothesis and returns one-sided or two-sided p-values as appropriate. o quadrat.test Can now perform Monte Carlo test as well (for use in small samples where the chi^2 approximation is inaccurate) o Softcore Improved numerical stability. New argument 'sigma0' for manual control over rescaling. o rescale If scale argument 's' is missing, then the data are rescaled to native units. For example if the current unit is 0.1 metres, coordinates will be re-expressed in metres. o psst Extra argument 'verbose=TRUE' o is.subset.owin Accelerated for polygonal windows o rmh.default 'track' is no longer a formal argument of rmh.default; it is now a parameter of rmhcontrol. However there is no change in usage: the argument 'track' can still be given to rmh.default. o clf.test Has been renamed 'dclf.test' to give proper attribution to Peter Diggle. o betacells This dataset has been restructured. The vector of cell profile areas, formerly given by betacells.extra$area, has now been included as a column of marks in the point pattern 'betacells'. o ants The function ants.extra$plot() has been renamed plotit() for conformity with other datasets. o redwoodfull The function redwoodfull.extra$plot() has been renamed plotit() for conformity with other datasets. o nbfires For conformity with other datasets, there is now an object nbfires.extra BUG FIXES o ripras Expansion factor was incorrect in the rectangular case. Fixed. o Triplets Crashed sometimes with error "dim(X) must have positive length". Fixed. o affine.im Crashed in the case of a diagonal transformation matrix! Spotted by Ege Rubak. Fixed. o envelope.envelope Ignored the argument 'global'. Fixed. o MultiStraussHard The printed output showed the hardcore radii as NULL. Spotted by Ege Rubak. Fixed. o "[.psp" Crashed if the data were generated by rpoisline(). Spotted by Marcelino de la Cruz. Fixed. o plot.linim If style="colour", the main title was always "x". Fixed. o plot.ppx Setting add=TRUE did not prevent the domain being plotted. Fixed. o rmh Crashed if x.start was an empty point pattern. Spotted by Ege Rubak. Fixed. o as.ppp.data.frame Crashed if any points lay outside the window. Spotted by Ege Rubak. Fixed. o Ripley isotropic edge correction Divide-by-zero error in rare cases. Spotted by Daniel Esser. Fixed. o summary functions For many of the summary functions (e.g. Kest, pcf), the result of saving the object to disc was an enormous file. Spotted by Robert John-Chandran. Fixed. o pcf.fv Default plot was wrongly coloured. Fixed. CHANGES IN spatstat VERSION 1.28-1 OVERVIEW o We thank Ege Rubak, Gopal Nair, Jens Oehlschlaegel and Mike Zamboni for contributions. o New approximation to the intensity of a fitted Gibbs model. o Minor improvements and bug fixes o spatstat now 'Suggests' the package 'gsl' NEW FUNCTIONS o intensity, intensity.ppp, intensity.ppm Calculate the intensity of a dataset or fitted model. Includes new approximation to the intensity of a fitted Gibbs model o LambertW Lambert's W-function SIGNIFICANT USER-VISIBLE CHANGES o envelope Improved plot labels for envelopes that were generated using the 'transform' argument. o plot.fv Improved algorithm for collision detection. o plot.im Now returns the colour map used. o plot.listof, plot.splitppp Slight change to handling of plot.begin and plot.end o square Now accepts vectors of length 2 o plot.fii Increased resolution of the plot obtained from plot(fitin(ppm(...))) o image.listof If equal.ribbon=TRUE, the colour ribbon will no longer be displayed repeatedly for each panel, but will now be plotted only once, at the right hand side of the plot array. BUG FIXES o vcov.ppm Results were sometimes incorrect for a Gibbs model with non-trivial trend. Spotted by Ege Rubak. Fixed. o nncross In rare cases the results could be slightly incorrect. Spotted by Jens Oehlschlaegel. Fixed. o plot.fv When add=TRUE, the x limits were sometimes truncated. Spotted by Mike Zamboni. Fixed. o plot.im Labels for the tick marks on the colour ribbon were sometimes ridiculous, e.g. "2.00000001". Fixed. CHANGES IN spatstat VERSION 1.28-0 OVERVIEW o We thank Farzaneh Safavimanesh, Andrew Hardegen and Tom Lawrence for contributions. o Improvements to 3D summary functions. o A multidimensional point pattern (ppx) can now have 'local' coordinates as well as spatial and temporal coordinates and marks. o Changed format for point patterns on a linear network (lpp). Changes are backward compatible. Many computations run faster. o More support for fitted cluster models (kppm). o split method for multidimensional point patterns (ppx) and point patterns on a linear network (lpp). o Fixed bug causing errors in plot.im o Miscellaneous improvements and bug fixes NEW FUNCTIONS o exactMPLEstrauss Fits the stationary Strauss point process model using an exact maximum pseudolikelihood technique. This is mainly intended for technical investigation of algorithms. o split.ppx Method for 'split' for multidimensional point patterns (class 'ppx'). This also works for point patterns on a linear network (class 'lpp'). o model.images This function is now generic, with methods for classes ppm, kppm, lppm o model.frame, model.matrix These generic functions now have methods for classes kppm, lppm o as.owin.kppm, as.owin.lppm New methods for 'as.owin' for objects of class kppm, lppm o as.linnet.lppm Extracts the linear network in which a point process model was fitted. SIGNIFICANT USER-VISIBLE CHANGES o class 'ppx' An object of class 'ppx' may now include 'local' coordinates as well as 'spatial' and 'temporal' coordinates, and marks. o ppx Arguments have changed. o class 'lpp' The internal format of lpp objects has been extended (but is backward-compatible). Many computations run faster. To convert an object to the new format: X <- lpp(as.ppp(X), as.linnet(X)). o F3est Calculation of theoretical Poisson curve ('theo') has changed, and is now controlled by the argument 'sphere'. o rmh, rmhstart The initial state ('start') can now be missing or null. o im, as.im The pixel coordinates in an image object are now generated more accurately. This avoids a numerical error in plot.im. o eval.fv, eval.fasp Evaluation is now applied only to columns that contain values of the function itself (rather than values of the derivative, hazard rate, etc). This is controlled by the new argument 'dotonly'. o spatstat.options New option 'nvoxel' o quad.ppm Now accepts kppm objects. o str This generic function (for inspecting the internal structure of an object) now produces sensible output for objects of class 'hyperframe', 'ppx', 'lpp' o ppx, coords.ppx, coords<-.ppx The arguments to these functions have changed. o lgcp.estK, Kmodel Computation can be greatly accelerated by setting spatstat.options(fastK.lgcp=TRUE). o G3est Computation accelerated. o envelope Computation slightly accelerated. o spatstat.options New option 'fastK.lgcp' BUG FIXES o nndist.psp Caused an error if length(k) > 1. Fixed. o plot.im Sometimes reported an error "useRaster=TRUE can only be used with a regular grid." This was due to numerical rounding effects on the coordinates of a pixel image. Fixed. o plot.fv If a formula was used to specify the plot, the names of variables in the formula were sometimes incorrectly matched to *functions*. Spotted by Farzaneh Safavimanesh. Fixed. o F3est Took a very long time if the containing box was very flat, due to the default value of 'vside'. Fixed. o rmh, rmhmodel An erroneous warning about 'outdated format of rmhmodel object' sometimes occurred. Fixed. o marks<-.ppx Names of result were incorrect. Fixed. o hyperframe class Various minor bug fixes. CHANGES IN spatstat VERSION 1.27-0 OVERVIEW o Variance estimates are now available for all Gibbs point process models. o Cressie-Loosmore-Ford test implemented o plot.fv now avoids collisions between the legend and the graphics. o Extension to predict.ppm o Improvements to envelopes and multitype summary functions. o Line transects of a pixel image. o Changes to defaults in Metropolis-Hastings simulations. o More geometrical operations o Bug fixes. o We thank Aruna Jammalamadaka for contributions. NEW FUNCTIONS o clf.test Perform the Cressie (1991)/ Loosmore and Ford (2006) test of CSR (or another model) o mad.test Perform the Maximum Absolute Deviation test of CSR (or another model). o convolve.im Compute convolution of pixel images. o Kmulti.inhom Counterpart of 'Kmulti' for spatially-varying intensity. o rmhexpand Specify a simulation window, or a rule for expanding the simulation window, in Metropolis-Hastings simulation (rmh) o transect.im Extract pixel values along a line transect. o affine.im Apply an affine transformation to a pixel image. o scalardilate Perform scalar dilation of a geometrical object relative to a specified origin. o reflect Reflect a geometrical object through the origin. o "[.lpp", "[.ppx" Subset operators for the classes "lpp" (point pattern on linear network) and "ppx" (multidimensional space-time point pattern). o is.rectangle, is.polygonal, is.mask Determine whether a window w is a rectangle, a domain with polygonal boundaries, or a binary pixel mask. o has.offset Determines whether a fitted model object (of any kind) has an offset. SIGNIFICANT USER-VISIBLE CHANGES o predict.ppm This function can now calculate the conditional intensity of a model relative to any point pattern X (not just the original data pattern). o vcov.ppm This function now handles all Gibbs point process models. o plot.fv Collisions between the legend box and the graphics are now detected and avoided. o rmh.ppm, rmh.default, simulate.ppm, qqplot.ppm, envelope.ppm These functions now have slightly different default behaviour because of changes to the handling of arguments to 'rmhcontrol'. o rmhcontrol The default value of the parameters 'periodic' and 'expand' has changed. o rmhcontrol The parameter 'expand' can now be in any format acceptable to rmhexpand(). o rmh.ppm, rmh.default, simulate.ppm Any 'rmhcontrol' parameter can now be given directly as an argument to rmh.ppm, rmh.default or simulate.ppm. o Kmulti, Gmulti, Jmulti The arguments I, J can now be any kind of subset index or can be functions that yield a subset index. o envelope.envelope In envelope(E, fun=NULL) if E does not contain simulated summary functions, but does contain simulated point patterns, then 'fun' now defaults to Kest, instead of flagging an error. o print.ppp, summary.ppp If the point pattern x was generated by Metropolis-Hastings simulation using 'rmh', then print(x) and summary(x) show information about the simulation parameters. o print.ppm Standard errors for the parameter estimates, and confidence intervals for the parameters, can now be printed for all Gibbs models (but are printed only for Poisson models by default). o eval.im Images with incompatible dimensions are now resampled to make them compatible (if harmonize=TRUE). o spatstat.options New option 'print.ppm.SE' controls whether standard errors and confidence intervals are printed for all Gibbs models, for Poisson models only, or are never printed. o inside.owin Now accepts the form list(x,y) for the first argument. o image.listof New argument 'equal.ribbon' allows several images to be plotted with the same colour map. o is.subset.owin Improved accuracy in marginal cases. o expand.owin Functionality extended to handle all types of expansion rule. o default.rmhcontrol, default.expand These functions now work with models of class 'rmhmodel' as well as 'ppm' o print.rmhcontrol Output improved. BUG FIXES o linearK, linearKinhom If any data points were located exactly at a vertex of the linear network, the weights for Ang's correction were incorrect, due to numerical error. This sometimes produced infinite or NA values of the linear K function. Fixed. o predict.ppm In some cases, predict.ppm(type="cif") generated a spurious warning that "number of rows of result is not a multiple of vector length." Fixed. o crossing.psp Results were sometimes incorrect due to numerical rounding error associated with GCC bug #323. Fixed. o MultiHard, MultiStrauss, MultiStraussHard If the mark values contained non-alphanumeric characters, the names of the interaction coefficients in coef(ppm(...)) were sometimes garbled. Fixed. o profilepl For edge corrections other than the border correction, an error message about 'rbord' would sometimes occur. Fixed. o is.marked, is.multitype These functions gave the wrong answer for 'lpp' objects. Fixed. o marks<-.lpp, marks<-.ppx Format of result was garbled if new columns of marks were added. Fixed. o reach.rmhmodel Gave the wrong answer for Geyer and BadGey models. Fixed. o envelope.envelope Ignored the argument 'savefuns'. Fixed. o BadGey Sometimes wrongly asserted that the parameter 'sat' was invalid. Occurred only in ppm(project=TRUE). Fixed. CHANGES IN spatstat VERSION 1.26-1 OVERVIEW o Variance-covariance matrix for Gibbs point process models. o Bootstrap confidence bands for pair correlation function and K function. o Bug fix in scan test. o Area-interaction model accelerated. o we thank Jean-Francois Coeurjolly and Ege Rubak for contributions. NEW FUNCTIONS o lohboot Computes bootstrap confidence bands for pair correlation function and K function using Loh's (2008) mark bootstrap. SIGNIFICANT USER-VISIBLE CHANGES o vcov.ppm Now works for all Gibbs point process models, thanks to new code (and theory) from Jean-Francois Coeurjolly and Ege Rubak o AreaInter Computations related to the area-interaction point process (ppm, predict.ppm, residuals.ppm, diagnose.ppm, qqplot.ppm) have been accelerated. BUG FIXES o scan.test Results were sometimes incorrect due to numerical instability (a 'Gibbs phenomenon'). Fixed. CHANGES IN spatstat VERSION 1.26-0 OVERVIEW o We thank Jens Oehlschlaegel for contributions. o Further substantial acceleration of spatstat functions. o Workaround for bug in RandomFields package. o Numerous modifications to internal code. NEW FUNCTIONS o RandomFieldsSafe There is a bug in the package 'RandomFields' (version <= 2.0.54) which causes a crash to occur, in the development version of R but not in R 2.15.0. To avoid crashing spatstat, we have written the temporary, undocumented function RandomFieldsSafe() which returns TRUE if it is safe to use the RandomFields package. Examples in the spatstat help files for kppm, lgcp.estK, lgcp.estpcf and rLGCP are only executed if RandomFieldsSafe() returns TRUE. SIGNIFICANT USER-VISIBLE CHANGES o Many functions Many spatstat functions now run faster, and will handle larger datasets, thanks to improvements in the internal code, following suggestions from Jens Oehlschlaegel. o Many functions The response to an 'Interrupt' signal is slightly slower. CHANGES IN spatstat VERSION 1.25-5 OVERVIEW o We thank Ya-Mei Chang, Jens Oehlschlaegel and Yong Song for contributions. o Extended functionality of 'rhohat' to local likelihood smoothing and bivariate smoothing. o Nearest neighbour distance computations accelerated. o spatstat now 'Suggests:' the package 'locfit' NEW FUNCTIONS o rho2hat Bivariate extension of 'rhohat' for estimating spatial residual risk, or intensity as a function of two covariates. SIGNIFICANT USER-VISIBLE CHANGES o rhohat Estimation can now be performed using local likelihood fitting with the 'locfit' package, or using kernel smoothing. o nncross Substantially accelerated. New arguments added to control the return value and the sorting of data. BUG FIXES o plot.msr Crashed if the argument 'box' was given. Fixed. CHANGES IN spatstat VERSION 1.25-4 OVERVIEW o We thank Jonathan Lee and Sergiy Protsiv for contributions. o Improvements and bug fixes to K function for very large datasets NEW FUNCTIONS o rStraussHard Perfect simulation for Strauss-hardcore process (with gamma <= 1) SIGNIFICANT USER-VISIBLE CHANGES o plot.im The colour ribbon can now be placed left, right, top or bottom using new argument 'ribside' o profilepl Does not generate warnings when some of the candidate models have zero likelihood - for example when fitting model with a hard core. o Kest Now includes fast algorithm for 'correction="none"' which will handle patterns containing millions of points. BUG FIXES o Kest, Lest Gave incorrect values in very large datasets, due to numerical overflow. `Very large' typically means about 1 million points in a random pattern, or 100,000 points in a tightly clustered pattern. [Overflow cannot occur unless there are at least 46,341 points.] [Spotted by Sergiy Protsiv.] Fixed. o Kest, Lest Ignored 'ratio=TRUE' if the argument 'domain' was given. [Spotted by Jonathan Lee.] Fixed. o rjitter Output was sometimes incorrect. [Spotted by Sergiy Protsiv.] Fixed. CHANGES IN spatstat VERSION 1.25-3 OVERVIEW o We thank Daniel Esser for contributions. o Improved support for fitted point process models. o Bug fixes. NEW FUNCTIONS o simulate.slrm Method for 'simulate' for spatial logistic regression models. o labels.ppm, labels.kppm, labels.slrm Methods for 'labels' for fitted point process models. o commonGrid Determine a common spatial domain and pixel resolution for several pixel images and/or binary masks SIGNIFICANT USER-VISIBLE CHANGES o effectfun Now has argument 'se.fit' allowing calculation of standard errors and confidence intervals. o [.msr Now handles character-valued indices. o print.summary.ppm Output gives a more precise description of the fitting method. o ppm, kppm, slrm Confidence intervals for the fitted trend parameters can now be obtained using 'confint' o predict.slrm New argument 'window' o union.owin Now handles a single argument: union.owin(A) returns A. BUG FIXES o selfcrossing.psp y coordinate values were incorrect. [Spotted by Daniel Esser.] Fixed. o as.im.owin Did not handle a binary mask with a 1 x 1 pixel array. Fixed. o predict.slrm Results of predict(object, newdata) were incorrect if the spatial domain of 'newdata' was larger than the original domain. Fixed. o ppm If the model was the uniform Poisson process, the argument 'rbord' was ignored. Fixed. o image subset assignment "[<-.im" Generated an error if the indexing argument 'i' was a point pattern containing zero points. Fixed. o hyperframe subset assignment "[<-.hyperframe" Did not correctly handle the case where a single column of the hyperframe was to be changed. Fixed. o help(bw.relrisk), help(rmh.ppm), help(plot.plotppm) These help files had the side-effect of changing some options in spatstat.options. Fixed. CHANGES IN spatstat VERSION 1.25-2 OVERVIEW o We thank Abdollah Jalilian and Thierry Onkelinx for contributions. o Very Important Bug fixes. o Improved mechanism for handling 'invalid' point processes NEW FUNCTIONS o as.matrix.owin Converts a window to a logical matrix. SIGNIFICANT USER-VISIBLE CHANGES o project.ppm Improved algorithm. Now handles terms in the trend formula as well as the interaction. The projected point process is now obtained by re-fitting the model, and is guaranteed to be the maximum pseudolikelihood fit. o plot.im Now handles many arguments recognised by plot.default such as 'cex.main'. Also handles argument 'box'. New argument 'ribargs' contains parameters controlling the ribbon plot only. o spatstat.options New option 'project.fast' allows a faster shortcut for project.ppm o spatstat.options New options 'rmh.p', 'rmh.q', 'rmh.nrep' determine the default values of the parameters p, q and nrep of the Metropolis-Hastings algorithm. See rmhcontrol o ppm Slightly accelerated. BUG FIXES o nncross, distfun, AreaInter Results of nncross were possibly incorrect when X and Y did not have the same window. This bug affected values of 'distfun' and may also have affected ppm objects with interaction 'AreaInter'. [Spotted by Thierry Onkelinx] Bug introduced in spatstat 1.9-4 (June 2006). Fixed. o rCauchy Simulations were incorrect in the sense that the value of 'omega' was inadvertently doubled (i.e. omega was incorrectly replaced by 2 * omega). Bug introduced in spatstat 1.25-0. Fixed. o plot.im White lines were present in the image display, on some graphics devices, due to changes in R 2.14. Fixed. o update.ppm The result of 'update(object, formula)' sometimes contained errors in the internal format. Bug introduced in spatstat 1.25-0. Fixed. o example(AreaInter), example(bw.smoothppp), example(Kest.fft), example(plot.owin), example(predict.ppm), example(simulate.ppm) Executing these examples had the side-effect of changing some of the parameters in spatstat.options. Fixed. CHANGES IN spatstat VERSION 1.25-1 OVERVIEW o We thank Neba Funwi-Gabga and Jorge Mateu for contributions. o New dataset of gorilla nest sites o New functions for perfect simulation o Bug fix for rare crashes in rStrauss o Code for ensuring a fitted point process model is a valid point process NEW DATASET o gorillas Gorilla nest sites in a National Park in Cameroon. Generously contributed by Neba Funwi-Gabga NEW FUNCTIONS o rDiggleGratton, rDGS, rHardcore Perfect simulation for the Diggle-Gratton process, Diggle-Gates-Stibbard process, and Hardcore process. o bw.scott Scott's rule of thumb for bandwidth selection in multidimensional smoothing o valid.ppm Checks whether a fitted point process model is a valid point process o project.ppm Forces a fitted point process model to be a valid point process SIGNIFICANT USER-VISIBLE CHANGES o ppm New argument 'project' determines whether the fitted model is forced to be a valid point process o linnet Substantially accelerated. o rStrauss Slightly accelerated. o summary.lpp Now prints the units of length. BUG FIXES o rStrauss Crashed rarely (once every 10 000 realisations) with a memory segmentation fault. Fixed. CHANGES IN spatstat VERSION 1.25-0 OVERVIEW o Leverage and influence for point process models o New cluster models (support for model-fitting and simulation). o Fit irregular parameters in trend of point process model o Third order summary statistic. o Improvements to speed and robustness of code. o spatstat now depends on R 2.14 o We thank Abdollah Jalilian and Rasmus Waagepetersen for contributions. NEW FUNCTIONS o leverage.ppm, influence.ppm, dfbetas.ppm Leverage and influence for point process models o ippm Experimental extension to 'ppm' which fits irregular parameters in trend by Fisher scoring algorithm. o Tstat Third order summary statistic for point patterns based on counting triangles. o rCauchy, rVarGamma simulation of a Neyman-Scott process with Cauchy clusters or Variance Gamma (Bessel) clusters. Contributed by Abdollah Jalilian. o rPoissonCluster simulation of a general Poisson cluster process o model.covariates Identify the covariates involved in a model (lm, glm, ppm etc) o as.im.distfun Converts a 'distfun' to a pixel image. o cauchy.estK, cauchy.estpcf, vargamma.estK, vargamma.estpcf Low-level model-fitting functions for the Neyman-Scott process with Cauchy or Variance-Gamma cluster kernel. Contributed by Abdollah Jalilian. SIGNIFICANT USER-VISIBLE CHANGES o kppm Now accepts clusters="Cauchy" or clusters="VarGamma" for the Neyman-Scott process with Cauchy or Variance-Gamma cluster kernel. Code contributed by Abdollah Jalilian. o rNeymanScott Argument 'rcluster' may now take a different format. o psst Argument 'funcorrection' changed to 'funargs' allowing greater flexibility. o plot.fv, plot.envelope New argument 'limitsonly' allows calculation of a common x,y scale for several plots. o overall speed spatstat is now byte-compiled and runs slightly faster. o user interrupt Long calculations in spatstat now respond to the Interrupt/Stop signal. o update.ppm Now runs faster and uses much less memory, when the update only affects the model formula (trend formula). o rNeymanScott, rThomas, rMatClust Accelerated thanks to Rasmus Waagepetersen. o multitype data and models Second order multitype statistics (such as Kcross, pcfcross) and multitype interaction models (such as MultiStrauss) now run faster, by a further 5%. BUG FIXES o distfun Some manipulations involving 'distfun' objects failed if the original data X in distfun(X) did not have a rectangular window. Fixed. CHANGES IN spatstat VERSION 1.24-2 OVERVIEW o Geyer's triplet interaction o more functionality for replicated point patterns o changed default for simulation window in point process simulation o changed default for edge correction in Kcom, Gcom o data in spatstat is now lazy-loaded o bug fixes NEW FUNCTIONS o Triplets Geyer's triplet interaction, for point process models o coef.summary.ppm New method coef.summary.ppm You can now type 'coef(summary(fit))' to extract a table of the fitted coefficients of the point process model 'fit' SIGNIFICANT USER-VISIBLE CHANGES o data in spatstat are now lazy-loaded so you don't have to type data(amacrine), etc. o rmh.default, rmh.ppm, simulate.ppm These now handle the 'triplets' interaction o fryplot Now has arguments 'to' and 'from', allowing selection of a subset of points. o fryplot, frypoints These functions now handle marked point patterns properly. o Kcross, Kdot, Kmulti New argument 'ratio' determines whether the numerator and denominator of the estimate of the multitype K-function will be stored. This enables analysis of replicated point patterns, using 'pool.rat()' to pool the K function estimates. o rmh.ppm, simulate.ppm, default.expand For point process models which have a trend depending only on x and y, the simulation window is now taken to be the same as the original window containing the data (by default). That is, `expansion' does not take place, by default. (In previous versions of spatstat the simulation window was larger than the original data window.) o rmh.ppm, simulate.ppm The argument sequence for these functions has changed. New argument 'expand' allows more explicit control over simulation domain. o Kcom, Gcom New argument 'conditional' gives more explicit control over choice of edge correction in compensator. Simplified defaults for edge correction. o Kinhom Improved plot labels. o profilepl Printed output improved. BUG FIXES o Lest The variance approximations (Lotwick-Silverman and Ripley) obtained with var.approx=TRUE, were incorrect for Lest (although they were correct for Kest) due to a coding error. Fixed. o simulate.ppm Ignored the argument 'control' in some cases. Fixed. o pcf and its relatives (pcfinhom, pcfcross.inhom, pcfdot.inhom) Sometimes gave a warning about 'extra arguments ignored'. Fixed. CHANGES IN spatstat VERSION 1.24-1 OVERVIEW o Spatial Scan Test o Functionality for replicated point patterns o Bug fixes NEW FUNCTIONS o scan.test Spatial scan test of clustering o rat New class of 'ratio objects' o pool.rat New method for 'pool'. Combines K function estimates for replicated point patterns (etc) by computing ratio-of-sums o unnormdensity Weighted kernel density with weights that do not sum to 1 and may be negative. o compatible New generic function with methods for 'fv', 'im', 'fasp' and 'units' SIGNIFICANT USER-VISIBLE CHANGES o Kest New argument 'ratio' determines whether the numerator and denominator of the estimate of the K-function will be stored. This enables analysis of replicated point patterns, using 'pool.rat()' to pool the K function estimates. o Lest Now handles theoretical variance estimates (using delta method) if var.approx=TRUE o as.mask Argument 'eps' can now be a 2-vector, specifying x and y resolutions. o default.expand Behaviour changed slightly. o plot.listof, plot.splitppp, contour.listof, image.listof The arguments 'panel.begin' and 'panel.end' can now be objects such as windows. BUG FIXES o rgbim, hsvim Did not work on images with non-rectangular domains. Fixed. o scaletointerval Did not handle NA's. Fixed. CHANGES IN spatstat VERSION 1.24-0 OVERVIEW o This version was not released publicly. CHANGES IN spatstat VERSION 1.23-6 OVERVIEW o Spatial covariance functions of windows and pixel images. o Area-interaction models can now be fitted in non-rectangular windows o Bug fix for envelope of inhomogeneous Poisson process o Bug fix for raster conversion o New vignette on 'Getting Started with Spatstat' o Code accelerated. NEW FUNCTIONS o imcov Spatial covariance function of pixel image or spatial cross-covariance function of two pixel images o harmonise.im Make several pixel images compatible by converting them to the same pixel grid o contour.listof, image.listof Methods for contour() and image() for lists of objects o dummify Convert data to numeric values by constructing dummy variables. SIGNIFICANT USER-VISIBLE CHANGES o setcov Can now compute the `cross-covariance' between two regions o AreaInter Point process models with the AreaInter() interaction can now be fitted to point pattern data X in any window. o areaGain, areaLoss These now handle arbitrary windows W. They are now more accurate when r is very small. o Kcom Computation vastly accelerated, for non-rectangular windows. o vignettes New vignette 'Getting Started with the Spatstat Package' o nncorr, nnmean, nnvario These functions now handle data frames of marks. BUG FIXES o envelope.ppm If the model was an inhomogeneous Poisson process, the resulting envelope object was incorrect (the simulations were correct, but the envelopes were calculated assuming the model was CSR). Bug was introduced in spatstat 1.23-5. Fixed. o envelope.ppm If the model was an inhomogeneous Poisson process with intensity a function of x and y only, overflow errors sometimes occurred ('insufficient storage' or 'attempting to generate a large number of random points'). Fixed. o as.im.im The result of as.im(X, W) was incorrect if 'W' did not cover 'X'. Fixed. o as.mask The result of as.mask(w, xy) was incorrect if 'xy' did not cover 'w'. Fixed. o plot.fv Legend was incorrectly labelled if 'shade' variables were not included in the plot formula. Fixed. o areaGain, areaLoss Crashed if the radius r was close to zero. Fixed. CHANGES IN spatstat VERSION 1.23-5 OVERVIEW o Bug fix to bandwidth selection. o Functions to pool data from several objects of the same class. o Improvements and bug fixes. o We thank Michael Sumner for contributions. NEW FUNCTIONS o pool Pool data from several objects of the same class o pool.envelope Pool simulated data from several envelope objects and create a new envelope o pool.fasp Pool simulated data from several function arrays and create a new array o envelope.envelope Recalculate an envelope from simulated data using different parameters SIGNIFICANT USER-VISIBLE CHANGES o bw.diggle, bw.relrisk, bw.smoothppp, bw.optim Plot method modified. o model.depends Now also recognises 'offset' terms. BUG FIXES o bw.diggle Bandwidth was too large by a factor of 2. Fixed. o plot.psp Crashed if any marks were NA. Fixed. o pointsOnLines Crashed if any segments had zero length. Ignored argument 'np' in some cases. Fixed. o stieltjes Crashed if M had only a single column of function values. Fixed. CHANGES IN spatstat VERSION 1.23-4 OVERVIEW o Bandwidth selection for density.ppp and smooth.ppp o Layered plots. o Model-handling facilities. o Improvements and bug fixes. NEW FUNCTIONS o bw.diggle Bandwidth selection for density.ppp by mean square error cross-validation. o bw.smoothppp Bandwidth selection for smooth.ppp by least-squares cross-validation. o layered, plot.layered A simple mechanism for controlling plots that consist of several successive layers of data. o model.depends Given a fitted model (of any kind), identify which of the covariates is involved in each term of the model. o model.is.additive Determine whether a fitted model (of any kind) is additive, in the sense that each term in the model involves at most one covariate. SIGNIFICANT USER-VISIBLE CHANGES o smooth.ppp Bandwidth 'sigma' is now selected by least-squares cross-validation o bw.relrisk Computation in large datasets accelerated. New arguments 'hmin', 'hmax' control the range of trial values of bandwidth. o Hest, Gfox, Jfox Improved algebraic labels for plot o spatstat.options New parameter 'n.bandwidth' o density.ppp, smooth.ppp Slightly accelerated. o point-in-polygon test Accelerated. BUG FIXES o with.fv Mathematical labels were incorrect in some cases. Fixed. o bw.relrisk Implementation of method="weightedleastsquares" was incorrect and was equivalent to method="leastsquares". Fixed. o smooth.ppp NaN values occurred if the bandwidth was very small. Fixed. CHANGES IN spatstat VERSION 1.23-3 OVERVIEW o Urgent bug fix. BUG FIXES o crossing.psp Crashed occasionally with a message about NA or NaN values. Fixed. o affine.ppp Crashed if the point pattern was empty. Fixed. CHANGES IN spatstat VERSION 1.23-2 OVERVIEW o Bug fixes. o Several functions have been accelerated. o We thank Marcelino de la Cruz and Ben Madin for contributions. NEW FUNCTIONS o sumouter, quadform Evaluate certain quadratic forms. o flipxy Exchange x and y coordinates. SIGNIFICANT USER-VISIBLE CHANGES o vcov.ppm Accelerated. o owin, as.owin Checking the validity of polygons has been accelerated. o crossing.psp, selfcrossing.psp Accelerated. BUG FIXES o split.ppp If drop=TRUE then some of the point patterns had the wrong windows. Spotted by Marcelino de la Cruz. Fixed. o split.ppp Crashed if the tessellation did not cover the point pattern. Fixed. o predict.ppm Crashed when type="se" if NA's were present. Spotted by Ben Madin. Fixed. o plot.ppp Incorrectly handled the case where both 'col' and 'cols' were present. Fixed. o polygon geometry The point-in-polygon test gave the wrong answer in some boundary cases. Fixed. CHANGES IN spatstat VERSION 1.23-1 OVERVIEW o Important bug fix to 'localpcf'. o Inverse-distance weighted smoothing. o Inhomogeneous versions of neighbourhood density functions. o Internal repairs and bug fixes. o We thank Mike Kuhn and Ben Madin for contributions. NEW FUNCTIONS o idw Inverse-distance weighted smoothing. o localKinhom, localLinhom, localpcfinhom Inhomogeneous versions of localK, localL, localpcf BUG FIXES o localpcf The columns of the result were in the wrong order. [i.e. pair correlation functions were associated with the wrong points.] Fixed. o delaunay If the union of several Delaunay triangles formed a triangle, this was erroneously included in the result of delaunay(). Fixed. o predict.ppm, plot.ppm Sometimes crashed with a warning about 'subscript out of bounds'. Fixed. o point-in-polygon test Vertices of a polygon were sometimes incorrectly classified as lying outside the polygon. Fixed. o Internal code Numerous tweaks and repairs to satisfy the package checker for the future R version 2.14. CHANGES IN spatstat VERSION 1.23-0 OVERVIEW o point patterns on a linear network: new tools including geometrically-corrected linear K function, pair correlation function, point process models, envelopes o changes to renormalisation of estimates in Kinhom and pcfinhom o new dataset: Chicago street crime o spatstat now 'Suggests:' the package RandomFields o spatstat now has a Namespace o we thank Mike Kuhn, Monia Mahling, Brian Ripley for contributions. NEW DATASET o chicago Street crimes in the University district of Chicago. A point pattern on a linear network. NEW FUNCTIONS o envelope.lpp Simulation envelopes for point patterns on a linear network o lineardisc Compute the 'disc' of radius r in a linear network o linearpcf Pair correlation for point pattern on a linear network o linearKinhom, linearpcfinhom Inhomogeneous versions of the K function and pair correlation function for point patterns on a linear network o lppm Fit point process models on a linear network. o anova.lppm Analysis of deviance for point process models on a linear network. o predict.lppm Prediction for point process models on a linear network. o envelope.lppm Simulation envelopes for point process models on a linear network. o linim Pixel image on a linear network o plot.linim Plot a pixel image on a linear network SIGNIFICANT USER-VISIBLE CHANGES o linearK New argument 'correction'. Geometrically-corrected estimation is performed by default (based on forthcoming paper by Ang, Baddeley and Nair) o Kinhom New argument 'normpower' allows different types of renormalisation. o pcfinhom Now performs renormalisation of estimate. Default behaviour changed - estimates are now renormalised by default. BUG FIXES o density.ppp Crashed if argument 'varcov' was given. Fixed. CHANGES IN spatstat VERSION 1.22-4 OVERVIEW o new diagnostics based on score residuals o new dataset o improvements to plotting summary functions o We thank Ege Rubak, Jesper Moller, George Leser, Robert Lamb and Ulf Mehlig for contributions. NEW FUNCTIONS o Gcom, Gres, Kcom, Kres New diagnostics for fitted Gibbs or Poisson point process models based on score residuals. Gcom is the compensator of the G function Gres is the residual of the G function Kcom is the compensator of the K function Kres is the residual of the K function o psst, psstA, psstG New diagnostics for fitted Gibbs or Poisson point process models based on pseudoscore residuals. psst is the pseudoscore diagnostic for a general alternative psstA is the pseudoscore diagnostic for an Area-interaction alternative psstG is the pseudoscore diagnostic for a Geyer saturation alternative o compareFit Computes and compares several point process models fitted to the same dataset, using a chosen diagnostic. o as.interact Extracts the interpoint interaction structure (without parameters) from a fitted point process model or similar object. NEW DATASET o flu Spatial point patterns giving the locations of influenza virus proteins on cell membranes. Kindly released by Dr George Leser and Dr Robert Lamb. SIGNIFICANT USER-VISIBLE CHANGES o pixel images and grids The default size of a pixel grid, given by spatstat.options("npixel"), has been changed from 100 to 128. A power of 2 gives faster and more accurate results in many cases. o residuals.ppm New arguments 'coefs' and 'quad' for advanced use (make it possible to compute residuals from a modified version of the fitted model.) o relrisk New argument 'casecontrol' determines whether a bivariate point pattern should be treated as case-control data. o plot.fv Further improvements in mathematical labels. o plot.fv The formula can now include the symbols .x and .y as abbreviation for the function argument and the recommended function value, respectively. o plot.fv New argument 'add' BUG FIXES o multitype summary functions (Kcross, Kdot, Gcross, Gdot, .....) Plotting these functions generated an error if the name of one of the types of points contained spaces, e.g. "Escherichia coli". Fixed. CHANGES IN spatstat VERSION 1.22-3 OVERVIEW o Important bug fix to simulation code o Miscellaneous improvements o spatstat now depends on R 2.13.0 or later o We thank Ege Rubak, Kaspar Stucki, Vadim Shcherbakov, Jesper Moller and Ben Taylor for contributions. NEW FUNCTIONS o is.stationary, is.poisson New generic functions for testing whether a point process model is stationary and/or Poisson. Methods for ppm, kppm, slrm etc o raster.xy raster coordinates of a pixel mask o zapsmall.im 'zapsmall' for pixel images SIGNIFICANT USER-VISIBLE CHANGES o density.ppp New argument 'diggle' allows choice of edge correction o rotate.owin, affine.owin These functions now handle binary pixel masks. New argument 'rescue' determines whether rectangles will be preserved BUG FIXES o rmh, simulate.ppm Serious bug - simulation was completely incorrect in the case of a multitype point process with an interaction that does not depend on the marks, such as ppm(betacells, ~marks, Strauss(60)) The calling parameters were garbled. Fixed. o effectfun Crashed if the covariate was a function(x,y). Fixed. o lurking Gave erroneous error messages about 'damaged' models. Fixed. o envelope.ppm Did not recognise when the fitted model was equivalent to CSR. Fixed. o plot.ppx Crashed in some cases. Fixed. CHANGES IN spatstat VERSION 1.22-2 OVERVIEW o Fitting and simulation of log-Gaussian Cox processes with any covariance function o More support for 'kppm' and 'rhohat' objects o K-function for point patterns on a linear network o Metropolis-Hastings algorithm now saves its transition history o Easier control of dummy points in ppm o Convert an 'fv' object to an R function o spatstat now depends on the package 'RandomFields' o We thank Abdollah Jalilian, Shen Guochun, Rasmus Waagepetersen, Ege Rubak and Ang Qi Wei for contributions. NEW FUNCTIONS o linearK Computes the Okabe-Yamada network K-function for a point pattern on a linear network. o pairdist.lpp Shortest-path distances between each pair of points on a linear network. o vcov.kppm Asymptotic variance-covariance matrix for regression parameters in kppm object. [Contributed by Abdollah Jalilian and Rasmus Waagepetersen] o rLGCP Simulation of log-Gaussian Cox processes [Contributed by Abdollah Jalilian and Rasmus Waagepetersen] o predict.rhohat Method for 'predict' for objects of class 'rhohat' Computes a pixel image of the predicted intensity. o Kmodel, pcfmodel Generic functions that compute the K-function or pair correlation function of a point process *model*. So far the only methods are for the class 'kppm'. o as.function.fv Converts a function value table (class 'fv') to a function in R o coef.kppm Method for 'coef' for objects of class 'kppm' o unitname, unitname<- These generic functions now have methods for fitted model objects (classes ppm, slrm, kppm, minconfit) and quadrature schemes (quad). o nobs.ppm Method for 'nobs' for class 'ppm'. Returns the number of points in the original data. SIGNIFICANT USER-VISIBLE CHANGES o kppm Can now fit a log-Gaussian Cox process o simulate.kppm Can now simulate a fitted log-Gaussian Cox process o lgcp.estK, lgcp.estpcf These functions previously fitted a log-Gaussian Cox process with exponential covariance. They can now fit a log-Gaussian Cox process with any covariance function implemented by the RandomFields package. o rmh If track=TRUE, the history of transitions of the Metropolis-Hastings algorithm is saved and returned. o ppm New argument 'nd' controls the number of dummy points. o as.fv Now handles objects of class kppm or minconfit. o rhohat If covariate = "x" or "y", the resulting object has the same 'unitname' as the original point pattern data. o rhohat Now has arguments 'eps, 'dimyx' to control pixel resolution. o MultiStrauss, MultiHard, MultiStraussHard Default value of 'types' has been changed to NULL. o data(ants) The auxiliary data 'ants.extra' now includes a function called 'side' determining whether a given location is in the scrub or field region. Can be used as a covariate in ppm, kppm, slrm. o print.ppm Now has argument 'what' to allow only selected information to be printed. BUG FIXES o profilepl Crashed in some cases involving multitype interactions. Fixed. o plot.splitppp Behaved incorrectly if 'main' was an expression. Fixed. o effectfun Crashed in trivial cases. Fixed. o kppm, thomas.estpcf, matclust.estpcf, lgcp.estpcf Gave a spurious warning message. Fixed. o step When applied to ppm objects this gave a spurious warning. Fixed. CHANGES IN spatstat VERSION 1.22-1 OVERVIEW o marked line segment patterns can now be plotted o multitype point process models are now 'self-starting' o new functions to manipulate colour images NEW FUNCTIONS o rgbim, hsvim Specify three colour channels. These functions convert three pixel images with numeric values into a single image whose pixel values are strings representing colours. o scaletointerval Generic utility function to rescale data (including spatial data) to a specified interval SIGNIFICANT USER-VISIBLE CHANGES o plot.im Can now plot images whose pixel values are strings representing colours. New argument 'valuesAreColours' o plot.psp Now handles marked line segment patterns and plots the marks as colours. o MultiHard, MultiStrauss, MultiStraussHard The argument 'types' can now be omitted; it will be inferred from the point pattern data. o rhohat Improved mathematical labels (when the result of rhohat is plotted) o plot.fv Minor improvements in graphics BUG FIXES o several minor bug fixes and improvements to satisfy R-devel CHANGES IN spatstat VERSION 1.22-0 OVERVIEW o support for point patterns on a linear network o 'superimpose' is now generic o improved mathematical labels when plotting functions NEW CLASSES o linnet An object of class 'linnet' represents a linear network, i.e. a connected network of line segments, such as a road network. Methods for this class include plot, print, summary etc. o lpp An object of class 'lpp' represents a point pattern on a linear network, such as a record of the locations of road accidents on a road network. Methods for this class include plot, print, summary etc. NEW FUNCTIONS o runiflpp Uniformly distributed random points on a linear network o rpoislpp Poisson point process on a linear network o clickjoin Interactive graphics to create a linear network o superimpose The function 'superimpose' is now generic, with methods for ppp, psp and a default method. o as.ppp.psp New method for as.ppp extracts the endpoints and marks from a line segment pattern NEW DATASETS o simplenet Simple example of a linear network SIGNIFICANT USER-VISIBLE CHANGES o superimposePSP This function is now deprecated in favour of 'superimpose' o superimpose Now handles data frames of marks. o plot.fv Argument 'legendmath' now defaults to TRUE. New argument 'legendargs' gives more control over appearance of legend. Increased default spacing between lines in legend. o eval.fv, with.fv Functions computed using eval.fv or with.fv now have better labels when plotted. o summary functions (Kest, Kest.fft, Kcross, Kdot, Kmulti, Kinhom, Kcross.inhom, Kdot.inhom, Kmulti.inhom, Lest, Lcross, Ldot, pcf, pcfcross, pcfdot, pcfinhom, pcfcross.inhom, pcfdot.inhom, Fest, Gest, Gcross, Gdot, Gmulti, Jest, Jcross, Jdot, Jmulti, Iest, localL, localK, markcorr, markvario, markconnect, Emark, Vmark, allstats, alltypes) Improved plot labels. BUG FIXES o superimpose If the marks components of patterns consisted of character vectors (rather than factors or non-factor numeric vectors) an error was triggered. Fixed. o plot.fv The y axis limits did not always cover the range of values if the argument 'shade' was used. Fixed. o plot.rhohat The y axis label was sometimes incorrect. Fixed. o plot.rhohat If argument 'xlim' was used, a warning was generated from 'rug'. Fixed. CHANGES IN spatstat VERSION 1.21-6 OVERVIEW o A line segment pattern can now have a data frame of marks. o Various minor extensions and alterations in behaviour NEW FUNCTIONS o nsegments Number of segments in a line segment pattern SIGNIFICANT USER-VISIBLE CHANGES o psp class A line segment pattern (object of class 'psp') can now have a data frame of marks. o density.ppp New argument 'adjust' makes it easy to adjust the smoothing bandwidth o plot.envelope If the upper envelope is NA but the lower envelope is finite, the upper limit is now treated as +Infinity o msr Argument 'continuous' renamed 'density' BUG FIXES o [.psp In X[W] if X is a line segment pattern and W is a polygonal window, marks were sometimes discarded, leading to an error. Fixed. o [.psp In X[W] if X is a line segment pattern and W is a rectangular window, if the marks of X were factor values, they were converted to integers. Fixed. o superimposePSP If the marks were a factor, they were mistakenly converted to integers. Fixed. o is.marked.ppp Did not generate a fatal error when na.action="fatal" as described in the help file. Fixed. CHANGES IN spatstat VERSION 1.21-5 OVERVIEW o Increased numerical stability. o New 'self-starting' feature of interpoint interactions. SIGNIFICANT USER-VISIBLE CHANGES o ppm Interaction objects may now be 'self-starting' i.e. initial parameter estimates can be computed from the point pattern dataset. So far, only the LennardJones() interaction has a self-starting feature. o LennardJones Increased numerical stability. New (optional) scaling argument 'sigma0'. Interpoint distances are automatically rescaled using 'self-starting' feature. o vcov.ppm New argument 'matrix.action' controls what happens when the matrix is ill-conditioned. Changed name of argument 'gamaction' to 'gam.action' o rmhmodel.ppm Default resolution of trend image has been increased. o is.poisson.ppm Accelerated. o ppm, kppm, qqplot.ppm Improved robustness to numerical error CHANGES IN spatstat VERSION 1.21-4 OVERVIEW o Urgent bug fix BUG FIXES o print.summary.ppm exited with an error message, if the model had external covariates. Fixed. CHANGES IN spatstat VERSION 1.21-3 OVERVIEW o Point process model covariates may now depend on additional parameters. o New class of signed measures, for residual analysis. o Miscellaneous improvements and bug fixes. NEW FUNCTIONS o clarkevans.test Classical Clark-Evans test of randomness o msr New class 'msr' of signed measures and vector-valued measures supporting residual analysis. o quadrat.test.quadratcount Method for 'quadrat.test' for objects of class 'quadratcount' (allows a chi-squared test to be performed on quadrat counts rather than recomputing from the original data) o tile.areas Computes areas of tiles in a tessellation (efficiently) SIGNIFICANT USER-VISIBLE CHANGES o ppm The spatial trend can now depend on additional parameters. This is done by allowing spatial covariate functions to have additional parameters: function(x, y, ...) where ... is controlled by the new argument 'covfunargs' to ppm o profilepl Can now maximise over trend parameters as well as interaction parameters o residuals.ppm The value returned by residuals.ppm is now an object of class 'msr'. It can be plotted directly. o eval.im When the argument 'envir' is used, eval.im() now recognises functions as well as variables in 'envir' o colourmap The argument 'col' can now be any kind of colour data o persp.im The 'colmap' argument can now be a 'colourmap' object o ppm The print and summary methods for 'ppm' objects now show standard errors for parameter estimates if the model is Poisson. o quadrat.test The print method for 'quadrattest' objects now displays information about the quadrats o lurking Improved format of x axis label o distmap.ppp Internal code is more robust. BUG FIXES o im Did not correctly handle 1 x 1 arrays. Fixed. o as.mask, pixellate.ppp Weird things happened if the argument 'eps' was set to a value greater than the size of the window. Fixed. CHANGES IN spatstat VERSION 1.21-2 OVERVIEW o New multitype hardcore interaction. o Nonparametric estimation of covariate effects on point patterns. o Output of 'Kmeasure' has been rescaled. o Numerous improvements and bug fixes. NEW FUNCTIONS o MultiHard multitype hard core interaction for use in ppm() o coords<- Assign new coordinates to the points in a point pattern o rhohat Kernel estimate for the effect of a spatial covariate on point process intensity SIGNIFICANT USER-VISIBLE CHANGES o as.ppp.matrix, as.ppp.data.frame These methods for 'as.ppp' now accept a matrix or data frame with any number of columns (>= 2) and interpret the additional columns as marks. o Kmeasure The interpretation of the output has changed: the pixel values are now density estimates. o rmh.ppm, rmhmodel.ppm These functions now accept a point process model fitted with the 'MultiHard' interaction o rmh.default, rmhmodel.default These functions now accept the option: cif='multihard' defining a multitype hard core interaction. o markcorr Now handles a data frame of marks o varblock Improved estimate in the case of the K function o colourmap, lut New argument 'range' makes it easier to specify a colour map or lookup table o [<-.hyperframe Now handles multiple columns o plot.fv Improved y axis labels o spatstat.options New option 'par.fv' controls default parameters for line plotting o rmhmodel More safety checks on parameter values. o quadratresample New argument 'verbose' o smooth.fv Default value of 'which' has been changed. BUG FIXES o Kest If the argument 'domain' was used, the resulting estimate was not correctly normalised. Fixed. o Kest The Lotwick-Silverman variance approximation was incorrectly calculated. (Spotted by Ian Dryden and Igor Chernayavsky). Fixed. o plot.owin, plot.ppp Display of binary masks was garbled if the window was empty or if it was equivalent to a rectangle. Fixed. o plot.bermantest One of the vertical lines for the Z1 test was in the wrong place. Fixed. o marks<-.ppx Crashed in some cases. Fixed. o is.convex An irrelevant warning was issued (for non-convex polygons). Fixed. CHANGES IN spatstat VERSION 1.21-1 OVERVIEW o Confidence intervals for K-function and other statistics o Bug fixes for smoothing and relative risk estimation NEW FUNCTIONS o varblock Variance estimation (and confidence intervals) for summary statistics such as Kest, using subdivision technique o bw.stoyan Bandwidth selection by Stoyan's rule of thumb. o which.max.im Applied to a list of images, this determines which image has the largest value at each pixel. o as.array.im Convert image to array SIGNIFICANT USER-VISIBLE CHANGES o smooth.ppp, markmean, sharpen.ppp, relrisk, bw.relrisk Further acceleration achieved. o Kest Argument 'correction' now explicitly overrides automatic defaults o plot.fv More robust handling of 'shade' BUG FIXES o relrisk Format of relrisk(at="points") was incorrect. Fixed. o bw.relrisk Result was incorrect in the default case method="likelihood" because of previous bug. Fixed. o Jdot, Jcross, Jmulti Return value did not include the hazard function, when correction="km" Fixed. o Jdot, Jcross, Jmulti Format of output was incompatible with format of Jest. Fixed. CHANGES IN spatstat VERSION 1.21-0 OVERVIEW o Implemented Spatial Logistic Regression o Implemented nonparametric estimation of relative risk with bandwidth selection by cross-validation. o Smoothing functions can handle a data frame of marks. o New options in Kinhom; default behaviour has changed. NEW FUNCTIONS o slrm Fit a spatial logistic regression model o anova.slrm, coef.slrm, fitted.slrm, logLik.slrm, plot.slrm, predict.slrm Methods for spatial logistic regression models o relrisk Nonparametric estimation of relative risk o bw.relrisk Automatic bandwidth selection by cross-validation o default.rmhcontrol Sets default values of Metropolis-Hastings parameters SIGNIFICANT USER-VISIBLE CHANGES o smooth.ppp, markmean These functions now accept a data frame of marks. o Kinhom Default behaviour has changed. New argument 'renormalise=TRUE' determines scaling of estimator and affects bias and variance in small samples. o residuals.ppm Now also computes the score residuals. o plot.im New argument 'ribscale' o plot.listof, plot.splitppp New arguments panel.begin, panel.end and panel.args o ppp Now checks for NA/NaN/Inf values in the coordinates o envelope.ppm Changed default value of 'control' New argument 'nrep' o qqplot.ppm Changed default value of 'control' BUG FIXES o marks<-.ppp, setmarks, %mark% A matrix of marks was accepted by ppp() but not by these assignment functions. Fixed. o density.ppp, smooth.ppp, sharpen.ppp, markmean Crashed if the bandwidth was extremely small. Fixed. CHANGES IN spatstat VERSION 1.20-5 OVERVIEW o Accelerated computations of kernel smoothing. o Implemented Choi-Hall data sharpening. NEW FUNCTIONS o sharpen.ppp Performs Choi-Hall data sharpening of a point pattern SIGNIFICANT USER-VISIBLE CHANGES o density.ppp, smooth.ppp Computation has been vastly accelerated for density(X, at="points") and smooth.ppp(X, at="points") o Kinhom Accelerated in case where lambda=NULL o Vignette 'shapefiles' updated CHANGES IN spatstat VERSION 1.20-4 OVERVIEW o New functions for inhomogeneous point patterns and local analysis. o Pair correlation function for 3D point patterns o Minor improvements and bug fixes to simulation code and image functions NEW FUNCTIONS o pcf3est Pair correlation function for 3D point patterns. o Kscaled, Lscaled Estimator of the template K function (and L-function) for a locally-scaled point process. o localpcf Local version of pair correlation function o identify.psp Method for 'identify' for line segment patterns. o as.im.matrix Converts a matrix to a pixel image SIGNIFICANT USER-VISIBLE CHANGES o rMaternI, rMaternII New argument 'stationary=TRUE' controls whether the simulated process is stationary (inside the simulation window). Default simulation behaviour has changed. o im New arguments 'xrange', 'yrange' o envelope Improvements to robustness of code. BUG FIXES o quadratcount If V was a tessellation created using a factor-valued image, quadratcount(X, tess=V) crashed with the error "Tessellation does not contain all the points of X". Fixed. o [.im If Z was a factor valued image and X was a point pattern then Z[X] was not a factor. Fixed. CHANGES IN spatstat VERSION 1.20-3 OVERVIEW o minor improvements (mostly internal). NEW FUNCTIONS o unmark.ppx Method for 'unmark' for general space-time point patterns SIGNIFICANT USER-VISIBLE CHANGES o plot.ppx Now handles marked patterns, in two-dimensional case o as.psp.psp Default value of argument 'check' set to FALSE CHANGES IN spatstat VERSION 1.20-2 OVERVIEW o Extensions to minimum contrast estimation. o Bug fix in simulation of Lennard-Jones model. o More support for distance functions. o Changes to point process simulations. NEW FUNCTIONS o thomas.estpcf Fit Thomas process model by minimum contrast using the pair correlation function (instead of the K-function). o matclust.estpcf Fit Matern Cluster model by minimum contrast using the pair correlation function (instead of the K-function). o lgcp.estpcf Fit log-Gaussian Cox process model by minimum contrast using the pair correlation function (instead of the K-function). o contour.distfun, persp.distfun Methods for 'contour' and 'persp' for distance functions o default.expand Computes default window for simulation of a fitted point process model. SIGNIFICANT USER-VISIBLE CHANGES o kppm Models can now be fitted using either the K-function or the pair correlation function. o ppm The list of covariates can now include windows (objects of class 'owin'). A window will be treated as a logical covariate that equals TRUE inside the window and FALSE outside it. o plot.distfun Pixel resolution can now be controlled. o envelope.ppm, qqplot.ppm The default value of 'control' has changed; simulation results may be slightly different. o rmh Slightly accelerated. BUG FIXES o rmh Simulation of the Lennard-Jones model (cif = 'lennard') was incorrect due to an obscure bug, introduced in spatstat 1.20-1. Fixed. o thomas.estK, matclust.estK, lgcp.estK The value of 'lambda' (if given) was ignored if X was a point pattern. Fixed. CHANGES IN spatstat VERSION 1.20-1 OVERVIEW o Further increases in speed and efficiency of ppm and rmh o New pairwise interaction model NEW FUNCTIONS o DiggleGatesStibbard Diggle-Gates-Stibbard pairwise interaction for use in ppm() SIGNIFICANT USER-VISIBLE CHANGES o ppm has been accelerated by a factor of 10 for the BadGey interaction. o rmh simulation of the Lennard-Jones model (cif='lennard') has been greatly accelerated. o rmh, rmhmodel.ppm Point process models fitted by ppm() using the DiggleGatesStibbard interaction can be simulated automatically using rmh. BUG FIXES o fitin The plot of a fitted Hardcore interaction was incorrect. Fixed. CHANGES IN spatstat VERSION 1.20-0 OVERVIEW o spatstat now contains over 1000 functions. o Substantial increase in speed and efficiency of model-fitting code. o Changes to factor-valued images. SIGNIFICANT USER-VISIBLE CHANGES o ppm has been accelerated by a factor of 10, and can handle datasets with 20,000 points, for the following interactions: DiggleGratton, Fiksel, Geyer, Hardcore, Strauss, StraussHard o predict.ppm accelerated by a factor of 3 (when type = "cif") with vastly reduced memory requirements for the following interactions: DiggleGratton, Fiksel, Geyer, Hardcore, Strauss, StraussHard o pixel images (class "im") The internal representation of factor-valued images has changed. Existing objects in the old format should still work. o im The syntax for creating a factor-valued image has changed. Argument 'lev' has been deleted. o ppm Some warnings have been reworded for greater clarity. BUG FIXES o [.im Mishandled some factor-valued images. Fixed. o hist.im Produced slightly erroneous output for some factor-valued images. Fixed. o plot.owin Filled polygons appeared to contain criss-cross lines on some graphics drivers. Fixed. o deltametric Did not handle windows with different enclosing frames (error message: 'dA and dB are incompatible') Fixed. o quadratcount Crashed if the pattern was empty and the window was a rectangle. (Noticed by Sandro Azaele) Fixed. o rNeymanScott Crashed if the parent process realisation was empty. (Noticed by Sandro Azaele) Fixed. CHANGES IN spatstat VERSION 1.19-3 ACKNOWLEDGEMENTS o We thank David Dereudre for contributions. OVERVIEW o Urgent bug fix to Metropolis-Hastings for Lennard-Jones model. o Miscellaneous additions to plotting and colour management. NEW FUNCTIONS o col2hex, rgb2hex, paletteindex, samecolour Functions for converting and comparing colours. o plot.envelope New method for plotting envelopes. By default the area between the upper and lower envelopes is shaded in grey. SIGNIFICANT USER-VISIBLE CHANGES o plot.fasp If the entries in the array are envelopes, they are plotted using plot.envelope (hence the envelope region is shaded grey). o plot.fv Now displays mathematical notation for each curve, if legendmath=TRUE. o print.fv Now prints the available range of 'r' values as well as the recommended range of 'r' values. BUG FIXES o rmh Simulation of Lennard-Jones model was incorrect; the simulations were effectively Poisson patterns. (Spotted by David Dereudre.) Fixed. o plot.fv Did not correctly handle formulas that included I( ) Fixed. CHANGES IN spatstat VERSION 1.19-2 ACKNOWLEDGEMENTS o We thank Jorge Mateu, Michael Sumner and Sebastian Luque for contributions. OVERVIEW o More support for fitted point process models and pixel images. o Improved plotting of pixel images and envelopes. o Simulation algorithm for Lennard-Jones process. o Improvements and bug fixes to envelopes. o Bug fixes to Metropolis-Hastings simulation. NEW FUNCTIONS o pairs.im Creates a scatterplot matrix for several pixel images. o model.frame.ppm Method for 'model.frame' for point process models. o sort.im Method for 'sort' for pixel images. SIGNIFICANT USER-VISIBLE CHANGES o plot.fv, plot.fasp New argument 'shade' enables confidence intervals or significance bands to be displayed as filled grey shading. o LennardJones The parametrisation of this interaction function has been changed. o rmh, rmhmodel These functions will now simulate a point process model that was fitted using the LennardJones() interaction. o rmh.default, rmhmodel.default These functions will now simulate a point process model with the Lennard-Jones interaction (cif='lennard'). o ecdf This function now works for pixel images. o dim, row, col These functions now work for pixel images. o order This function now works for pixel images. o [.im and [<-.im The subset index can now be any valid subset index for a matrix. o density.ppp, smooth.ppp The return value now has attributes 'sigma' and 'varcov' reporting the smoothing bandwidth. o plot.im The argument 'col' can now be a 'colourmap' object. This makes it possible to specify a fixed mapping between numbers and colours (e.g. so that it is consistent between plots of several different images). o rmh, spatstat.options spatstat.options now recognises the parameter 'expand' which determines the default window expansion factor in rmh. o rmh Improved handling of ppm objects with covariates. o kstest The 'covariate' can now be one of the characters "x" or "y" indicating the Cartesian coordinates. BUG FIXES o model.matrix.ppm For a fitted model that used a large number of quadrature points, model.matrix.ppm sometimes reported an internal error about mismatch between the model matrix and the quadrature scheme. Fixed. o plot.ppx Minor bugs fixed. o rmh In rare cases, the simulated point pattern included multiple points at the origin (0,0). (Bug introduced in spatstat 1.17-0.) Fixed. o rmh, rmhmodel.ppm Crashed when applied to a fitted multitype point process model if the model involved more than one covariate image. (Spotted by Jorge Mateu) Fixed. o density.psp If any segment had zero length, the result contained NaN values. (Spotted by Michael Sumner and Sebastian Luque.) Fixed. o envelope Crashed with fun=Lest or fun=Linhom if the number of points in a simulated pattern exceeded 3000. Fixed. o plot.kstest Main title was corrupted if the covariate was a function. Fixed. CHANGES IN spatstat VERSION 1.19-1 OVERVIEW o New dataset: replicated 3D point patterns. o Improvements to Metropolis-Hastings simulation code. o More support for hyperframes. o Bug fixes. NEW DATASETS o osteo: Osteocyte Lacunae data: replicated 3D point patterns NEW FUNCTIONS o rbind.hyperframe: Method for rbind for hyperframes. o as.data.frame.hyperframe: Converts a hyperframe to a data frame. SIGNIFICANT USER-VISIBLE CHANGES o Fiksel: Fitted point process models (class ppm) with the Fiksel() double exponential interaction can now be simulated by rmh. o rmh.default: Point processes with the Fiksel interaction can now be simulated by specifying parameters in rmh.default. o logLik.ppm: New argument 'warn' controls warnings. o profilepl: No longer issues spurious warnings. BUG FIXES o Hardcore, rmh: Simulation of the 'Hardcore' process was incorrect. The hard core radius was erroneously set to zero so that the simulated patterns were Poisson. Fixed. o fitin: A plot of the pairwise interaction function of a fitted model, generated by plot(fitin(model)) where model <- ppm(...), was sometimes incorrect when the model included a hard core. Fixed. CHANGES IN spatstat VERSION 1.19-0 OVERVIEW o Numerous bugs fixed in the implementation of the Huang-Ogata approximate maximum likelihood method. o New interpoint interaction model. NEW FUNCTIONS o Fiksel: new interpoint interaction: Fiksel's double exponential model. SIGNIFICANT USER-VISIBLE CHANGES o runifpoint, rpoispp, envelope These functions now issue a warning if the number of random points to be generated is very large. This traps a common error in simulation experiments. BUG FIXES o predict.ppm, fitted.ppm: Predictions and fitted values were incorrect for objects fitted using ppm(..., method="ho"). Fixed. o logLik, AIC: Values of logLik() and AIC() were incorrect for objects fitted using ppm(..., method="ho"). Fixed. o profilepl: Results were incorrect if the argument 'method="ho"' was used. Fixed. o fitin The result of fitin() was incorrect for objects fitted using ppm(..., method="ho"). Fixed. o rmhcontrol: rmhcontrol(NULL) generated an error. Fixed. CHANGES IN spatstat VERSION 1.18-4 ACKNOWLEDGEMENTS o We thank Michael Sumner for contributions. BUG FIXES o pixellate.psp: segments shorter than one pixel width were measured incorrectly if the 'weights' argument was present. Fixed. NEW FUNCTIONS o pairdist.ppx, crossdist.ppx, nndist.ppx, nnwhich.ppx: Methods for pairdist, crossdist, nndist, nnwhich for multidimensional point patterns (class 'ppx') o runifpointx, rpoisppx: Random point patterns in any number of dimensions o boxx: Multidimensional box in any number of dimensions o diameter.boxx, volume.boxx, shortside.boxx, eroded.volumes.boxx: Geometrical computations for multidimensional boxes o sum.im, max.im, min.im: Methods for sum(), min(), max() for pixel images. o as.matrix.ppx: Convert a multidimensional point pattern to a matrix SIGNIFICANT USER-VISIBLE CHANGES o plot.ppp: New argument 'zap' o diameter: This function is now generic, with methods for "owin", "box3" and "boxx" o eroded.volumes: This function is now generic, with methods for "box3" and "boxx" CHANGES IN spatstat VERSION 1.18-3 ACKNOWLEDGEMENTS o We thank Michael Sumner for contributions. BUG FIXES o pixellate.psp: segments shorter than one pixel width were measured incorrectly. Fixed. o fv: 'alim' not handled correctly. Fixed. NEW FUNCTIONS o smooth.fv: Applies spline smoothing to the columns of an fv object. CHANGES IN spatstat VERSION 1.18-2 ACKNOWLEDGEMENTS o We thank Michael Sumner for contributions. NEW FUNCTIONS o Gfox, Jfox: Foxall's G and J functions o as.owin.distfun: New method for as.owin extracts the domain of a distfun object. SIGNIFICANT USER-VISIBLE CHANGES o distfun: objects of class 'distfun', when called as functions, will now accept either two vectors (x,y) or a point pattern x. o Hest: this function can now compute the Hanisch estimator. It now has arguments 'r', 'breaks' and 'correction', like other summary functions. o Hest: new argument 'conditional'. BUG FIXES o pixellate.psp: Values were sometimes incorrect due to coding error. (Spotted by Michael Sumner) Fixed. o kstest: Crashed if the covariate contained NA's. Fixed. o kstest: Crashed if X was a multitype point pattern in which some mark values were unrepresented. Fixed. o lurking: Minor bug in handling of NA values. Fixed. o Hest: labels of columns were incorrect. Fixed. CHANGES IN spatstat VERSION 1.18-1 ACKNOWLEDGEMENTS o we thank Andrew Bevan and Ege Rubak for suggestions. NEW FUNCTIONS o Hardcore: Hard core interaction (for use in ppm) o envelope.pp3: simulation envelopes for 3D point patterns o npoints: number of points in a point pattern of any kind SIGNIFICANT USER-VISIBLE CHANGES o rmh.ppm, rmhmodel.ppm: It is now possible to simulate Gibbs point process models that are fitted to multitype point patterns using a non-multitype interaction, e.g. data(amacrine) fit <- ppm(amacrine, ~marks, Strauss(0.1)) rmh(fit, ...) o rmh.ppm, rmhmodel.ppm, rmh.default, rmhmodel.default: Hard core models can be simulated. o rmh.default, rmhmodel.default: The argument 'par' is now required to be a list, in all cases (previously it was sometimes a list and sometimes a vector). o Fest: Calculation has been accelerated in some cases. o summary.pp3 now returns an object of class 'summary.pp3' containing useful summary information. It is plotted by 'plot.summary.pp3'. o F3est, G3est, K3est: these functions now accept 'correction="best"' o union.owin, intersect.owin: these functions now handle any number of windows. o envelope.ppp, envelope.ppm, envelope.kppm: argument lists have changed slightly BUG FIXES o Fest: The result of Fest(X, correction="rs") had a slightly corrupted format, so that envelope(X, Fest, correction="rs") in fact computed the envelopes based on the "km" correction. (Spotted by Ege Rubak). Fixed. o rmh (rmh.ppm, rmhmodel.ppm): rmh sometimes failed for non-stationary point process models, with a message about "missing value where TRUE/FALSE needed". (Spotted by Andrew Bevan). Fixed. o diagnose.ppm, lurking: Calculations were not always correct if the model had conditional intensity equal to zero at some locations. Fixed. o ppm, profilepl: If data points are illegal under the model (i.e. if any data points have conditional intensity equal to zero) the log pseudolikelihood should be -Inf but was sometimes returned as a finite value. Thus profilepl did not always work correctly for models with a hard core. Fixed. o F3est, G3est: Debug messages were printed unnecessarily. Fixed. CHANGES IN spatstat VERSION 1.18-0 ACKNOWLEDGEMENTS o we thank Ege Rubak and Tyler Dean Rudolph for suggestions. HEADLINES o A point pattern is now allowed to have a data frame of marks (previously the marks had to be a vector). o Extended capabilities for 'envelope' and 'kstest'. NEW FUNCTIONS o pixellate.psp, as.mask.psp Convert a line segment pattern to a pixel image or binary mask o as.data.frame.im Convert a pixel image to a data frame SIGNIFICANT USER-VISIBLE CHANGES o A point pattern is now allowed to have a data frame of marks (previously the marks had to be a vector). o Many functions in spatstat now handle point patterns with a data frame of marks. These include print.ppp, summary.ppp, plot.ppp, split.ppp. o finpines, nbfires, shapley: The format of these datasets has changed. They are now point patterns with a data frame of marks. o envelope() is now generic, with methods for "ppp", "ppm" and "kppm". o kstest() now handles multitype point patterns and multitype point process models. o nnclean() now returns a point pattern with a data frame of marks. o plot.ppp() has new argument 'which.marks' to select one column from a data frame of marks to be plotted. o plot.ppp() now handles marks that are POSIX times. o complement.owin now handles any object acceptable to as.owin. BUG FIXES o erosion(w) and opening(w) crashed if w was not a window. Fixed. o diameter() and eroded.areas() refused to work if w was not a window. Fixed. CHANGES IN spatstat VERSION 1.17-6 ACKNOWLEDGEMENTS o We thank Simon Byers and Adrian Raftery for generous contributions. OVERVIEW o Nearest neighbour clutter removal algorithm o New documentation for the 'fv' class. o Minor improvements and bug fixes. NEW FUNCTIONS o nnclean: Nearest neighbour clutter removal for recognising features in spatial point patterns. Technique of Byers and Raftery (1998) [From original code by Simon Byers and Adrian Raftery, adapted for spatstat.] o marks.ppx, marks<-.ppx: Methods for extracting and changing marks in a multidimensional point pattern o latest.news: print news about the current version of the package SIGNIFICANT USER-VISIBLE CHANGES o news: spatstat now has a NEWS file which can be printed by typing news(package="spatstat"). o areaGain, areaLoss: New algorithms in case exact=TRUE. Syntax slightly modified. o with.hyperframe: - The result now inherits 'names' from the row names of the hyperframe. - New argument 'enclos' controls the environment in which the expression is evaluated. - The algorithm is now smarter at simplifying the result when simplify=TRUE. o update.ppm: Tweaked to improve the ability of ppm objects to be re-fitted in different contexts. ADVANCED USERS ONLY o Documentation for the class 'fv' of function value tables - fv: Creates an object of class 'fv' - cbind.fv, collapse.fv: Combine objects of class 'fv' - bind.fv: Add additional columns of data to an 'fv' object BUG FIXES o "$<-.hyperframe" destroyed the row names of the hyperframe. Fixed. o model.matrix.ppm had minor inconsistencies. Fixed. o ppm: The fitted coefficient vector had incorrect format in the default case of a uniform Poisson process. Fixed. o plot.ppx: Crashed if the argument 'main' was given. Fixed. o envelope.ppp: Crashed if the object returned by 'fun' did not include a column called "theo". Fixed. spatstat/R/0000755000176200001440000000000014243060071012313 5ustar liggesusersspatstat/R/news.R0000644000176200001440000000306114243360777013432 0ustar liggesusers# # news.R # # News and warnings # latest.news <- function(package=spatstat.family(), doBrowse=FALSE, major=TRUE) { stopifnot(is.character(package)) ## news from spatstat.core is subsumed in spatstat.explore + spatstat.model defunctpackages <- "spatstat.core" package <- setdiff(package, defunctpackages) n <- length(package) result <- vector(mode="list", length=n) names(result) <- package for(i in seq_len(n)) { pack <- package[i] ## get version number v <- read.dcf(file=system.file("DESCRIPTION", package=pack), fields="Version") if(major) { ## the current major version vp <- package_version(v) vv <- unlist(vp) v <- paste0(vv[1], ".", vv[2]) } ne <- eval(substitute(news(Version >= v0, package=pack), list(v0=v))) if(n > 1 && !doBrowse) { hdr <- paste0("Package ", sQuote(pack), ":") if(interactive()) readline(hdr) else cat(paste(hdr, "\n")) } page(ne, method="print", doBrowse=doBrowse) result[[i]] <- ne } if(n == 1) result <- result[[1]] return(invisible(result)) } class(latest.news) <- "autoexec" spatstat.family <- function(subpackages=TRUE, extensions=FALSE) { sub <- c("spatstat.utils", "spatstat.data", "spatstat.sparse", "spatstat.geom", "spatstat.random", "spatstat.explore", "spatstat.model", "spatstat.linnet", "spatstat") ext <- c("spatstat.gui", "spatstat.local", "spatstat.Knet") result <- c(if(subpackages) sub else NULL, if(extensions) ext else NULL) as.character(result) } spatstat/R/bugtable.R0000644000176200001440000000723014332336032014227 0ustar liggesusers#' #' bugtable.R #' #' $Revision: 1.10 $ $Date: 2021/09/06 05:28:59 $ bugfixes <- function(sinceversion=NULL, sincedate=NULL, package=spatstat.family(), show=TRUE) { package <- as.character(package) is.spat <- package %in% spatstat.family(TRUE, TRUE) if("book" %in% list(sinceversion, sincedate)) { ## special usage sinceversion <- NULL sincedate <- "2015-06-05" } n <- length(package) z <- NULL for(i in seq_len(n)) { packi <- package[i] if("all" %in% list(sinceversion, sincedate)) { ## no date constraint - show all news a <- eval(substitute(news(grepl("^BUG", Category), package=packi))) } else if(!is.null(sincedate) && !is.spat[i]) { #' news items after specified date #' non-spatstat package ne <- news(package=packi) if(is.null(ne) || is.null(ne$Date) || anyNA(ne$Date)) stop(paste(if(is.null(ne)) "News" else "Date", "information is not available for package", sQuote(packi)), call.=FALSE) a <- eval(substitute(news(Date >= SD & grepl("^BUG", Category), package=packi), list(SD=sincedate))) } else { #' determine a corresponding version number if(is.null(sinceversion) && is.null(sincedate)) { #' default is latest version dfile <- system.file("DESCRIPTION", package=packi) sinceversion <- read.dcf(file=dfile, fields="Version") } else if(!is.null(sincedate) && is.spat[i]) { #' read release history table fname <- system.file("doc", "packagesizes.txt", package=packi) p <- read.table(fname, header=TRUE, stringsAsFactors=FALSE) #' find earliest package version on or after the given date imin <- with(p, min(which(as.Date(date) >= sincedate))) sinceversion <- p[imin, "version"] } a <- eval(substitute(news(Version >= sv & grepl("^BUG", Category), package=packi), list(sv=sinceversion))) } #' convert format if(is.data.frame(a) && nrow(a) > 0) { #' split each entry into lines alines <- strsplit(a$Text, "\n") #' extract first line f <- unname(sapply(alines, "[", i=1L)) #' extract body b <- unname(lapply(alines, "[", i=-1L)) b <- unname(sapply(b, paste, collapse="\n")) #' extract header from first line h <- unname(sapply(strsplit(f, ":"), "[", i=1L)) h <- unname(sapply(strsplit(h, ","), "[", i=1L)) h <- unname(sapply(strsplit(h, " "), "[", i=1L)) #' rebuild zi <- data.frame(Header=h, Firstline=f, Body=b, Version=a$Version, stringsAsFactors=FALSE) if(n > 1) zi$Package <- packi z <- if(i == 1) zi else rbind(z, zi) } } if(is.null(z)) return(NULL) #' sort by header oo <- with(z, order(Header, Firstline)) z <- z[oo, ] #' wrap up class(z) <- c("bugtable", class(z)) if(show) page(z, method="print") return(invisible(z)) } class(bugfixes) <- "autoexec" print.bugtable <- function(x, ...) { hprev <- "" haspack <- "Package" %in% colnames(x) for(i in seq_len(nrow(x))) { h <- x$Header[i] f <- x$Firstline[i] v <- x$Version[i] p <- if(haspack) x$Package[i] else NULL b <- x$Body[i] if(h != hprev) { # new main header cat("\n***", h, "***\n", fill=TRUE) } if(haspack) cat(p, v, ":", f, fill=TRUE) else cat(v, ":", f, fill=TRUE) cat(b, "\n", fill=TRUE) hprev <- h } return(invisible(NULL)) } spatstat/R/First.R0000644000176200001440000000166614330617550013545 0ustar liggesusers## spatstat/R/First.R .onLoad <- function(...) { reset.spatstat.options() ## umf <- system.file("doc", "umbrella.txt", package="spatstat") ## isum <- !is.null(umf) && file.exists(umf) ## putSpatstatVariable("Spatstat.Is.Umbrella", isum) putSpatstatVariable("Spatstat.Is.Umbrella", TRUE) invisible(NULL) } .onAttach <- function(libname, pkgname) { vs <- read.dcf(file=system.file("DESCRIPTION", package="spatstat"), fields="Version") vs <- as.character(vs) putSpatstatVariable("SpatstatVersion", vs) ## nickfile <- system.file("doc", "Nickname.txt", package="spatstat") ## ni <- scan(file=nickfile, what=character(), n=1, quiet=TRUE) msg <- paste("\nspatstat", vs, ## " ", ## paren(paste("nickname:", sQuote(ni))), "\nFor an introduction to spatstat, type", sQuote("beginner"), "\n") packageStartupMessage(msg) return(invisible(NULL)) } spatstat/R/beginner.R0000644000176200001440000000174114332336032014234 0ustar liggesusers# # beginner.R # # Helpful information for beginners # # $Revision: 1.3 $ $Date: 2015/10/21 09:06:57 $ # print.autoexec <- function(x, ...) { x() } beginner <- function(package="spatstat") { package <- as.character(substitute(package)) RShowDoc("BEGINNER.txt", type="txt", package=package) return(invisible(NULL)) } class(beginner) <- "autoexec" foo <- local({ fooText <- paste0("Error: object 'foo' not found.\n\n", "'foo' is not a defined variable or function.\n", "It is a placeholder name, which serves only to ", "demonstrate a concept. It represents the name of ", "any desired object or function. 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right=1.5cm, left=1.5cm, top=1.5cm]{geometry} % \VignetteIndexEntry{Summary of Recent Updates to the Spatstat Family} % $Revision: 1.68 $ $Date: 2023/10/21 09:22:40 $ \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} <>= library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) @ \title{Summary of recent updates to \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{\today} \maketitle \thispagestyle{empty} <>= readSizeTable <- function(fname) { if(is.null(fname) || !file.exists(fname)) return(NULL) a <- read.table(fname, header=TRUE) a$date <- as.Date(a$date) return(a) } getSizeTable <- function(packagename="spatstat", tablename="packagesizes.txt") { fname <- system.file("doc", tablename, package=packagename) readSizeTable(fname) } RemoveDevel <- function(sizetable) { ## remove entries with fractional version numbers if(is.null(sizetable)) return(NULL) ver <- sizetable$version isdevel <- sapply(ver, function(x) { length(unlist(package_version(x))) > 3 }) st <- if(all(isdevel)) NULL else sizetable[!isdevel, , drop=FALSE] return(st) } counts <- c("nhelpfiles", "nobjects", "ndatasets", "Rlines", "srclines") mergeSizeTables <- function(a, b, breakupdate, allow.devel=FALSE) { #' a is the running total for spatstat; b is a sub-package. #' breakupdate is the date when the code in b was removed from spatstat #' so that the size of 'b' must be added to 'a' for all dates >= breakupdate if(!allow.devel) b <- RemoveDevel(b) if(is.null(b)) return(a) adates <- a$date bdates <- b$date alldates <- sort(unique(c(adates,bdates))) if(missing(breakupdate)) breakupdate <- min(bdates) #' functions to determine, for any given date, #' the relevant (latest) row of the table aok <- rev(!duplicated(rev(adates))) arowfun <- approxfun(adates[aok], seq_along(adates)[aok], method="constant", f=0, rule=2, yleft=0) bok <- rev(!duplicated(rev(bdates))) browfun <- approxfun(bdates[bok], seq_along(bdates)[bok], method="constant", f=0, rule=2, yleft=0) result <- NULL for(k in seq_along(alldates)) { thedate <- alldates[k] i <- arowfun(thedate) j <- browfun(thedate) #' i > 0 because spatstat's founding date is earlier than any sub-package nextrow <- a[i, ] if(j > 0 && thedate >= breakupdate) { #' add contribution from 'b' nextrow[, counts] <- nextrow[, counts] + b[j, counts] } result <- rbind(result, nextrow) } return(result) } ## Get histories of all sub-packages ## Package formerly known as 'spatstat' z <- getSizeTable() ## installed sub-packages - access via the installed sub-packages zutils <- getSizeTable("spatstat.utils") zdata <- getSizeTable("spatstat.data") zsparse <- getSizeTable("spatstat.sparse") zgeom <- getSizeTable("spatstat.geom") zrandom <- getSizeTable("spatstat.random") zexplore <- getSizeTable("spatstat.explore") zmodel <- getSizeTable("spatstat.model") zlinnet <- getSizeTable("spatstat.linnet") ## other sub-packages - access via stored copies of package size files ## defunct package spatstat.core zcore <- getSizeTable("spatstat", "spatstatcoresize.txt") ## extension packages zlocal <- getSizeTable("spatstat", "spatstatlocalsize.txt") zgui <- getSizeTable("spatstat", "spatstatguisize.txt") zKnet <- getSizeTable("spatstat", "spatstatKnetsize.txt") ## Merge histories starting at the 'split dates' z <- mergeSizeTables(z, zutils, "2017-03-22") z <- mergeSizeTables(z, zdata, "2017-09-23") z <- mergeSizeTables(z, zsparse, "2020-11-04") BigSplitDay <- "2020-12-14" z <- mergeSizeTables(z, zgeom, BigSplitDay) z <- mergeSizeTables(z, zcore, BigSplitDay) z <- mergeSizeTables(z, zlinnet, BigSplitDay) z <- mergeSizeTables(z, zrandom, "2022-02-12") CoreSplitDay <- "2020-05-25" # size of 'core' drops to 0 on this date z <- mergeSizeTables(z, zexplore, CoreSplitDay) z <- mergeSizeTables(z, zmodel, CoreSplitDay) ## extension packages: these never overlapped spatstat z <- mergeSizeTables(z, zlocal) z <- mergeSizeTables(z, zgui) z <- mergeSizeTables(z, zKnet) ## Now summarise currentcount <- z[nrow(z), counts] bookcount <- z[z$version == "1.42-0", counts] changes <- currentcount - bookcount newobj <- changes[["nobjects"]] newdat <- changes[["ndatasets"]] + 1 # counting rule doesn't detect redwood3 newcode <- changes[["Rlines"]] + changes[["srclines"]] bookcode <- bookcount[["Rlines"]] + bookcount[["srclines"]] currentcode <- currentcount[["Rlines"]] + currentcount[["srclines"]] growth <- signif((100 * newcode)/bookcode, digits=2) @ %$ This is a summary of changes to the \spst\ package that have occurred since the publication of the book \cite{baddrubaturn15} in 2015. Since then, the \spst\ family has grown by \Sexpr{growth}\%, including \Sexpr{newobj} new functions and \Sexpr{newdat} new datasets, and now contains more than \Sexpr{10 * floor(currentcode/10000)},000 lines of code. This document summarises the most important changes. <>= options(SweaveHooks=list(fig=function() par(mar=0.2+c(2,4,2,0)))) Plot <- function(fmla, ..., dat=z) { yvals <- eval(as.expression(fmla[[2]]), envir=dat) plot(fmla, ..., data=dat, type="l", xlab="", lwd=2, ylim=c(0, max(yvals))) } @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.5\textwidth} \centerline{ <>= Plot((Rlines + srclines)/1000 ~ date, ylab="Lines of code (x 1000)", main="Spatstat growth") lines(srclines/1000 ~ date, data=z) text(as.Date("2015-01-01"), 9.5, "C code") text(as.Date("2015-01-01"), 60, "R code") @ } \setcounter{tocdepth}{1} \tableofcontents \newpage \newpage \section{Version information} The book \cite{baddrubaturn15}, published in December 2015, covered \spst\ version \texttt{1.42-0}, released in May 2015. <>= ## Tabulate latest version numbers of packages vtable <- data.frame(package="spatstat", version=sversion, date=as.Date(sdate)) AppendVersion <- function(pkg, sizetable, v, allow.devel=FALSE) { if(!allow.devel) sizetable <- RemoveDevel(sizetable) if(is.null(sizetable)) return(v) lastrow <- sizetable[nrow(sizetable), , drop=FALSE] if(is.null(lastrow)) return(v) rbind(v, data.frame(package=pkg, version=lastrow[,"version"], date=as.Date(lastrow[,"date"]))) } vtable <- AppendVersion("spatstat.geom", zgeom, vtable) vtable <- AppendVersion("spatstat.random", zrandom, vtable) vtable <- AppendVersion("spatstat.explore", zexplore, vtable) vtable <- AppendVersion("spatstat.model", zmodel, vtable) ##vtable <- AppendVersion("spatstat.core", zcore, vtable) vtable <- AppendVersion("spatstat.linnet", zlinnet, vtable) vtable <- AppendVersion("spatstat.sparse", zsparse, vtable) vtable <- AppendVersion("spatstat.data", zdata, vtable) vtable <- AppendVersion("spatstat.utils", zutils, vtable) vtable <- AppendVersion("spatstat.local", zlocal, vtable) vtable <- AppendVersion("spatstat.Knet", zKnet, vtable) vtable <- AppendVersion("spatstat.gui", zgui, vtable) @ The current versions of the \spst\ family of packages (used to produce this document) are: <>= print(vtable[,c(3,1,2)], row.names=FALSE) @ \section{Package structure} The original \pkg{spatstat} package grew to be very large. It has now been split into a family of packages, to satisfy the requirements of CRAN. This should not affect the user: existing code will continue to work in the same way. Typing \code{library(spatstat)} will load the familiar \pkg{spatstat} package which can be used as before. \subsection{Sub-packages} Currently there are nine sub-packages, called \pkg{spatstat.utils}, \pkg{spatstat.data}, \pkg{spatstat.sparse}, \pkg{spatstat.geom}, \pkg{spatstat.random}, \pkg{spatstat.explore}, \pkg{spatstat.model}, \pkg{spatstat.linnet}, and \pkg{spatstat}. \begin{itemize} \item The \code{spatstat} package now contains only documentation and introductory material. It provides beginner's introductions, vignettes, interactive demonstration scripts, and a few help files summarising the package. \item The \pkg{spatstat.data} package now contains all the datasets for \pkg{spatstat}. \item The \pkg{spatstat.utils} package contains utility functions for \pkg{spatstat}. \item The \pkg{spatstat.sparse} package contains functions for manipulating sparse arrays and performing linear algebra. \item The \pkg{spatstat.geom} package contains definitions of spatial objects (such as point patterns, windows and pixel images) and code which performs geometrical operations. \item The \pkg{spatstat.random} package contains functions for random generation of spatial patterns and random simulation of models. \item The \pkg{spatstat.explore} package contains the code for exploratory data analysis and nonparametric analysis of spatial data. \item The \pkg{spatstat.model} package contains the code for model-fitting, model diagnostics, and formal inference. \item The \pkg{spatstat.linnet} package defines spatial data on a linear network, and performs geometrical operations and statistical analysis on such data. \end{itemize} \noindent\textbf{Installing:} If you install \pkg{spatstat}, then the system will install all the other sub-packages listed above. \noindent\textbf{Running:} If you type \code{library(spatstat)} in an \textsf{R} session, the system will automatically load \pkg{spatstat.data}, \pkg{spatstat.geom}, \pkg{spatstat.random}, \pkg{spatstat.explore}, \pkg{spatstat.model} and \pkg{spatstat.linnet}. It will also silently \textbf{``import''} \pkg{spatstat.utils} and \pkg{spatstat.sparse}. To access the functions in \pkg{spatstat.utils} directly, you would need to type \code{library(spatstat.utils)}. Similarly for \pkg{spatstat.sparse}. \subsection{Extension packages} There are also extension packages which provide additional capabilities and must be loaded explicitly when you need them. Currently there are three extension packages, with a fourth in development: \begin{itemize} \item \pkg{spatstat.local} for local model-fitting, \item \pkg{spatstat.Knet} provides additional code for analysing point patterns on a network. \item \pkg{spatstat.gui} containing interactive graphics functions, \item \pkg{spatstat.sphere} for analysing point patterns on a sphere (under development!) \end{itemize} %\pagebreak \section{Precis of all changes} Here is the text from the `overview' sections of the News and Release Notes for each update. \begin{itemize} \item The \spst\ family no longer depends on the packages \pkg{maptools}, \pkg{sp} and \pkg{RandomFields}. \item code for fitting and simulating log-Gaussian Cox models has changed. \item New vignette on function objects (class \texttt{"fv"} and \texttt{"envelope"}) \item Vignette on shapefiles temporarily removed. \item Integration of functions. \item \texttt{clarkevans.test} modified. %% \item The package \texttt{spatstat.core} has been split into two packages called %% \texttt{spatstat.explore} (for exploratory data analysis) %% and \texttt{spatstat.model} (for modelling and formal inference). %% \item The original \spst\ package has now been split into 9 sub-packages %% (\pkg{spatstat.utils}, \pkg{spatstat.data}, \pkg{spatstat.sparse}, %% \pkg{spatstat.geom}, \pkg{spatstat.random}, %% \pkg{spatstat.explore}, \pkg{spatstat.model}, \pkg{spatstat.linnet} %% and \pkg{spatstat}). \item Improvements to \texttt{envelope} methods. \item Conditional simulation for Matern cluster process. \item Improvements to \texttt{runifpoint} and \texttt{rpoispp}. \item Extension of distance transform algorithm. \item Improvement to progress reports. \item Suppress annoying warnings. \item Changed the calculation of standard errors in \texttt{density.ppp} and \texttt{relrisk.ppp}. \item Inline arithmetic for function tables (class \texttt{"fv"}) and arrays (class \texttt{"fasp"}) \item Standard error calculation for \texttt{Smooth.ppp} (experimental) \item multitype pair correlation functions can save numerator and denominator. \item multitype inhomogeneous $J$ functions. \item More support for automatic bandwidth selection. \item Standard errors are now available for \texttt{ppm} models fitted using \texttt{gam}. \item \texttt{linearKinhom} and \texttt{linearpcfinhom} now automatically estimate the intensity. \item \texttt{density.lpp} accepts bandwidth selection rules, and has a simple default bandwidth. \item Pair correlation functions allow more control over smoothing parameters. \item Extension to support for one-dimensional smoothing kernels. \item Improvements to \texttt{update} methods for point process models. \item New \texttt{update} methods for classes \texttt{dppm} and \texttt{rppm}. \item Generate truncated Poisson random variables. \item reciprocal moment of Poisson variable conditioned to be positive. \item Methods for \verb![[! and \verb![[<-! for hyperframes. \item Colour map for \textsf{pH} values. \item Restrict a colour map to a narrower range of values. \item Integral of a one-dimensional density estimate. \item \texttt{kppm} has been accelerated when \texttt{method="palm"} or \texttt{"clik2"}. \item \texttt{kppm} can save the history of the optimisation algorithm. \item Faster algorithms for simulating cluster processes. \item Penalised model-fitting for Neyman-Scott cluster process models. \item Index of the strength of clustering in a Neyman-Scott cluster process model. \item Probability of having any siblings. \item More information is printed about Neyman-Scott cluster process models. \item Palm intensity diagnostic plot. \item Convert several factors or factor-valued images to a common set of levels. \item Extension to \texttt{rjitter} \item Alternative to \texttt{rjitter} \item Quantile function as a function \item Periodic edge correction for \textit{K} function. \item Changed denominator in \textit{K} function and pair correlation function. \item Bandwidth selection for adaptive kernel estimation of intensity. \item U-shaped and inverted-U-shaped curves in \texttt{rhohat}. \item Radial cumulative integral of an image. \item New dataset \texttt{stonetools}. \item Regularized model-fitting in \texttt{ppm} and \texttt{kppm}. \item Residuals for recursively-partitioned models. \item Residuals for any observed point pattern and an estimate of its intensity. \item Weighted measures and weighted integrals. \item Improved approximation of intensity of Gibbs models. \item Experimental code to represent (theoretical) point process models \item Extract more information about a point process model. \item kernel smoothing on a linear network. \item linear network $K$ function and pair correlation function based on Euclidean distance. \item inhomogeneous linear network $J$ function. \item Terminal vertices of a network. \item A point pattern on a network can be plotted as cross-ticks. \item The interactive graphics functions \texttt{iplot} and \texttt{istat} have been removed from \spst\ into a new extension package \pkg{spatstat.gui}. \item The packages \pkg{tcltk} and \pkg{rpanel} are no longer Suggested by \spst. \item \spst\ now Imports the package \pkg{spatstat.sparse}. \item \spst\ now Imports the package \pkg{spatstat.utils}. \item \spst\ now requires the package \pkg{spatstat.data} which contains the datasets. \item \spst\ now suggests the package \pkg{fftwtools}. \item Conditional simulation in \texttt{kppm}. \item More diagnostics for spatial logistic regression models. \item Increased numerical stability in \texttt{kppm}. \item Simulation of the product shot noise Cox process. \item Information criteria for model selection in \texttt{kppm}. \item Estimation of the spatial covariance function of a pixel image \item Modified handling of covariates in \texttt{slrm} \item New options for \texttt{weighted.quantile} \item Buffer tessellation \item New function for jittering point patterns on a network. \item Extensions to \texttt{rhohat} \item \texttt{densityfun.ppp} handles query points outside original window \item Extension to \texttt{discretise}. \item Improvement to \texttt{densityEqualSplit}. \item summary method for spatial logistic regression models \item New options for \texttt{distmap.psp} \item Improved output in \texttt{summary.mppm} \item Increased speed for large datasets. \item Variance calculations handle larger datasets. \item Relative risk estimation on a network. \item Leave-one-out density estimation on a network. \item Add new vertices to a linear network. \item More support for multi-dimensional patterns. \item \texttt{predict.mppm} now works for multitype point process models. \item Improved handling of \texttt{newdata} in \texttt{predict.mppm} \item New datasets \texttt{concrete} and \texttt{btb}. \item Changed default value of \texttt{stringsAsFactors}. \item Function \texttt{lengths.psp} has been renamed \verb!lengths_psp!. \item Tessellations on a linear network can now have marks. \item More functions for manipulating tessellations on a linear network. \item New functions for simulating point processes on a linear network. \item Nearest Neighbour Index function can now return mark values. \item Index of repulsion strength for determinantal point process models. \item Nearest neighbours between two point patterns in any number of dimensions. \item More options for handling bad simulation outcomes in \texttt{envelope}. \item \texttt{mppm} accepts case weights. \item Bandwidth selectors warn about extreme values of bandwidth. \item Fast kernel estimation on a linear network using 2D kernels. \item Extension of Scott's rule for bandwidth selection. \item Cross-validated bandwidth selection on a linear network. \item Random thinning and random labelling of spatial patterns extended to different types of pattern. \item Confidence intervals for multitype $K$ function. \item Envelopes for balanced two-stage test \item Extensions to adaptive intensity estimators \item `Dartboard' tessellation using polar coordinates. \item Standard error calculation for inverse-distance weighting. \item Kernel estimate of intensity as a \texttt{function(x,y)}. \item Extract discrete and continuous components of a measure. \item Improvements and extensions to leverage and influence code. \item Plot a line segment pattern using line widths. \item Find connected components of each tile in a tessellation. \item Geometrical operations on \texttt{distfun} objects. \item Join vertices in a linear network. \item Distance map and contact distribution for rectangular structuring element. \item Lurking variable plot for models fitted to several point patterns. \item New dataset \code{cetaceans}. \item Gamma correction for colour maps and image plots. \item Class \code{units} has been renamed \code{unitname} to avoid package collision. \item More support for tessellations. \item Fixed longstanding bug in leverage and influence diagnostics. \item Improvements and bug fixes for leverage and influence diagnostics. \item Tighter bounding box for \code{psp}, \code{lpp}, \code{linnet} objects. \item Improved layout in \code{plot.solist} \item Tools to increase colour saturation. \item Connected components of a 3D point pattern. \item Accelerated computations on linear networks. \item Accelerated simulation of determinantal point processes. \item Improved printing of 3D point patterns. \item Minor corrections to handling of unitnames. \item Improvements to \texttt{ppm} and \texttt{update.ppm}. \item Correction to \texttt{lohboot} \item Numerous bug fixes for linear networks code. \item Now handles disconnected linear networks. \item Effect function is now available for all types of fitted model. \item Geometric-mean smoothing. \item A model can be fitted or re-fitted to a sub-region of data. \item New fast algorithm for kernel smoothing on a linear network. \item Leverage and influence diagnostics extended to Poisson/Gibbs models fitted by logistic composite likelihood. \item Two-stage Monte Carlo test. \item Dirichlet/Voronoi tessellation on a linear network. \item Thinning of point patterns on a linear network. \item More support for functions and tessellations on a linear network. \item Bandwidth selection for pair correlation function. \item Pooling operations improved. \item Operations on signed measures. \item Operations on lists of pixel images. \item Improved pixellation of point patterns. \item Stieltjes integral extended. \item Subset operators extended. \item Greatly accelerated \texttt{rmh} when using \texttt{nsave} \item Sufficient Dimension Reduction for point processes. \item Alternating Gibbs Sampler for point process simulation. \item New class of spatially sampled functions. \item ROC and AUC extended to other types of point patterns and models. \item More support for linear networks. \item More support for infinite straight lines. \item \spst\ now depends on the packages \pkg{nlme} and \pkg{rpart}. \item Important bug fix in \code{linearK}, \code{linearpcf} \item Changed internal format of \code{linnet} and \code{lpp} objects. \item Faster computation in linear networks. \item Bias correction techniques. \item Bounding circle of a spatial object. \item Option to plot marked points as arrows. \item Kernel smoothing accelerated. \item Workaround for bug in some graphics drivers affecting image orientation. \item Non-Gaussian smoothing kernels. \item Improvements to inhomogeneous multitype $K$ and $L$ functions. \item Variance approximation for pair correlation function. \item Leverage and influence for multitype point process models. \item Functions for extracting components of vector-valued objects. \item Recursive-partition point process models. \item Minkowski sum, morphological dilation and erosion with any shape. \item Minkowski sum also applicable to point patterns and line segment patterns. \item Important bug fix in Smooth.ppp \item Important bug fix in spatial CDF tests. \item More bug fixes for replicated patterns. \item Simulate a model fitted to replicated point patterns. \item Inhomogeneous multitype $F$ and $G$ functions. \item Summary functions recognise \texttt{correction="all"} \item Leverage and influence code handles bigger datasets. \item More support for pixel images. \item Improved progress reports. \item New dataset \texttt{redwood3} \item Fixed namespace problems arising when spatstat is not loaded. \item Important bug fix in leverage/influence diagnostics for Gibbs models. \item Surgery with linear networks. \item Tessellations on a linear network. \item Laslett's Transform. \item Colour maps for point patterns with continuous marks are easier to define. \item Pair correlation function estimates can be pooled. \item Stipulate a particular version of a package. \item More support for replicated point patterns. \item More support for tessellations. \item More support for multidimensional point patterns and point processes. \item More options for one-sided envelopes. \item More support for model comparison. \item Convexifying operation. \item Subdivide a linear network. \item Penttinen process can be simulated (by Metropolis-Hastings or CFTP). \item Calculate the predicted variance of number of points. \item Accelerated algorithms for linear networks. \item Quadrat counting accelerated, in some cases. \item Simulation algorithms have been accelerated; simulation outcomes are \emph{not} identical to those obtained from previous versions of \spst. \item Determinantal point process models. \item Random-effects and mixed-effects models for replicated patterns. \item Dao-Genton test, and corresponding simulation envelopes. \item Simulated annealing and simulated tempering. \item spatstat colour tools now handle transparent colours. \item Improvements to \verb![! and \texttt{subset} methods \item Extensions to kernel smoothing on a linear network. \item Support for one-dimensional smoothing kernels. \item Mark correlation function may include weights. \item Cross-correlation version of the mark correlation function. \item Penttinen pairwise interaction model. \item Improvements to simulation of Neyman-Scott processes. \item Improvements to fitting of Neyman-Scott models. \item Extended functionality for pixel images. \item Fitted intensity on linear network \item Triangulation of windows. \item Corrected an edge correction. \end{itemize} \section{New datasets} The following new datasets have been added. These are now provided in the sub-package \pkg{spatstat.data}. \begin{itemize} \item \texttt{austates}: The states and large mainland territories of Australia represented as polygonal regions forming a tessellation. \item \texttt{redwood3}: a more accurate version of the \texttt{redwood} data. \item \texttt{cetaceans}: point patterns of whale and dolphin sightings. \item \texttt{concrete}: air bubbles in concrete. \item \texttt{btb}: bovine tuberculosis occurrences. \item \texttt{stonetools}: palaeolithic stone tools and bone fragments. \end{itemize} \section{New classes} The following new classes of objects may be of use. \begin{itemize} \item \texttt{traj}: Trajectory (history of function evaluations) in a model that was fitted by optimisation. \item \texttt{metric}: Class of distance metrics. An object of class \texttt{metric} represents a distance metric between points in two-dimensional space. See \texttt{help(metric.object)}. \item \texttt{ssf}: Class of spatially sampled functions. An object of class \texttt{"ssf"} represents a spatial function which has been evaluated or sampled at an irregular set of points. See \texttt{help(ssf)}. \item \texttt{zclustermodel}: Experimental. An object of class \texttt{zclustermodel} represents a Neyman-Scott cluster point process model with specified parameter values (whereas \texttt{kppm} represents such a model fitted to data). \item \texttt{zgibbsmodel}: Experimental. An object of class \texttt{zgibbsmodel} represents a Gibbs point process model with specified parameter values (whereas \texttt{ppm} represents such a model fitted to data). \end{itemize} \section{New Functions} Following is a list of all the functions that have been added, starting with the most recent additions. \begin{itemize} \item \texttt{integral.fv}: Compute the integral of a function object. \item \texttt{compileCDF}: Low level utility for calculating cumulative distribution function of distance variable. \item \texttt{Math.fv}, \texttt{Complex.fv}, \texttt{Summary.fv}, \texttt{Ops.fv}: Methods for arithmetic operations for function tables (class \texttt{"fv"}) \item \texttt{Math.fasp}, \texttt{Complex.fasp}, \texttt{Summary.fasp}, \texttt{Ops.fasp}: Methods for arithmetic operations for function arrays (class \texttt{"fasp"}) \item \texttt{Gcross.inhom}, \texttt{Gdot.inhom}: Multitype $G$ functions for inhomogeneous point processes. \item \texttt{Jcross.inhom}, \texttt{Jdot.inhom}, \texttt{Jmulti.inhom}: Multitype $J$ functions for inhomogeneous point processes. \item \texttt{summary.bw.optim}, \texttt{print.summary.bw.optim}: Method for \texttt{summary} of optimised bandwidth objects (class \texttt{bw.optim}). These are the objects produced by the bandwidth selection functions such as \texttt{bw.diggle}, \texttt{bw.scott}, \texttt{bw.pcf} \item \texttt{psp2mask}: Function \texttt{as.mask.psp} has been renamed \texttt{psp2mask}. The old function \texttt{as.mask.psp} still exists but will soon be deprecated and later removed. \item \texttt{update.dppm}: Update method for determinantal point process models. \item \texttt{update.rppm}: Update method for recursively partitioned point process models. \item \verb![[.hyperframe!, \verb![[<-.hyperframe!: Methods for \verb![[! and \verb![[<-! for hyperframes. \item \texttt{pHcolourmap}, \texttt{pHcolour}: Colour map for values of pH \item \texttt{restrict.colourmap}: Restrict a colourmap to a narrower range of values. \item \texttt{integral.density}: Compute the integral of a one-dimensional kernel density estimate. \item \texttt{as.colourmap}: Extract colour information from an object. \item \texttt{panysib}: Probability that a point in a cluster process has \emph{any} siblings. \item \texttt{is.poissonclusterprocess}: Detects whether a given model is a Poisson cluster process (which includes Neyman-Scott processes). \item \texttt{traj}, \texttt{print.traj}, \texttt{plot.traj}, \texttt{lines.traj}: Extract, print and plot the trajectory of function evaluations. \item \texttt{rpoisnonzero}: Generate Poisson random variables conditioned to be positive. \item \texttt{rpoistrunc}: Generate `truncated' Poisson random variables, conditioned to be greater than or equal to a specified minimum value. \item \texttt{recipEnzpois}: Calculate the first reciprocal moment of nonzero Poisson variable. \item \texttt{rclusterBKBC}: (Advanced use) Internal algorithm to simulate any Neyman-Scott cluster process using either the naive, Brix-Kendall, or Baddeley-Chang algorithm. \item \texttt{palmdiagnose}, \texttt{plot.palmdiag}: Palm intensity diagnostic plot for cluster process models proposed by Tanaka, Ogata and Stoyan. \item \texttt{harmoniseLevels}: Given several factors or factor-valued pixel images, convert them all to have the same set of factor levels. \item \texttt{rexplode}: ``Explode'' a point pattern by randomly displacing each group of duplicated points to make a circular pattern around the original location. An alternative to \texttt{rjitter}. \item \texttt{quantilefun}: Return a function that computes any quantiles of a given dataset. \item \texttt{bw.CvL.adaptive}: Bandwidth selection for adaptive kernel estimation of intensity. \item \texttt{radcumint}: Radial cumulative integral of an image. \item \texttt{Smooth.lpp}: kernel smoothing on a linear network. \item \texttt{residuals.rppm}: Residual measure for a recursively-partitioned point process model. \item \texttt{residualMeasure}: Residual measure for any observed point pattern and any estimate of its intensity. \item \texttt{linearKEuclid, linearpcfEuclid, linearKEuclidInhom, linearpcfEuclidInhom}: Linear network $K$ function and pair correlation function based on Euclidean distances. \item \texttt{linearJinhom}: Inhomogeneous $J$ function on a linear network. \item \texttt{terminalvertices}: Extract the terminal vertices of a linear network. \item \texttt{bw.relrisk.lpp}: This function replaces \texttt{bw.relrisklpp} and is a method for the generic \texttt{bw.relrisk}. \item \texttt{measureWeighted}: weighted version of a measure. \item \texttt{harmonicmean, harmonicsum}: The harmonic mean of a set of numbers, calculated robustly. \item \texttt{which.min.fair, which.max.fair} (in \texttt{spatstat.utils}): Find the location of the minimum or maximum entry in a vector; if there are multiple minima or maxima, choose one of them at random. \item \texttt{hardcoredist}: Extract the hard core distance of a point process model. \item \texttt{interactionorder}: Extract the order of interpoint interaction of a point process model. \item \texttt{zgibbsmodel}: Experimental. Create an object of class \texttt{zgibbsmodel}. \item \texttt{print.zgibbsmodel}: Experimental. Print an object of class \texttt{zgibbsmodel}. \item \texttt{is.poisson.zgibbsmodel}, \texttt{is.stationary.zgibbsmodel}: Experimental. Methods for class \texttt{zgibbsmodel}. \item \texttt{indefinteg}: Numerically computes the indefinite integral of a function \item \texttt{framedist.pixels}: Computes distance from each pixel to the enclosing rectangle. \item \texttt{lurking.slrm}: Lurking variable plot for spatial logistic regression models. \item \texttt{eem.slrm}: Exponential energy marks for spatial logistic regression models. \item \texttt{eem.ppm}: Exponential energy marks for Gibbs and Poisson point process models (this function was previously called \texttt{eem}). \item \texttt{transformquantiles}: Transform the quantiles of a vector, matrix, array or pixel image. \item \texttt{convexmetric}: Distance metric based on a convex set. \texttt{invoke.metric}: Low level function to perform a desired operation using a given metric. \item \texttt{mean.ecdf, mean.ewcdf} Calculate the mean of an empirical cumulative distribution function. \item \texttt{rjitter.ppp} This function was previously called \texttt{rjitter}. It is now a method for the new generic function \texttt{rjitter}. \item \texttt{bufftess}: Distance buffer tessellation \item \texttt{ic}: Information criteria for model selection in ppm and kppm. Kindly contributed by Achmad Choiruddin, Jean-Francois Coeurjolly and Rasmus Waagepetersen. \item \texttt{rPSNCP}: Generate simulated realisations of the product shot noise Cox process. Contributed by Abdollah Jalilian, Yongtao Guan and Rasmus Waagepetersen. \item \texttt{spatcov}: Estimate the spatial covariance function of a pixel image. \item \texttt{summary.slrm}, \texttt{print.summary.slrm} Summary method for spatial logistic regression models \item \texttt{coef.summary.slrm}: Print the fitted coefficients, confidence interval and p-values for a spatial logistic regression model. \item \texttt{pairMean}: Compute the mean of a specified function of interpoint distance between random points in a window. \item \texttt{rjitterlpp}: Apply random displacements to the points on a linear network. \item \texttt{intersect.boxx}: Compute intersection of boxes in multi-dimensional space \item \texttt{scale.boxx}, \texttt{scale.ppx}: Methods for \texttt{scale} for boxes and patterns in multi-dimensional space \item \texttt{shift.boxx}, \texttt{shift.ppx}: Methods for \texttt{shift} for boxes and patterns in multi-dimensional space \item \texttt{is.boxx}: Determine whether an object is a multidimensional box \item \texttt{relrisk.lpp}: nonparametric estimation of relative risk on a network. \item \texttt{bw.relrisklpp}: Bandwidth selection for relative risk estimation on a network. \item \texttt{bw.lppl}: Bandwidth selection for kernel density estimation of point patterns on a linear network, using likelihood cross-validation. \item \texttt{densityfun.lpp}: a method for \texttt{densityfun} for point patterns on a linear network. \item \texttt{addVertices}: Add new vertices to a network, at locations outside the existing network. \item \verb!lengths_psp!: this is the new name of the function \texttt{lengths.psp}, which had to be changed because of a conflict with the generic \texttt{lengths}. \item \texttt{densityEqualSplit}: The equal-split algorithm for kernel density estimation on a network is now visible as a separate function. \item \texttt{densityHeat}: The heat-equation algorithm for kernel density estimation on a network is now visible as a separate function. It has also been extended to computing leave-one-out density estimates at the data points. \item \texttt{hotrod}: Compute the heat kernel $\kappa(u,v)$ on a one-dimensional line segment. \item \texttt{heatkernelapprox}: Calculate an approximation to the value of the heat kernel on a network evaluated at the source point, $\kappa(u,u)$. \item \texttt{is.linim}: test whether an object is a pixel image on a linear network (class \verb!"linim"!). \item \texttt{rcelllpp}: Simulate the cell point process on a linear network. \item \texttt{rSwitzerlpp}: Simulate the Switzer-type point process on a linear network. \item \texttt{intersect.lintess}: Form the intersection of two tessellations on a linear network. \item \texttt{chop.linnet}: Divide a linear network into tiles using infinite lines. \item \texttt{repairNetwork}: Detect and repair inconsistencies in internal data in a \texttt{linnet} or \texttt{lpp} object. \item \verb!marks<-.lintess!, \texttt{unmark.lintess}: Assign marks to the tiles of a tessellation on a linear network. \item \texttt{marks.lintess}: Extract the marks of the tiles of a tessellation on a linear network. \item \texttt{tilenames.lintess}: Extract the names of the tiles in a tessellation on a linear network \item \verb!tilenames<-.lintess!: Change the names of the tiles in a tessellation on a linear network \item \texttt{nobjects.lintess}: Count the number of tiles in a tessellation on a linear network \item \texttt{as.data.frame.lintess}: Convert a tessellation on a linear network into a data frame. \item \texttt{repul}: Repulsiveness index for a determinantal point process model. \item \texttt{reach.kppm}: Reach (interaction distance) for a Cox or cluster point process model. \item \texttt{summary.dppm}, \texttt{print.summary.dppm}: Summary method for determinantal point process models. \item \texttt{nncross.ppx}: Nearest neighbours between two point patterns in any number of dimensions. \item \texttt{rthinclumps}: Divide a spatial region into clumps and randomly delete some of them. \item \texttt{densityQuick.lpp}: Fast kernel estimator of point process intensity on a network using 2D smoothing kernel. \item \texttt{data.lppm}: Extract the original point pattern dataset (on a linear network) to which the model was fitted. \item \texttt{bw.scott.iso}: Isotropic version of Scott's rule (for point patterns in any dimension). \item \texttt{bits.envelope}: Global simulation envelope corresponding to \texttt{bits.test}, the balanced independent two-stage Monte Carlo test. \item \texttt{extrapolate.psp}: Extrapolate line segments to obtain infinite lines. \item \texttt{uniquemap}: Map duplicate points to unique representatives. Generic with methods for \texttt{ppp}, \texttt{lpp}, \texttt{ppx} \item \texttt{uniquemap.data.frame}, \texttt{uniquemap.matrix}: Map duplicate rows to unique representatives \item \texttt{localKcross}, \texttt{localLcross}, \texttt{localKdot}, \texttt{localLdot}, \texttt{localKcross.inhom}, \texttt{localLcross.inhom}: Multitype local $K$ functions. \item \texttt{polartess}: tessellation using polar coordinates. \item \texttt{densityVoronoi}: adaptive estimate of point process intensity using tessellation methods. \item \texttt{densityAdaptiveKernel}: adaptive estimate of point process intensity using variable kernel methods. \item \texttt{bw.abram}: compute adaptive smoothing bandwidths using Abramson's rule. \item \texttt{coords.quad}: method for \texttt{coords}, to extract the coordinates of the points in a quadrature scheme. \item \texttt{lineartileindex}: low-level function to classify points on a linear network according to which tile of a tessellation they fall inside. \item \texttt{markmarkscatter}: Mark--mark scatterplot. \item \texttt{bw.CvL}: Cronie-van Lieshout bandwidth selection for density estimation. \item \texttt{subset.psp}: subset method for line segment patterns. \item \texttt{densityfun}, \texttt{densityfun.ppp}: Compute a kernel estimate of intensity of a point pattern and return it as a function of spatial location. \item \texttt{as.im.densityfun}: Convert \texttt{function(x,y)} to a pixel image. \item \texttt{measureDiscrete}, \texttt{measureContinuous}: Extract the discrete and continuous components of a measure. \item \texttt{connected.tess}: Find connected components of each tile in a tessellation and make a new tessellation composed of these pieces. \item \texttt{dffit.ppm}: Effect change diagnostic \texttt{DFFIT} for spatial point process models. \item \texttt{shift.distfun}, \texttt{rotate.distfun}, \texttt{reflect.distfun}, \texttt{flipxy.distfun}, \texttt{affine.distfun}, \texttt{scalardilate.distfun}: Methods for geometrical operations on \texttt{distfun} objects. \item \texttt{rescale.distfun}: Change the unit of length in a \texttt{distfun} object. \item \texttt{plot.indicfun}: Plot method for indicator functions created by \texttt{as.function.owin}. \item \texttt{Smooth.leverage.ppm}, \texttt{Smooth.influence.ppm}: Smooth a leverage function or an influence measure. \item \texttt{integral.leverage.ppm}, \texttt{integral.influence.ppm}: Compute the integral of a leverage function or an influence measure. \item \texttt{mean.leverage.ppm}: Compute the mean value of a leverage function. \item \texttt{rectdistmap}: Distance map using rectangular metric. \item \texttt{rectcontact}: Contact distribution function using rectangular structuring element. \item \texttt{joinVertices}: Join specified vertices in a linear network. \item \code{summary.ssf}: Summary method for a spatially sampled function (class \code{ssf}). \item \code{unstack.tess}: Given a tessellation with multiple columns of marks, take the columns one at a time, and return a list of tessellations, each carrying only one of the original columns of marks. \item \code{contour.leverage.ppm}: Method for \code{contour} for leverage functions of class \code{leverage.ppm} \item \code{lurking}: New generic function for lurking variable plots. \item \code{lurking.ppp}, \code{lurking.ppm}: These are equivalent to the original function \code{lurking}. They are now methods for the new generic \code{lurking}. \item \code{lurking.mppm}: New method for class \code{mppm}. Lurking variable plot for models fitted to several point patterns. \item \code{print.lurk}: Prints information about the object returned by the function \code{lurking} representing a lurking variable plot. \item \code{model.matrix.mppm}: Method for \code{model.matrix} for models of class \code{mppm}. \item \code{test.crossing.psp}, \code{test.selfcrossing.psp}: Previously undocumented functions for testing whether segments cross. \item \code{to.saturated}: Convert a colour value to the corresponding fully-saturated colour. \item \code{intensity.psp}: Compute the average total length of segments per unit area. \item \code{boundingbox.psp}: Bounding box for line segment patterns. This produces a tighter bounding box than the previous default behaviour. \item \code{boundingbox.lpp}: Bounding box for point patterns on a linear network. This produces a tighter bounding box than the previous default behaviour. \item \code{boundingbox.linnet}: Bounding box for a linear network. This produces a tighter bounding box than the previous default behaviour. \item \verb!"Frame<-.default"!: New default method for assigning bounding frame to a spatial object. \item \code{connected.pp3}: Connected components of a 3D point pattern. \item \code{colouroutputs}, \verb!"colouroutputs<-"!: Extract or assign colour values in a colour map. (Documented a previously-existing function) \item \texttt{fitin.profilepl}: Extract the fitted interaction from a model fitted by profile likelihood. \item \verb![<-.linim!: Subset assignment method for pixel images on a linear network. \item \texttt{nnfromvertex}: Given a point pattern on a linear network, find the nearest data point from each vertex of the network. \item \texttt{tile.lengths}: Calculate the length of each tile in a tessellation on a network. \item \texttt{text.ppp}, \texttt{text.lpp}, \texttt{text.psp}: Methods for \texttt{text} for spatial patterns. \item \texttt{as.data.frame.envelope}: Extract function data from an envelope object, including the functions for the simulated data ('simfuns') if they were saved. \item \texttt{is.connected}, \texttt{is.connected.default}, \texttt{is.connected.linnet}: Determines whether a spatial object consists of one topologically connected piece, or several pieces. \item \texttt{is.connected.ppp}: Determines whether a point pattern is connected after all pairs of points closer than distance R are joined. \item \texttt{hist.funxy}: Histogram of values of a spatial function. \item \texttt{model.matrix.ippm}: Method for \texttt{model.matrix} which allows computation of regular and irregular score components. \item \texttt{harmonise.msr}: Convert several measures (objects of class \texttt{msr}) to a common quadrature scheme. \item \texttt{bits.test}: Balanced Independent Two-Stage Monte Carlo test, an improvement on the Dao-Genton test. \item \texttt{lineardirichlet}: Computes the Dirichlet-Voronoi tessellation associated with a point pattern on a linear network. \item \texttt{domain.lintess}, \texttt{domain.linfun}: Extract the linear network from a \texttt{lintess} or \texttt{linfun} object. \item \texttt{summary.lintess}: Summary of a tessellation on a linear network. \item \texttt{clicklpp}: Interactively add points on a linear network. \item \texttt{envelopeArray}: Generate an array of envelopes using a function that returns \texttt{fasp} objects. \item \texttt{bw.pcf}: Bandwidth selection for pair correlation function. \item \texttt{grow.box3}: Expand a three-dimensional box. \item \texttt{hexagon}, \texttt{regularpolygon}: Create regular polygons. \item \texttt{Ops.msr}: Arithmetic operations for measures. \item \texttt{Math.imlist}, \texttt{Ops.imlist}, \texttt{Summary.imlist}, \texttt{Complex.imlist}: Arithmetic operations for lists of pixel images. \item \texttt{measurePositive}, \texttt{measureNegative}, \texttt{measureVariation}, \texttt{totalVariation}: Positive and negative parts of a measure, and variation of a measure. \item \texttt{as.function.owin}: Convert a spatial window to a \texttt{function(x,y)}, the indicator function. \item \texttt{as.function.ssf}: Convert an object of class \texttt{ssf} to a \texttt{function(x,y)} \item \texttt{as.function.leverage.ppm} Convert an object of class \texttt{leverage.ppm} to a \texttt{function(x,y)} \item \texttt{sdr}, \texttt{dimhat}: Sufficient Dimension Reduction for point processes. \item \texttt{simulate.rhohat}: Simulate a Poisson point process with the intensity estimated by \texttt{rhohat}. \item \texttt{rlpp}: Random points on a linear network with a specified probability density. \item \texttt{cut.lpp}: Method for \texttt{cut} for point patterns on a linear network. \item \texttt{has.close}: Faster way to check whether a point has a close neighbour. \item \texttt{psib}: Sibling probability (index of clustering strength in a cluster process). \item \texttt{rags}, \texttt{ragsAreaInter}, \texttt{ragsMultiHard}: Alternating Gibbs Sampler for point processes. \item \texttt{bugfixes}: List all bug fixes in recent versions of a package. \item \texttt{ssf}: Create a spatially sampled function \item \texttt{print.ssf}, \texttt{plot.ssf}, \texttt{contour.ssf}, \texttt{image.ssf}: Display a spatially sampled function \item \texttt{as.im.ssf}, \texttt{as.ppp.ssf}, \texttt{marks.ssf}, \verb!marks<-.ssf!, \texttt{unmark.ssf}, \verb![.ssf!, \texttt{with.ssf}: Manipulate data in a spatially sampled function \item \texttt{Smooth.ssf}: Smooth a spatially sampled function \item \texttt{integral.ssf}: Approximate integral of spatially sampled function \item \texttt{roc.kppm}, \texttt{roc.lppm}, \texttt{roc.lpp}: Methods for \texttt{roc} for fitted models of class \texttt{"kppm"} and \texttt{"lppm"} and point patterns of class \texttt{"lpp"} \item \texttt{auc.kppm}, \texttt{auc.lppm}, \texttt{auc.lpp}: Methods for \texttt{auc} for fitted models of class \texttt{"kppm"} and \texttt{"lppm"} and point patterns of class \texttt{"lpp"} \item \texttt{timeTaken}: Extract the timing data from a \texttt{"timed"} object or objects. \item \texttt{rotate.infline}, \texttt{shift.infline}, \texttt{reflect.infline}, \texttt{flipxy.infline}: Geometrical transformations for infinite straight lines. \item \texttt{whichhalfplane}: Determine which side of an infinite line a point lies on. \item \texttt{matrixpower}, \texttt{matrixsqrt}, \texttt{matrixinvsqrt}: Raise a matrix to any power. \item \texttt{points.lpp}: Method for \texttt{points} for point patterns on a linear network. \item \texttt{pairs.linim}: Pairs plot for images on a linear network. \item \texttt{closetriples}: Find close triples of points. \item \texttt{anyNA.im}: Method for \texttt{anyNA} for pixel images. \item \texttt{bc}: Bias correction (Newton-Raphson) for fitted model parameters. \item \texttt{rex}: Richardson extrapolation for numerical integrals and statistical model parameter estimates. \item \texttt{boundingcircle}, \texttt{boundingcentre}: Find the smallest circle enclosing a window or point pattern. \item \verb![.linim! : Subset operator for pixel images on a linear network. \item \texttt{mean.linim}, \texttt{median.linim}, \texttt{quantile.linim}: The mean, median, or quantiles of pixel values in a pixel image on a linear network. \item \texttt{weighted.median}, \texttt{weighted.quantile}: Median or quantile of numerical data with associated weights. \item \verb!"[.linim"!: Subset operator for pixel images on a linear network. \item \texttt{mean.linim}, \texttt{median.linim}, \texttt{quantile.linim}: The mean, median, or quantiles of pixel values in a pixel image on a linear network. \item \texttt{boundingcircle}, \texttt{boundingcentre}: Smallest circle enclosing a spatial object. \item \texttt{split.msr}: Decompose a measure into parts. \item \texttt{unstack.msr}: Decompose a vector-valued measure into its component measures. \item \texttt{unstack.ppp}, \texttt{unstack.psp}, \texttt{unstack.lpp}: Given a spatial pattern with several columns of marks, separate the columns and return a list of spatial patterns, each having only one column of marks. \item \texttt{kernel.squint}: Integral of squared kernel, for the kernels used in density estimation. \item \texttt{as.im.data.frame}: Build a pixel image from a data frame of coordinates and pixel values. \item \texttt{covering}: Cover a window using discs of a given radius. \item \texttt{dilationAny}, \texttt{erosionAny}, \verb!%(-)%! : Morphological dilation and erosion by any shape. \item \texttt{FmultiInhom}, \texttt{GmultiInhom} Inhomogeneous multitype/marked versions of the summary functions \texttt{Fest}, \texttt{Gest}. \item \texttt{kernel.moment} Moment or incomplete moment of smoothing kernel. \item \texttt{MinkowskiSum}, \verb!%(+)%!: Minkowski sum of two windows: \verb!A %(+)% B!, or \texttt{MinkowskiSum(A,B)} \item \texttt{nobjects}: New generic function for counting the number of 'things' in a dataset. There are methods for \texttt{ppp}, \texttt{ppx}, \texttt{psp}, \texttt{tess}. \item \texttt{parameters.interact}, \texttt{parameters.fii}: Extract parameters from interpoint interactions. (These existing functions are now documented.) \item \texttt{ppmInfluence}: Calculate \texttt{leverage.ppm}, \texttt{influence.ppm} and \texttt{dfbetas.ppm} efficiently. \item \texttt{rppm}, \texttt{plot.rppm}, \texttt{predict.rppm}, \texttt{prune.rppm}: Recursive-partition point process models. \item \texttt{simulate.mppm} Simulate a point process model fitted to replicated point patterns. \item \texttt{update.interact}: Update the parameters of an interpoint interaction. [This existing function is now documented.] \item \texttt{where.max}, \texttt{where.min} Find the spatial location(s) where a pixel image achieves its maximum or minimum value. \item \texttt{compileK}, \texttt{compilepcf}: make a $K$ function or pair correlation function given the pairwise distances and their weights. [These existing internal functions are now documented.] \item \texttt{laslett}: Laslett's Transform. \item \texttt{lintess}: Tessellation on a linear network. \item \texttt{divide.linnet}: Divide a linear network into pieces demarcated by a point pattern. \item \texttt{insertVertices}: Insert new vertices in a linear network. \item \texttt{thinNetwork}: Remove vertices and/or segments from a linear network etc. \item \texttt{connected.linnet}: Find connected components of a linear network. \item \texttt{nvertices}, \texttt{nvertices.linnet}, \texttt{nvertices.owin}: Count the number of vertices in a linear network or vertices of the boundary of a window. \item \texttt{as.data.frame.linim}, \texttt{as.data.frame.linfun}: Extract a data frame of spatial locations and function values from an object of class \texttt{linim} or \texttt{linfun}. \item \texttt{as.linfun}, \texttt{as.linfun.linim}, \texttt{as.linfun.lintess}: Convert other kinds of data to a \texttt{linfun} object. \item \texttt{requireversion}: Require a particular version of a package (for use in stand-alone R scripts). \item \texttt{as.function.tess}: Convert a tessellation to a \texttt{function(x,y)}. The function value indicates which tile of the tessellation contains the point $(x,y)$. \item \texttt{tileindex}: Determine which tile of a tessellation contains a given point $(x,y)$. \item \texttt{persp.leverage.ppm}: Method for persp plots for objects of class \texttt{leverage.ppm} \item \texttt{AIC.mppm}, \texttt{extractAIC.mppm}: AIC for point process models fitted to replicated point patterns. \item \texttt{nobs.mppm}, \texttt{terms.mppm}, \texttt{getCall.mppm}: Methods for point process models fitted to replicated point patterns. \item \texttt{rPenttinen}: Simulate the Penttinen process using perfect simulation. \item \texttt{varcount}: Given a point process model, compute the predicted variance of the number of points falling in a window. \item \texttt{inside.boxx}: Test whether multidimensional points lie inside a specified multidimensional box. \item \texttt{lixellate}: Divide each segment of a linear network into smaller segments. \item \texttt{nsegments.linnet}, \texttt{nsegments.lpp}: Count the number of line segments in a linear network. \item \texttt{grow.boxx}: Expand a multidimensional box. \item \texttt{deviance.ppm}, \texttt{deviance.lppm}: Deviance for a fitted point process model. \item \texttt{pseudoR2}: Pseudo-R-squared for a fitted point process model. \item \texttt{tiles.empty} Checks whether each tile of a tessellation is empty or nonempty. \item \texttt{summary.linim}: Summary for a pixel image on a linear network. \item Determinantal Point Process models: \begin{itemize} \item \texttt{dppm}: Fit a determinantal point process model. \item \texttt{fitted.dppm}, \texttt{predict.dppm}, \texttt{intensity.dppm}: prediction for a fitted determinantal point process model. \item \texttt{Kmodel.dppm}, \texttt{pcfmodel.dppm}: Second moments of a determinantal point process model. \item \texttt{rdpp}, \texttt{simulate.dppm}: Simulation of a determinantal point process model. \item \texttt{logLik.dppm}, \texttt{AIC.dppm}, \texttt{extractAIC.dppm}, \texttt{nobs.dppm}: Likelihood and AIC for a fitted determinantal point process model. \item \texttt{print.dppm}, \texttt{reach.dppm}, \texttt{valid.dppm}: Basic information about a \texttt{dpp} model. \item \texttt{coef.dppm}, \texttt{formula.dppm}, \texttt{print.dppm}, \texttt{terms.dppm}, \texttt{labels.dppm}, \texttt{model.frame.dppm}, \texttt{model.matrix.dppm}, \texttt{model.images.dppm}, \texttt{is.stationary.dppm}, \texttt{reach.dppm}, \texttt{unitname.dppm}, \verb!unitname<-.dppm!, \texttt{Window.dppm}: Various methods for \texttt{dppm} objects. \item \texttt{parameters.dppm}: Extract meaningful list of model parameters. \item \texttt{objsurf.dppm}: Objective function surface of a \texttt{dppm} object. \item \texttt{residuals.dppm}: Residual measure for a \texttt{dppm} object. \end{itemize} \item Determinantal Point Process model families: \begin{itemize} \item \texttt{dppBessel}, \texttt{dppCauchy}, \texttt{dppGauss}, \texttt{dppMatern}, \texttt{dppPowerExp}: Determinantal Point Process family functions. \item \texttt{detpointprocfamilyfun}: Create a family function. \item \texttt{update.detpointprocfamily}: Set parameter values in a determinantal point process model family. \item \texttt{simulate.dppm}: Simulation. \item \texttt{is.stationary.detpointprocfamily}, \texttt{intensity.detpointprocfamily}, \texttt{Kmodel.detpointprocfamily}, \texttt{pcfmodel.detpointprocfamily}: Moments. \item \texttt{dim.detpointprocfamily}, \texttt{dppapproxkernel}, \texttt{dppapproxpcf}, \texttt{dppeigen}, \texttt{dppkernel}, \texttt{dppparbounds}, \texttt{dppspecdenrange}, \texttt{dppspecden}: Helper functions. \end{itemize} \item \texttt{dg.envelope}: Simulation envelopes corresponding to Dao-Genton test. \item \texttt{dg.progress}: Progress plot (envelope representation) for the Dao-Genton test. \item \texttt{dg.sigtrace}: significance trace for the Dao-Genton test. \item \texttt{markcrosscorr}: Mark cross-correlation function for point patterns with several columns of marks. \item \texttt{rtemper}: Simulated annealing or simulated tempering. \item \texttt{rgb2hsva}: Convert RGB to HSV data, like \texttt{rgb2hsv}, but preserving transparency. \item \texttt{superimpose.ppplist}, \texttt{superimpose.splitppp}: New methods for 'superimpose' for lists of point patterns. \item \texttt{dkernel}, \texttt{pkernel}, \texttt{qkernel}, \texttt{rkernel}: Probability density, cumulative probability, quantiles and random generation from distributions used in basic one-dimensional kernel smoothing. \item \texttt{kernel.factor}: Auxiliary calculations for one-dimensional kernel smoothing. \item \texttt{spatdim}: Spatial dimension of any object in the \spst\ package. \item \texttt{as.boxx}: Convert data to a multi-dimensional box. \item \texttt{intensity.ppx}: Method for \texttt{intensity} for multi-dimensional space-time point patterns. \item \texttt{fourierbasis}: Evaluate Fourier basis functions in any number of dimensions. \item \texttt{valid}: New generic function, with methods \texttt{valid.ppm}, \texttt{valid.lppm}, \texttt{valid.dppm}. \item \texttt{emend}, \texttt{emend.ppm}, \texttt{emend.lppm}: New generic function with methods for \texttt{ppm} and \texttt{lppm}. \texttt{emend.ppm} is equivalent to \texttt{project.ppm}. \item \texttt{Penttinen}: New pairwise interaction model. \item \texttt{quantile.density}: Calculates quantiles from kernel density estimates. \item \texttt{CDF.density}: Calculates cumulative distribution function from kernel density estimates. \item \texttt{triangulate.owin}: decompose a spatial window into triangles. \item \texttt{fitted.lppm}: fitted intensity values for a point process on a linear network. \item \texttt{parameters}: Extract all parameters from a fitted model. \end{itemize} \section{Alphabetical list of changes} Here is a list of all changes made to existing functions, listed alphabetically. \begin{itemize} %%A \item \texttt{adaptive.density}: This function can now perform adaptive estimation by three methods: tessellation-based methods, variable-bandwidth kernel estimation, and nearest-neighbour intensity estimation. The calculations are performed by \texttt{densityVoronoi}, \texttt{densityAdaptiveKernel} or \texttt{nndensity}. \item \texttt{affine.owin}: Allows transformation matrix to be singular, if the window is polygonal. \item \texttt{alltypes}: If \texttt{envelope=TRUE} and the envelope computation reaches the maximum permitted number of errors (\texttt{maxnerr}) in evaluating the summary function for the simulated patterns, then instead of triggering a fatal error, the envelope limits will be set to \texttt{NA}. \item \texttt{anova.mppm}: \begin{itemize} \item Now handles Gibbs models, and performs the adjusted composite likelihood ratio test. \item New argument \texttt{fine}. \item Issues a warning when applied to random-effects models (models fitted using the argument \texttt{random}). \end{itemize} \item \texttt{anyDuplicated.ppp}: Accelerated. \item \texttt{append.psp}: arguments may be \texttt{NULL}. \item \texttt{applynbd}: Now works for point patterns in three dimensions (class \texttt{"pp3"}) and point patterns on a network (class \texttt{"lpp"}). \item \texttt{as.function.tess}: New argument \texttt{values} specifies the function values. \item \texttt{as.im}: Many methods for \texttt{as.im} now have argument \texttt{rule.eps}. \item \texttt{as.im.distfun}: New argument \texttt{approx} specifies the choice of algorithm. \item \texttt{as.im.tess}: New argument \texttt{values}. \item \texttt{as.im.function}: \begin{itemize} \item New argument \texttt{strict}. \item New argument \texttt{stringsAsFactors}. \item The formal default value of \texttt{stringsAsFactors} has been changed to \texttt{NULL} to conform to changes in R. (The actual default value is \texttt{TRUE} for \texttt{R < 4.1.0} and \texttt{FALSE} for \texttt{R >= 4.1.0}). \end{itemize} \item \texttt{as.im.leverage.ppm}: New argument \texttt{what}. \item \texttt{as.im.nnfun}: New argument \texttt{approx} chooses between a fast, approximate algorithm and a slow, exact algorithm. \item \texttt{as.im.smoothfun}: New argument \texttt{approx} chooses between a fast, approximate algorithm and a slow, exact algorithm. \item \texttt{as.layered}: Default method now handles a (vanilla) list of spatial objects. \item \texttt{as.linfun.lintess}: \begin{itemize} \item New argument \texttt{values} specifies the function value for each tile. \item The default \texttt{values} are the marks, if present. \item New argument \texttt{navalue}. \item Computation accelerated. \end{itemize} \item \texttt{as.linim.default}: \begin{itemize} \item New arguments \texttt{delta} and \texttt{nd} control spacing of sample points in internal data. \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{as.linim.linfun}: \begin{itemize} \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{as.linnet.linnet}: New argument \texttt{maxsize}. \item \texttt{as.linnet.psp}: \begin{itemize} \item If the line segment pattern has marks, then the resulting linear network also carries these marks in the \verb!$lines! component. \item Computation accelerated. \item The resulting network has attribute \texttt{"camefrom"} indicating the provenance of each line segment in the network. \end{itemize} \item \texttt{as.lpp}: accepts more data formats: \begin{itemize} \item Now handles the case where coordinates \texttt{seg} and \texttt{tp} are given but \texttt{x} and \texttt{y} are missing. \item Now handles the case where \texttt{x} is a data frame with columns named \texttt{x,y,seg,tp} or \texttt{x,y} or \texttt{seg,tp}. \end{itemize} \item \texttt{as.mask:} New argument \texttt{rule.eps} specifies what to do when the desired pixel size is not a divisor of the frame size. \item \texttt{as.owin.default}: \begin{itemize} \item Now refuses to convert a \code{box3} to a two-dimensional window. \item Now accepts a structure with entries named \code{xmin},\code{xmax}, \code{ymin}, \code{ymax} in any order. This handles objects of class \code{bbox} in the \pkg{sf} package. \item Now detects objects of class \code{SpatialPolygons} and issues a more helpful error message. \end{itemize} \item \texttt{as.owin.data.frame}: New argument \texttt{step} \item \texttt{as.polygonal}: \begin{itemize} \item Can now repair errors in polygon data, if \texttt{repair=TRUE}. \item Accelerated when \texttt{w} is a pixel mask. \end{itemize} \item \texttt{as.psp}: now permits a data frame of marks to have only one column, instead of coercing it to a vector. \item \texttt{as.solist}: The argument \texttt{x} can now be a spatial object; \texttt{as.solist(cells)} is the same as \texttt{solist(cells)}. %%B \item \texttt{bdist.pixels}: Accelerated for polygonal windows. New argument \texttt{method}. \item \texttt{bdist.points}: Accelerated for polygonal windows. \item \texttt{beachcolours}: \begin{itemize} \item Improved positioning of the yellow colour band. \item If \texttt{sealevel} lies outside \texttt{srange}, then \texttt{srange} will be extended to include it (without a warning). \end{itemize} \item \texttt{beachcolourmap}: Improved positioning of the yellow colour band. \item \texttt{bilinearform}: This function has been moved to the sub-package \texttt{spatstat.sparse}. \item \texttt{bind.fv}: New argument \texttt{clip}. \item \texttt{blur}: New argument \texttt{kernel}. \item \texttt{bw.abram}: \begin{itemize} \item This function is now generic, with a method for class \texttt{ppp}. \item New argument \texttt{smoother} determines how the pilot estimate is computed. \item Formal arguments rearranged. \end{itemize} \item \texttt{bw.diggle}, \texttt{bw.ppl}, \texttt{bw.relrisk}, \texttt{bw.smoothppp}: \begin{itemize} \item These functions now extract and store the name of the unit of length from the point pattern dataset. When the bandwidth selection criterion is plotted, the name of the unit of length is shown on the x-axis. \item A warning is issued if the optimal value of the cross-validation criterion occurs at an endpoint of the search interval. New argument \texttt{warn}. \end{itemize} \item \texttt{bw.ppl}: \begin{itemize} \item New arguments \texttt{weights} and \texttt{sigma}. \item New argument \texttt{shortcut} allows faster computation. \item Additional arguments \verb!...! are now passed to \texttt{density.ppp}. \end{itemize} \item \texttt{bw.relrisk}: This function is now generic, with methods for class \texttt{"ppp"} and \texttt{"lpp"}. \item \texttt{bw.relrisk.lpp}: When \texttt{method="likelihood"}, the cross-validation criterion is now defined as the \underline{\emph{negative}} likelihood. This is consistent with \texttt{bw.relrisk.ppp}, and ensures that the optimum bandwidth is always found by minimising the cross-validation criterion. \item \texttt{bw.relrisk.ppp}: Additional arguments \texttt{...} are now passed to \texttt{density.ppp}. \item \texttt{bw.scott}: \begin{itemize} \item the two bandwidth values in the result now have names \texttt{sigma.x} and \texttt{sigma.y}. \item Now handles point patterns of any dimension. \item New arguments \texttt{isotropic} and \texttt{d}. \end{itemize} \item \texttt{bw.stoyan}: The rule has been modified so that, if the pattern is empty, it is now treated as if it contained 1 point, so that a finite bandwidth value is returned. %%C \item \texttt{cbind.hyperframe}: \begin{itemize} \item The result now retains the \texttt{row.names} of the original arguments. \item \end{itemize} \item \texttt{cdf.test}: \begin{itemize} \item Calculations are more robust against numerical rounding effects. \item The methods for classes \texttt{ppp}, \texttt{ppm}, \texttt{lpp}, \texttt{lppm}, \texttt{slrm} have a new argument \texttt{interpolate}. \item Monte Carlo test runs much faster. \item More jittering is applied when \texttt{jitter=TRUE}. Warnings about tied values should not occur any more. \end{itemize} \item \texttt{cdf.test.ppm}: \begin{itemize} \item Recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{cdf.test.mppm}: \begin{itemize} \item Now handles Gibbs models. \item Now recognises \texttt{covariate="x"} or \texttt{"y"}. \end{itemize} \item \texttt{circdensity}: Improved output of \texttt{print} method. \item \texttt{clarkevans}: The argument \texttt{correction="all"} is now recognised: it selects all the available options. [This is also the default.] \item \texttt{clarkevans.test}: \begin{itemize} \item The asymptotic test is now available for any choice of edge correction. \item New argument \texttt{method} determines whether to use the asymptotic test or Monte Carlo test. The default has changed to \texttt{method="asymptotic"}. \item Default edge correction has changed, to avoid bias. \end{itemize} \item \texttt{clickpoly}: The polygon is now drawn progressively as the user clicks new vertices. \item \texttt{closepairs.ppp}: New argument \code{periodic}. \item \texttt{closepairs.ppp}, \texttt{closepairs.pp3}: \begin{itemize} \item New arguments \texttt{distinct} and \texttt{neat} allow more options. \item Argument \texttt{ordered} has been replaced by \texttt{twice} (but \texttt{ordered} is still accepted, with a warning). \item Performance improved (computation time and memory requirements reduced.) This should improve the performance of many functions in \texttt{spatstat}. \end{itemize} \item \texttt{closepairs.pp3}: Argument \texttt{what} can take the value \texttt{"ijd"} \item \texttt{clusterset}: Improved behaviour. \item \texttt{clusterfit}: \begin{itemize} \item New argument \texttt{algorithm} specifies the choice of optimisation algorithm. \item Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments \texttt{q,p,rmax,rmin} now take precedence over entries with the same names in the list \texttt{ctrl}. \item New argument \texttt{verbose}. \end{itemize} \item \texttt{colourmap}: argument \texttt{col} have have length 1, representing a trivial colour map in which all data values are mapped to the same colour. \item \texttt{collapse.fv}: \begin{itemize} \item This is now treated as a method for the \texttt{nlme} generic \texttt{collapse}. Its syntax has been adjusted slightly. \item Recognises the abbreviations used by \texttt{fvnames()}. \end{itemize} \item \texttt{connected.im}: Now handles a logical-valued image properly. Arguments \texttt{...} now determine pixel resolution. \item \texttt{connected.owin}: Arguments \texttt{...} now determine pixel resolution. \item \texttt{contour.im}: \begin{itemize} \item New argument \texttt{col} specifies the colour of the contour lines. If \texttt{col} is a colour map, then the contours are drawn in different colours. \item New argument \texttt{log} specifies whether the contour lines should be equally spaced on a logarithmic scale. \end{itemize} \item \texttt{convolve.im}: the name of the unit of length is preserved. \item \texttt{crossdist.lpp}: \begin{itemize} \item Now handles much larger networks, using the sparse representation of the network. \item New argument \texttt{check}. \end{itemize} \item \texttt{crossing.psp}: New argument \texttt{details} gives more information about the intersections between the segments. \item \texttt{crosspairs.ppp}: \begin{itemize} \item New argument \texttt{periodic} specifies whether to use periodic (toroidal) distances. \item New arguments \texttt{iX}, \texttt{iY} make it possible to eliminate pairs in which the two points are identical. \end{itemize} \item \texttt{crosspairs.pp3}: Argument \texttt{what} can take the value \texttt{"ijd"} \item \texttt{cut.ppp}: Argument \texttt{z} can be \texttt{"x"} or \texttt{"y"} indicating one of the spatial coordinates. %%D \item \texttt{dclf.test, mad.test, dclf.progress, mad.progress,} \texttt{dclf.sigtrace, mad.sigtrace}, \texttt{dg.progress, dg.sigtrace}: \begin{itemize} \item New argument \texttt{clamp} determines the test statistic for one-sided tests. \item New argument \texttt{rmin} determines the left endpoint of the test interval. \item New argument \texttt{leaveout} specifies how to calculate discrepancy between observed and simulated function values. \item New argument \texttt{scale} allows summary function values to be rescaled before the comparison is performed. \item New argument \texttt{interpolate} supports interpolation of $p$-value. \item Function values which are infinite, \texttt{NaN} or \texttt{NA} are now ignored in the calculation (with a warning) instead of causing an error. Warning messages are more detailed. \end{itemize} \item \texttt{default.rmhcontrol, default.rmhexpand}: New argument \texttt{w}. \item \texttt{densityfun.ppp}: The resulting function can now handle query points which lie outside the window of the original data, and has argument \texttt{drop=TRUE} which specifies how to handle them. \item \texttt{densityEqualSplit}: New arguments \texttt{at} and \texttt{leaveoneout} for consistency with other functions. \item \texttt{densityHeat}: \begin{itemize} \item default behaviour has changed slightly. \item new argument \texttt{finespacing}. \end{itemize} \item \texttt{density.lpp}: \begin{itemize} \item Argument \texttt{sigma} can now be a function in the R language, assumed to provide a bandwidth selection rule. This function will be applied to the point pattern \texttt{x} to compute the bandwidth. \item Argument \texttt{sigma=NULL} is now accepted. The default value is one-eighth of the length of the shortest side of the bounding box of \texttt{x}. \item New fast algorithm (up to 1000 times faster) for the default case where \texttt{kernel="gaussian"} and \texttt{continuous=TRUE}. Generously contributed by Greg McSwiggan. \item Fast algorithm has been further accelerated. \item Further accelerated when the point pattern contains duplicated points. \item New argument \texttt{kernel} specifies the smoothing kernel. Any of the standard one-dimensional smoothing kernels can be used. \item Now supports both the `equal-split continuous' and `equal-split discontinuous' smoothers. New argument \texttt{continuous} determines the choice of smoother. \item New arguments \texttt{weights} and \texttt{old}. \item New argument \texttt{distance} offers a choice of different kernel methods. \item Infinite bandwidth (\texttt{sigma=Inf}) is now permitted, and results in a density estimate that is constant over all locations. \end{itemize} \item \texttt{density.ppp}: \begin{itemize} \item A non-Gaussian kernel can now be specified using the argument \texttt{kernel}. \item Standard error calculation is now available with any smoothing kernel. \item The interpretation of \texttt{weights} in the calculation of standard error has changed. New argument \texttt{wtype} controls this interpretation. \item Argument \texttt{weights} can now be a pixel image. \item Infinite bandwidth \texttt{sigma=Inf} is supported. \item Accelerated by about 30\% when \texttt{at="pixels"}. \item Accelerated by about 15\% in the case where \texttt{at="points"} and \texttt{kernel="gaussian"}. \item Accelerated in the cases where weights are given or \texttt{diggle=TRUE}. \item New argument \texttt{verbose}. \end{itemize} \item \texttt{densityQuick.lpp}: Argument \texttt{X} changed to \texttt{x} for consistency. \item \texttt{density.psp}: \begin{itemize} \item New argument \texttt{method}. \item Accelerated by 1 to 2 orders of magnitude. \end{itemize} \item \texttt{density.splitppp}: New argument \texttt{weights}. \item \texttt{dfbetas.ppm}: \begin{itemize} \item For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. \item Increased the default resolution of the pixel images. Spatial resolution can now be controlled by the arguments \code{dimyx}, \code{eps}. \item Recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{diagnose.ppm}: \begin{itemize} \item Infinite values of \texttt{rbord} are now ignored and treated as zero. This ensures that \texttt{diagnose.ppm} has a sensible default when the fitted model has infinite reach. \item Accelerated, when \texttt{type="inverse"}, for models without a hard core. \end{itemize} \item \texttt{diagnose.ppm, plot.diagppm}: \begin{itemize} \item New arguments \texttt{col.neg, col.smooth} control the colour maps. \item Accelerated, when \texttt{type="inverse"}, for models without a hard core. \end{itemize} \item \texttt{dilation.ppp}: Improved geometrical accuracy. Now accepts arguments to control resolution of polygonal approximation. \item \texttt{dirichletEdges}: New argument \texttt{clip}. \item \texttt{discretise}: \begin{itemize} \item New argument \texttt{move.points} determines whether the point coordinates are also discretised. \item New argument \texttt{rule.eps} \end{itemize} \item \texttt{discs}: \begin{itemize} \item Now accepts a single numeric value for \texttt{radii}. \item New argument \texttt{npoly}. \item Accelerated in some cases. \end{itemize} \item \texttt{distcdf}: \begin{itemize} \item Arguments which are \texttt{NULL} will be treated as missing. \item New argument \texttt{savedenom}. \end{itemize} \item \texttt{distfun}: \begin{itemize} \item When the user calls a distance function that was created by \texttt{distfun}, the user may now give a \texttt{ppp} or \texttt{lpp} object for the argument \texttt{x}, instead of giving two coordinate vectors \texttt{x} and \texttt{y}. \item New argument \texttt{rule.eps} \end{itemize} \item \texttt{distfun.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item Computation accelerated. \end{itemize} \item \texttt{distmap.owin}: \begin{itemize} \item New argument \texttt{connect}. \item Behaviour has been altered so that, when \texttt{X} is a binary mask, the results of \texttt{distmap(X, invert=TRUE)} and \texttt{distmap(complement.owin(X))} are identical. This affects a few pixels close to the edge of the frame. \end{itemize} \item \texttt{distmap.ppp}: New argument \texttt{clip}. \item \texttt{distmap.psp}: New arguments \texttt{extras} and \texttt{clip}. \item \texttt{dppm}: Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments \texttt{q,p,rmax,rmin} now take precedence over entries with the same names in the list \texttt{ctrl}. \item \texttt{duplicated.ppp}: accelerated. %%E \item \texttt{edge.Trans}: New argument \texttt{gW} for efficiency. \item \texttt{eem}: The function \texttt{eem} is now generic, with methods for \texttt{ppm} and \texttt{slrm}. The function previously named \texttt{eem} is now called \texttt{eem.ppm}. \item \texttt{effectfun}: \begin{itemize} \item Now works for \texttt{ppm}, \texttt{kppm}, \texttt{lppm}, \texttt{dppm}, \texttt{rppm} and \texttt{profilepl} objects. \item New argument \texttt{nvalues}. \item Standard error calculation (\texttt{se.fit=TRUE}) now works for models fitted with \texttt{use.gam=TRUE}. \end{itemize} \item \texttt{envelope}: \begin{itemize} \item All methods for \texttt{envelope} now accept a summary function in which the function argument is not named \texttt{r}. This includes functions such as \texttt{transect.im} and \texttt{roc}. \item New argument \texttt{clamp} gives greater control over one-sided envelopes. \item New argument \texttt{funargs} \item New argument \texttt{scale} allows global envelopes to have width proportional to a specified function of $r$, rather than constant width. \item New argument \texttt{funYargs} contains arguments to the summary function when applied to the data pattern only. \item The argument \texttt{simulate} can now be a function (such as \texttt{rlabel}). The function will be applied repeatedly to the original data pattern. \item \texttt{rejectNA} and \texttt{silent}. \end{itemize} \item \texttt{envelope.lpp}, \texttt{envelope.lppm}: \begin{itemize} \item New arguments \texttt{fix.n} and \texttt{fix.marks} allow envelopes to be computed using simulations conditional on the observed number of points. \item New arguments \texttt{maxnerr}, \texttt{rejectNA} and \texttt{silent}. \end{itemize} \item \texttt{eval.im}: New argument \texttt{warn}. \item \texttt{eval.linim}: New argument \texttt{warn}. \item \texttt{eval.fasp}: automatically generated labels have been improved. \item \texttt{ewcdf}: \begin{itemize} \item Argument \texttt{weights} can now be \texttt{NULL}. \item New arguments \texttt{normalise} and \texttt{adjust}. \item Computation accelerated. \item The result does not inherit class \texttt{"ecdf"} if \texttt{normalise=FALSE}. \end{itemize} %%F \item \texttt{Fest}: Additional checks for errors in input data. \item \texttt{Finhom}: \begin{itemize} \item A warning is issued if bias is likely to occur because of undersmoothing. \item New arguments \texttt{warn.bias} and \texttt{savelambda}. \end{itemize} \item \texttt{fitted.lppm}: New argument \texttt{leaveoneout} allows leave-one-out computation of fitted value. \item \texttt{fitted.ppm}: \begin{itemize} \item New option, \texttt{type="link"}. \item New argument \code{ignore.hardcore}. \end{itemize} \item \texttt{funxy}: \begin{itemize} \item When the user calls a function that was created by \texttt{funxy}, the user may now give a \texttt{ppp} or \texttt{lpp} object for the argument \texttt{x}, instead of giving two coordinate vectors \texttt{x} and \texttt{y}. \item Functions of class \texttt{"funxy"} can now be applied to quadrature schemes. \item The result now has a \texttt{unitname}, inherited from the argument \texttt{W}. \end{itemize} %%G \item \texttt{Gcross}: Function labels (shown on the plot legend) have been improved when \texttt{i = j}. \item \texttt{Geyer}: The saturation parameter \texttt{sat} can now be less than 1. \item \texttt{Ginhom}: \begin{itemize} \item A warning is issued if bias is likely to occur because of undersmoothing. \item New arguments \texttt{warn.bias} and \texttt{savelambda}. \end{itemize} \item \texttt{grow.rectangle}: New argument \texttt{fraction}. %%H \item \texttt{harmonise.im}: The result belongs to classes \texttt{solist} and \texttt{imlist} so that it can be plotted. \item \texttt{Hest}: \begin{itemize} \item Argument \texttt{X} can now be a pixel image with logical values. \item New argument \texttt{W}. [Based on code by Kassel Hingee.] \item Additional checks for errors in input data. \end{itemize} \item \texttt{hist.im}: New argument \texttt{xname}. \item \texttt{hyperframe}: \begin{itemize} \item The formal default value of \texttt{stringsAsFactors} has been changed to \texttt{NULL} to conform to changes in R. (The actual default value is \texttt{TRUE} for \texttt{R < 4.1.0} and \texttt{FALSE} for \texttt{R >= 4.1.0}). \end{itemize} %%I \item \texttt{identify.psp}: Improved placement of labels. Arguments can be passed to \texttt{text.default} to control the plotting of labels. \item \texttt{idw}: Standard errors can now be calculated by setting \texttt{se=TRUE}. \item \texttt{imcov}: the name of the unit of length is preserved. \item \texttt{im.apply}: \begin{itemize} \item Computation accelerated \item New argument \texttt{fun.handles.na} \item New argument \texttt{check} \end{itemize} \item \texttt{influence.ppm}: For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. \item \texttt{integral.im}: \begin{itemize} \item New argument \texttt{weight} specifies a weight function for the integration. \item Accelerated in the case where \texttt{domain} is a tessellation. \end{itemize} \item \texttt{integral.linfun}: \begin{itemize} \item New argument \texttt{weight} specifies a weight function for the integration. \item New argument \texttt{delta} controls step length of approximation to integral. \item New argument \texttt{nd} controls approximate number of sample points used to calculate integral. \item Argument \code{domain} can be a tessellation. \item Now handles complex-valued functions. \end{itemize} \item \texttt{integral.linim}: \begin{itemize} \item New argument \texttt{weight} specifies a weight function for the integration. \item Argument \code{domain} can be a tessellation. \item Now handles complex-valued functions. \end{itemize} \item \texttt{integral.msr}: New argument \texttt{weight} specifies a weight (integrand) for the integration. \item \texttt{integral.ssf}: Argument \code{domain} can be a tessellation. \item \texttt{intensity.ppm}: \begin{itemize} \item Intensity approximation is now implemented for area-interaction model, and Geyer saturation model. \item Can now calculate the Coeurjolly-Lavancier DPP approximation of intensity. [Code kindly contributed by Frederic Lavancier] \item New argument \texttt{approx} specifies the choice of approximation. \end{itemize} \item \texttt{interp.im}: New argument \texttt{bilinear}. \item \texttt{intersect.tess}: New argument \texttt{keepempty}. \item \texttt{invoke.symbolmap}: new argument \texttt{angleref}. \item \texttt{iplot}, \texttt{iplot.ppp}, \texttt{iplot.layered}, \texttt{iplot.linnet}, \texttt{iplot.default}: These interactive plotting functions have been removed from \pkg{spatstat} into a new package \pkg{spatstat.gui}. \item \texttt{ippm}: \begin{itemize} \item Accelerated. \item The internal format of the result has been extended slightly. \item Improved defaults for numerical algorithm parameters. \end{itemize} \item \texttt{istat}: This interactive analysis function has been removed from \pkg{spatstat} into a new package \pkg{spatstat.gui}. %%J \item \texttt{Jcross}: Function labels (shown on the plot legend) have been improved when \texttt{i = j}. \item \texttt{Jfox}: new argument \texttt{warn.trim}. \item \texttt{Jinhom}: \begin{itemize} \item A warning is issued if bias is likely to occur because of undersmoothing. \item New arguments \texttt{warn.bias} and \texttt{savelambda}. \end{itemize} %%K \item \texttt{Kcross}: \begin{itemize} \item Function labels (shown on the plot legend) have been improved when \texttt{i = j}. \item Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \end{itemize} \item \texttt{Kcross.inhom}, \texttt{Kdot.inhom}, \texttt{Kmulti.inhom}: \begin{itemize} \item These functions now allow intensity values to be given by a fitted point process model. \item New arguments \texttt{update}, \texttt{leaveoneout}, \texttt{lambdaX}. \item Leave-one-out calculation is now implemented when \texttt{lambbdaX} is a fitted model of class \texttt{"dppm"}. \end{itemize} \item \texttt{Kdot}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{Kest} \begin{itemize} \item Accelerated computation (for translation and rigid corrections) when window is an irregular shape. \item Calculation of isotropic edge correction for polygonal windows has changed slightly. Results are believed to be more accurate. Computation has been accelerated by about 20 percent in typical cases. \item Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \end{itemize} \item \texttt{Kest.fft}: Now has \verb!...! arguments allowing control of spatial resolution. \item \texttt{Kinhom}: \begin{itemize} \item New argument \texttt{ratio}. \item Stops gracefully if \texttt{lambda} contains any zero values. \item Leave-one-out calculation is implemented when \texttt{lambda} is a fitted model of class \texttt{"dppm"}. \end{itemize} \item \texttt{Kmulti}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{kernel.moment}: \begin{itemize} \item New arguments \texttt{mean} and \texttt{sd}. \item Computation accelerated for \texttt{kernel='cosine'} or \texttt{'optcosine'}. \item All cases are now computed using analytic expressions, for \texttt{m=0,1,2}. \end{itemize} \item \texttt{kppm}: \begin{itemize} \item The code for fitting log-Gaussian Cox process models (\texttt{clusters="LGCP"}) has been re-implemented without using the package \pkg{RandomFields}. The current code supports the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance models. \item Computation accelerated when \texttt{method="palm"} or \texttt{method="clik2"}. [Kindly contributed by Bethany Macdonald.] \item New argument \texttt{trajectory} specifies whether to save the history of function evaluations performed by the optimization algorithm. \item New argument \texttt{penalised} supports penalised model-fitting with a penalty against extremely large or small values of the cluster scale. \item New arguments \texttt{ppm.improve.type} and \texttt{ppm.improve.args}. \item The first order trend is fitted using a regularized fitting algorithm when \texttt{ppm.improve.type="enet"}. \item New default settings ensure greater numerical stability of the optimization algorithm against the effects of the scale of the spatial coordinates. New argument \texttt{stabilize} specifies whether the optimization algorithm should be numerically stabilized. \item Fitting a model with \texttt{clusters="LGCP"} no longer requires the package \pkg{RandomFields} to be loaded explicitly. \item New argument \texttt{algorithm} specifies the choice of optimisation algorithm. \item Left hand side of formula can now involve entries in the list \texttt{data}. \item refuses to fit a log-Gaussian Cox model with anisotropic covariance. \item A warning about infinite values of the summary function no longer occurs when the default settings are used. Also affects \texttt{mincontrast}, \texttt{cauchy.estpcf}, \texttt{lgcp.estpcf}, \texttt{matclust.estpcf}, \texttt{thomas.estpcf}, \texttt{vargamma.estpcf}. \item Changed precedence rule for handling the algorithm parameters in the minimum contrast algorithm. Individually-named arguments \texttt{q,p,rmax,rmin} now take precedence over entries with the same names in the list \texttt{ctrl}. \item Improved printed output. \item Improved numerical robustness. \end{itemize} %%L \item \texttt{latest.news}: Now prints news documentation for the current major version, by default. New argument \texttt{major}. \item \texttt{Lcross}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{Ldot}: Now accepts the option \texttt{correction="periodic"} to compute the periodic (toroidal) edge correction estimate. \item \texttt{Lcross.inhom}, \texttt{Ldot.inhom}: These functions now allow intensity values to be given by a fitted point process model. New arguments \texttt{update}, \texttt{leaveoneout}, \texttt{lambdaX}. \item \texttt{lengths.psp}: \begin{itemize} \item New argument \texttt{squared}. \item This function will soon be Deprecated in favour of the new name \verb!lengths_psp!. \end{itemize} \item \texttt{Lest}, \texttt{Linhom}, \texttt{Ldot}, \texttt{Lcross}, \texttt{Ldot.inhom}, \texttt{Lcross.inhom}: These summary functions now have explicit argument \texttt{"correction"}. \item \texttt{leverage.ppm}: \begin{itemize} \item For Gibbs models, memory usage has been dramatically reduced, so the code can handle larger datasets and finer quadrature schemes. \item Increased the default resolution of the pixel images. Spatial resolution can now be controlled by the arguments \code{dimyx}, \code{eps}. \item Recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: \begin{itemize} \item These methods now work for models that were fitted by logistic composite likelihood (\texttt{method='logi'}). \item Computation has been vastly accelerated for models with Geyer interaction fitted using isotropic or translation edge corrections. \item Faster computation in many cases. \item Virtually all models and edge corrections are now supported, using a ``brute force'' algorithm. This can be slow in some cases. \end{itemize} \item \texttt{lgcp.estK}, \texttt{lgcp.estpcf}: This code for fitting log-Gaussian Cox process models has been re-implemented without using the package \pkg{RandomFields}. The current code supports the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance models. \item \texttt{lineardisc}: \begin{itemize} \item New argument \texttt{add}. \item Default plotting behaviour has changed. \end{itemize} \item \texttt{linearK}, \texttt{linearpcf} and relatives: \\ \begin{itemize} \item substantially accelerated. \item ratio calculations are now supported. \item new argument \texttt{ratio}. \end{itemize} \item \texttt{linearKEuclidInhom}, \texttt{linearpcfEuclidInhom}: Argument \texttt{lambda=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. A warning is issued that this is different from the previous behaviour. \item \texttt{linearKinhom}: new argument \texttt{normpower}. \item \texttt{linearKdot.inhom}, \texttt{linearpcfdot.inhom} Argument \texttt{lambdaI=NULL} or \texttt{lambdadot=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. \item \texttt{linearKcross.inhom}, \texttt{linearpcfcross.inhom} Argument \texttt{lambdaI=NULL} or \texttt{lambdaJ=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. \item \texttt{linearKinhom}, \texttt{linearpcfinhom}: \begin{itemize} \item Argument \texttt{lambda=NULL} is now interpreted to mean that the intensity should be estimated by kernel smoothing. A warning is issued that this is different from the previous behaviour. \item Changed behaviour when \texttt{lambda} is a fitted model. \item New arguments \texttt{update} and \texttt{leaveoneout}. \end{itemize} \item \texttt{linearpcf}: new argument \texttt{normpower}. \item \texttt{linearpcfinhom}: New arguments \texttt{adjust.sigma}, \texttt{bw} and \texttt{adjust.bw}. \item \texttt{linearpcfcross.inhom}, \texttt{linearpcfdot.inhom}: New arguments \texttt{adjust.sigma}, \texttt{bw} and \texttt{adjust.bw}. \item \texttt{linearpcfEuclidInhom}: New arguments \texttt{adjust.sigma}, \texttt{bw} and \texttt{adjust.bw}. \item \texttt{linim}: \begin{itemize} \item The image \texttt{Z} is now automatically restricted to the network. \item New argument \texttt{restrict}. \end{itemize} \item \texttt{linnet}: \begin{itemize} \item The internal format of a \texttt{linnet} (linear network) object has been changed. Existing datasets of class \texttt{linnet} are still supported. However, computation will be faster if they are converted to the new format. To convert a linnet object \texttt{L} to the new format, use \verb!L <- as.linnet(L)!. \item If the argument \texttt{edges} is given, then this argument now determines the ordering of the sequence of line segments. For example, the \texttt{i}-th row of \texttt{edges} specifies the \texttt{i}-th line segment in \texttt{as.psp(L)}. \item New argument \texttt{warn}. \item When argument \texttt{edges} is specified, the code now checks whether any edges are duplicated. \end{itemize} \item \texttt{lintess}: \begin{itemize} \item Argument \texttt{df} can be missing or \texttt{NULL}, resulting in a tesellation with only one tile. \item Tessellations can now have marks. New argument \texttt{marks}. \end{itemize} \item \texttt{localpcf}: New argument \texttt{rvalue}. \item \texttt{localpcfinhom}: \begin{itemize} \item New arguments \texttt{update}, \texttt{leaveoneout}, \texttt{rvalue}. \end{itemize} \item \texttt{logLik.ppm}: \begin{itemize} \item New argument \texttt{absolute}. \item The warning about pseudolikelihood (`log likelihood not available') is given only once, and is not repeated in subsequent calls, within a spatstat session. \end{itemize} \item \texttt{logLik.mppm}: new argument \texttt{warn}. \item \texttt{lohboot}: \begin{itemize} \item Algorithm has been corrected and extended thanks to Christophe Biscio and Rasmus Waagepetersen. \item New arguments \texttt{block}, \texttt{basicboot}, \texttt{Vcorrection}. \item Accelerated when the window is a rectangle. \item Now works for multitype $K$ functions \texttt{Kcross}, \texttt{Kdot}, \texttt{Lcross}, \texttt{Ldot}, \texttt{Kcross.inhom}, \texttt{Lcross.inhom} \item Confidence bands for \texttt{Lest}, \texttt{Linhom}, \texttt{Lcross}, \texttt{Ldot}, \texttt{Lcross.inhom} are now computed differently. First a confidence band is computed for the corresponding $K$ function \texttt{Kest}, \texttt{Kinhom}, \texttt{Kcross}, \texttt{Kdot}, \texttt{Kcross.inhom} respectively. Then this is transformed to a confidence band for the $L$ function by applying the square root transformation. \end{itemize} \item \texttt{lpp}: \begin{itemize} \item The internal format of an \texttt{lpp} object has been changed. Existing datasets of class \texttt{lpp} are still supported. However, computation will be faster if they are converted to the new format. To convert an \texttt{lpp} object \texttt{X} to the new format, use \verb!X <- as.lpp(X)!. \item \texttt{X} can be missing or \texttt{NULL}, resulting in an empty point pattern. \item Now handles the case where coordinates \texttt{seg} and \texttt{tp} are given but \texttt{x} and \texttt{y} are missing. \end{itemize} \item \texttt{lppm}: \begin{itemize} \item Covariates can be objects of class \texttt{lintess}. \item New argument \texttt{random} controls placement of dummy points. \item Computation accelerated. \end{itemize} \item \texttt{lurking.ppm}: accelerated. \item \texttt{lut}: argument \texttt{outputs} may have length 1, representing a lookup table in which all data values are mapped to the same output value. %%M \item \texttt{markconnect}: Accepts the argument \texttt{weights} which is passed to \texttt{markcorr}. \item \texttt{markcorr}: \begin{itemize} \item New argument \texttt{weights} allows computation of the weighted version of the mark correlation function. Weights can be an expression to be evaluated, or a function, or a pixel image, or a numeric vector. \item Now allows negative mark values, when \texttt{normalise=FALSE}. \end{itemize} \item \texttt{markcrosscorr}: Now allows negative mark values, when \texttt{normalise=FALSE}. \item \texttt{markstat}: Now works for point patterns in three dimensions (class \texttt{"pp3"}) and point patterns on a network (class \texttt{"lpp"}). \item \texttt{marktable}: Now works for point patterns in three dimensions (class \texttt{"pp3"}) and point patterns on a network (class \texttt{"lpp"}). \item \texttt{markvario}: Accepts the argument \texttt{weights} which is passed to \texttt{markcorr}. \item \texttt{mincontrast}: New argument \texttt{action.bad.values} specifies what action is taken when the summary function produces \texttt{NA} or \texttt{NaN} or infinite values. \item \texttt{minnndist}, \texttt{maxnndist}: New argument \texttt{by} makes it possible to find the minimum or maximum nearest neighbour distance between each pair of possible types in a multitype pattern. \item \texttt{mppm}: \begin{itemize} \item Now handles models with a random effect component. (This is covered in \cite[Chap.\ 16]{baddrubaturn15}.) \item New argument \texttt{random} is a formula specifying the random effect. (This is covered in \cite[Chap.\ 16]{baddrubaturn15}.) \item Performs more checks for consistency of the input data. \item New arguments \texttt{gcontrol} and \texttt{reltol.pql} control the fitting algorithm. \item New argument \texttt{weights} specifies case weights for each row of data. \end{itemize} \item \texttt{msr}: Infinite and \texttt{NA} values are now detected (if \texttt{check=TRUE}) and are reset to zero, with a warning. %%N \item \texttt{nbfires}: \begin{itemize} \item the unit of length for the coordinates is now specified in this dataset. \item This dataset now includes information about the different land and sea borders of New Brunswick. \end{itemize} \item \texttt{nncorr,nnmean,nnvario}: New argument \texttt{na.action}. \item \texttt{nncross.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item Computation accelerated. \end{itemize} \item \texttt{nncross.ppp}: When \texttt{X} is a point pattern and \texttt{Y} is a line segment pattern, higher order neighbours (\verb!k > 1!) are now supported. \item \texttt{nndist.pp3}: New argument \texttt{by} allows computation of the nearest distance to each group of points. \item \texttt{nndist.ppx}: New argument \texttt{by} allows computation of the nearest distance to each group of points. \item \texttt{nndist.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item new argument \texttt{by} allows computation of the nearest distance to each group of points. \item Computation accelerated. \end{itemize} \item \texttt{nnwhich.lpp}: \begin{itemize} \item New argument \texttt{k} allows computation of $k$-th nearest point. \item new argument \texttt{by} allows computation of the nearest distance to each group of points. \item Computation accelerated. \end{itemize} \item \texttt{nnfun}: new argument \texttt{rule.eps}. \texttt{nnfun.lpp}: \begin{itemize} \item New argument \texttt{k}. \item New argument \texttt{value} specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. \end{itemize} \texttt{nnfun.ppp}: \begin{itemize} \item New argument \texttt{value} specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. \end{itemize} \texttt{nnfun.psp}: \begin{itemize} \item New argument \texttt{value} specifies whether to return the index of the nearest neighbour or the mark value of the nearest neighbour. \end{itemize} %%O \item \texttt{owin}: If argument \texttt{mask} is a logical matrix, \texttt{NA} entries will be accepted, and converted to \texttt{FALSE}. \item \texttt{owin2mask}: New options \texttt{op="majority"} and \texttt{op="minority"}. If \texttt{op="majority"}, a pixel belongs to the resulting mask if at least half of the pixel area is covered by the window. %%P \item \texttt{padimage}: New argument \texttt{W} allows an image to be padded out to fill any window. \item \texttt{pairdist.lpp}: Now handles much larger networks, using the sparse representation of the network. \item \texttt{pairorient}: Default edge corrections now include \texttt{"bord.modif"}. \item \texttt{pairs.im}: new argument \texttt{drop}. \item \texttt{parres}: the argument \texttt{covariate} is allowed to be missing if the model only depends on one covariate. \item \texttt{pcf.ppp}: \begin{itemize} \item New argument \code{close} for advanced use. \item New argument \texttt{ratio} allows several estimates of pcf to be pooled. \item Now calculates an analytic approximation to the variance of the estimate of the pair correlation function (when \texttt{var.approx=TRUE}). \item Now returns the smoothing bandwidth used, as an attribute of the result. \item New argument \texttt{close} for advanced use. \item Now accepts \texttt{correction="none"}. \end{itemize} \item \texttt{pcfcross}, \texttt{pcfdot}, \texttt{pcfmulti}: New argument \texttt{ratio} makes it possible to save the numerator and denominator of the function estimates, so that estimates can be pooled. \item \texttt{pcfcross.inhom}, \texttt{pcfdor.inhom}: New arguments \texttt{adjust.sigma} and \texttt{adjust.bw} allow separate adjustment of the one-dimensional smoothing bandwidth \texttt{bw} and the spatial smoothing bandwidth \texttt{sigma}. \item \texttt{pcfinhom}: \begin{itemize} \item New arguments \texttt{adjust.sigma} and \texttt{adjust.bw} allow separate adjustment of the one-dimensional smoothing bandwidth \texttt{bw} and the spatial smoothing bandwidth \texttt{sigma}. \item New argument \code{close} for advanced use. \item Default behaviour is changed when \texttt{lambda} is a fitted model. The default is now to re-fit the model to the data before computing pcf. New arguments \texttt{update} and \texttt{leaveoneout} control this. \item New argument \texttt{close} for advanced use. \item Now handles \texttt{correction="good"} \item Leave-one-out calculation is implemented when \texttt{lambda} is a fitted model of class \texttt{"dppm"}. \end{itemize} \item \code{persp.funxy}: Improved $z$-axis label. \item \texttt{pixellate.ppp}: \begin{itemize} \item If the pattern is empty, the result is an integer-valued image (by default) for consistency with the results for non-empty patterns. \item Accelerated in the case where weights are given. \item New arguments \texttt{fractional} and \texttt{preserve} for more accurate discretisation. \item New argument \texttt{savemap}. \end{itemize} \item \texttt{plot.anylist}: \begin{itemize} \item If a list entry \verb!x[[i]]! belongs to class \texttt{"anylist"}, it will be expanded so that each entry \verb!x[[i]][[j]]! will be plotted as a separate panel. \item New arguments \texttt{panel.begin.args}, \texttt{panel.end.args} \item Result is now an (invisible) list containing the result from executing the plot of each panel. \end{itemize} \item \texttt{plot.bermantest}: Improved layout for plots of Berman's \texttt{Z2} test. \item \texttt{plot.colourmap}: \begin{itemize} \item Now handles a colour map for a zero-length interval [a,a] \item New argument \texttt{increasing} specifies whether the colours are displayed in order left-to-right/bottom-to-top. \item Changed default behaviour for discrete colour maps when \texttt{vertical=FALSE}. \end{itemize} \item \texttt{plot.fv}: New argument \texttt{clip.xlim}. \item \texttt{plot.im}: \begin{itemize} \item New argument \texttt{addcontour} specifies that contour lines should be drawn over the image plot. \item Now handles complex-valued images. \item New argument \texttt{workaround} to avoid a bug in some MacOS device drivers that causes the image to be displayed in the wrong spatial orientation. \item The number of tick marks in the colour ribbon can now be controlled using the argument \texttt{nint} in \texttt{ribargs}. \item Improved behaviour when all pixel values are \texttt{NA}. \item Improved handling of tickmarks on colour ribbon. \item Improved behaviour when the image values are almost constant. \item New argument \texttt{riblab}. \item Axes are prevented from extending outside the image rectangle. \item New argument \texttt{zap}. \item Some warnings are suppressed when \texttt{do.plot=FALSE}. \end{itemize} \item \texttt{plot.imlist}: Result is now an (invisible) list containing the result from executing the plot of each panel. \item \texttt{plot.influence.ppm}: New argument \texttt{multiplot}. \item \texttt{plot.kppm}: \begin{itemize} \item New arguments \texttt{pause} and \texttt{xname}. \item The argument \texttt{what="all"} is now recognised: it selects all the available options. [This is also the default.] \end{itemize} \item \texttt{plot.leverage.ppm}: \begin{itemize} \item New arguments \texttt{multiplot} and \code{what}. \item A contour line showing the average value of leverage is now drawn on the colour ribbon, as well as on the main image. New argument \texttt{args.contour}. \end{itemize} \item \texttt{plot.linfun}: \begin{itemize} \item Now passes arguments to the function being plotted. \item A scale bar is now plotted when \texttt{style="width"}. \item New argument \texttt{legend}. \item The return value has a different format. \end{itemize} \item \texttt{plot.linim}: \begin{itemize} \item The return value has a different format. \item New argument \texttt{fatten} improves visual appearance when \texttt{style="colour"}. \item A scale bar is now plotted when \texttt{style="width"}. \item When \texttt{style="width"}, negative values are plotted in red (by default). New argument \texttt{negative.args} controls this. \item New argument \texttt{zlim} specifies the range of values to be mapped. \item New explicit argument \texttt{box} determines whether to plot a bounding box; default is \texttt{FALSE} in all cases. \end{itemize} \item \texttt{plot.lintess}: \begin{itemize} \item Improved plot method, with more options. \item Modified to display the marks attached to the tiles. \item Options: \verb!style=c("colour", "width", "image")!. \end{itemize} \item \texttt{plot.lpp}: \begin{itemize} \item New argument \texttt{show.network}. \item For a point pattern with continuous marks (``real numbers'') the colour arguments \texttt{cols}, \texttt{fg}, \texttt{bg} can now be vectors of colour values, and will be used to determine the default colour map for the marks. \item If \texttt{shape="crossticks"}, the points will be drawn as short line segments perpendicular to the network. \end{itemize} \item \texttt{plot.mppm}: \begin{itemize} \item New argument \texttt{main}. \item New argument \texttt{se}. \end{itemize} \item \texttt{plot.msr}: \begin{itemize} \item Now handles multitype measures. \item New argument \texttt{multiplot}. \item New argument \texttt{massthresh}. \item New arguments \texttt{equal.markscale} and \texttt{equal.ribbon}. \end{itemize} \item \texttt{plot.onearrow:} Graphical parameters, specified when the object was created, are now taken as the defaults for graphical parameters to the plot. \item \texttt{plot.owin:} New argument \texttt{use.polypath} controls how to plot a filled polygon when it has holes. \item \texttt{plot.profilepl}: This function has now been documented, and the graphics improved. \item \texttt{plot.psp}: \begin{itemize} \item Segments can be plotted with widths proportional to their mark values. \item New argument \texttt{style}. \item New argument \texttt{col} gives control over the colour map representing the values of marks attached to the segments. \item The code for \texttt{style="width"} has been completely rewritten, so that it no longer depends on \texttt{plot.linim}, and is much more efficient. \item The formal argument list has been extended. \end{itemize} \item \texttt{plot.pp3}: New arguments \texttt{box.front}, \texttt{box.back} control plotting of the box. \item \texttt{plot.ppp}: \begin{itemize} \item For multitype point patterns, a warning is issued if the plot legend does not represent every possible type of point due to space restrictions. \item The default colour for the points is now a transparent grey, if this is supported by the plot device. \item For a point pattern with continuous marks (``real numbers'') the colour arguments \texttt{cols}, \texttt{fg}, \texttt{bg} can now be vectors of colour values, and will be used to determine the default colour map for the marks. \item Now recognises graphics parameters for text, such as \texttt{family} and \texttt{srt} \item When \texttt{clipwin} is given, any parts of the boundary of the window of \texttt{x} that lie inside \texttt{clipwin} will also be plotted. \item Improved placement of symbol map legend when argument \texttt{symap} is given. \end{itemize} \item \code{plot.tess}: \begin{itemize} \item This plot method can now fill each tile with a different colour. \item New arguments \code{do.col}, \code{values}, \code{col} and \code{ribargs}. Old argument \code{col} has been renamed \code{border} for consistency. \item Now generates a separate plot panel for each column of marks, if \texttt{do.col=TRUE}. \item New argument \texttt{multiplot}. \end{itemize} \item \texttt{plot.palmdiag}: Improved calculation of $y$ axis limits. \item \texttt{plot.profilepl} ,\texttt{plot.quadratcount}, \texttt{plot.quadrattest}, \texttt{plot.tess}: Now recognise graphics parameters for text, such as \texttt{family} and \texttt{srt} \item \texttt{plot.solist}: \begin{itemize} \item When \texttt{equal.ribbon=TRUE}, the images may now be factor-valued or character-valued. Character-valued images will be converted to factor-valued images. The common colour map will combine the levels of all the factor images. \item New arguments \texttt{panel.begin.args}, \texttt{panel.end.args} \item Result is now an (invisible) list containing the result from executing the plot of each panel. \end{itemize} \item \texttt{plot.studpermutest}: This existing function now has a help file. \item \texttt{plot.symbolmap}: \begin{itemize} \item New argument \texttt{colour.only} makes it possible to display only the colour map information in a symbol map. \item New argument \texttt{warn}. \item Issues a warning if the plot of a discrete symbol map does not represent every possible input value, due to space restrictions. \item New argument \texttt{nsymbols} controls the number of symbols plotted. \end{itemize} \item \code{ponderosa}: In this installed dataset, the function \code{ponderosa.extra\$plotit} has changed slightly (to accommodate the dependence on the package \pkg{spatstat.utils}). \item \texttt{polynom}: This function now has a help file. \item \texttt{pool.fv}: \begin{itemize} \item The default plot of the pooled function no longer includes the variance curves. \item New arguments \texttt{relabel} and \texttt{variance}. \end{itemize} \item \texttt{pool.rat}: New arguments \texttt{weights}, \texttt{relabel} and \texttt{variance}. \item \texttt{ppm}: \begin{itemize} \item Now supports regularized model-fitting. \item Huang-Ogata approximate maximum likelihood can be applied to logistic fits. \item New argument \texttt{improve.type}. \begin{itemize} \item Option \texttt{method="ho"} is replaced by \texttt{improve.type="ho"}. \item Regularized model-fitting is performed when \texttt{improve.type="enet"}. \item Huang-Ogata approximate maximum likelihood can be applied to logistic fits by setting \texttt{method="logi"} and \texttt{improve.type="ho"}. \end{itemize} \item Argument \code{interaction} can now be a function that makes an interaction, such as \code{Poisson}, \code{Hardcore}, \code{MultiHard}. \item Argument \texttt{subset} can now be a window (class \texttt{"owin"}) specifying the sub-region of data to which the model should be fitted. \end{itemize} \item \texttt{ppm.ppp, ppm.quad}: \begin{itemize} \item New argument \texttt{emend}, equivalent to \texttt{project}. \item New arguments \texttt{subset} and \texttt{clipwin}. \end{itemize} \item \code{ppmInfluence}: The result now belongs to class \code{ppmInfluence}, for which there are methods for \code{leverage}, \code{influence}, \code{dfbetas} which extract the desired component. \item \texttt{ppp}: \begin{itemize} \item New argument \texttt{checkdup}. \item If the coordinate vectors \code{x} and \code{y} contain \code{NA}, \code{NaN} or infinite values, these points are deleted with a warning, instead of causing a fatal error. \end{itemize} \item \texttt{pp3}: New argument \texttt{marks}. \item \texttt{predict.kppm, residuals.kppm} Now issues a warning when the calculation ignores the cluster/Cox component and treats the model as if it were Poisson. (This currently happens in predict.kppm when se=TRUE or interval != "none", and in residuals.kppm when type != "raw"). \item \texttt{predict.lppm}: Argument \texttt{locations} can now be an \texttt{lpp} object. \item \texttt{predict.mppm}: \begin{itemize} \item The argument \texttt{type="all"} is now recognised: it selects all the available options. [This is also the default.] \item Now supports multitype point process models. \item Improved handling of argument \texttt{newdata}. \end{itemize} \item \texttt{predict.ppm}: \begin{itemize} \item Now recognises the arguments \code{dimyx} and \code{eps} for specifying the resolution of the grid of prediction points. \item New argument \code{ignore.hardcore}. \item Accelerated for models fitted with \texttt{method="VBlogi"} \item Standard error calculation (\texttt{se=TRUE}) now works for models fitted with \texttt{use.gam=TRUE}. \end{itemize} \item \texttt{predict.rhohat}: New argument \texttt{what} determines which value should be calculated: the function estimate, the upper/lower confidence limits, or the standard error. \item \texttt{print.kppm}: Additional characteristics of the fitted model are reported, including the cluster strength \texttt{phi} and the sibling probability. \item \texttt{print.linim}: More information is printed. \item \texttt{print.lintess}: Output includes information about marks. \item \texttt{print.lppm}: The name of the original point pattern dataset (to which the model was fitted) is now printed. \item \texttt{print.quad}: More information is printed. \item \texttt{print.rmhmodel}: More information is printed. \item \texttt{progressreport} \begin{itemize} \item The estimated time of completion is also printed, if the remaining time is longer than 10 minutes. \item Behaviour improved. \item New arguments \texttt{state}, \texttt{tick}, \texttt{showtime}. \item New option: \verb!style="tk"! \item New argument \texttt{formula} controls the calculation of estimated time remaining. \item New argument \texttt{savehistory} specifies whether to save the elapsed times when the function was called. \end{itemize} \item \code{pseudoR2.ppm}, \code{pseudoR2.lppm}: \begin{itemize} \item The null model now includes any offset terms, by default. \item New argument \code{keepoffset}. \end{itemize} %%Q \item \texttt{quadform}: This function has been moved to the sub-package \texttt{spatstat.sparse}. \item \texttt{quadratcount.ppp}: Computation accelerated in some cases. \item \texttt{quadrat.test.ppm}: Computation accelerated in some cases. \item \texttt{quantess}: \begin{itemize} \item The covariate \texttt{Z} can now be \texttt{"rad"} or \texttt{"ang"} representing polar coordinates. \item New argument \texttt{origin} specifies the origin of polar coordinates. \item New argument \texttt{eps} controls the accuracy of the calculation. \end{itemize} \item \texttt{quantile.ewcdf}: The function is now normalised to the range \verb![0,1]! before the quantiles are computed. This can be suppressed by setting \texttt{normalise=FALSE}. \item \texttt{qqplot.ppm} Argument \texttt{expr} can now be a list of point patterns, or an envelope object containing a list of point patterns. %%R \item Most random generators: now accept \texttt{nsim=0} and return a zero-length list. \item \texttt{rbind.hyperframe}: The result now retains the \texttt{row.names} of the original arguments. \item \texttt{rcellnumber}: New argument \texttt{mu}. \item \texttt{rebound.owin}: Now preserves unitnames of the objects. \item \texttt{rescale.owin}, \texttt{rescale.ppp}, \texttt{rescale.psp}: The geometrical type of the window is now preserved in all cases. (Previously if the window was polygonal but was equivalent to a rectangle, the rescaled window was a rectangle.) \item \texttt{rgbim, hsvim}: New argument \texttt{A} controls the alpha (transparency) channel. \item \texttt{rgb2hex, col2hex, paletteindex, is.colour, samecolour,} \texttt{complementarycolour, is.grey, to.grey} These colour tools now handle transparent colours. \item \texttt{rgb2hex}: New argument \texttt{maxColorValue} \item \texttt{relrisk.ppp}: \begin{itemize} \item If \texttt{se=TRUE} and \texttt{at="pixels"}, the result belongs to class \texttt{solist}. \item The arguments \texttt{adjust}, \texttt{edge}, \texttt{diggle} are now explicit formal arguments. \item New argument \texttt{weights}. \item Ratios which are close to 0/0 are handled more effectively, reducing the likelihood of strange-looking plots when \texttt{sigma} is very small. \item Issues a warning if numerical underflow is detected. \item The interpretation of \texttt{weights} in the calculation of standard error has changed. New argument \texttt{wtype} controls this interpretation. \item New argument \texttt{fudge} specifies a constant numeric value that will be added to each estimate of point process intensity before calculation of relative risk. \end{itemize} \texttt{rhohat}: \begin{itemize} \item The result now includes the ``average'' intensity $\overline\rho$. \item New options \texttt{smoother="piecewise"} computes a piecewise-constant estimate of $\rho(z)$. \item Nonparametric maximum likelihood estimation is now supported, assuming the intensity is a monotone function of the covariate. \item New options \texttt{smoother="increasing"} and \texttt{smoother="decreasing"} for estimating a monotone increasing or monotone decreasing curve. \item New options \texttt{smoother="mountain"} and \texttt{smoother="valley"} for estimating a unimodal function (U-shaped or inverted-U-shaped curve). \item New argument \texttt{subset} allows computation for a subset of the data. \item New argument \texttt{positiveCI} specifies whether confidence limits should always be positive. \item If the covariate is a \texttt{distfun}, the name of the unit of length is saved and displayed on the plot. \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{rhohat.lpp}: \begin{itemize} \item New argument \texttt{random} controls placement of dummy points. \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{rhohat.lppm}: \begin{itemize} \item New argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item\texttt{rhohat.ppm}: New argument \texttt{rule.eps} passed to \texttt{as.mask}. \item \texttt{rjitter.ppp}: If \texttt{trim=TRUE}, the displacement radius will be constrained to be less than or equal to the distance from the data point to the window boundary. This guarantees that all displaced points fall inside the window, and accelerates the computation. \item \texttt{rlabel}: \begin{itemize} \item New argument \texttt{group} specifies that the points are divided into several groups, and that relabelling is applied within each group. \item New arguments \texttt{nsim} and \texttt{drop}. \item \texttt{X} can now be a point pattern of any type (\texttt{ppp}, \texttt{lpp}, \texttt{pp3}, \texttt{ppx}) or a line segment pattern (\texttt{psp}). \end{itemize} \item \texttt{rlabel.ppp}: New argument \texttt{group} specifies that the points are divided into several groups, and that relabelling is applied within each group. \item \texttt{rLGCP}: \begin{itemize} \item This function has been completely re-implemented so that it no longer requires the package \pkg{RandomFields}, which is defunct (and sadly missed). \item The current implementation supports only the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance functions. \item Now recognises argument \texttt{rule.eps} passed to \texttt{as.mask}. \end{itemize} \item \texttt{rMaternI, rMaternII}: These functions can now generate random patterns in three dimensions and higher dimensions, when the argument \texttt{win} is of class \texttt{box3} or \texttt{boxx}. \item \texttt{rMatClust}: \begin{itemize} \item Can now perform conditional simulation given a fixed number of points. \item New arguments \texttt{n.cond} and \texttt{w.cond}. \end{itemize} \item \texttt{rmh}: \begin{itemize} \item Accelerated, in the case where multiple patterns are saved using \texttt{nsave}. \item The printed output of the debugger (invoked by \texttt{snoop=TRUE}) has been improved. \end{itemize} \item \texttt{rmh.ppm, rmhmodel.ppm, simulate.ppm}: A model fitted using the \texttt{Penttinen} interaction can now be simulated. \item \texttt{rmh.default, rmhmodel.default}: \begin{itemize} \item These functions now recognise \verb!cif='penttinen'! for the Penttinen interaction. \item New arguments \texttt{nsim}, \texttt{saveinfo}. \item The printed output of the debugger (invoked by \texttt{snoop=TRUE}) has been improved. \end{itemize} \item \texttt{rmhcontrol}: \begin{itemize} \item New parameter \texttt{pstage} determines when to generate random proposal points. \item The parameter \texttt{nsave} can now be a vector of integers. \end{itemize} \item \texttt{rNeymanScott}: \begin{itemize} \item Argument \texttt{lmax} has been replaced by \texttt{kappamax}. \item New argument 'mumax'. \end{itemize} \item \texttt{rose.default} New argument \texttt{weights}. \item \texttt{rose} New arguments \texttt{start} and \texttt{clockwise} specify the convention for measuring and plotting angles. \item \texttt{rotmean}: \begin{itemize} \item New argument \texttt{padzero}. \item Default behaviour has changed. \item Improved algorithm stability. \item The result now has the same \texttt{unitname} as the input object. \item New argument \texttt{adjust} controls the smoothing bandwidth. \end{itemize} \item \texttt{rpoint}: New argument \texttt{forcewin} forces the code to use the window \texttt{win} when \texttt{f} is a pixel image. \item \texttt{rpoispp}: Accelerated, when \texttt{lambda} is a pixel image. \item \texttt{rpoisppx}: New argument \code{drop}. \item \texttt{rpoisline}: Also returns information about the original infinite random lines. \item \texttt{rpoislpp}: If \texttt{lambda} is a list of \texttt{"linim"} or \texttt{"linfun"} objects, then the argument \texttt{L} can be omitted. \item \texttt{rPoissonCluster}: Argument \texttt{lmax} has been replaced by \texttt{kappamax}. \item \texttt{rshift.ppp}, \texttt{rshift.splitppp}: new argument \texttt{nsim}. \item \texttt{rSSI}: Accelerated. \item \texttt{rStrauss, rHardcore, rStraussHard, rDiggleGratton, rDGS, rPenttinen:} New argument \texttt{drop}. \item \texttt{rtemper:} new argument \texttt{track}. \item \texttt{rthin} \begin{itemize} \item Accelerated, when \texttt{P} is a single number. \item \texttt{X} can now be a point pattern of any type (\texttt{ppp}, \texttt{lpp}, \texttt{pp3}, \texttt{ppx}) or a line segment pattern (\texttt{psp}). \end{itemize} \item \texttt{rThomas, rMatClust, rCauchy, rVarGamma}: \begin{itemize} \item These algorithms have been accelerated by several orders of magnitude in the case where the cluster radius is large. \item These functions now offer a choice of simulation algorithms. \item Formal arguments have changed. \item When the model is approximately Poisson, it is simulated using rpoispp. This avoids computations which would require huge amounts of memory. New argument \texttt{poisthresh} controls this behaviour. \item New argument \texttt{saveparents}. \end{itemize} \item \texttt{runiflpp}, \texttt{rpoislpp}: The simulation parameters can be determined from an example point pattern, given as the argument \texttt{ex}. \item \texttt{runifpointOnLines}, \texttt{rpoisppOnLines}: New argument \code{drop}. \item \texttt{runifpointx}: New argument \code{drop}. %%S \item \texttt{selfcut.psp}: \begin{itemize} \item Computation accelerated. \item The result now has an attribute \texttt{"camefrom"} indicating the provenance of each segment in the result. \item No longer checks for validity of the resulting segments. \end{itemize} \item \texttt{sessionInfo}: Output now includes a list of packages that are imported but not loaded. \item \texttt{sessionLibs}: Package names are now sorted alphabetically \item \texttt{setcov}: the name of the unit of length is preserved. \item \code{shapley}: In this installed dataset, the function \code{shapley.extra\$plotit} has changed slightly (to accommodate the dependence on the package \pkg{spatstat.utils}). \item \texttt{shift.im}, \texttt{shift.owin}, \texttt{shift.ppp}, \texttt{shift.psp}: More options for the argument \texttt{origin}. \item Simulation: Several basic simulation algorithms have been accelerated. Consequently, simulation outcomes are not identical to those obtained with previous versions of \spst, even when the same random seed is used. To ensure compatibility with previous versions of spatstat, revert to the slower code by setting \texttt{spatstat.options(fastthin=FALSE, fastpois=FALSE)}. \item \texttt{simulate.kppm}: \begin{itemize} \item For log-Gaussian Cox process models (\texttt{clusters="LGCP"}) the simulation algorithm has been completely re-implemented without using the package \pkg{RandomFields}. The current code supports the \texttt{"exponential"}, \texttt{"gauss"}, \texttt{"stable"}, \texttt{"gencauchy"} and \texttt{"matern"} covariance models. \item Conditional simulation of the model, given a fixed number of points, is now supported using the new arguments \texttt{n.cond} and \texttt{w.cond}. \item Additional arguments \verb!...! are now passed to the function that performs the simulation. \end{itemize} \item \texttt{simulate.ppm}: \begin{itemize} \item New argument \texttt{w} controls the window of the simulated patterns. \item New argument \texttt{verbose}. \item Now recognises the argument \texttt{window} as an alternative to \texttt{w}. \end{itemize} \item \texttt{slrm}: \begin{itemize} \item In the default case (where \texttt{dataAtPoints} is not given) all spatial covariates, including the spatial coordinates \texttt{x} and \texttt{y}, are now evaluated at the centre of each pixel. This improves consistency with other implementations of spatial logistic regression. \item Silently ignores any arguments \verb!'...'! that are not recognised by \texttt{as.mask} \end{itemize} \item \texttt{Smooth.ppp}: \begin{itemize} \item A non-Gaussian kernel can now be specified using the argument \texttt{kernel}. \item Argument \texttt{weights} can now be a pixel image, a function, a numeric vector or an expression to be evaluated. \item Infinite bandwidth \texttt{sigma=Inf} is supported. \item Accelerated by about 30\% in the case where \texttt{at="pixels"}. \item Accelerated by about 15\% in the case where \texttt{at="points"} and \texttt{kernel="gaussian"}. \item Now exits gracefully if any mark values are \texttt{NA}, \texttt{NaN} or \texttt{Inf}. \item New argument \texttt{geometric} supports geometric-mean smoothing. \item The arguments \texttt{adjust}, \texttt{edge}, \texttt{diggle} and \texttt{kernel} are now explicit formal arguments. \item Standard error calculation is now supported (Experimental). \end{itemize} \item \texttt{solist}: New argument \verb!.NameBase! \item \texttt{spatialcdf}: \begin{itemize} \item Computation accelerated. \item The result does not inherit class \texttt{"ecdf"} if \texttt{normalise=FALSE}. \end{itemize} \item \texttt{spatstat.options} New options \texttt{fastthin} and \texttt{fastpois} enable fast simulation algorithms. Set these options to \texttt{FALSE} to reproduce results obtained with previous versions of \spst. \item \texttt{split.ppp}, \texttt{split.ppx}: The splitting variable \texttt{f} can now be a logical vector. \item \verb!split<-.ppp!: The default for argument \texttt{un} in \verb!split<-.ppp! now agrees with the default for the same argument in \texttt{split.ppp}. \item \texttt{square}: Handles a common error in the format of the arguments. \item \texttt{step}: now works for models of class \texttt{"mppm"}. \item \texttt{stieltjes}: Argument \texttt{M} can be a stepfun object (such as an empirical CDF). \item \texttt{subset.ppp}, \texttt{subset.lpp}, \texttt{subset.pp3}, \texttt{subset.ppx}: The argument \texttt{subset} can now be any argument acceptable to the \verb!"["! method. \item \texttt{Summary.linim} (methods for the operations \texttt{range}, \texttt{max}, \texttt{min} etc): Recognises the argument \texttt{finite} so that \texttt{range(x, finite=TRUE)} works for a linim object \texttt{x}. \item summary functions: The argument \texttt{correction="all"} is now recognised: it selects all the available options. \begin{quote} This applies to \texttt{Fest}, \texttt{F3est}, \texttt{Gest}, \texttt{Gcross}, \texttt{Gdot}, \texttt{Gmulti}, \texttt{G3est}, \texttt{Gfox}, \texttt{Gcom}, \texttt{Gres}, \texttt{Hest}, \texttt{Jest}, \texttt{Jmulti}, \texttt{Jcross}, \texttt{Jdot}, \texttt{Jfox}, \texttt{Kest}, \texttt{Kinhom}, \texttt{Kmulti}, \texttt{Kcross}, \texttt{Kdot}, \texttt{Kcom}, \texttt{Kres}, \texttt{Kmulti.inhom}, \texttt{Kcross.inhom}, \texttt{Kdot.inhom}, \texttt{Kscaled}, \texttt{Ksector}, \texttt{Kmark}, \texttt{K3est}, \texttt{Lscaled}, \texttt{markcorr}, \texttt{markcrosscorr}, \texttt{nnorient}, \texttt{pairorient}, \texttt{pcfinhom}, \texttt{pcfcross.inhom}, \texttt{pcfcross}, \texttt{pcf}, \texttt{Tstat}. \end{quote} \item \texttt{summary.distfun}, \texttt{summary.funxy}: \begin{itemize} \item More information is printed. \item Pixel resolution can now be controlled. \end{itemize} \item \texttt{summary.im}: Output improved when the image is empty (i.e. when all pixel values are undefined). \item \texttt{summary.kppm}: prints more information about algorithm convergence. \item \texttt{summary.lintess}: prints information about marks. \item \texttt{summary.lppm}: The name of the original point pattern dataset (to which the model was fitted) is now printed. \item \texttt{summary.mppm}: Improved summary of the dependence of the interpoint interaction on the covariates. \item \texttt{summary.ppm}: New argument \texttt{fine} selects the algorithm for variance estimation. \item \texttt{summary.owin}, \texttt{summary.im}: The fraction of frame area that is occupied by the window/image is now reported. \item \texttt{sumouter}: \begin{itemize} \item New argument \texttt{y} allows computation of asymmetric outer products. \item This function has now been moved to the sub-package \texttt{spatstat.sparse} \end{itemize} \item \texttt{symbolmap}: \begin{itemize} \item Now accepts a vector of colour values for the arguments \texttt{col}, \texttt{cols}, \texttt{fg}, \texttt{bg} if the argument \texttt{range} is given. \item New option: \texttt{shape="arrows"}. \end{itemize} %%T \item \texttt{tess}: Argument \texttt{window} is ignored when xgrid, ygrid are given. \item \texttt{texturemap}: Argument \texttt{textures} can be missing or NULL. \item \texttt{textureplot}: Argument \texttt{x} can now be something acceptable to \texttt{as.im}. \item \texttt{tilenames}, \verb!tilenames<-!: These functions are now generic, with methods for \texttt{tess} and \texttt{lintess}. \item \texttt{to.grey} New argument \texttt{transparent}. \item \texttt{transect.im}: new argument \texttt{nsample}. %%U \item \texttt{union.owin}: Improved behaviour when there are more than 2 windows. \item \texttt{unnormdensity}: \begin{itemize} \item Suppress annoying warning messages from \texttt{density.default}. This affects many functions in the \spst\ family of packages. \item Argument \texttt{weights} may have length 1. \item New argument \texttt{defaults}. \item Computation accelerated. \end{itemize} \item \texttt{unstack.lintess}: now handles marks. \item \texttt{update}: now works for models of class \texttt{"mppm"}. \item \texttt{update.kppm}: \begin{itemize} \item New argument \texttt{evaluate}. \item Now handles additional arguments in any order, with or without names. \item Changed arguments. \item Improved behaviour. \end{itemize} \item \texttt{update.ppm}: For the case \texttt{update(model, X)} where \texttt{X} is a point pattern, if the window of \texttt{X} is different from the original window, then the model is re-fitted from scratch (i.e. \texttt{use.internal=FALSE}). %%V \item \texttt{valid.ppm} This is now a method for the generic function \texttt{valid}. \item \texttt{varcount}: New argument \texttt{relative} supports calculation of the overdispersion index. \item \texttt{vcov.mppm}: \begin{itemize} \item Now handles models with Gibbs interactions. \item New argument \texttt{nacoef.action} specifies what to do if some of the fitted coefficients are \texttt{NA}, \texttt{NaN} or \texttt{Inf}. \end{itemize} \item \texttt{vcov.ppm}: \begin{itemize} \item Performance slightly improved, for Gibbs models. \item Variance calculations now handle larger datasets because they use sparse arrays, by default. \item New argument \texttt{nacoef.action} specifies what to do if some of the fitted model coefficients are \texttt{NA}, \texttt{NaN} or infinite. \end{itemize} %%W %%X %%Y %%Z \item \verb![<-.hyperframe!: Improved error message when the format of the index is not supported. \item \verb![<-.im! \begin{itemize} \item Accepts an array for \texttt{value}. \item The subset index \texttt{i} can now be a linear network. Then the result of \verb!x[i, drop=FALSE]! is a pixel image of class \texttt{linim}. \item New argument \texttt{drop} controls behaviour when indices are missing as in \verb!x[] <- value! \end{itemize} \item \verb![.layered!: \begin{itemize} \item Subset index \texttt{i} can now be an \texttt{owin} object. \item Additional arguments \verb!...! are now passed to other methods. \end{itemize} \item \verb![.leverage.ppm!: New argument \texttt{update}. \item \verb![.linnet!: \begin{itemize} \item New argument \texttt{snip} determines what to do with segments of the network that cross the boundary of the window. Default behaviour has changed. \item More robust against artefacts when the subset index is a pixel mask. \end{itemize} \item \verb![.linim!: \begin{itemize} \item More robust against artefacts. \item Accelerated. \end{itemize} \item \verb![.lpp!: New argument \texttt{snip} determines what to do with segments of the network that cross the boundary of the window. Default behaviour has changed. \item \verb![.ppx!: \begin{itemize} \item The subset index \texttt{i} may now be a spatial domain of class \texttt{boxx} or \texttt{box3}. \item New argument \texttt{clip}. \end{itemize} \item \verb![.ppp!: \begin{itemize} \item New argument \texttt{clip} determines whether the window is clipped. \item The previously-unused argument \texttt{drop} now determines whether to remove unused levels of a factor. \end{itemize} \item \verb![.pp3!, \verb![.lpp!, \verb![.ppx!, \texttt{subset.ppp, subset.pp3, subset.lpp, subset.ppx}: These methods now have an argument \texttt{drop} which determines whether to remove unused levels of a factor. \item \verb![.psp!: \begin{itemize} \item accelerated. \item New argument \texttt{fragments} specifies whether to keep fragments of line segments that are cut by the new window, or only to retain segments that lie entirely inside the window. \end{itemize} \item \verb![.solist!: Subset index \texttt{i} can now be an \texttt{owin} object. \end{itemize} \begin{thebibliography}{1} \bibitem{badd10wshop} A.~Baddeley. \newblock Analysing spatial point patterns in {{R}}. \newblock Technical report, CSIRO, 2010. \newblock Version 4. \newblock URL \texttt{https://research.csiro.au/software/r-workshop-notes/} \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \end{thebibliography} \vspace*{\fill} \noindent Current total package size: \Sexpr{currentcount[["ndatasets"]]} datasets, \Sexpr{currentcount[["nobjects"]]} functions, \Sexpr{currentcount[["Rlines"]]} lines of R, \Sexpr{currentcount[["srclines"]]} lines of C, \Sexpr{currentcode} total lines of code. \end{document} spatstat/inst/doc/updates.pdf0000644000176200001440000067641214517616251016027 0ustar liggesusers%PDF-1.5 % 4 0 obj << /Length 851 /Filter /FlateDecode >> stream xڕUˎ:Wds%G"&~&Y2<$Zl w➎&&v@] 4RU.S>Ue*ʙ1ͨ*MEud.D>hLŶvʹ&`u[?d༤7YlƧP1Fx-X#(YkZ5xtKo&`+uvwy3t]W].h^6gbdJ"ٻWE"L$4\ǜy)iGhZԃ(d4&mi W< r1?YdTY5jr+EL)d$=5=]vy= }k|?O?_-Ep"|Osi)8^0s%S4/RF *#C=*QP)Tb <dBg7Yɷ C0pd?)HoX~WujJ؟2z}܄\ RZoM9 @>(>VʲDc'bco,/L09]GDƾXśˣ#c;{1()#Baw_vTM#c2nmǒۑem sQI*Hr 3׊_-J]j9yI%t0eypE>PX6D8/#>PGсyRo{}.'{kO9@;_dH0c 5Àȇz ^!oiz90̛P/`n's+0`F$/GcS1aȩRoޞw endstream endobj 1 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpSNURb9/Rbuild2b205e814247/spatstat/vignettes/updates-004.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 14 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 15 0 R/F3 16 0 R>> /ExtGState << >>/ColorSpace << /sRGB 17 0 R >>>> /Length 2956 /Filter /FlateDecode >> stream xI߯Ha J"$R1 ["??{? ][UK ~<|~ =u? ?{G1|b/C < C oËَH:j -K1֣LyLHLzaeY؏5QQ?s:2s8HKfr=CVG88fTkZQKEͣ%G(ձbbхyMBǘdG~/b9ͥ1[vح1jF 1;68Dd%fubmbBs-*͞#iFH!ZQ11DPbE|8[ lA2w)7#&쎫f 0 3"z:͞pEǖLGR9MO", =-s2W:,B[ ]!(L(a#m1%,th߮ ͣ!ZCkXbHYà 475NZ]Nl +yUI e bYb@!zuf jDeqhmFo@2 z X/a ftQmNB5C-Ѽih]`{rXͻ9X-kd?]Z kѪA  gIΘY_0)&{Nq'&Ee| FQ)WzR|Qp+ݞkrjeR'x8{^bgu|,gCCN z:km:<.$exaV v4> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 22 0 obj << /Length 2518 /Filter /FlateDecode >> stream xڽZK۸-T%^[V9Rީ\{%1E*|؞n<(43^F6u7/w?*9o#m/n5۔,K-Yb66Jl}ߴGcowgVnJR,GI&eR |yq"iOWt>V{0{ߴ8_͖&;s2(X2*5[eI:Yc9uf~UI] I(& "q38lE$+2o'ԸG=I9TQRʅnwCۚ{}L!ͽY e*G[=g9V>̆eMz;h{j7@tkޮ"BB9',+u%%[2T Qbe1,6 i'R {Rdaxj &qžg$ >ȐN2;BSմf"C\#G'IM5nXZSmumq)47e5hve gސ4+2f Y9FSTNG ^Y(fO5eOJș`'8$NVR 0ȿ @!;<îZL* i֠/ K @WKisXx0ѷ* G}߃ӗ4N Ǹ0?^bPbn7F3o:# f.XOuqnv}@[XjևÑ7 MMiNaFk} @Bhm4hl( LrU("Y2|LqU!(0)c]2๐)5QLJ ,NT췪E[( /ש7]J-y.RbH^.љ3*slf\YeyK"^ eN.dZRjIԣ1ιbrz6zg%YǁSy X6xQZn2pg! 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RT?"'򛥦,-tB@eپe,="C!ef9@.%LROOXVODa(>E.AQۊ⚾(uJ~V*@P$[EJUVī$ZKU=\ڵj:m5vIsZP[ս(=ko@sZC֬&m\(MC^МiR3R[+`m.B[kRiOXHX'esge]np:z[K2~ _6@9 Dq!T a6 7FPK8-m~|&+ΓnRi3`9n9ia\IQJ6Z .PtiEJ.^ (K[Ki][_WjtGV*֨׊u5a}wȑk=;N8۪lSjtRvzx_$_BwX =-Tcg:ऊ礝NgɳZƹ*p\P]QpI;.,5N~~nh#wTwԫPHGc(Os="^RV^?]{c䭊^>TnEQIT6܂/E૎oz]Fђ.bp8U\ "R.y3%.Z ݑ endstream endobj startxref 182311 %%EOF spatstat/inst/doc/bugfixes.Rnw0000644000176200001440000012247414514656205016165 0ustar liggesusers\documentclass[10pt]{article} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage[bottom=0.5cm, right=1.5cm, left=1.5cm, top=1.5cm]{geometry} % \VignetteIndexEntry{Bugs Fixed in Spatstat} % $Revision: 1.32 $ $Date: 2023/05/17 01:17:05 $ \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} <>= library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) @ \title{Bugs fixed in \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle \thispagestyle{empty} This vignette lists all \emph{important} bugs detected and fixed in the \spst\ package since 2010. It also explains how to search the list of all recorded bugs in the \spst\ family of packages. <>= nbugs <- nrow(bugfixes("all", show=FALSE)) nbugssince <- nrow(bugfixes("book", show=FALSE)) @ \tableofcontents \pagebreak \section{Bug history} Thousands of bugs have been detected and fixed in \spst\ since it was first released in 2001. We started recording the bug history in 2010. \subsection{Documentation of bugs} Bugs that may have affected the user are listed in the package \texttt{NEWS} file, and can be searched using the \R\ command \texttt{news} or the \spst\ command \texttt{bugfixes}. To see the bugs which have just been fixed in the latest version of \spst, type <>= bugfixes @ To see all bugs which were fixed after a particular version of \spst, for example, bugs that were fixed in version \texttt{1.50-0} or later, type <>= bugfixes(sinceversion="1.50-0") @ To see all bugs in \spst\ that were fixed after a particular date, for example 30 June 2017, type <>= bugfixes(sincedate="2017-06-30") @ To see all bugs fixed after the book \cite{baddrubaturn15} was written, type <>= bugfixes("book") @ To see all bugs in the entire recorded history of \spst, type <>= bugfixes("all") @ which currently produces a list of \Sexpr{nbugs} bugs, of which \Sexpr{nbugssince} were detected after publication of the book \cite{baddrubaturn15}. \subsection{Bugs in the \texttt{spatstat} family of packages} Recently \spst\ was divided into a family of sub-packages. The command \texttt{bugfixes} now covers the bug history in all of these sub-packages. See the help for \texttt{bugfixes} for further details. <>= getstuff <- function(pkg) { x <- read.dcf(file=system.file("DESCRIPTION", package=pkg), fields=c("Version", "Date")) xversion <- as.character(x[,"Version"]) xdate <- as.character(x[,"Date"]) data.frame(date=as.Date(xdate), package=pkg, version=xversion) } vtable <- do.call(rbind, lapply(c("spatstat.utils", "spatstat.data", "spatstat.sparse", "spatstat.geom", "spatstat.random", "spatstat.explore", "spatstat.model", "spatstat.linnet", "spatstat"), getstuff)) @ The current versions of the \spst\ family of packages (used to produce this document) are: <>= print(vtable, row.names=FALSE) @ \pagebreak \section{List of bugs} Following is a list of the {\bf most serious bugs}, in decreasing order of potential impact. A bug is classified as ``serious'' if it produced incorrect results without the user's knowledge. Bugs which cause an error exit are not listed here, because the presence of a bug is obvious, and the bug would not have misled the user. \newcommand\bugger[4]{% \\ {} % {\small (Bug introduced in \texttt{spatstat {#1}}, {#2}; % fixed in \texttt{spatstat {#3}}, {#4})}% } \newcommand\bugin[4]{% \\ {} % {\small (Bug introduced in \texttt{{#1}}, {#2}; % fixed in \texttt{{#3}}, {#4})}% } \newcommand\bugbefore[4]{% \\ {} % {\small (Bug introduced before \texttt{{#1}}, {#2}; % fixed in \texttt{{#3}}, {#4})}% } \newcommand\bugalways[2]{\bugbefore{spatstat 1.19-0}{may 2010}{#1}{#2}} %%% LEVEL 1 \subsection{Serious Bugs, Always Wrong, Broad Impact} \begin{itemize} \item \texttt{nncross.ppp}: Results were completely incorrect if $k > 1$. \bugger{1.31-2}{april 2013}{1.35-0}{december 2013} \item \texttt{nncross.pp3}: Results were completely incorrect in some cases. \bugger{1.32-0}{august 2013}{1.34-0}{october 2013} \item \texttt{cdf.test.ppm}: Calculation of $p$-values was incorrect for Gibbs models: $1-p$ was computed instead of $p$. \bugger{1.40-0}{december 2014}{1.45-2}{may 2016} \item \texttt{Smooth.ppp}: Results of \verb!Smooth(X, at="points", leaveoneout=FALSE)! were completely incorrect. \bugger{1.20-5}{august 2010}{1.46-0}{july 2016} \item \texttt{rmh}: \begin{itemize} \item Simulation was completely incorrect in the case of a multitype point process with an interaction that does not depend on the marks, such as \verb!ppm(betacells, ~marks, Strauss(60))! due to a coding error in the \texttt{C} interface. \bugger{1.22-3}{march 2010}{1.22-3}{june 2011} \item Simulation of the Area-Interaction model was completely incorrect. \bugger{1.23-6}{october 2011}{1.31-0}{january 2013} \item Simulation of the Geyer saturation process was completely incorrect. \bugger{1.31-0}{january 2013}{1.31-1}{march 2013} \item Simulation of the Strauss-Hard Core process was partially incorrect, giving point patterns with a slightly lower intensity. \bugger{1.31-0}{january 2013}{1.37-0}{may 2014} \item Simulation of the \emph{multitype} hard core model was completely incorrect (the interaction was effectively removed, changing the model into a Poisson process). \bugger{1.31-0}{january 2013}{1.63-0}{january 2020} \item The result of simulating a model with a hard core did not necessarily respect the hard core constraint, and simulation of a model with strong inhibition did not necessarily converge. This only happened if the first order trend was large, the starting state (\texttt{n.start} or \texttt{x.start}) was not given, and the number of iterations \texttt{nrep} was not very large. It occurred because of a poor choice for the default starting state. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.40-0}, december 2014)} \item Simulation was incorrect in the case of an inhomogeneous multitype model with \texttt{fixall=TRUE} (i.e.\ with a fixed number of points of each type) if the model was segregated (i.e.\ if different types of points had different first order trend). The effect of the error was that all types of points had the same first order trend. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.43-0}, september 2015)} \item Simulation of the Geyer saturation process was incorrectly initialised, so that the results of a short run (i.e. small value of \texttt{nrep}) were incorrect, while long runs were correct. \bugger{1.17-0}{october 2009}{1.31-1}{march 2013} \end{itemize} \item \texttt{nnmark, as.im.ssf}: If \code{marks(X)} was a matrix rather than a data frame, the results were completely incorrect. \bugger{1.32-0}{august 2013}{1.55-1}{april 2018} \item \texttt{rVarGamma}: Simulations were incorrect; they were generated using the wrong value of the parameter \texttt{nu.ker}. \bugger{1.25-0}{december 2011}{1.35-0}{december 2013} \item \texttt{rCauchy}: Simulations were incorrect; they were generated using the wrong value of the parameter \texttt{omega}. \bugger{1.25-0}{december 2011}{1.25-2}{january 2012} \item \texttt{lppm}: For multitype patterns, the fitted model was completely incorrect due to an error in constructing the quadrature scheme. \bugger{1.23-0}{july 2011}{1.30-0}{december 2012} \item \verb![.lpp!: The local coordinate \texttt{seg} was completely incorrect, when \texttt{i} was a window. \bugger{1.31-2}{april 2013}{1.45-0}{march 2016} \item \texttt{lohboot}: Implementation was completely incorrect. \bugger{1.26-1}{april 2012}{1.53-2}{october 2017} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were incorrect for non-Poisson processes due to a mathematical error. \bugger{1.25-0}{december 2011}{1.51-0}{may 2017} \end{itemize} %%% LEVEL 2 \subsection{Serious Bugs, Often Completely Wrong, Moderate Impact} \begin{itemize} \item \texttt{pairdist.lpp}: Results could have been completely incorrect, due to an internal bug, if the linear network data was in the non-sparse representation. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.linnet 3.0-3}{january 2023} \item \texttt{matrixsqrt}, \texttt{matrixinvsqrt}, \texttt{matrixpower}: If the result was a complex-valued matrix, the values were completely incorrect. \bugin{spatstat 1.48-0}{december 2016}{spatstat.sparse 2.1-1}{february 2021} \item \texttt{pcf.ppp}: Estimates were incorrectly scaled (they were incorrectly multiplied by the area of the window.) Spotted by Maximilian Hesselbarth. \bugin{spatstat.explore 3.0-0}{may 2022}{spatstat.explore 3.0-6}{january 2023} \item \texttt{rLGCP}, \texttt{simulate.kppm}: Simulation results for log-Gaussian Cox processes were incorrect unless the pixel dimensions and pixel spacings were identical on the horizontal and vertical axes. (If pixel dimensions were not specified, then the results were incorrect whenever the Frame of the simulation window was not a square.) \bugger{1.22-2}{june 2011}{1.65-0}{december 2020} \item \texttt{deviance.lppm}, \texttt{pseudoR2.lppm}: Results were completely incorrect, due to a coding error. \bugger{1.44-0}{december 2015}{1.64-2}{november 2020} \item \texttt{kppm}: Results were sometimes incorrect for method='clik2' and method='palm' because the log composite likelihood was erroneously truncated to positive values. Any fitted model for which \verb!logLik(model) = 2.2e-16! should be suspected of being incorrect. \bugin{spatstat.core 2.0-0}{march 2021}{spatstat.core 2.3-2}{november 2021} \item \texttt{bw.pcf}: Results were totally incorrect due to a typo. \bugger{1.51-0}{may 2017}{1.52-0}{august 2017} \item \texttt{density.ppp}: edge correction factors were calculated incorrectly when the window was not a rectangle, causing a negative bias in the estimated intensity. \bugger{1.57-0}{oct 2018}{1.64-0}{april 2020}. \item \texttt{density.ppp}: The standard error (calculated when \texttt{se=TRUE}) was incorrect when \texttt{sigma} was a single numeric value. The output was equal to \texttt{sqrt(sigma)} times the correct answer. \bugger{1.41-1}{february 2015}{1.57-0}{october 2018} \item \texttt{density.ppp}: The standard error (calculated when \texttt{se=TRUE}) was incorrect for non-Gaussian kernels. \bugbefore{spatstat 1.57-0}{october 2018}{spatstat.explore 3.2-0}{may 2023} \item \texttt{relrisk.ppp}: The standard error (calculated when \texttt{se=TRUE}) was incorrect for non-Gaussian kernels. \bugbefore{spatstat 1.57-0}{october 2018}{spatstat.explore 3.2-0}{may 2023} \item \texttt{rthin}: If \texttt{P} was close to 1, the result was sometimes an empty point pattern when it should have been identical to \texttt{X}. \bugger{1.43-0}{october 2015}{1.57-0}{october 2018} \item \texttt{predict.mppm}: If the model included random effects, and if the library \pkg{MASS} was not loaded, the predictions were on the log scale (i.e.\ they were logarithms of the correct values). \bugger{1.43-0}{october 2015}{1.55-1}{april 2018} \item \texttt{nnmap}, \texttt{nnmark}: Values were incorrect if the resulting pixel image had unequal numbers of rows and columns. \bugger{1.35-0}{december 2013}{1.55-0}{january 2018} \item \texttt{vcov.mppm}: Format was incorrect (rows/columns were omitted) in some cases. \bugger{1.45-1}{may 2016}{1.55-0}{january 2018} \item \texttt{model.matrix.ppm}, \texttt{model.frame.ppm}: Values were sometimes incorrect when applied to the result of \texttt{subfits}. To be precise, if \texttt{fit} was an \texttt{mppm} object fitted to a hyperframe that included ``design covariates'' (covariates that take a constant value in each row of the hyperframe), and if \verb!futs <- subfits(fit)!, then \verb!model.matrix(futs[[i]])! gave incorrect values in the columns corresponding to the design covariates. \bugger{1.45-1}{may 2016}{1.55-0}{january 2018} \item \texttt{predict.rhohat}, \texttt{simulate.rhohat}: Results were incorrect for a \texttt{rhohat} object computed from linear network data (class \texttt{"lpp"} or \texttt{"lppm"}). \bugger{1.31-0}{march 2013}{1.63-1}{february 2020} \item \texttt{predict.rho2hat}: Results were incorrect for a \texttt{rho2hat} object computed from a point pattern. \bugger{1.42-0}{may 2015}{1.52-0}{august 2017} \item \texttt{density.ppp}: Result was incorrect for non-Gaussian kernels when \texttt{at="points"} and \texttt{leaveoneout=FALSE}. \bugger{1.47-0}{october 2016}{1.57-0}{october 2018} \item \texttt{envelope.ppm}: If the model was an inhomogeneous Poisson process, the resulting envelope object was incorrect (the simulations were correct, but the envelopes were calculated assuming the model was CSR). \bugger{1.23-5}{september 2011}{1.23-6}{october 2011} \item \texttt{linearK}, \texttt{linearpcf}, \texttt{linearKinhom}, \texttt{linearpcfinhom} and multitype versions: These functions were sometimes greatly underestimated when the network had segments shorter than 10 coordinate units. \bugger{1.44-0}{december 2015}{1.46-2}{july 2016} \item \texttt{nncross}, \texttt{distfun}, \texttt{AreaInter}: Results of \texttt{nncross} were possibly incorrect when \code{X} and \code{Y} did not have the same window. This bug affected values of \texttt{distfun} and may also have affected ppm objects with interaction \texttt{AreaInter}. \bugger{1.9-4}{june 2006}{1.25-2}{january 2012} \item \texttt{update.kppm}: \begin{itemize} \item Did not function correctly when several additional arguments were given. \bugger{1.42-2}{june 2015}{1.54-0}{november 2017} \item If the call to \texttt{update} did not include a formula argument or a point pattern argument, then all arguments were ignored. Example: \texttt{update(fit, improve.type="quasi")} was identical to \texttt{fit}. \bugger{1.42-2}{june 2015}{1.45-0}{march 2016} \end{itemize} \item \texttt{markcorrint}: Results were completely incorrect. \bugger{1.39-0}{october 2014}{1.40-0}{december 2014} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were slightly incorrect for models with a hard core, due to a mathematical error. \bugger{1.51-0}{may 2017}{1.55-1}{april 2018} \item \texttt{Ops.msr}: If the input data contained a pixel image of the smoothed density, this image was not updated; it was copied to the output unchanged. Plots of the resulting measure were incorrect. \bugger{1.52-0}{august 2017}{1.55-1}{april 2018} \item \verb![.linnet!: in calculating \verb!L[W]! where \texttt{W} is a window, the code ignored segments of \code{L} that crossed \code{W} without having a vertex in \code{W}. \bugger{1.53-0}{september 2017}{1.55-1}{april 2015} \item \verb!as.im.function!: if the function domain was not a rectangle and the function values were categorical (factor) values, the result was an empty image. \bugger{1.42-0}{may 2015}{1.57-0}{october 2018} \item \texttt{density.lpp}: If \texttt{weights} were given, the results were completely incorrect if \texttt{leaveoneout=TRUE} (the default) and \texttt{at="points"}. \bugin{spatstat 1.51-0}{may 2017}{spatstat.linnet 3.0-0}{june 2022} \end{itemize} %%% LEVEL 3 \subsection{Bugs, Substantially Incorrect, Moderate Impact} \begin{itemize} \item \texttt{rpoislinetess}: Results were incorrect unless the window was centred at the origin. \bugbefore{spatstat 1.42-0}{may 2015}{spatstat.random 3.2-1}{october 2023} \item \texttt{closepairs.ppp}: If \texttt{distinct=FALSE} and \texttt{what="all"}, the resulting vectors \texttt{yi} and \texttt{yj} contained incorrect values, and had the wrong length. \bugbefore{spatstat 1.56-0}{june 2018}{spatstat.geom 2.4-0}{march 2022} \item \texttt{nncross.ppp}: When \texttt{k > 1}, distance values were incorrectly replaced by \texttt{Inf} in some cases. \bugbefore{spatstat 1.56-0}{june 2018}{spatstat.geom 2.4-0}{march 2022} \item \texttt{nncross.ppp}: If the argument \texttt{by} was given, some of the results were incorrect. [Spotted by Hank Stevens.] \bugger{1.32-0}{august 2013}{2.2-0}{june 2021} \item \verb!"[<-.im"!: Incorrect values were assigned in \verb!x[] <- v! when \texttt{x} and \texttt{v} were both factor-valued but with different sets of levels. \bugbefore{spatstat 1.56-0}{june 2018}{spatstat.geom 3.0-5}{january 2023} \item \texttt{vcov.mppm}: For Gibbs (non-Poisson) models, the variance matrix was calculated incorrectly in some cases. \bugin{spatstat 1.45-1}{may 2016}{spatstat.core 2.4-1}{may 2022} \item \texttt{vcov.mppm}: Results were sometimes incorrect if the two models had different interactions (e.g. Strauss vs Poisson). \bugin{spatstat 1.45-1}{may 2016}{spatstat.core 2.4-1}{may 2022} \item \texttt{distmap.owin}: If the window was a binary mask, the distance values were slightly too large (by a factor \texttt{1 + 1/n} where \texttt{n} is the pixel grid dimension). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{distfun.owin}: If the window was a binary mask, the distance values were slightly too small (typically reduced by 1/20 of a pixel width). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{nncross.ppp}, \texttt{nncross.pp3}: If \texttt{iX} and \texttt{iY} were given, some of the results were incorrect. \bugger{1.32-0}{august 2013}{2.2-0}{june 2021} \item \texttt{vcov.ppm}: Result was sometimes incorrect for Gibbs models. The Fisher information was slightly underestimated. \bugger{1.31-1}{march 2013}{1.64-1}{may 2020} \item \texttt{as.linnet.psp}: Sometimes produced a network with duplicated segments. [Such objects can be repaired using \texttt{repairNetwork}.] \bugger{1.41-1}{february 2015}{1.62-0}{december 2019} \item \texttt{addvar}: If the covariate contained \texttt{NA}, \texttt{NaN} or \texttt{Inf} values, the calculations were sometimes incorrect. \bugger{1.45-0}{march 2016}{.core 2.4-1}{march 2022} \item \texttt{rlpp}: The resulting pattern was unmarked even when it should have been multitype. \bugger{1.48-0}{december 2016}{1.63-0}{january 2020} \item \texttt{spatialcdf}: Argument \texttt{weights} was ignored, unless it was a fitted model. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{ppp}: Points inside the window were erroneously rejected as lying outside the window, if the window was a polygon equivalent to a rectangle with sides longer than $10^6$ units. {\small (Bug was present since the beginning. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{inside.owin}: All results were \texttt{FALSE} if the window was a polygon equivalent to a rectangle with sides longer than $10^6$ units. {\small (Bug was present since the beginning. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{sumouter}: result was incorrect (all entries were zero) if \texttt{w} was missing and \texttt{y} was given. \bugger{1.47-0}{october 2016}{1.59-0}{march 2019} \item \texttt{simulate.dppm}, \texttt{simulate.detpointprocfamily}: In dimensions higher than 2, the result was shifted so that it was centred at the origin. \bugger{1.54-0}{december 2017}{1.55-0}{january 2018} \item \texttt{integral.msr}: If the result was a matrix, it was the transpose of the correct answer. \bugger{1.35-0}{december 2012}{1.55-1}{april 2018} \item \texttt{density.ppp}: Values of \verb!density(X, at="points")! and \verb!Smooth(X, at="points")! were sometimes incorrect, due to omission of the contribution from the data point with the smallest $x$ coordinate. \bugger{1.26-0}{april 2012}{1.46-1}{july 2016} \item \texttt{multiplicity.default}: The first occurrence of any value in the input was incorrectly assigned a multiplicity of 1. \bugger{1.32-0}{december 2013}{1.57-1}{november 2018} \item \texttt{update.ppm}: If the argument \texttt{Q} was given, the results were usually incorrect, or an error was generated. \bugger{1.38-0}{august 2014}{1.38-1}{august 2014} \item \texttt{subfits}: The interaction coefficients of the submodels were incorrect for Gibbs models with a multitype interaction (\texttt{MultiStrauss}, etc). \bugger{1.35-0}{december 2013}{1.45-2}{may 2016} \item \texttt{F3est}: Estimates of $F(r)$ for the largest value of $r$ were wildly incorrect. {\small (Bug was present since about 2010. Fixed in \texttt{spatstat 1.48-0}, december 2016)} \item \texttt{kppm}, \texttt{matclust.estpcf}, \texttt{pcfmodel}: The pair correlation function of the M\'atern Cluster Process was evaluated incorrectly at distances close to 0. This could have affected the fitted parameters in \texttt{matclust.estpcf()} or \texttt{kppm(clusters="MatClust")}. \bugger{1.20-2}{august 2010}{1.33-0}{september 2013} \item \texttt{ppm}: Results were incorrect for the Geyer saturation model with a non-integer value of the saturation parameter \texttt{sat}. \bugger{1.20-0}{july 2010}{1.31-2}{april 2013} \item \texttt{clip.infline}: Results were incorrect unless the midpoint of the window was the coordinate origin. \bugger{1.15-1}{april 2009}{1.48-0}{december 2016} \item \texttt{intensity.ppm}: Result was incorrect for Gibbs models if the model was exactly equivalent to a Poisson process (i.e. if all interaction coefficients were exactly zero). \bugger{1.28-1}{june 2012}{1.47-0}{october 2016} \item \texttt{idw}: Results were incorrect if \texttt{se=TRUE} and \verb!at="pixels"! and \texttt{power} was not equal to 2. The pixel values of \verb!$estimate! were all equal to zero. \bugger{1.58-0}{january 2019}{1.63-0}{january 2020} \item \texttt{funxy}: Did not correctly handle one-line functions. The resulting objects evaluated the wrong function in some cases. \bugger{1.45-0}{march 2016}{1.46-0}{july 2016} \item \texttt{kernel.moment}: Result was incorrect for \texttt{kernel="cosine"} and \texttt{kernel="optcosine"}. \bugger{1.45-2}{may 2016}{1.56-0}{june 2018} \item \verb![.msr!: Format was mangled if the subset contained exactly one quadrature point. \bugger{1.21-3}{january 2011}{1.56-0}{june 2018} \item \texttt{hyperframe}: Did not correctly handle date-time values (columns of class \texttt{"Date"}, etc). \bugger{1.19-1}{may 2010}{1.63-0}{january 2020} \item \texttt{tess}: If a list of tiles was given, and the tiles were pixel images or masks, their pixel resolutions were ignored, and reset to the default $128 \times 128$. {\small (Bug fixed in \texttt{spatstat 1.56-0}, june 2018)} \item \texttt{nnorient}: crashed if the point pattern was empty. \bugger{1.40-0}{december 2015}{1.57-0}{october 2018} \item \verb!as.im.data.frame!: Results were incorrect for factor-valued data. \bugger{1.45-2}{may 2016}{1.63-0}{january 2020} \item \texttt{predict.ppm}: Argument \texttt{new.coef} was ignored in calculating the standard error when \texttt{se=TRUE}. \bugin{spatstat 1.29-0}{october 2012}{spatstat.model 3.0-0}{june 2022} \item \texttt{predict.ppm}: Argument \texttt{new.coef} was ignored in calculating the standard error (and therefore the width of the interval) when \texttt{type="count"} and (\texttt{interval="confidence"} or \texttt{interval="prediction"}). \bugin{spatstat 1.29-0}{october 2012}{spatstat.model 3.0-0}{june 2022} \item \texttt{unnormdensity}: If \texttt{weights} was a single numerical value \texttt{w}, the calculation incorrectly assigned the weight for each observation to be \texttt{w/n} where \texttt{n=length(x)}. \bugalways{spatstat.geom 3.0-7}{march 2023} \end{itemize} %% LEVEL 4: \subsection{Partially Incorrect} \begin{itemize} \item \texttt{kernel.squint}: The return value was incorrect if the argument \texttt{bw} was given. \bugin{spatstat 1.43-0}{october 2015}{spatstat.explore 3.2-4}{october 2023} \item \texttt{pcf} The variance approximation was calculated incorrectly (due to a bug in \texttt{kernel.squint}). \bugin{spatstat 1.43-0}{october 2015}{spatstat.explore 3.2-4}{october 2023} \item \texttt{rjitter.ppp}: If \texttt{retry=FALSE}, marks were ignored. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.geom 3.0-5}{january 2023} \item \texttt{rhohat.lpp}: The argument \texttt{subset} was not handled correctly in the internal data. The estimated function \texttt{rho} was correct, but if \texttt{predict.rhohat} was applied, predictions were computed only in the \texttt{subset}, and were possibly incorrect values. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.linnet 3.0-0}{june 2022} \item \texttt{rhohat.ppp}: The argument \texttt{subset} was not handled correctly in the internal data. The estimated function \texttt{rho} was correct, but if \texttt{predict.rhohat} was applied, predictions were computed only in the \texttt{subset}, and were possibly incorrect values. \bugbefore{spatstat 1.65-0}{december 2020}{spatstat.explore 3.0-0}{june 2022} \item \texttt{density.lpp}: The result had the wrong length if \texttt{x} contained duplicated points when \texttt{weights} were given and \texttt{at="points"}. [Spotted by Andrea Gilardi] \bugger{1.55-1}{april 2018}{2.2-0}{june 2021} \item \texttt{crossdist.pp3}: Results with \texttt{periodic=TRUE} were partially incorrect. \bugger{1.34-1}{dec 2013}{1.65-0}{dec 2020} \item \texttt{mppm}: Internal data were malformed if the interaction was \texttt{Hardcore} or \texttt{MultiHard} or a hybrid involving these interactions. This caused various errors when the fitted model was used. \bugger{1.61-0}{september 2019}{1.64-0}{april 2020}. \item \texttt{mppm}: Ignored the arguments \texttt{nd} and \texttt{eps} controlling the quadrature scheme. \bugger{1.35-0}{december 2013}{1.64-0}{april 2020}. \item \texttt{edge.Ripley}: Results were incorrect for data points lying exactly at the corners of a rectangle. \bugbefore{spatstat 1.21-0}{november 2010}{spatstat.core 2.3-2}{november 2021} \item \texttt{kppm}, \texttt{AIC}: For kppm models fitted with \verb!method='clik2'!, the resulting value of \texttt{logLik()} was equal to $1/2$ of the correct value. This would have affected model comparison using AIC, and model selection using \texttt{step}. \bugger{1.42-0}{may 2015}{1.63-0}{january 2020}. \item \texttt{edge.Ripley}, \texttt{Kest}, \texttt{Kinhom}: Isotropic correction weights for polygonal windows were sometimes incorrect for small radius \texttt{r} if the polygon contained many small segments or if the polygon was very long and thin. \bugger{1.60-0}{june 2019}{1.62-0}{december 2019}. \item \texttt{edge.Ripley}, \texttt{Kest}, \texttt{Kinhom}: Isotropic edge correction weight was computed incorrectly for a data point lying exactly on a corner of a rectangular window. \bugin{spatstat 1.60-0}{june 2019}{spatstat.explore 3.0-4}{november 2022} \item \texttt{beachcolours}, \texttt{beachcolourmap}: The number of colours was not always equal to \texttt{ncolours}. \bugger{1.32-0}{august 2013}{1.59-0}{march 2019} \item \texttt{extractbranch.lpp}: Point pattern coordinates were sometimes erroneously set to \texttt{NA}. \bugger{1.42-0}{may 2015}{1.59-0}{march 2019} \item \texttt{rotmean}: When \texttt{result="im"} the resulting image did not have the same dimensions as the input. \bugger{1.42-2}{june 2015}{1.58-0}{january 2019} \item \texttt{quadratcount.ppp}: Sometimes issued an incorrect warning that data points were outside the tessellation, when \texttt{tess} was a tessellation represented by a pixel image. {\small (Bug fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{quadrat.test}: the $p$-value was \texttt{NA} if one of the observed counts was zero, for the Cressie-Read tests with \texttt{CR} not equal to $1$ or $-1$. \bugger{1.38-0}{august 2014}{1.59-0}{march 2019} \item \texttt{quadrat.test}: argument \texttt{CR} was ignored if \texttt{method="MonteCarlo"}. \bugger{1.38-0}{august 2014}{1.61-0}{september 2019} \item \texttt{rotmean}: If argument \texttt{origin} was given, and if \texttt{result="im"} was specified, the resulting image was wrongly displaced. \bugger{1.42-2}{june 2015}{1.58-0}{january 2019} \item \texttt{runifpointx}: Result was mangled when \texttt{n=0} or \texttt{n=1}. \bugger{1.50-0}{march 2017}{1.58-0}{january 2019} \item \texttt{model.matrix.ppm}: The attribute \texttt{assign} was omitted in some cases. \bugger{1.45-1}{may 2016}{1.55-0}{january 2018} \item \texttt{model.matrix.mppm}: Sometimes returned a matrix with the wrong number of rows. \bugger{1.55-0}{january 2018}{1.63-0}{january 2020} \item \texttt{density.ppp}: If the smoothing bandwidth \texttt{sigma} was very small (e.g.\ less than the width of a pixel), results were inaccurate if the default resolution was used, and completely incorrect if a user-specified resolution was given. \bugger{1.26-0}{april 2012}{1.52-0}{august 2017} \item \texttt{selfcrossing.psp}: $y$ coordinate values were incorrect. \bugger{1.23-2}{august 2011}{1.25-3}{february 2012} \item \texttt{Geyer}: For point process models with the \texttt{Geyer} interaction, \texttt{vcov.ppm} and \texttt{suffstat} sometimes gave incorrect answers. \bugger{1.27-0}{may 2012}{1.30-0}{december 2012} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Calculations were incorrect for a Geyer model fitted using an edge correction other than \texttt{"border"} or \texttt{"none"}. \bugger{1.25-0}{december 2011}{1.51-0}{may 2017} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were slightly incorrect for models fitted using the border correction. \bugger{1.25-0}{december 2011}{1.54-0}{november 2017} \item \texttt{leverage.ppm}: The mean leverage value (shown as a contour level in \texttt{plot.leverage.ppm}) was slightly incorrect for Gibbs models. \bugger{1.25-0}{december 2011}{1.54-0}{november 2017} \item \texttt{vcov.ppm}, \texttt{suffstat}: These functions sometimes gave incorrect values for marked point process models. \bugger{1.27-0}{may 2012}{1.29-0}{october 2012} \item \texttt{diagnose.ppm}: When applied to a model obtained from \texttt{subfits()}, in the default case (\texttt{oldstyle=FALSE}) the variance calculations were incorrect. Consequently the dotted lines representing significance bands were incorrect. An error or warning about negative variances occurred sometimes. However, calculations with \texttt{oldstyle=TRUE} were correct. The default has now been changed to \texttt{oldstyle=TRUE} for such models. \bugger{1.35-0}{december 2013}{1.45-0}{march 2016} \item \texttt{Smooth.ppp}: Results for \verb!at="points"! were garbled, for some values of \texttt{sigma}, if \texttt{X} had more than one column of marks. \bugger{1.38-0}{october 2014}{1.46-0}{july 2016} \item \texttt{linearK}, \texttt{linearKinhom}: If any data points were located exactly at a vertex of the linear network, the weights for Ang's correction were incorrect, due to numerical error. This sometimes produced infinite or NA values of the linear $K$ function. \bugger{1.23-0}{july 2011}{1.27-0}{may 2012} \item \texttt{Kinhom}, \texttt{Linhom}: the results were not renormalised (even if \texttt{renormalise=TRUE}) in some cases. \bugger{1.21-0}{december 2010}{1.37-0}{may 2014} \item \texttt{Kinhom}, \texttt{Linhom}: Ignored argument \texttt{reciplambda2} in some cases. \bugger{1.39-0}{october 2014}{1.40-0}{december 2014} \item \texttt{Kinhom}, \texttt{Linhom}: Calculations were incorrect if \texttt{lambda} was a fitted point process model. \bugger{1.38-0}{august 2014}{1.38-1}{august 2014} \item \texttt{integral.linim}, \texttt{integral.linfun}: \begin{itemize} \item results were inaccurate because of a bias in the distribution of sample points. \bugger{1.41-0}{february 2015}{1.47-0}{october 2016} \item results were inaccurate if many of the segment lengths were shorter than the width of a pixel. \bugger{1.41-0}{february 2015}{1.48-0}{december 2016} \item results were wildly inaccurate in some extreme cases where many segments were very short. \bugger{1.41-0}{february 2015}{1.54-0}{november 2017} \end{itemize} \item \texttt{predict.ppm}: Calculation of the conditional intensity omitted the edge correction if \texttt{correction='translate'} or \texttt{correction='periodic'}. \bugger{1.17-0}{october 2009}{1.31-3}{may 2013} \item \texttt{varblock}: Calculations were incorrect if more than one column of edge corrections was computed. \bugger{1.21-1}{november 2010}{1.39-0}{october 2014} \item \texttt{scan.test} Results were sometimes incorrect due to numerical instability (a 'Gibbs phenomenon'). \bugger{1.24-1}{october 2011}{1.26-1}{april 2012} \item \texttt{relrisk}: When \verb!at="pixels"!, a small fraction of pixel values were sometimes wildly inaccurate, due to numerical errors. This affected the range of values in the result, and therefore the appearance of plots. {\small (Bug fixed in \texttt{spatstat 1.40-0}, december 2014)} \item \texttt{predict.slrm}: Results of \texttt{predict(object, newdata)} were incorrect if the spatial domain of \texttt{newdata} was larger than the original domain. \bugger{1.21-0}{november 2010}{1.25-3}{february 2012} \item \texttt{Lest}: The variance approximations (Lotwick-Silverman and Ripley) obtained with \texttt{var.approx=TRUE} were incorrect for \texttt{Lest} (although they were correct for \texttt{Kest}) due to a coding error. \bugger{1.24-1}{october 2011}{1.24-2}{november 2011} \item \texttt{bw.diggle}: Bandwidth was too large by a factor of 2. \bugger{1.23-4}{september 2011}{1.23-5}{september 2011} \item pair correlation functions (\texttt{pcf.ppp}, \texttt{pcfdot}, \texttt{pcfcross} etc:) The result had a negative bias at the maximum $r$ value, because contributions to the pcf estimate from interpoint distances greater than \texttt{max(r)} were mistakenly omitted. {\small (Bugs fixed in \texttt{spatstat 1.35-0}, december 2013)} \item \texttt{Kest}, \texttt{Lest}: Gave incorrect values in very large datasets, due to numerical overflow. `Very large' typically means about 1 million points in a random pattern, or 100,000 points in a tightly clustered pattern. [Overflow cannot occur unless there are at least 46,341 points.] \item \texttt{bw.relrisk}: Implementation of \texttt{method="weightedleastsquares"} was incorrect and was equivalent to \texttt{method="leastsquares"}. \bugger{1.21-0}{november 2010}{1.23-4}{september 2011} \item \texttt{triangulate.owin}: Results were incorrect in some special cases. \bugger{1.42-2}{june 2015}{1.44-0}{december 2015} \item \texttt{crosspairs}: If \texttt{X} and \texttt{Y} were identical point patterns, the result was not necessarily symmetric (on some machines) due to numerical artifacts. \bugger{1.35-0}{december 2013}{1.44-0}{december 2015} \item \texttt{bdist.tiles}: Values were incorrect in some cases due to numerical error. {\small (Bug fixed in \texttt{spatstat 1.29-0}, october 2012)} \item \texttt{Kest.fft}: Result was incorrectly normalised. \bugger{1.21-2}{january 2011}{1.44-0}{december 2015} \item \texttt{crossdist.ppp}: Ignored argument \texttt{squared} if \texttt{periodic=FALSE}. {\small (Bug fixed in \texttt{spatstat 1.38-0}, july 2014)} \item polygon geometry: The point-in-polygon test gave the wrong answer in some boundary cases. {\small (Bug fixed in \texttt{spatstat 1.23-2}, august 2011)} \item \texttt{MultiStraussHard}: If a fitted model with \texttt{MultiStraussHard} interaction was invalid, \texttt{project.ppm} sometimes yielded a model that was still invalid. {\small (Bug fixed in \texttt{spatstat 1.42-0}, may 2015)} \item \texttt{pool.envelope}: Did not always respect the value of \texttt{use.theory}. \bugger{1.23-5}{september 2011}{1.43-0}{september 2015} \item \texttt{nncross.lpp}, \texttt{nnwhich.lpp}, \texttt{distfun.lpp}: Sometimes caused a segmentation fault. \bugger{1.44-0}{december 2015}{1.44-1}{december 2015} \item \texttt{anova.ppm}: If a single \texttt{object} was given, and it was a Gibbs model, then \texttt{adjust} was effectively set to \texttt{FALSE}. \bugger{1.39-0}{october 2014}{1.44-1}{december 2015} \item \verb![.linim!: the result sometimes had the wrong class. \bugger{1.53-0}{september 2017}{1.55-1}{april 2015} \item \verb![.linim!: factor values were erroneously converted to integers, in some cases. \bugger{1.53-0}{september 2017}{1.61-0}{september 2019} \item \verb!is.subset.owin!: sometimes gave the wrong result for polygonal windows due to numerical rounding error. {\small (Bug was always present. Fixed in \texttt{spatstat 1.59-0}, march 2019)} \item \texttt{plot.tess}: the legend showed the tile names in lexicographical order, rather than their original order. \bugger{1.55-1}{april 2018}{1.59-0}{march 2019} \item \texttt{rThomas}, \texttt{rMatClust}, \texttt{rCauchy}, \texttt{rVarGamma}: If the simulation window was not a rectangle, the attribute \texttt{Lambda} was a numeric vector, rather than a pixel image as intended. \bugger{1.43-0}{october 2015}{1.59-0}{march 2019} \item \texttt{effectfun}: In a multitype point process model, \texttt{effectfun} ignored any user-specified value of \texttt{marks}. \bugger{1.52-0}{august 2017}{1.61-0}{september 2019} \item \verb!"[<-.hyperframe"!: Some classes of objects were not handled correctly. \bugger{1.37-0}{may 2014}{1.61-0}{september 2019} \item \texttt{relrisk.ppp}: Crashed if there were more than 2 types of points and \texttt{method = "leastsquares"} or \texttt{method = "weightedleastsquares"}. \bugger{1.23-4}{september 2011}{1.63-0}{january 2020} \item \texttt{nncross.ppp}: Format of output was incorrect if \texttt{X} was an empty pattern. \bugger{1.56-0}{june 2018}{1.63-0}{january 2020} \item \texttt{rmh}, \texttt{rmh.default}: For a marked point process, the debugger did not display the marks. (The \texttt{rmh} debugger is invoked by calling \texttt{rmh} with \texttt{snoop=TRUE}). \bugger{1.31-1}{march 2013}{1.63-0}{january 2020} \item \texttt{model.matrix.mppm}: If the model was fitted using \texttt{gam}, the resulting matrix did not have an \texttt{"assign"} attribute. \bugger{1.55-0}{march 2018}{2.2-0}{june 2021} \item \texttt{update.slrm}: Failed to find covariates that were provided in \texttt{env}. \bugger{1.33-0}{september 2013}{2.2-0}{june 2021} \item \texttt{distmap.owin}: Values were incorrect if X was an empty window (\texttt{is.empty(X) = TRUE}). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{distmap.ppp}, \texttt{distmap.psp}: Values were incorrect if X was an empty pattern (\texttt{npoints(X) = 0}). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{distmap.psp}: Values were incorrect if X was an empty pattern (\texttt{nsegments(X) = 0}). \bugalways{spatstat.geom 2.4-0}{march 2022} \item \texttt{envelope} methods: Results were malformed if the name of the function argument was not \texttt{r}. \bugalways{spatstat.explore 3.2-3}{september 2023} \end{itemize} \begin{thebibliography}{1} \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \end{thebibliography} \end{document} spatstat/inst/doc/packagesizes.txt0000644000176200001440000002425014517616230017061 0ustar liggesusersdate version nhelpfiles nobjects ndatasets Rlines srclines "2001-08-08" "1.0-1" 109 196 0 706 1370 "2002-05-17" "1.1-3" 116 220 0 1140 1370 "2002-08-06" "1.2-1" 129 237 0 1786 1474 "2003-03-12" "1.3-1" 134 242 0 1955 1474 "2003-05-05" "1.3-2" 148 257 0 2024 1474 "2003-07-28" "1.3-3" 148 266 0 2034 1474 "2003-11-12" "1.3-4" 148 261 0 2033 1474 "2004-01-27" "1.4-3" 166 296 0 3641 1437 "2004-02-11" "1.4-4" 166 296 0 3641 1437 "2004-03-25" "1.4-5" 166 296 0 3646 1439 "2004-05-23" "1.4-6" 166 296 0 3689 1514 "2004-06-17" "1.5-1" 166 300 0 4255 1514 "2004-09-01" "1.5-3" 171 311 0 4636 1514 "2004-09-24" "1.5-4" 174 315 0 4642 1514 "2004-10-21" "1.5-5" 180 319 0 4686 1514 "2004-11-15" "1.5-6" 180 319 0 4686 1512 "2004-11-27" 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"2.0-0" 516 1206 0 72205 14748 "2021-06-16" "2.2-0" 520 1216 0 72952 14748 "2021-10-28" "2.3-1" 530 1266 0 74240 14230 "2021-11-25" "2.3-2" 531 1271 0 74653 14236 "2022-02-12" "2.4-0" 463 1134 0 66214 6891 "2022-03-29" "2.4-1" 467 1159 0 66653 6891 "2022-04-01" "2.4-2" 467 1159 0 66653 6891 "2022-04-27" "2.4-3" 467 1160 0 66717 6891 "2022-05-21" "2.4-4" 467 1168 0 67357 6891 "2022-05-25" "2.4-99" 0 0 0 0 0 spatstat/inst/doc/datasets.Rnw0000644000176200001440000007110614366376353016164 0ustar liggesusers\documentclass[11pt]{article} % \VignetteIndexEntry{Datasets Provided for the Spatstat Package} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \usepackage{graphicx} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \newcommand{\sdat}{\pkg{spatstat.data}} \newcommand{\Sdat}{\pkg{Spatstat.data}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") spatstat.options(transparent=FALSE) options(useFancyQuotes=FALSE) @ \title{Datasets provided for \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle This document is an overview of the spatial datasets that are provided for the \spst\ package. To flick through a nice display of all the data sets that come with \spst\ type \texttt{demo(data)}. To see information about a given data set, type \texttt{help({\em name})} where \emph{name} is the name of the data set. To plot a given data set, type \texttt{plot({\em name})}. Datasets in \spst\ are ``lazy-loaded'', which means that they can be accessed simply by typing their name. Not all packages do this; in some packages you have to type \texttt{data({\em name})} in order to access a data set. To list all the datasets in \spst, you need to type \texttt{data(package="spatstat.data")}. This is because, for efficiency, the datasets are actually installed in a sub-package \sdat. This is the only time you should ever need to mention \sdat\ explicitly. When the \spst\ package is loaded by the command \texttt{library(spatstat)}, the sub-package \sdat\ is automatically loaded. \section{List of datasets} \subsection{Point patterns in 2D} Here is a list of the standard point pattern data sets that are supplied with the current installation of \sdat: \newcommand{\recto}{\framebox{\hphantom{re}\vphantom{re}}} \newcommand{\irregpoly}{\includegraphics*[width=6mm]{irregpoly}} \newcommand{\masque}{\includegraphics*[width=6mm]{mask}} \newcommand{\convpoly}{\includegraphics*[width=4mm]{hexagon}} \newcommand{\disc}{$\bigcirc$} \newcommand{\nomarks}{$\cdot$} \newcommand{\nocov}{$\cdot$} \begin{tabular}{l|l|ccc} {\sf name} & {\sf description} & {\sf marks} & {\sf covariates} & {\sf window} \\ \hline {\tt amacrine} & rabbit amacrine cells & cell type & \nocov & \recto \\ {\tt anemones} & sea anemones & diameter & \nocov & \recto \\ {\tt ants} & ant nests& species & zones & \convpoly \\ {\tt bdspots} & breakdown spots & \nomarks & \nocov & \disc \\ {\tt bei} & rainforest trees & \nomarks & topography & \recto \\ {\tt betacells} & cat retinal ganglia & cell type, area & \nocov & \recto \\ {\tt bramblecanes} & bramble canes & age & \nocov & \recto \\ {\tt bronzefilter} & bronze particles & diameter & \nocov & \recto \\ {\tt btb} & bovine tuberculosis & type, year & \nocov & \irregpoly \\ {\tt cells} & biological cells & \nomarks &\nocov & \recto \\ {\tt chorley} & cancers & case/control &\nocov & \irregpoly \\ {\tt clmfires} & forest fires & cause, size, date & \shortstack[c]{elevation, orientation,\\ slope, land use} & \irregpoly \\ {\tt concrete} & air bubbles in concrete & \nomarks & \nocov & \masque \\ {\tt copper} & copper deposits & \nomarks & fault lines & \recto \\ {\tt demopat} & artificial data & type & \nocov & \irregpoly \\ {\tt finpines} & trees & diam, height & \nocov & \recto \\ {\tt gordon} & people in a park & \nomarks & \nocov & \irregpoly \\ {\tt gorillas} & gorilla nest sites & group, season & \shortstack[c]{terrain, vegetation,\\ heat, water} & \irregpoly \\ {\tt hamster} & hamster tumour cells & cell type &\nocov & \recto \\ {\tt humberside} & child leukaemia & case/control & \nocov & \irregpoly\\ {\tt hyytiala} & mixed forest & species &\nocov & \recto \\ {\tt japanesepines} & Japanese pines & \nomarks &\nocov & \recto \\ {\tt lansing} & mixed forest & species & \nocov & \recto \\ {\tt longleaf} & trees & diameter & \nocov & \recto \\ {\tt mucosa} & gastric mucosa cells & cell type & \nocov & \recto \\ {\tt murchison} & gold deposits & \nomarks & faults, rock type & \irregpoly \\ {\tt nbfires} & wildfires & several & \nocov & \irregpoly \\ {\tt nztrees} & trees & \nomarks & \nocov & \recto \\ {\tt paracou} & trees & adult/juvenile & \nocov & \recto \\ {\tt ponderosa} & trees & \nomarks & \nocov & \recto \\ {\tt redwood} & saplings & \nomarks & \nocov & \recto \\ {\tt redwood3} & saplings & \nomarks & \nocov & \recto \\ {\tt redwoodfull} & saplings & \nomarks & zones & \recto \\ {\tt shapley} & galaxies & magnitude, recession, SE & \nocov & \convpoly \\ {\tt simdat} & simulated pattern & \nomarks & \nocov & \recto \\ {\tt sporophores} & fungi & species & \nocov & \disc \\ {\tt spruces} & trees & diameter & \nocov & \recto \\ {\tt stonetools} & palaeolithic tools & type, depth & \nocov & \irregpoly \\ {\tt swedishpines} & trees & \nomarks & \nocov & \recto \\ {\tt urkiola} & mixed forest & species & \nocov & \irregpoly \\ {\tt vesicles} & synaptic vesicles & \nomarks & zones & \irregpoly \\ {\tt waka} & trees & diameter & \nocov & \recto \\ \hline \end{tabular} \bigskip \noindent The shape of the window containing the point pattern is indicated by the symbols \recto\ (rectangle), \disc\ (disc), \convpoly\ (convex polygon), \irregpoly\ (irregular polygon) and \masque (binary mask). Additional information about the data set \texttt{\em name} may be stored in a separate list \texttt{{\em name}.extra}. Currently these are the available options: \begin{tabular}[!h]{ll} {\sc Name} & {\sc Contents} \\ \hline {\tt ants.extra} & field and scrub subregions; \\ & additional map elements; plotting function \\ {\tt bei.extra} & covariate images \\ {\tt chorley.extra} & incinerator location; plotting function \\ {\tt gorillas.extra} & covariate images\\ {\tt nbfires.extra} & inscribed rectangle; border type labels \\ {\tt ponderosa.extra} & data points of interest; plotting function\\ {\tt redwoodfull.extra} & subregions; plotting function \\ {\tt shapley.extra} & individual survey fields; plotting function \\ {\tt vesicles.extra} & anatomical regions \\ \hline \end{tabular} For demonstration and instruction purposes, raw data files are available for the datasets \texttt{vesicles}, \texttt{gorillas} and \texttt{osteo}. \subsection{Other Data Types} There are also the following spatial data sets which are not 2D point patterns: \begin{tabular}[c]{l|l|l} {\sf name} & {\sf description} & {\sf format} \\ \hline {\tt austates} & Australian states & tessellation \\ {\tt cetaceans} & marine survey & replicated 2D point patterns \\ {\tt chicago} & crimes & point pattern on linear network \\ {\tt demohyper} & simulated data & replicated 2D point patterns with covariates\\ {\tt dendrite} & dendritic spines & point pattern on linear network \\ {\tt flu} & virus proteins & replicated 2D point patterns \\ {\tt heather} & heather mosaic & binary image (three versions) \\ {\tt osteo} & osteocyte lacunae & replicated 3D point patterns with covariates\\ {\tt pyramidal} & pyramidal neurons & replicated 2D point patterns in 3 groups\\ {\tt residualspaper} & data \& code from Baddeley et al (2005) & 2D point patterns, \R\ function \\ {\tt simba} & simulated data & replicated 2D point patterns in 2 groups\\ {\tt spiders} & spider webs & point pattern on linear network \\ {\tt waterstriders} & insects on water & replicated 2D point patterns\\ \hline \end{tabular} Additionally there is a dataset \texttt{Kovesi} containing several colour maps with perceptually uniform contrast. \section{Information on each dataset} Here we give basic information about each dataset. For further information, consult the help file for the particular dataset. <>= opa <- par() ## How to set all margins to zero and eliminate all outer spaces zeromargins <- function() { par( mar=rep(0,4), omd=c(0,1,0,1), xaxs="i", yaxs="i" ) invisible(NULL) } ## Set 'mar' setmargins <- function(...) { x <- c(...) x <- rep(x, 4)[1:4] par(mar=x) invisible(NULL) } @ \subsubsection*{\texttt{amacrine}: Amacrine cells} Locations of displaced amacrine cells in the retina of a rabbit. There are two types of points, ``on'' and ``off''. \SweaveOpts{width=5.5,height=3}\setkeys{Gin}{width=0.8\textwidth} <>= plot(amacrine) @ <>= setmargins(0,1,2,0) plot(amacrine) @ \subsubsection*{\texttt{anemones}: Sea Anemones} These data give the spatial locations and diameters of sea anemones on a boulder near sea level. \SweaveOpts{width=7,height=4.5}\setkeys{Gin}{width=0.8\textwidth} <>= plot(anemones, markscale=1) @ <>= setmargins(0,0,2,0) plot(anemones, markscale=1) @ \subsubsection*{\texttt{ants}: Ants' nests} Spatial locations of nests of two species of ants at a site in Greece. The full dataset (supplied here) has an irregular polygonal boundary, while most analyses have been confined to two rectangular subsets of the pattern (also supplied here). % Parameters for Ants data with key at right \SweaveOpts{width=6.3,height=4}\setkeys{Gin}{width=0.7\textwidth} <>= ants.extra$plotit() @ %$ <>= setmargins(0,0,1,0) ants.extra$plotit() @ %$ \subsubsection*{\texttt{austates}: Australian states} The states and large mainland territories of Australia are represented as polygonal regions forming a tessellation. <>= plot(austates) @ \subsubsection*{\texttt{bdspots}: Breakdown spots} A list of three point patterns, each giving the locations of electrical breakdown spots on a circular electrode in a microelectronic capacitor. \SweaveOpts{width=12,height=6}\setkeys{Gin}{width=\textwidth} <>= plot(bdspots, equal.scales=TRUE, pch="+", panel.args=function(i)list(cex=c(0.15, 0.2, 0.7)[i])) @ <>= zeromargins() plot(bdspots, equal.scales=TRUE, pch="+", main="", mar.panel=0, hsep=1, panel.args=function(i)list(cex=c(0.15, 0.2, 0.7)[i])) @ \subsubsection*{\texttt{bei}: Beilschmiedia data} Locations of 3605 trees in a tropical rain forest. Accompanied by covariate data giving the elevation (altitude) and slope of elevation in the study region. \SweaveOpts{width=12,height=6}\setkeys{Gin}{width=0.8\textwidth} <>= plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) @ <>= setmargins(0,0,2,0) plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) @ The following command gives a perspective display similar to the front cover of Baddeley, Rubak and Turner (2015): <>= M <- persp(bei.extra$elev, theta=-45, phi=18, expand=7, border=NA, apron=TRUE, shade=0.3, box=FALSE, visible=TRUE, main="") perspPoints(bei, Z=bei.extra$elev, M=M, pch=16, cex=0.3) @ \subsubsection*{\texttt{betacells}: Beta ganglion cells} Locations of beta ganglion cells in cat retina, each cell classified as `on' or `off' and also labelled with the cell profile area. <>= plot(betacells) @ \subsubsection*{\texttt{bramblecanes}: Bramble canes} <>= plot(bramblecanes, cols=1:3) @ Try the following <>= plot(split(bramblecanes)) @ \subsubsection*{\texttt{bronzefilter}: Bronze filter section profiles} Spatially inhomogeneous pattern of circular section profiles of particles, observed in a longitudinal plane section through a gradient sinter filter made from bronze powder. <>= plot(bronzefilter,markscale=2) @ \subsubsection*{\texttt{btb}: bovine tuberculosis} Locations of farms where bovine tuberculosis was detected, marked by year of detection and spoligotype of tuberculosis. <>= plot(btb, which.marks="spoligotype", cols=2:5, chars=1:4) @ \subsubsection*{\texttt{cells}: Biological cells} Locations of the centres of 42 biological cells observed under optical microscopy in a histological section. Often used as a demonstration example. <>= plot(cells) @ \subsubsection*{\texttt{cetaceans}: Survey of marine species} Recorded sightings of whales, dolphins and other marine species in a series of surveys. Replicated 2D marked point patterns. <>= plot(cetaceans.extra$patterns, main="Cetaceans data", cols=1:5, hsep=1) @ \subsubsection*{\texttt{chicago}: Chicago crimes} Locations (street addresses) of crimes reported in a two-week period in an area close to the University of Chicago. A multitype point pattern on a linear network. <>= plot(chicago, main="Chicago Crimes", col="grey", cols=c("red", "blue", "black", "blue", "red", "blue", "blue"), chars=c(16,2,22,17,24,15,6), leg.side="left", show.window=FALSE) @ \subsubsection*{\texttt{chorley}: Chorley-Ribble cancer data} Spatial locations of cases of cancer of the larynx and cancer of the lung, and the location of a disused industrial incinerator. A marked point pattern, with an irregular window and a simple covariate. <>= chorley.extra$plotit() @ %$ \subsubsection*{\texttt{clmfires}: Castilla-La Mancha Fires} Forest fires in the Castilla-La Mancha region of Spain between 1998 and 2007. A point pattern with 4 columns of marks: \begin{tabular}{ll} \texttt{cause} & cause of fire\\ \texttt{burnt.area} & total area burned, in hectares \\ \texttt{date} & date of fire \\ \texttt{julian.date} & date of fire in days since 1.1.1998 \end{tabular} <>= plot(clmfires, which.marks="cause", cols=2:5, cex=0.25, main="Castilla-La Mancha forest fires") @ The accompanying dataset \texttt{clmfires.extra} is a list of two items \texttt{clmcov100} and \texttt{clmcov200} containing covariate information for the entire Castilla-La Mancha region. Each of these two elements is a list of four pixel images named \texttt{elevation}, \texttt{orientation}, \texttt{slope} and \texttt{landuse}. <>= plot(clmfires.extra$clmcov100$elevation, main="Elevation") @ %$ \subsubsection*{\texttt{concrete}: Air bubbles in concrete} Prof.\ Shin-ichi Igarashi's data: a point pattern of the locations of centroids of air bubbles seen in a cross-section of concrete. Air bubbles are present in the matrix of cement paste which surrounds the particles of aggregate. The outline of the aggregate, and the locations of the centroids of the bubble profiles, are recorded. <>= plot(concrete,chars="+",cols="blue",col="yellow") @ \subsubsection*{\texttt{copper}: Queensland copper data} These data come from an intensive geological survey in central Queensland, Australia. They consist of 67 points representing copper ore deposits, and 146 line segments representing geological `lineaments', mostly faults. <>= plot(copper$Points, main="Copper") plot(copper$Lines, add=TRUE) @ \subsubsection*{\texttt{demohyper}} A synthetic example of a \texttt{hyperframe} for demonstration purposes. <>= plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) }), parargs=list(mar=rep(1,4))) @ \subsubsection*{\texttt{demopat}} A synthetic example of a point pattern for demonstration purposes. <>= plot(demopat) @ \subsubsection*{\texttt{dendrite}} Dendrites are branching filaments which extend from the main body of a neuron (nerve cell) to propagate electrochemical signals. Spines are small protrusions on the dendrites. This dataset gives the locations of 566 spines observed on one branch of the dendritic tree of a rat neuron. The spines are classified according to their shape into three types: mushroom, stubby or thin. <>= plot(dendrite, leg.side="bottom", main="", cex=0.75, cols=2:4) @ \subsubsection*{\texttt{finpines}: Finnish pine saplings} Locations of 126 pine saplings in a Finnish forest, their heights and their diameters. <>= plot(finpines, main="Finnish pines") @ \subsubsection*{\texttt{flu}: Influenza virus proteins} The \texttt{flu} dataset contains replicated spatial point patterns giving the locations of two different virus proteins on the membranes of cells infected with influenza virus. It is a \texttt{hyperframe} containing point patterns and explanatory variables. <>= wildM1 <- with(flu, virustype == "wt" & stain == "M2-M1") plot(flu[wildM1, 1, drop=TRUE], main=c("flu data", "wild type virus, M2-M1 stain"), chars=c(16,3), cex=0.4, cols=2:3) @ \subsubsection*{\texttt{gordon}: People in Gordon Square} Locations of people sitting on a grass patch on a sunny afternoon. <>= plot(gordon, main="People in Gordon Square", pch=16) @ \subsubsection*{\texttt{gorillas}: Gorilla nesting sites} Locations of nesting sites of gorillas, and associated covariates, in a National Park in Cameroon. \texttt{gorillas} is a marked point pattern (object of class \texttt{"ppp"}) representing nest site locations. \texttt{gorillas.extra} is a named list of 7 pixel images (objects of class \texttt{"im"}) containing spatial covariates. It also belongs to the class \texttt{"listof"}. <>= plot(gorillas, which.marks=1, chars=c(1,3), cols=2:3, main="Gorilla nest sites") @ The \texttt{vegetation} covariate is also available as a raw ASCII format file, <>= system.file("rawdata/gorillas/vegetation.asc", package="spatstat") @ \subsubsection*{\texttt{hamster}: Hamster kidney cells} Cell nuclei in hamster kidney, each nucleus classified as either `dividing' or `pyknotic'. A multitype point pattern. <>= plot(hamster, cols=c(2,4)) @ \subsubsection*{\texttt{heather}: Heather mosaic} The spatial mosaic of vegetation of the heather plant, recorded in a 10 by 20 metre sampling plot in Sweden. A list with three entries, representing the same data at different spatial resolutions. <>= plot(heather$coarse) @ Type the following to see all three images: <>= plot(heather) @ \subsubsection*{\texttt{humberside}: Childhood Leukemia and Lymphoma} Spatial locations of cases of childhood leukaemia and lymphoma, and randomly-selected controls, in North Humberside. A marked point pattern. <>= plot(humberside) @ The dataset \texttt{humberside.convex} is an object of the same format, representing the same point pattern data, but contained in a larger, 5-sided convex polygon. \subsubsection*{\texttt{hyytiala}: Mixed forest} Spatial locations and species classification for trees in a Finnish forest. <>= plot(hyytiala, cols=2:5) @ \subsubsection*{\texttt{japanesepines}: Japanese black pine saplings} Locations of Japanese black pine saplings in a square sampling region in a natural forest. Often used as a standard example. <>= plot(japanesepines) @ \subsubsection*{\texttt{lansing}: Lansing Woods} Locations and botanical classification of trees in a forest. A multitype point pattern with 6 different types of points. Includes duplicated points. <>= plot(lansing) @ Type the following to see 6 plots, each showing the location of one of the types of points: <>= plot(split(lansing)) @ \subsubsection*{\texttt{longleaf}: Longleaf Pines} Locations and diameters of Longleaf pine trees. <>= plot(longleaf) @ \subsubsection*{\texttt{mucosa}: Gastric Mucosa Cells} A bivariate inhomogeneous point pattern, giving the locations of the centres of two types of cells in a cross-section of the gastric mucosa of a rat. <>= plot(mucosa, chars=c(1,3), cols=c("red", "green")) plot(mucosa.subwin, add=TRUE, lty=3) @ \subsubsection*{\texttt{murchison}: Murchison Gold Deposits} Spatial locations of gold deposits and associated geological features in the Murchison area of Western Australia. A list of three elements: \begin{itemize} \item \texttt{gold}, the point pattern of gold deposits; \item \texttt{faults}, the line segment pattern of geological faults; \item \texttt{greenstone}, the subregion of greenstone outcrop. \end{itemize} Type the following to see the full Murchison dataset: <>= plot(murchison$greenstone, main="Murchison data", col="lightgreen") plot(murchison$gold, add=TRUE, pch=3, col="blue") plot(murchison$faults, add=TRUE, col="red") @ Some analysis of the data uses a rectangle close to the abandoned town of Reedy: <<>>= reedy <- owin(c(580, 650) * 1000, c(6986, 7026) * 1000) @ Here are the data in that area: <>= plot(murchison$greenstone[reedy], main="Murchison data", col="lightgreen") plot(murchison$gold[reedy], add=TRUE, pch=3, col="blue") plot(murchison$faults[reedy], add=TRUE, col="red") @ \subsubsection*{\texttt{nbfires}: New Brunswick Fires} Fires in New Brunswick (Canada) with marks giving information about each fire. <>= plot(nbfires, use.marks=FALSE, pch=".") @ The following command would show the data for each year in a separate panel: <>= plot(split(nbfires), use.marks=FALSE, chars=".") @ <>= par(mar=c(0,0,2,0)) plot(split(nbfires)$"2000", which.marks="fire.type", main=c("New Brunswick fires 2000", "by fire type"), cols=c("blue", "green", "red", "cyan"), leg.side="left") @ \subsubsection*{\texttt{nztrees}: New Zealand Trees} Locations of trees in a forest plot in New Zealand. Often used as a demonstration example. <>= plot(nztrees) plot(trim.rectangle(as.owin(nztrees), c(0,5), 0), add=TRUE, lty=3) @ \subsubsection*{\texttt{osteo}: Osteocyte Lacunae} Replicated three-dimensional point patterns: the three-dimensional locations of osteocyte lacunae observed in rectangular volumes of solid bone using a confocal microscope. A \texttt{hyperframe} containing 3D point patterns and explanatory variables. <>= plot(osteo[1:4,], main.panel="", pch=21, bg='white') @ For demonstration and instruction purposes, the raw data from the 36th point pattern are available in a plain ascii file in the \texttt{spatstat} installation, <>= system.file("rawdata/osteo/osteo36.txt", package="spatstat") @ \subsubsection*{\texttt{paracou}: Kimboto trees} Point pattern of adult and juvenile Kimboto trees recorded at Paracou in French Guiana. A bivariate point pattern. <>= plot(paracou, cols=2:3, chars=c(16,3)) @ \subsubsection*{\texttt{ponderosa}: Ponderosa Pines} Locations of Ponderosa Pine trees in a forest. Several special points are identified. <>= ponderosa.extra$plotit() @ %$ \subsubsection*{\texttt{pyramidal}: Pyramidal Neurons in Brain} Locations of pyramidal neurons in sections of human brain. There is one point pattern from each of 31 human subjects. The subjects are divided into three groups: controls (12 subjects), schizoaffective (9 subjects) and schizophrenic (10 subjects). To reduce space, we show only the odd-numbered patterns: <<>>= pyr <- pyramidal[c(FALSE,TRUE), ] @ <>= pyr$grp <- abbreviate(pyr$group, minlength=7) plot(pyr, quote(plot(Neurons, pch=16, main=grp)), main="Pyramidal Neurons") @ \subsubsection*{\texttt{redwood}, \texttt{redwood3}, \texttt{redwoodfull}: Redwood seedlings and saplings} California Redwood seedlings and saplings in a forest. There are two versions of this dataset: \texttt{redwood} and \texttt{redwoodfull}. The \texttt{redwoodfull} dataset is the full data. It is spatially inhomogeneous in density and spacing of points. The \texttt{redwood} dataset is a subset of the full data, selected because it is apparently homogeneous, and has often been used as a demonstration example. This comes in two versions commonly used in the literature: \texttt{redwood} (coordinates given to 2 decimal places) and \texttt{redwood3} (coordinates given to 3 decimal places). <>= plot(redwood) plot(redwood3, add=TRUE, pch=20) @ <>= redwoodfull.extra$plotit() @ %$ \subsubsection*{\texttt{residualspaper}: Data from residuals paper} Contains the point patterns used as examples in \begin{quote} A. Baddeley, R. Turner, J. M{\o}ller and M. Hazelton (2005) Residual analysis for spatial point processes. \emph{Journal of the Royal Statistical Society, Series B} \textbf{67}, 617--666 \end{quote} along with {\sf R} code. <>= plot(as.solist(residualspaper[c("Fig1", "Fig4a", "Fig4b", "Fig4c")]), main="") @ \subsubsection*{\texttt{shapley}: Shapley Galaxy Concentration} Sky positions of 4215 galaxies in the Shapley Supercluster (mapped by radioastronomy). <>= shapley.extra$plotit(main="Shapley") @ %$ \subsubsection*{\texttt{simdat}: Simulated data} Another simulated dataset used for demonstration purposes. <>= plot(simdat) @ \subsubsection*{\texttt{spiders}: Spider webs} Spider webs across the mortar lines of a brick wall. A point pattern on a linear network. <>= plot(spiders, pch=16, show.window=FALSE) @ \subsubsection*{\texttt{sporophores}: Sporophores} Sporophores of three species of fungi around a tree. <>= plot(sporophores, chars=c(16,1,2), cex=0.6) points(0,0,pch=16, cex=2) text(15,8,"Tree", cex=0.75) @ \subsubsection*{\texttt{stonetools}: Palaeolithic stone tools and bone fragments} Palaeolithic tools and bone fragments uncovered in an archaeological dig in Tanzania. Each find is marked by its type (either \texttt{BONE} or \texttt{LITHIC}) and by the height $z$ (above a reference level) of the location in the soil where it was found. <>= plot(stonetools, which.marks=2, cols=c(2,3), chars=c(1,3), cex=0.5) @ \subsubsection*{\texttt{spruces}: Spruces in Saxony} Locations of Norwegian spruce trees in a natural forest stand in Saxonia, Germany. Each tree is marked with its diameter at breast height. <>= plot(spruces, maxsize=min(nndist(spruces))) @ \subsubsection*{\texttt{swedishpines}: Swedish Pines} Locations of pine saplings in a Swedish forest. Often used as a demonstration example. <>= plot(swedishpines) @ \subsubsection*{\texttt{urkiola}: trees in a wood} Locations of birch and oak trees in a secondary wood in Urkiola Natural Park (Basque country, northern Spain). Irregular window, bivariate point pattern. <>= plot(urkiola, cex=0.5, cols=2:3) @ \subsubsection*{\texttt{waka}: trees in Waka National Park} Spatial coordinates of each tree, marked by the tree diameter at breast height. <>= par(mar=c(0,0,2,0)) plot(waka, markscale=0.04, main=c("Waka national park", "tree diameters")) @ \subsubsection*{\texttt{vesicles}: synaptic vesicles} Point pattern of synaptic vesicles observed in rat brain tissue. <>= v <- rotate(vesicles, pi/2) ve <- lapply(vesicles.extra, rotate, pi/2) plot(v, main="Vesicles") plot(ve$activezone, add=TRUE, lwd=3) @ The auxiliary dataset \texttt{vesicles.extra} is a list with entries\\ \begin{tabular}{ll} \texttt{presynapse} & outer polygonal boundary of presynapse \\ \texttt{mitochondria} & polygonal boundary of mitochondria \\ \texttt{mask} & binary mask representation of vesicles window \\ \texttt{activezone} & line segment pattern representing the active zone. \end{tabular} For demonstration and training purposes, the raw data files for this dataset are also provided in the \pkg{spatstat} package installation:\\ \begin{tabular}{ll} \texttt{vesicles.txt} & spatial locations of vesicles \\ \texttt{presynapse.txt} & vertices of \texttt{presynapse} \\ \texttt{mitochondria.txt} & vertices of \texttt{mitochondria} \\ \texttt{vesiclesimage.tif} & greyscale microscope image \\ \texttt{vesiclesmask.tif} & binary image of \texttt{mask} \\ \texttt{activezone.txt} & coordinates of \texttt{activezone} \end{tabular} The files are in the folder \texttt{rawdata/vesicles} in the \texttt{spatstat} installation directory. The precise location of the files can be obtained using \texttt{system.file}, for example <>= system.file("rawdata/vesicles/mitochondria.txt", package="spatstat") @ \subsubsection*{\texttt{waterstriders}: Insects on a pond} Three independent replications of a point pattern formed by insects on the surface of a pond. <>= plot(waterstriders) @ \end{document} spatstat/inst/doc/spatstatlocalsize.txt0000644000176200001440000000130314360753512020154 0ustar liggesusersdate version nhelpfiles nobjects ndatasets Rlines srclines "2017-03-30" "3.5-6" 21 85 0 4677 0 "2018-01-30" "3.5-7" 21 85 0 4677 0 "2019-04-13" "3.6-0" 21 84 0 4663 0 "2021-01-10" "4.0-0" 21 84 0 4663 0 "2021-03-31" "4.1-0" 21 84 0 4663 0 "2021-04-20" "4.1-1" 21 84 0 4663 0 "2022-01-12" "4.1-2" 21 84 0 4663 0 "2022-02-17" "4.1-3" 21 84 0 4663 0 "2022-02-23" "4.1-4" 21 84 0 4663 0 "2022-04-01" "4.1-5" 21 84 0 4678 0 "2022-04-05" "4.1-5" 21 84 0 4678 0 "2022-04-05" "4.1-6" 21 84 0 4678 0 "2022-04-05" "4.1-6" 21 84 0 4678 0 "2022-04-05" "4.1-6" 21 84 0 4678 0 "2022-06-13" "4.1-7" 21 84 0 4678 0 "2022-10-30" "5.0-0" 21 84 0 4678 0 "2022-10-30" "5.0-0" 21 84 0 4678 0 "2022-11-12" "5.0-1" 21 84 0 4678 0 spatstat/inst/doc/spatstatguisize.txt0000644000176200001440000000107614514653072017656 0ustar liggesusersdate version nhelpfiles nobjects ndatasets Rlines srclines "2020-03-03" "1.0-0" 4 10 0 865 0 "2020-03-03" "1.0-1" 4 10 0 865 0 "2020-03-13" "1.0-2" 4 10 0 868 0 "2021-01-04" "1.1-0" 4 10 0 868 0 "2021-01-10" "1.1-1" 4 10 0 868 0 "2021-02-15" "1.65-0" 4 10 0 868 0 "2021-02-15" "1.2-0" 4 10 0 868 0 "2021-03-31" "2.0-0" 4 10 0 868 0 "2022-01-12" "2.0-1" 4 10 0 868 0 "2022-01-12" "2.0-2" 4 10 0 868 0 "2022-01-12" "2.0-2" 4 10 0 868 0 "2022-10-30" "3.0-0" 4 10 0 868 0 "2022-10-30" "3.0-0" 4 10 0 868 0 "2022-11-12" "3.0-1" 4 10 0 868 0 "2023-10-08" "3.0-1.001" 4 10 0 868 0 spatstat/inst/doc/replicated.R0000644000176200001440000004062614517616250016115 0ustar liggesusers### R code from vignette source 'replicated.Rnw' ################################################### ### code chunk number 1: replicated.Rnw:29-30 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) ################################################### ### code chunk number 2: replicated.Rnw:35-42 ################################################### library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) ################################################### ### code chunk number 3: replicated.Rnw:180-181 ################################################### waterstriders ################################################### ### code chunk number 4: replicated.Rnw:199-200 ################################################### getOption("SweaveHooks")[["fig"]]() plot(waterstriders, main="") ################################################### ### code chunk number 5: replicated.Rnw:207-208 ################################################### summary(waterstriders) ################################################### ### code chunk number 6: replicated.Rnw:216-217 ################################################### X <- listof(rpoispp(100), rpoispp(100), rpoispp(100)) ################################################### ### code chunk number 7: replicated.Rnw:222-224 ################################################### getOption("SweaveHooks")[["fig"]]() plot(X) X ################################################### ### code chunk number 8: replicated.Rnw:253-254 (eval = FALSE) ################################################### ## hyperframe(...) ################################################### ### code chunk number 9: replicated.Rnw:279-281 ################################################### H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) H ################################################### ### code chunk number 10: replicated.Rnw:289-294 ################################################### G <- hyperframe(X=1:3, Y=letters[1:3], Z=factor(letters[1:3]), W=list(rpoispp(100),rpoispp(100), rpoispp(100)), U=42, V=rpoispp(100), stringsAsFactors=FALSE) G ################################################### ### code chunk number 11: replicated.Rnw:324-325 ################################################### simba ################################################### ### code chunk number 12: replicated.Rnw:338-339 ################################################### pyramidal ################################################### ### code chunk number 13: replicated.Rnw:345-346 ################################################### ws <- hyperframe(Striders=waterstriders) ################################################### ### code chunk number 14: replicated.Rnw:353-355 ################################################### H$X H$Y ################################################### ### code chunk number 15: replicated.Rnw:365-367 ################################################### H$U <- letters[1:3] H ################################################### ### code chunk number 16: replicated.Rnw:372-376 ################################################### G <- hyperframe() G$X <- waterstriders G$Y <- 1:3 G ################################################### ### code chunk number 17: replicated.Rnw:384-388 ################################################### H[,1] H[2,] H[2:3, ] H[1,1] ################################################### ### code chunk number 18: replicated.Rnw:394-397 ################################################### H[,1,drop=TRUE] H[1,1,drop=TRUE] H[1,2,drop=TRUE] ################################################### ### code chunk number 19: replicated.Rnw:410-411 (eval = FALSE) ################################################### ## plot.listof(x, ..., main, arrange = TRUE, nrows = NULL, ncols = NULL) ################################################### ### code chunk number 20: replicated.Rnw:426-427 ################################################### getOption("SweaveHooks")[["fig"]]() plot(waterstriders, pch=16, nrows=1) ################################################### ### code chunk number 21: replicated.Rnw:442-443 ################################################### getOption("SweaveHooks")[["fig"]]() plot(simba) ################################################### ### code chunk number 22: replicated.Rnw:455-457 ################################################### getOption("SweaveHooks")[["fig"]]() H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) plot(H$Y) ################################################### ### code chunk number 23: replicated.Rnw:469-470 (eval = FALSE) ################################################### ## plot(h, e) ################################################### ### code chunk number 24: replicated.Rnw:479-480 ################################################### getOption("SweaveHooks")[["fig"]]() plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) })) ################################################### ### code chunk number 25: replicated.Rnw:492-494 ################################################### getOption("SweaveHooks")[["fig"]]() H <- hyperframe(Bugs=waterstriders) plot(H, quote(plot(Kest(Bugs))), marsize=1) ################################################### ### code chunk number 26: replicated.Rnw:507-509 ################################################### df <- data.frame(A=1:10, B=10:1) with(df, A-B) ################################################### ### code chunk number 27: replicated.Rnw:522-523 (eval = FALSE) ################################################### ## with(h,e) ################################################### ### code chunk number 28: replicated.Rnw:533-536 ################################################### H <- hyperframe(Bugs=waterstriders) with(H, npoints(Bugs)) with(H, distmap(Bugs)) ################################################### ### code chunk number 29: replicated.Rnw:559-560 ################################################### with(simba, npoints(Points)) ################################################### ### code chunk number 30: replicated.Rnw:567-569 ################################################### H <- hyperframe(Bugs=waterstriders) K <- with(H, Kest(Bugs)) ################################################### ### code chunk number 31: replicated.Rnw:577-578 ################################################### getOption("SweaveHooks")[["fig"]]() plot(K) ################################################### ### code chunk number 32: replicated.Rnw:583-585 ################################################### H <- hyperframe(Bugs=waterstriders) with(H, nndist(Bugs)) ################################################### ### code chunk number 33: replicated.Rnw:591-592 ################################################### with(H, min(nndist(Bugs))) ################################################### ### code chunk number 34: replicated.Rnw:604-605 ################################################### simba$Dist <- with(simba, distmap(Points)) ################################################### ### code chunk number 35: replicated.Rnw:618-622 ################################################### getOption("SweaveHooks")[["fig"]]() lambda <- rexp(6, rate=1/50) H <- hyperframe(lambda=lambda) H$Points <- with(H, rpoispp(lambda)) plot(H, quote(plot(Points, main=paste("lambda=", signif(lambda, 4))))) ################################################### ### code chunk number 36: replicated.Rnw:628-629 ################################################### H$X <- with(H, rpoispp(50)) ################################################### ### code chunk number 37: replicated.Rnw:658-659 ################################################### getOption("SweaveHooks")[["fig"]]() plot(simba, quote(plot(density(Points), main="")), nrows=2) ################################################### ### code chunk number 38: replicated.Rnw:678-680 ################################################### getOption("SweaveHooks")[["fig"]]() rhos <- with(demohyper, rhohat(Points, Image)) plot(rhos) ################################################### ### code chunk number 39: replicated.Rnw:698-699 (eval = FALSE) ################################################### ## mppm(formula, data, interaction, ...) ################################################### ### code chunk number 40: replicated.Rnw:709-710 (eval = FALSE) ################################################### ## mppm(Points ~ group, simba, Poisson()) ################################################### ### code chunk number 41: replicated.Rnw:743-744 ################################################### mppm(Points ~ 1, simba) ################################################### ### code chunk number 42: replicated.Rnw:751-752 ################################################### mppm(Points ~ group, simba) ################################################### ### code chunk number 43: replicated.Rnw:758-759 ################################################### mppm(Points ~ id, simba) ################################################### ### code chunk number 44: replicated.Rnw:769-770 ################################################### mppm(Points ~ Image, data=demohyper) ################################################### ### code chunk number 45: replicated.Rnw:788-789 (eval = FALSE) ################################################### ## mppm(Points ~ offset(log(Image)), data=demohyper) ################################################### ### code chunk number 46: replicated.Rnw:801-802 (eval = FALSE) ################################################### ## mppm(Points ~ log(Image), data=demop) ################################################### ### code chunk number 47: replicated.Rnw:819-820 (eval = FALSE) ################################################### ## mppm(formula, data, interaction, ..., iformula=NULL) ################################################### ### code chunk number 48: replicated.Rnw:870-871 ################################################### radii <- with(simba, mean(nndist(Points))) ################################################### ### code chunk number 49: replicated.Rnw:878-880 ################################################### Rad <- hyperframe(R=radii) Str <- with(Rad, Strauss(R)) ################################################### ### code chunk number 50: replicated.Rnw:885-887 ################################################### Int <- hyperframe(str=Str) mppm(Points ~ 1, simba, interaction=Int) ################################################### ### code chunk number 51: replicated.Rnw:914-917 ################################################### h <- hyperframe(Y=waterstriders) g <- hyperframe(po=Poisson(), str4 = Strauss(4), str7= Strauss(7)) mppm(Y ~ 1, data=h, interaction=g, iformula=~str4) ################################################### ### code chunk number 52: replicated.Rnw:928-929 ################################################### fit <- mppm(Points ~ 1, simba, Strauss(0.07), iformula = ~Interaction*group) ################################################### ### code chunk number 53: replicated.Rnw:947-948 ################################################### fit ################################################### ### code chunk number 54: replicated.Rnw:951-953 ################################################### co <- coef(fit) si <- function(x) { signif(x, 4) } ################################################### ### code chunk number 55: replicated.Rnw:964-965 ################################################### coef(fit) ################################################### ### code chunk number 56: replicated.Rnw:1022-1023 (eval = FALSE) ################################################### ## interaction=hyperframe(po=Poisson(), str=Strauss(0.07)) ################################################### ### code chunk number 57: replicated.Rnw:1028-1029 (eval = FALSE) ################################################### ## iformula=~ifelse(group=="control", po, str) ################################################### ### code chunk number 58: replicated.Rnw:1039-1040 (eval = FALSE) ################################################### ## iformula=~I((group=="control")*po) + I((group=="treatment") * str) ################################################### ### code chunk number 59: replicated.Rnw:1050-1055 ################################################### g <- hyperframe(po=Poisson(), str=Strauss(0.07)) fit2 <- mppm(Points ~ 1, simba, g, iformula=~I((group=="control")*po) + I((group=="treatment") * str)) fit2 ################################################### ### code chunk number 60: replicated.Rnw:1178-1180 ################################################### H <- hyperframe(P=waterstriders) mppm(P ~ 1, H, random=~1|id) ################################################### ### code chunk number 61: replicated.Rnw:1187-1188 (eval = FALSE) ################################################### ## mppm(Neurons ~ AstroIm, random=~AstroIm|WellNumber) ################################################### ### code chunk number 62: replicated.Rnw:1211-1214 ################################################### H <- hyperframe(W=waterstriders) fit <- mppm(W ~ 1, H) subfits(fit) ################################################### ### code chunk number 63: replicated.Rnw:1235-1236 (eval = FALSE) ################################################### ## subfits <- subfits.new ################################################### ### code chunk number 64: replicated.Rnw:1248-1250 ################################################### H <- hyperframe(W=waterstriders) with(H, ppm(W)) ################################################### ### code chunk number 65: replicated.Rnw:1273-1275 ################################################### fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) ################################################### ### code chunk number 66: replicated.Rnw:1285-1286 ################################################### getOption("SweaveHooks")[["fig"]]() plot(res) ################################################### ### code chunk number 67: replicated.Rnw:1291-1293 ################################################### getOption("SweaveHooks")[["fig"]]() smor <- with(hyperframe(res=res), Smooth(res, sigma=4)) plot(smor) ################################################### ### code chunk number 68: replicated.Rnw:1305-1308 ################################################### fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) totres <- sapply(res, integral.msr) ################################################### ### code chunk number 69: replicated.Rnw:1314-1321 ################################################### getOption("SweaveHooks")[["fig"]]() fit <- mppm(Points~Image, data=demohyper) resids <- residuals(fit, type="Pearson") totres <- sapply(resids, integral.msr) areas <- with(demohyper, area.owin(as.owin(Points))) df <- as.data.frame(demohyper[, "Group"]) df$resids <- totres/areas plot(resids~Group, df) ################################################### ### code chunk number 70: replicated.Rnw:1342-1345 ################################################### getOption("SweaveHooks")[["fig"]]() fit <- mppm(P ~ 1, hyperframe(P=waterstriders)) sub <- hyperframe(Model=subfits(fit)) plot(sub, quote(diagnose.ppm(Model))) ################################################### ### code chunk number 71: replicated.Rnw:1358-1366 ################################################### H <- hyperframe(P = waterstriders) fitall <- mppm(P ~ 1, H) together <- subfits(fitall) separate <- with(H, ppm(P)) Fits <- hyperframe(Together=together, Separate=separate) dr <- with(Fits, unlist(coef(Separate)) - unlist(coef(Together))) dr exp(dr) ################################################### ### code chunk number 72: replicated.Rnw:1383-1392 ################################################### H <- hyperframe(X=waterstriders) # Poisson with constant intensity for all patterns fit1 <- mppm(X~1, H) quadrat.test(fit1, nx=2) # uniform Poisson with different intensity for each pattern fit2 <- mppm(X ~ id, H) quadrat.test(fit2, nx=2) ################################################### ### code chunk number 73: replicated.Rnw:1421-1422 (eval = FALSE) ################################################### ## kstest.mppm(model, covariate) spatstat/inst/doc/updates.R0000644000176200001440000001562414517616251015447 0ustar liggesusers### R code from vignette source 'updates.Rnw' ################################################### ### code chunk number 1: updates.Rnw:20-26 ################################################### library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) ################################################### ### code chunk number 2: updates.Rnw:36-133 ################################################### readSizeTable <- function(fname) { if(is.null(fname) || !file.exists(fname)) return(NULL) a <- read.table(fname, header=TRUE) a$date <- as.Date(a$date) return(a) } getSizeTable <- function(packagename="spatstat", tablename="packagesizes.txt") { fname <- system.file("doc", tablename, package=packagename) readSizeTable(fname) } RemoveDevel <- function(sizetable) { ## remove entries with fractional version numbers if(is.null(sizetable)) return(NULL) ver <- sizetable$version isdevel <- sapply(ver, function(x) { length(unlist(package_version(x))) > 3 }) st <- if(all(isdevel)) NULL else sizetable[!isdevel, , drop=FALSE] return(st) } counts <- c("nhelpfiles", "nobjects", "ndatasets", "Rlines", "srclines") mergeSizeTables <- function(a, b, breakupdate, allow.devel=FALSE) { #' a is the running total for spatstat; b is a sub-package. #' breakupdate is the date when the code in b was removed from spatstat #' so that the size of 'b' must be added to 'a' for all dates >= breakupdate if(!allow.devel) b <- RemoveDevel(b) if(is.null(b)) return(a) adates <- a$date bdates <- b$date alldates <- sort(unique(c(adates,bdates))) if(missing(breakupdate)) breakupdate <- min(bdates) #' functions to determine, for any given date, #' the relevant (latest) row of the table aok <- rev(!duplicated(rev(adates))) arowfun <- approxfun(adates[aok], seq_along(adates)[aok], method="constant", f=0, rule=2, yleft=0) bok <- rev(!duplicated(rev(bdates))) browfun <- approxfun(bdates[bok], seq_along(bdates)[bok], method="constant", f=0, rule=2, yleft=0) result <- NULL for(k in seq_along(alldates)) { thedate <- alldates[k] i <- arowfun(thedate) j <- browfun(thedate) #' i > 0 because spatstat's founding date is earlier than any sub-package nextrow <- a[i, ] if(j > 0 && thedate >= breakupdate) { #' add contribution from 'b' nextrow[, counts] <- nextrow[, counts] + b[j, counts] } result <- rbind(result, nextrow) } return(result) } ## Get histories of all sub-packages ## Package formerly known as 'spatstat' z <- getSizeTable() ## installed sub-packages - access via the installed sub-packages zutils <- getSizeTable("spatstat.utils") zdata <- getSizeTable("spatstat.data") zsparse <- getSizeTable("spatstat.sparse") zgeom <- getSizeTable("spatstat.geom") zrandom <- getSizeTable("spatstat.random") zexplore <- getSizeTable("spatstat.explore") zmodel <- getSizeTable("spatstat.model") zlinnet <- getSizeTable("spatstat.linnet") ## other sub-packages - access via stored copies of package size files ## defunct package spatstat.core zcore <- getSizeTable("spatstat", "spatstatcoresize.txt") ## extension packages zlocal <- getSizeTable("spatstat", "spatstatlocalsize.txt") zgui <- getSizeTable("spatstat", "spatstatguisize.txt") zKnet <- getSizeTable("spatstat", "spatstatKnetsize.txt") ## Merge histories starting at the 'split dates' z <- mergeSizeTables(z, zutils, "2017-03-22") z <- mergeSizeTables(z, zdata, "2017-09-23") z <- mergeSizeTables(z, zsparse, "2020-11-04") BigSplitDay <- "2020-12-14" z <- mergeSizeTables(z, zgeom, BigSplitDay) z <- mergeSizeTables(z, zcore, BigSplitDay) z <- mergeSizeTables(z, zlinnet, BigSplitDay) z <- mergeSizeTables(z, zrandom, "2022-02-12") CoreSplitDay <- "2020-05-25" # size of 'core' drops to 0 on this date z <- mergeSizeTables(z, zexplore, CoreSplitDay) z <- mergeSizeTables(z, zmodel, CoreSplitDay) ## extension packages: these never overlapped spatstat z <- mergeSizeTables(z, zlocal) z <- mergeSizeTables(z, zgui) z <- mergeSizeTables(z, zKnet) ## Now summarise currentcount <- z[nrow(z), counts] bookcount <- z[z$version == "1.42-0", counts] changes <- currentcount - bookcount newobj <- changes[["nobjects"]] newdat <- changes[["ndatasets"]] + 1 # counting rule doesn't detect redwood3 newcode <- changes[["Rlines"]] + changes[["srclines"]] bookcode <- bookcount[["Rlines"]] + bookcount[["srclines"]] currentcode <- currentcount[["Rlines"]] + currentcount[["srclines"]] growth <- signif((100 * newcode)/bookcode, digits=2) ################################################### ### code chunk number 3: updates.Rnw:145-150 ################################################### options(SweaveHooks=list(fig=function() par(mar=0.2+c(2,4,2,0)))) Plot <- function(fmla, ..., dat=z) { yvals <- eval(as.expression(fmla[[2]]), envir=dat) plot(fmla, ..., data=dat, type="l", xlab="", lwd=2, ylim=c(0, max(yvals))) } ################################################### ### code chunk number 4: updates.Rnw:156-161 ################################################### getOption("SweaveHooks")[["fig"]]() Plot((Rlines + srclines)/1000 ~ date, ylab="Lines of code (x 1000)", main="Spatstat growth") lines(srclines/1000 ~ date, data=z) text(as.Date("2015-01-01"), 9.5, "C code") text(as.Date("2015-01-01"), 60, "R code") ################################################### ### code chunk number 5: updates.Rnw:178-199 ################################################### ## Tabulate latest version numbers of packages vtable <- data.frame(package="spatstat", version=sversion, date=as.Date(sdate)) AppendVersion <- function(pkg, sizetable, v, allow.devel=FALSE) { if(!allow.devel) sizetable <- RemoveDevel(sizetable) if(is.null(sizetable)) return(v) lastrow <- sizetable[nrow(sizetable), , drop=FALSE] if(is.null(lastrow)) return(v) rbind(v, data.frame(package=pkg, version=lastrow[,"version"], date=as.Date(lastrow[,"date"]))) } vtable <- AppendVersion("spatstat.geom", zgeom, vtable) vtable <- AppendVersion("spatstat.random", zrandom, vtable) vtable <- AppendVersion("spatstat.explore", zexplore, vtable) vtable <- AppendVersion("spatstat.model", zmodel, vtable) ##vtable <- AppendVersion("spatstat.core", zcore, vtable) vtable <- AppendVersion("spatstat.linnet", zlinnet, vtable) vtable <- AppendVersion("spatstat.sparse", zsparse, vtable) vtable <- AppendVersion("spatstat.data", zdata, vtable) vtable <- AppendVersion("spatstat.utils", zutils, vtable) vtable <- AppendVersion("spatstat.local", zlocal, vtable) vtable <- AppendVersion("spatstat.Knet", zKnet, vtable) vtable <- AppendVersion("spatstat.gui", zgui, vtable) ################################################### ### code chunk number 6: updates.Rnw:205-206 ################################################### print(vtable[,c(3,1,2)], row.names=FALSE) spatstat/inst/doc/getstart.pdf0000644000176200001440000065164714517616255016226 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 3973 /Filter /FlateDecode /N 63 /First 520 >> stream x[Yܶ~ϯrifʥ*-[r)+;vdj9R~}pg] ALJ(g)V;40T-e ̱nPdu#PLнfRٚ(Y2ѺVLif0-,geZ)wLw5L;p)0! uɌ/chf18$n ˜$Lrs͚ V2+\V\1i0 AH($*eİAAaJb 4& YEzƠјK ؉up=kq =4$ lFѳ`F]34z6ΠMCmгFVB0خj4F+I0 /_ͪny-!h ^.߶[hC˪Ǹ~<<޴j}dkٽ'\`k0/ڭ7OX]iG}}nmO7;,^/ ;il~cvd8A:tjɅ&s~Fzص- 4VGOx]э^zr"NRoOxf}ݡ哧x{i<( It[nϒ#EIRTJ5hԱVS/&ըM5jR k5$P@E%4o.Ngf0<(RdprYyF/87 /h|GDU>kyG8X̷c/)MM V-`͢o&kDjz pU! 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How to set all margins to zero and eliminate all outer spaces zeromargins <- function() { par( mar=rep(0,4), omd=c(0,1,0,1), xaxs="i", yaxs="i" ) invisible(NULL) } ## Set 'mar' setmargins <- function(...) { x <- c(...) x <- rep(x, 4)[1:4] par(mar=x) invisible(NULL) } ################################################### ### code chunk number 4: datasets.Rnw:247-248 (eval = FALSE) ################################################### ## plot(amacrine) ################################################### ### code chunk number 5: datasets.Rnw:250-252 ################################################### getOption("SweaveHooks")[["fig"]]() setmargins(0,1,2,0) plot(amacrine) ################################################### ### code chunk number 6: datasets.Rnw:261-262 (eval = FALSE) ################################################### ## plot(anemones, markscale=1) ################################################### ### code chunk number 7: datasets.Rnw:264-266 ################################################### getOption("SweaveHooks")[["fig"]]() setmargins(0,0,2,0) plot(anemones, markscale=1) ################################################### ### code chunk number 8: datasets.Rnw:279-280 (eval = FALSE) ################################################### ## ants.extra$plotit() ################################################### ### code chunk number 9: datasets.Rnw:282-284 ################################################### getOption("SweaveHooks")[["fig"]]() setmargins(0,0,1,0) ants.extra$plotit() ################################################### ### code chunk number 10: datasets.Rnw:292-293 ################################################### getOption("SweaveHooks")[["fig"]]() plot(austates) ################################################### ### code chunk number 11: datasets.Rnw:303-305 (eval = FALSE) ################################################### ## plot(bdspots, equal.scales=TRUE, pch="+", ## panel.args=function(i)list(cex=c(0.15, 0.2, 0.7)[i])) ################################################### ### code chunk number 12: datasets.Rnw:307-311 ################################################### getOption("SweaveHooks")[["fig"]]() zeromargins() plot(bdspots, equal.scales=TRUE, pch="+", main="", mar.panel=0, hsep=1, panel.args=function(i)list(cex=c(0.15, 0.2, 0.7)[i])) ################################################### ### code chunk number 13: datasets.Rnw:321-323 (eval = FALSE) ################################################### ## plot(bei.extra$elev, main="Beilschmiedia") ## plot(bei, add=TRUE, pch=16, cex=0.3) ################################################### ### code chunk number 14: datasets.Rnw:325-328 ################################################### getOption("SweaveHooks")[["fig"]]() setmargins(0,0,2,0) plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) ################################################### ### code chunk number 15: datasets.Rnw:334-340 (eval = FALSE) ################################################### ## M <- persp(bei.extra$elev, ## theta=-45, phi=18, expand=7, ## border=NA, apron=TRUE, shade=0.3, ## box=FALSE, visible=TRUE, ## main="") ## perspPoints(bei, Z=bei.extra$elev, M=M, pch=16, cex=0.3) ################################################### ### code chunk number 16: datasets.Rnw:349-350 ################################################### getOption("SweaveHooks")[["fig"]]() plot(betacells) ################################################### ### code chunk number 17: datasets.Rnw:355-356 ################################################### getOption("SweaveHooks")[["fig"]]() plot(bramblecanes, cols=1:3) ################################################### ### code chunk number 18: datasets.Rnw:361-362 (eval = FALSE) ################################################### ## plot(split(bramblecanes)) ################################################### ### code chunk number 19: datasets.Rnw:372-373 ################################################### getOption("SweaveHooks")[["fig"]]() plot(bronzefilter,markscale=2) ################################################### ### code chunk number 20: datasets.Rnw:381-382 ################################################### getOption("SweaveHooks")[["fig"]]() plot(btb, which.marks="spoligotype", cols=2:5, chars=1:4) ################################################### ### code chunk number 21: datasets.Rnw:391-392 ################################################### getOption("SweaveHooks")[["fig"]]() plot(cells) ################################################### ### code chunk number 22: datasets.Rnw:400-401 ################################################### getOption("SweaveHooks")[["fig"]]() plot(cetaceans.extra$patterns, main="Cetaceans data", cols=1:5, hsep=1) ################################################### ### code chunk number 23: datasets.Rnw:410-413 ################################################### getOption("SweaveHooks")[["fig"]]() plot(chicago, main="Chicago Crimes", col="grey", cols=c("red", "blue", "black", "blue", "red", "blue", "blue"), chars=c(16,2,22,17,24,15,6), leg.side="left", show.window=FALSE) ################################################### ### code chunk number 24: datasets.Rnw:423-424 ################################################### getOption("SweaveHooks")[["fig"]]() chorley.extra$plotit() ################################################### ### code chunk number 25: datasets.Rnw:440-442 ################################################### getOption("SweaveHooks")[["fig"]]() plot(clmfires, which.marks="cause", cols=2:5, cex=0.25, main="Castilla-La Mancha forest fires") ################################################### ### code chunk number 26: datasets.Rnw:452-453 ################################################### getOption("SweaveHooks")[["fig"]]() plot(clmfires.extra$clmcov100$elevation, main="Elevation") ################################################### ### code chunk number 27: datasets.Rnw:464-465 ################################################### getOption("SweaveHooks")[["fig"]]() plot(concrete,chars="+",cols="blue",col="yellow") ################################################### ### code chunk number 28: datasets.Rnw:476-478 ################################################### getOption("SweaveHooks")[["fig"]]() plot(copper$Points, main="Copper") plot(copper$Lines, add=TRUE) ################################################### ### code chunk number 29: datasets.Rnw:485-487 ################################################### getOption("SweaveHooks")[["fig"]]() plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) }), parargs=list(mar=rep(1,4))) ################################################### ### code chunk number 30: datasets.Rnw:494-495 ################################################### getOption("SweaveHooks")[["fig"]]() plot(demopat) ################################################### ### code chunk number 31: datasets.Rnw:509-510 ################################################### getOption("SweaveHooks")[["fig"]]() plot(dendrite, leg.side="bottom", main="", cex=0.75, cols=2:4) ################################################### ### code chunk number 32: datasets.Rnw:518-519 ################################################### getOption("SweaveHooks")[["fig"]]() plot(finpines, main="Finnish pines") ################################################### ### code chunk number 33: datasets.Rnw:532-536 ################################################### getOption("SweaveHooks")[["fig"]]() wildM1 <- with(flu, virustype == "wt" & stain == "M2-M1") plot(flu[wildM1, 1, drop=TRUE], main=c("flu data", "wild type virus, M2-M1 stain"), chars=c(16,3), cex=0.4, cols=2:3) ################################################### ### code chunk number 34: datasets.Rnw:544-545 ################################################### getOption("SweaveHooks")[["fig"]]() plot(gordon, main="People in Gordon Square", pch=16) ################################################### ### code chunk number 35: datasets.Rnw:560-561 ################################################### getOption("SweaveHooks")[["fig"]]() plot(gorillas, which.marks=1, chars=c(1,3), cols=2:3, main="Gorilla nest sites") ################################################### ### code chunk number 36: datasets.Rnw:565-566 (eval = FALSE) ################################################### ## system.file("rawdata/gorillas/vegetation.asc", package="spatstat") ################################################### ### code chunk number 37: datasets.Rnw:575-576 ################################################### getOption("SweaveHooks")[["fig"]]() plot(hamster, cols=c(2,4)) ################################################### ### code chunk number 38: datasets.Rnw:586-587 ################################################### getOption("SweaveHooks")[["fig"]]() plot(heather$coarse) ################################################### ### code chunk number 39: datasets.Rnw:591-592 (eval = FALSE) ################################################### ## plot(heather) ################################################### ### code chunk number 40: datasets.Rnw:602-603 ################################################### getOption("SweaveHooks")[["fig"]]() plot(humberside) ################################################### ### code chunk number 41: datasets.Rnw:615-616 ################################################### getOption("SweaveHooks")[["fig"]]() plot(hyytiala, cols=2:5) ################################################### ### code chunk number 42: datasets.Rnw:625-626 ################################################### getOption("SweaveHooks")[["fig"]]() plot(japanesepines) ################################################### ### code chunk number 43: datasets.Rnw:635-636 ################################################### getOption("SweaveHooks")[["fig"]]() plot(lansing) ################################################### ### code chunk number 44: datasets.Rnw:642-643 (eval = FALSE) ################################################### ## plot(split(lansing)) ################################################### ### code chunk number 45: datasets.Rnw:650-651 ################################################### getOption("SweaveHooks")[["fig"]]() plot(longleaf) ################################################### ### code chunk number 46: datasets.Rnw:660-662 ################################################### getOption("SweaveHooks")[["fig"]]() plot(mucosa, chars=c(1,3), cols=c("red", "green")) plot(mucosa.subwin, add=TRUE, lty=3) ################################################### ### code chunk number 47: datasets.Rnw:678-681 ################################################### getOption("SweaveHooks")[["fig"]]() plot(murchison$greenstone, main="Murchison data", col="lightgreen") plot(murchison$gold, add=TRUE, pch=3, col="blue") plot(murchison$faults, add=TRUE, col="red") ################################################### ### code chunk number 48: datasets.Rnw:687-688 ################################################### reedy <- owin(c(580, 650) * 1000, c(6986, 7026) * 1000) ################################################### ### code chunk number 49: datasets.Rnw:693-696 ################################################### getOption("SweaveHooks")[["fig"]]() plot(murchison$greenstone[reedy], main="Murchison data", col="lightgreen") plot(murchison$gold[reedy], add=TRUE, pch=3, col="blue") plot(murchison$faults[reedy], add=TRUE, col="red") ################################################### ### code chunk number 50: datasets.Rnw:704-705 ################################################### getOption("SweaveHooks")[["fig"]]() plot(nbfires, use.marks=FALSE, pch=".") ################################################### ### code chunk number 51: datasets.Rnw:711-712 (eval = FALSE) ################################################### ## plot(split(nbfires), use.marks=FALSE, chars=".") ################################################### ### code chunk number 52: datasets.Rnw:715-720 ################################################### getOption("SweaveHooks")[["fig"]]() par(mar=c(0,0,2,0)) plot(split(nbfires)$"2000", which.marks="fire.type", main=c("New Brunswick fires 2000", "by fire type"), cols=c("blue", "green", "red", "cyan"), leg.side="left") ################################################### ### code chunk number 53: datasets.Rnw:728-730 ################################################### getOption("SweaveHooks")[["fig"]]() plot(nztrees) plot(trim.rectangle(as.owin(nztrees), c(0,5), 0), add=TRUE, lty=3) ################################################### ### code chunk number 54: datasets.Rnw:743-744 ################################################### getOption("SweaveHooks")[["fig"]]() plot(osteo[1:4,], main.panel="", pch=21, bg='white') ################################################### ### code chunk number 55: datasets.Rnw:750-751 (eval = FALSE) ################################################### ## system.file("rawdata/osteo/osteo36.txt", package="spatstat") ################################################### ### code chunk number 56: datasets.Rnw:760-761 ################################################### getOption("SweaveHooks")[["fig"]]() plot(paracou, cols=2:3, chars=c(16,3)) ################################################### ### code chunk number 57: datasets.Rnw:769-770 ################################################### getOption("SweaveHooks")[["fig"]]() ponderosa.extra$plotit() ################################################### ### code chunk number 58: datasets.Rnw:782-783 ################################################### pyr <- pyramidal[c(FALSE,TRUE), ] ################################################### ### code chunk number 59: datasets.Rnw:786-788 ################################################### getOption("SweaveHooks")[["fig"]]() pyr$grp <- abbreviate(pyr$group, minlength=7) plot(pyr, quote(plot(Neurons, pch=16, main=grp)), main="Pyramidal Neurons") ################################################### ### code chunk number 60: datasets.Rnw:808-810 ################################################### getOption("SweaveHooks")[["fig"]]() plot(redwood) plot(redwood3, add=TRUE, pch=20) ################################################### ### code chunk number 61: datasets.Rnw:813-814 ################################################### getOption("SweaveHooks")[["fig"]]() redwoodfull.extra$plotit() ################################################### ### code chunk number 62: datasets.Rnw:828-830 ################################################### getOption("SweaveHooks")[["fig"]]() plot(as.solist(residualspaper[c("Fig1", "Fig4a", "Fig4b", "Fig4c")]), main="") ################################################### ### code chunk number 63: datasets.Rnw:838-839 ################################################### getOption("SweaveHooks")[["fig"]]() shapley.extra$plotit(main="Shapley") ################################################### ### code chunk number 64: datasets.Rnw:846-847 ################################################### getOption("SweaveHooks")[["fig"]]() plot(simdat) ################################################### ### code chunk number 65: datasets.Rnw:855-856 ################################################### getOption("SweaveHooks")[["fig"]]() plot(spiders, pch=16, show.window=FALSE) ################################################### ### code chunk number 66: datasets.Rnw:863-866 ################################################### getOption("SweaveHooks")[["fig"]]() plot(sporophores, chars=c(16,1,2), cex=0.6) points(0,0,pch=16, cex=2) text(15,8,"Tree", cex=0.75) ################################################### ### code chunk number 67: datasets.Rnw:878-879 ################################################### getOption("SweaveHooks")[["fig"]]() plot(stonetools, which.marks=2, cols=c(2,3), chars=c(1,3), cex=0.5) ################################################### ### code chunk number 68: datasets.Rnw:888-889 ################################################### getOption("SweaveHooks")[["fig"]]() plot(spruces, maxsize=min(nndist(spruces))) ################################################### ### code chunk number 69: datasets.Rnw:898-899 ################################################### getOption("SweaveHooks")[["fig"]]() plot(swedishpines) ################################################### ### code chunk number 70: datasets.Rnw:908-909 ################################################### getOption("SweaveHooks")[["fig"]]() plot(urkiola, cex=0.5, cols=2:3) ################################################### ### code chunk number 71: datasets.Rnw:916-918 ################################################### getOption("SweaveHooks")[["fig"]]() par(mar=c(0,0,2,0)) plot(waka, markscale=0.04, main=c("Waka national park", "tree diameters")) ################################################### ### code chunk number 72: datasets.Rnw:925-929 ################################################### getOption("SweaveHooks")[["fig"]]() v <- rotate(vesicles, pi/2) ve <- lapply(vesicles.extra, rotate, pi/2) plot(v, main="Vesicles") plot(ve$activezone, add=TRUE, lwd=3) ################################################### ### code chunk number 73: datasets.Rnw:954-955 (eval = FALSE) ################################################### ## system.file("rawdata/vesicles/mitochondria.txt", package="spatstat") ################################################### ### code chunk number 74: datasets.Rnw:963-964 ################################################### getOption("SweaveHooks")[["fig"]]() plot(waterstriders) spatstat/inst/CITATION0000755000176200001440000000357114375270731014251 0ustar liggesusersc( bibentry(bibtype = "Book", title = "Spatial Point Patterns: Methodology and Applications with {R}", author = c(person("Adrian", "Baddeley"), person("Ege", "Rubak"), person("Rolf", "Turner")), year = "2015", publisher = "Chapman and Hall/CRC Press", address = "London", isbn = 9781482210200, url = "https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200/", header = "To cite spatstat in publications, please use:" ), bibentry(bibtype = "Article", title = "Hybrids of Gibbs Point Process Models and Their Implementation", author = c(person("Adrian", "Baddeley"), person("Rolf", "Turner"), person("Jorge", "Mateu"), person("Andrew", "Bevan")), journal = "Journal of Statistical Software", year = "2013", volume = "55", number = "11", pages = "1--43", doi = "10.18637/jss.v055.i11", header = "If you use hybrid models, please also cite:" ), bibentry(bibtype = "Article", title = "{spatstat}: An {R} Package for Analyzing Spatial Point Patterns", author = c(person("Adrian", "Baddeley"), person("Rolf", "Turner")), journal = "Journal of Statistical Software", year = "2005", volume = "12", number = "6", pages = "1--42", doi = "10.18637/jss.v012.i06", header = "In survey articles, please also cite the original paper on spatstat:" ) )

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M:Uendstream endobj 471 0 obj << /Filter /FlateDecode /Length 341 >> stream x]n0D|l)KrɡU1"п̒Dzس;oq/Ӹ:ǯ8kϏ5Kq*cܞ8gI9.apKgSMq} 1aXr;2:cSQ[0= =$2@kDe=kQȢվ9Me;"|n^uݛ43*&"PGpp?EQ=Q9DAT@z=^G@!Kk="Px})c]Ӵ:pK)m2/tP endstream endobj 472 0 obj << /Type /XRef /Length 704 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Info 3 0 R /Root 2 0 R /Size 473 /ID [<4412529edfb53bcfc0605cefd23ffacc><1269f4a3503e5763a3dc23ff13f2e394>] >> stream xMOQZmm TAEQ"(hН1a"޿qNcc5~Q?PAb! ]<{߹sϜV\w. ~v~^}'ZS(mLLvBn>/gn23f,"N$g` O#^5O_5(VplT҃[f@Fy3^;Q|U &21B}fftX^:"iD<8ސws̑ژh֏4Ҥt 9RuXVOӻռyի#xO}T{ܕ<i>h1B+c|Ghj> /DԉfoL<=M,e)+QeRL7v-X}y%8gM߿ fQ2K4QZS Z uo\r[LEH8_Ua :MV=J{Ւ:q\*]u?7Alj$7vD /*޽Ex~*07ެ/?!Nhп\ qZ`w9$O=?M0Qоl҉ܫ=H>Yo rOdKVrw\wS endstream endobj startxref 217044 %%EOF spatstat/inst/doc/umbrella.txt0000644000176200001440000000000414243060071016172 0ustar liggesusersyes spatstat/inst/doc/getstart.Rnw0000644000176200001440000003126414243060071016167 0ustar liggesusers\documentclass[11pt]{article} % \VignetteIndexEntry{Getting Started with Spatstat} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \usepackage{graphicx} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) @ \title{Getting started with \texttt{spatstat}} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle Welcome to \spst, a package in the \R\ language for analysing spatial point patterns. This document will help you to get started with \spst. It gives you a quick overview of \spst, and some cookbook recipes for doing basic calculations. \section*{What kind of data does \spst\ handle?} \Spst\ is mainly designed for analysing \emph{spatial point patterns}. For example, suppose you are an ecologist studying plant seedlings. You have pegged out a $10 \times 10$ metre rectangle for your survey. Inside the rectangle you identify all the seedlings of the species you want, and record their $(x,y)$ locations. You can plot the $(x,y)$ locations: <>= data(redwood) plot(redwood, pch=16, main="") @ This is a \emph{spatial point pattern} dataset. Methods for analysing this kind of data are summarised in the highly recommended book by Diggle \cite{digg03}, or our own book \cite{baddrubaturn15}, or other references in the bibliography below. \nocite{handbook10,bivapebegome08} Alternatively the points could be locations in one dimension (such as road accidents recorded on a road network) or in three dimensions (such as cells observed in 3D microscopy). You might also have recorded additional information about each seedling, such as its height, or the number of fronds. Such information, attached to each point in the point pattern, is called a \emph{mark} variable. For example, here is a stand of pine trees, with each tree marked by its diameter at breast height (dbh). The circle radii represent the dbh values (not to scale). <>= data(longleaf) plot(longleaf, main="") @ You might also have recorded supplementary data, such as the terrain elevation, which might serve as explanatory variables. These data can be in any format. \Spst\ does not usually provide capabilities for analysing such data in their own right, but \spst\ does allow such explanatory data to be taken into account in the analysis of a spatial point pattern. \Spst\ is \underline{\bf not} designed to handle point data where the $(x,y)$ locations are fixed (e.g.\ temperature records from the state capital cities in Australia) or where the different $(x,y)$ points represent the same object at different times (e.g.\ hourly locations of a tiger shark with a GPS tag). These are different statistical problems, for which you need different methodology. \section*{What can \spst\ do?} \Spst\ supports a very wide range of popular techniques for statistical analysis for spatial point patterns, for example \begin{itemize} \item kernel estimation of density/intensity \item quadrat counting and clustering indices \item detection of clustering using Ripley's $K$-function \item spatial logistic regression \item model-fitting \item Monte Carlo tests \end{itemize} as well as some advanced statistical techniques. \Spst\ is one of the largest packages available for \R, containing over 1000 commands. It is the product of 25 years of software development by leading researchers in spatial statistics. \section*{How do I start using \spst?} \begin{enumerate} \item Install \R\ on your computer \begin{quote} Go to \texttt{r-project.org} and follow the installation instructions. \end{quote} \item Install the \spst\ package in your \R\ system \begin{quote} Start \R\ and type \verb!install.packages("spatstat")!. If that doesn't work, go to \texttt{r-project.org} to learn how to install Contributed Packages. \end{quote} \item Start \R\ \item Type \texttt{library(spatstat)} to load the package. \item Type \texttt{help(spatstat)} for information. \end{enumerate} \section*{How do I get my data into \spst?} <>= data(finpines) mypattern <- unmark(finpines) mydata <- round(as.data.frame(finpines), 2) @ Here is a cookbook example. Suppose you've recorded the $(x,y)$ locations of seedlings, in an Excel spreadsheet. You should also have recorded the dimensions of the survey area in which the seedlings were mapped. \begin{enumerate} \item In Excel, save the spreadsheet into a comma-separated values (CSV) file. \item Start \R\ \item Read your data into \R\ using \texttt{read.csv}. \begin{quote} If your CSV file is called \texttt{myfile.csv} then you could type something like <>= mydata <- read.csv("myfile.csv") @ to read the data from the file and save them in an object called \texttt{mydata} (or whatever you want to call it). You may need to set various options inside the \texttt{read.csv()} command to get this to work for your file format: type \texttt{help(read.csv)} for information. \end{quote} \item Check that \texttt{mydata} contains the data you expect. \begin{quote} For example, to see the first few rows of data from the spreadsheet, type <<>>= head(mydata) @ To select a particular column of data, you can type \texttt{mydata[,3]} to extract the third column, or \verb!mydata$x! to extract the column labelled \texttt{x}. \end{quote} \item Type \texttt{library(spatstat)} to load the \spst\ package \item Now convert the data to a point pattern object using the \spst\ command \texttt{ppp}. \begin{quote} Suppose that the \texttt{x} and \texttt{y} coordinates were stored in columns 3 and 7 of the spreadsheet. Suppose that the sampling plot was a rectangle, with the $x$ coordinates ranging from 100 to 200, and the $y$ coordinates ranging from 10 to 90. Then you would type <>= mypattern <- ppp(mydata[,3], mydata[,7], c(100,200), c(10,90)) @ The general form is <>= ppp(x.coordinates, y.coordinates, x.range, y.range) @ Note that this only stores the seedling locations. If you have additional columns of data (such as seedling height, seedling sex, etc) these can be added as \emph{marks}, later. \end{quote} \item Check that the point pattern looks right by plotting it: <>= plot(mypattern) @ \item Now you are ready to do some statistical analysis. Try the following: \begin{itemize} \item Basic summary of data: type <>= summary(mypattern) @ \item Ripley's $K$-function: <>= options(SweaveHooks=list(fig=function() par(mar=rep(4,4)+0.1))) @ <>= plot(Kest(mypattern)) @ For more information, type \texttt{help(Kest)} \item Envelopes of $K$-function: <>= plot(envelope(mypattern,Kest)) @ <>= env <- envelope(mypattern,Kest, nsim=39) @ <>= plot(env, main="envelope(mypattern, Kest)") @ <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ For more information, type \texttt{help(envelope)} \item kernel smoother of point density: <>= plot(density(mypattern)) @ For more information, type \texttt{help(density.ppp)} \end{itemize} \item Next if you have additional columns of data recording (for example) the seedling height and seedling sex, you can add these data as \emph{marks}. Suppose that columns 5 and 9 of the spreadsheet contained such values. Then do something like <>= marks(mypattern) <- mydata[, c(5,9)] @ <>= mypattern <-finpines @ Now you can try things like the kernel smoother of mark values: <>= plot(Smooth(mypattern)) @ \setkeys{Gin}{width=0.8\textwidth} <>= plot(Smooth(mypattern, sigma=1.2), main="Smooth(mypattern)") @ \setkeys{Gin}{width=0.4\textwidth} \item You are airborne! Now look at the book \cite{baddrubaturn15} for more hints. \end{enumerate} \section*{How do I find out which command to use?} Information sources for \spst\ include: \begin{itemize} \item the Quick Reference guide: a list of the most useful commands. \begin{quote} To view the quick reference guide, start \R, then type \texttt{library(spatstat)} and then \texttt{help(spatstat)}. Alternatively you can download a pdf of the Quick Reference guide from the website \texttt{www.spatstat.org} \end{quote} \item online help: \begin{quote} The online help files are useful --- they give detailed information and advice about each command. They are available when you are running \spst. To get help about a particular command \texttt{blah}, type \texttt{help(blah)}. There is a graphical help interface, which you can start by typing \texttt{help.start()}. Alternatively you can download a pdf of the entire manual (1000 pages!) from the website \texttt{www.spatstat.org}. \end{quote} \item vignettes: \begin{quote} \Spst\ comes installed with several `vignettes' (introductory documents with examples) which can be accessed using the graphical help interface. They include a document about \texttt{Handling shapefiles}. \end{quote} \item book: \begin{quote} Our book \cite{baddrubaturn15} contains a complete course on \texttt{spatstat}. \end{quote} \item website: \begin{quote} Visit the \spst\ package website \texttt{www.spatstat.org} \end{quote} \item forums: \begin{quote} Join the forum \texttt{R-sig-geo} by visiting \texttt{r-project.org}. Then email your questions to the forum. Alternatively you can ask the authors of the \spst\ package (their email addresses are given in the package documentation). \end{quote} \end{itemize} \begin{thebibliography}{10} % \bibitem{badd10wshop} % A. Baddeley. % \newblock Analysing spatial point patterns in {{R}}. % \newblock Technical report, CSIRO, 2010. % \newblock Version 4. % \newblock URL \texttt{https://research.csiro.au/software/r-workshop-notes/} % \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \bibitem{bivapebegome08} R. Bivand, E.J. Pebesma, and V. G{\'{o}}mez-Rubio. \newblock {\em Applied spatial data analysis with {R}}. \newblock Springer, 2008. \bibitem{cres93} N.A.C. Cressie. \newblock {\em Statistics for Spatial Data}. \newblock {John Wiley and Sons}, {New York}, second edition, 1993. \bibitem{digg03} P.J. Diggle. \newblock {\em Statistical Analysis of Spatial Point Patterns}. \newblock Hodder Arnold, London, second edition, 2003. \bibitem{fortdale05} M.J. Fortin and M.R.T. Dale. \newblock {\em Spatial analysis: a guide for ecologists}. \newblock Cambridge University Press, Cambridge, UK, 2005. \bibitem{fothroge09handbook} A.S. Fotheringham and P.A. Rogers, editors. \newblock {\em The {SAGE} {H}andbook on {S}patial {A}nalysis}. \newblock SAGE Publications, London, 2009. \bibitem{gaetguyo09} C. Gaetan and X. Guyon. \newblock {\em Spatial statistics and modeling}. \newblock Springer, 2009. \newblock Translated by Kevin Bleakley. \bibitem{handbook10} A.E. Gelfand, P.J. Diggle, M. Fuentes, and P. Guttorp, editors. \newblock {\em Handbook of Spatial Statistics}. \newblock CRC Press, 2010. \bibitem{illietal08} J. Illian, A. Penttinen, H. Stoyan, and D. Stoyan. \newblock {\em Statistical Analysis and Modelling of Spatial Point Patterns}. \newblock John Wiley and Sons, Chichester, 2008. \bibitem{mollwaag04} J. M{\o}ller and R.P. Waagepetersen. \newblock {\em Statistical Inference and Simulation for Spatial Point Processes}. \newblock Chapman and Hall/CRC, Boca Raton, 2004. \bibitem{pfeietal08} D.U. Pfeiffer, T. Robinson, M. Stevenson, K. Stevens, D. Rogers, and A. Clements. \newblock {\em Spatial analysis in epidemiology}. \newblock Oxford University Press, Oxford, UK, 2008. \bibitem{wallgotw04} L.A. Waller and C.A. Gotway. \newblock {\em Applied spatial statistics for public health data}. \newblock Wiley, 2004. \end{thebibliography} \end{document} spatstat/inst/doc/getstart.R0000644000176200001440000001154514517616243015636 0ustar liggesusers### R code from vignette source 'getstart.Rnw' ################################################### ### code chunk number 1: getstart.Rnw:5-6 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) ################################################### ### code chunk number 2: getstart.Rnw:25-32 ################################################### library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) ################################################### ### code chunk number 3: getstart.Rnw:56-58 ################################################### getOption("SweaveHooks")[["fig"]]() data(redwood) plot(redwood, pch=16, main="") ################################################### ### code chunk number 4: getstart.Rnw:80-82 ################################################### getOption("SweaveHooks")[["fig"]]() data(longleaf) plot(longleaf, main="") ################################################### ### code chunk number 5: getstart.Rnw:139-142 ################################################### data(finpines) mypattern <- unmark(finpines) mydata <- round(as.data.frame(finpines), 2) ################################################### ### code chunk number 6: getstart.Rnw:157-158 (eval = FALSE) ################################################### ## mydata <- read.csv("myfile.csv") ################################################### ### code chunk number 7: getstart.Rnw:169-170 ################################################### head(mydata) ################################################### ### code chunk number 8: getstart.Rnw:185-186 (eval = FALSE) ################################################### ## mypattern <- ppp(mydata[,3], mydata[,7], c(100,200), c(10,90)) ################################################### ### code chunk number 9: getstart.Rnw:189-190 (eval = FALSE) ################################################### ## ppp(x.coordinates, y.coordinates, x.range, y.range) ################################################### ### code chunk number 10: getstart.Rnw:199-200 ################################################### getOption("SweaveHooks")[["fig"]]() plot(mypattern) ################################################### ### code chunk number 11: getstart.Rnw:207-208 (eval = FALSE) ################################################### ## summary(mypattern) ################################################### ### code chunk number 12: getstart.Rnw:212-213 ################################################### options(SweaveHooks=list(fig=function() par(mar=rep(4,4)+0.1))) ################################################### ### code chunk number 13: getstart.Rnw:215-216 ################################################### getOption("SweaveHooks")[["fig"]]() plot(Kest(mypattern)) ################################################### ### code chunk number 14: getstart.Rnw:222-223 (eval = FALSE) ################################################### ## plot(envelope(mypattern,Kest)) ################################################### ### code chunk number 15: getstart.Rnw:225-226 ################################################### env <- envelope(mypattern,Kest, nsim=39) ################################################### ### code chunk number 16: getstart.Rnw:228-229 ################################################### getOption("SweaveHooks")[["fig"]]() plot(env, main="envelope(mypattern, Kest)") ################################################### ### code chunk number 17: getstart.Rnw:231-232 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) ################################################### ### code chunk number 18: getstart.Rnw:238-239 ################################################### getOption("SweaveHooks")[["fig"]]() plot(density(mypattern)) ################################################### ### code chunk number 19: getstart.Rnw:249-250 (eval = FALSE) ################################################### ## marks(mypattern) <- mydata[, c(5,9)] ################################################### ### code chunk number 20: getstart.Rnw:252-253 ################################################### mypattern <-finpines ################################################### ### code chunk number 21: getstart.Rnw:256-257 (eval = FALSE) ################################################### ## plot(Smooth(mypattern)) ################################################### ### code chunk number 22: getstart.Rnw:260-261 ################################################### getOption("SweaveHooks")[["fig"]]() plot(Smooth(mypattern, sigma=1.2), main="Smooth(mypattern)") spatstat/inst/doc/spatstatcoreNEWS0000755000176200001440000005707614243357434017026 0ustar liggesusers CHANGES IN spatstat.core VERSION 2.4-4.010 OVERVIEW o Internal improvements. CHANGES IN spatstat.core VERSION 2.4-4 OVERVIEW o Bug fixes and minor improvements. BUG FIXES o rhohat.ppp The argument 'subset' was not handled correctly in the internal data. The estimated function 'rho' was correct, but if 'predict.rhohat' was applied, predictions were computed only in the 'subset', and were possibly incorrect values. Fixed. o Gfox, Jfox Warnings were issued about text formatting errors (mentioning 'sprintf' or 'fmt'). Fixed. CHANGES IN spatstat.core VERSION 2.4-3 OVERVIEW o We thank Art Stock for contributions. o Bug fixes and minor improvements. BUG FIXES o Smooth.ppp Crashed when 'kernel' was a function, 'at="points"' and 'scalekernel=FALSE'. Fixed. o Finhom, Ginhom, Jinhom Crashed when ratio=TRUE. [Spotted by Art Stock.] Fixed. o envelope Crashed for some of the summary functions when ratio=TRUE. [Spotted by Art Stock.] Fixed. o "[.rat" Crashed in some cases. Fixed. o Kcross The result of Kcross() was 'invisible', i.e. it was not automatically printed. Fixed. CHANGES IN spatstat.core VERSION 2.4-2 OVERVIEW o Internal bug fixes. CHANGES IN spatstat.core VERSION 2.4-1 OVERVIEW o We thank Frederic Lavancier, Sebastian Meyer, Suman Rakshit and Sven Wagner for contributions. o Improved approximation of intensity of Gibbs models. o Experimental code to represent (theoretical) point process models o Extract more information about a point process model. o Internal improvements and bug fixes. NEW CLASSES o zgibbsmodel Experimental. An object of class 'zgibbsmodel' represents a Gibbs point process model with specified parameter values (whereas 'ppm' represents a model fitted to data). NEW FUNCTIONS o hardcoredist Extract the hard core distance of a point process model. o interactionorder Extract the order of interpoint interaction of a point process model. o zgibbsmodel Experimental. Create an object of class 'zgibbsmodel'. o print.zgibbsmodel Experimental. Print an object of class 'zgibbsmodel'. o is.poisson.zgibbsmodel, is.stationary.zgibbsmodel Experimental. Methods for class 'zgibbsmodel' SIGNIFICANT USER-VISIBLE CHANGES o intensity.ppm Can now calculate the Coeurjolly-Lavancier DPP approximation of intensity. [Code kindly contributed by Frederic Lavancier] New argument 'approx' specifies the choice of approximation. o relrisk.ppp Ratios which are close to 0/0 are handled more effectively, reducing the likelihood of strange-looking plots when 'sigma' is very small. BUG FIXES o density.ppp Crashed if the observation window had zero area. Fixed. o dirichletVoronoi.ppp Crashed randomly, with obscure error messages from 'im' or 'eval.im', when argument 'f' had a small value. [Spotted by Suman Rakshit.] Fixed. o dirichletVoronoi.ppp Rarely, produced an image containing NA values. [Spotted by Suman Rakshit.] Fixed. o vcov.ppm Crashed in some cases, with message 'object lamdel not found'. [Spotted by Sven Wagner.] Fixed. CHANGES IN spatstat.core VERSION 2.4-0 OVERVIEW o We thank Sriram Ramamurthy for contributions. o spatstat.core now depends on the new package 'spatstat.random'. o Functions for generating random patterns have been removed. o Important bug fixes in anova.mppm and vcov.mppm. o Minor improvements and bug fixes SIGNIFICANT USER-VISIBLE CHANGES o package structure The code for generating random spatial patterns (including 'rpoispp', 'rMatClust', 'rThomas', 'rNeymanScott', 'rStrauss', 'rmh') has been removed from 'spatstat.core' and placed in a new package 'spatstat.random'. This new package is required by 'spatstat.core'. o anova.mppm Improved labelling of models in output. o qqplot.ppm, plot.qqppm Improved the text label indicating the type of residuals. o reload.or.compute New argument 'context' BUG FIXES o vcov.mppm For Gibbs (non-Poisson) models, the variance matrix was calculated incorrectly in some cases. Fixed. o anova.mppm Results were sometimes incorrect if the two models had different interactions (e.g. Strauss vs Poisson). Fixed. o anova.mppm Crashed for some models with a message about 'coefficient missing from new.coef'. Fixed. o anova.mppm Gave a warning for some models about "Internal error: unable to map submodels to full model". Fixed. o addvar If the covariate contained NA, NaN or Infinite values, the calculations were sometimes incorrect. Fixed. o pcfmodel.ppm Refused to handle an inhomogeneous Poisson process. Fixed. o fitted.ppm Crashed if leaveoneout=TRUE and the model had no fitted coefficients. Fixed. o reload.or.compute Scoping error (crashed sometimes if called from a non-global environment). Fixed. CHANGES IN spatstat.core VERSION 2.3-2 OVERVIEW o More diagnostics for spatial logistic regression models. o Important bug fix in kppm. o Increased numerical stability in kppm. o Minor improvements and bug fixes. o We thank Jonas Brehmer for contributions. NEW FUNCTIONS o lurking.slrm Lurking variable plot for spatial logistic regression models. o eem.slrm Exponential energy marks for spatial logistic regression models. o eem.ppm Exponential energy marks for Gibbs and Poisson point process models (this function was previously called 'eem'). SIGNIFICANT USER-VISIBLE CHANGES o eem The function 'eem' is now generic, with methods for 'ppm' and 'slrm'. The function previously named 'eem' is now called 'eem.ppm'. o objsurf.dppm, objsurf.kppm, objsurf.mincontrast New arguments 'xlim', 'ylim' determine the range of parameter values to be considered. o Hybrid Printed output from hybrid models has been improved slightly. o kppm New default settings ensure greater numerical stability of the optimization algorithm against the effects of the scale of the spatial coordinates. New argument 'stabilize' specifies whether the optimization algorithm should be numerically stabilized. o pcf Improved error message BUG FIXES o kppm Results were sometimes incorrect for method='clik2' and method='palm' because the log composite likelihood was erroneously truncated to positive values. Any fitted model for which logLik(model) = 2.2e-16 should be suspected of being incorrect. Fixed. o edge.Ripley Results were incorrect for data points lying exactly at the corners of a rectangle. Fixed. o MultiHard A hybrid of 'MultiHard' with another multitype interaction caused an error. Fixed. o simulate.dppm, simulate.detpointprocfamily Crashed, rarely, with error message 'The dimension differs from the number of columns in index'. Fixed. o as.fv.kppm, as.fv.dppm The default plot labels in 'as.fv(x)' implied that the model 'x' was inhomogeneous. Fixed. CHANGES IN spatstat.core VERSION 2.3-1 OVERVIEW o Covariates in ppm and mppm may be functions that depend on the marks as well as the spatial coordinates. o Automatic selection of threshold for defining a binary predictor. o Random perturbation of line segments. o Minor extensions, performance improvements, and bug fixes. NEW FUNCTIONS o thresholdSelect, thresholdCI Select the optimal threshold for converting a numerical predictor to a binary predictor. o coef<-.fii Changes the coefficients of a fitted interaction object (a method for the generic "coef<-") o rjitter.psp Random perturbation of a spatial pattern of line segments. A method for 'rjitter' for class 'psp'. SIGNIFICANT USER-VISIBLE CHANGES o ppm Covariates which are functions may now depend on the marks as well as the spatial coordinates: function(x,y,marks). o mppm Covariates which are functions may now depend on the marks as well as the spatial coordinates: function(x,y,marks). o reach.kppm Now returns a result for LGCP models as well as cluster models. o distcdf Improved regularisation algorithm. Argument 'nr=NULL' is now accepted. New argument 'delta' allows the result to be interpolated onto a finer grid. o collapse.fv Columns identified by the arguments 'same' and 'different' may now be absent from some of the 'fv' objects that will be collapsed. o Kest When the argument 'domain' is given, the calculation of estimates of K(r) has changed slightly, to adhere more closely to the description in the help file. o reload.or.compute Now prints a message indicating whether the data were recomputed or reloaded from file. New argument 'verbose'. o update.kppm New argument 'envir'. o clusterfit Minor changes to the argument list. o mincontrast Minor changes to the argument list. o mincontrast Improved algorithm for handling NA, NaN or infinite values. o varcount Argument B has a sensible default. o pool.envelope Now uses the value of 'nrank' which was used in the original envelopes. o Kmulti New argument 'rmax'. o Kinhom No longer issues a warning about changed behaviour in the case where 'lambda' is a fitted model. o pcfinhom No longer issues a warning about changed behaviour in the case where 'lambda' is a fitted model. BUG FIXES o subfits The fitted interaction coefficients were garbled. If 'mfit' is the mppm object and 'a <- subfits(mfit)[[i]]' is one of the sub-models, then coef(a) was correct, but coef(fitin(a)) was incorrect. The fitted interaction was shown correctly by printing 'mfit' but incorrectly by printing 'a'. Fixed. o varcount The result was very inaccurate if the cluster radius was small compared to the size of the window 'B', due to discretisation error. Fixed. o segregation.test The test statistic was calculated as the mean, rather than the sum, of discrepancies between probabilities. (The p-value was not affected.) Fixed. o Kest If 'domain' was specified, 'rmax' was ignored. Fixed. o edge.Ripley Value was incorrect for a point lying exactly on a corner. Fixed. o edge.Ripley Crashed when method="interpreted", if a point lay exactly on a corner. Fixed. o plot.fv, plot.envelope Crashed when trying to display a significance band of width zero around a constant function. Fixed. o collapse.fv Crashed if 'length(same) > 1'. Fixed. CHANGES IN spatstat.core VERSION 2.3-0 OVERVIEW o We thank Chiara Fend for contributions. o Extensive support for spatial logistic regression models. o New fitting method in kppm and dppm. o Transect of an image along a curve. o Image cross-correlation and cross-covariance. o Summary method for 'objsurf'. o Minor bug fixes. NEW FUNCTIONS o response Generic function which extracts the values of the response in a fitted model. There are methods for lm, glm (which extract the numeric vector of responses), and ppm, kppm, slrm, lppm and mppm (which extract the original data point pattern). o cov.im, cor.im Correlation or covariance between several pixel images. o summary.objsurf, print.summary.objsurf Summary method for 'objsurf' o residuals.slrm Residuals for spatial logistic regression models. o leverage.slrm, influence.slrm, dfbetas.slrm, dffit.slrm Leverage and influence diagnostics for spatial logistic regression models. o rhohat.slrm Method for 'rhohat' for spatial logistic regression models. o envelope.slrm Method for 'envelope' for spatial logistic regression models. o intensity.slrm Method for 'intensity' for spatial logistic regression models. o deviance.slrm Method for 'deviance' for spatial logistic regression models. o pseudoR2.slrm Method for 'pseudoR2' for spatial logistic regression models. o quadrat.test.slrm Method for 'quadrat.test' for spatial logistic regression models. o parameters.slrm Method for 'parameters' for spatial logistic regression models. o valid.slrm Method for 'valid' for spatial logistic regression models. o emend.slrm Method for 'emend' for spatial logistic regression models. o roc.slrm Method for 'roc' for spatial logistic regression models. o auc.slrm Method for 'auc' for spatial logistic regression models. o Window.slrm, as.owin.slrm Methods for 'Window' and 'as.owin' for spatial logistic regression models. SIGNIFICANT USER-VISIBLE CHANGES o kppm, dppm New option 'method="adapcl"' performs adaptive composite likelihood fitting. [Contributed by Chiara Fend.] o transect.im New argument 'curve' allows the user to specify a curved transect. BUG FIXES o clusterfield Values of the cluster field were slightly incorrect (slightly higher than the correct values) near the edge of the window, because an 'edge correction' was mistakenly applied. Fixed. o rhohat The rug plot (produced by plot.rhohat) was incorrect when rhohat was called with method="piecewise". Fixed. o markcrosscorr Did not recognise the option 'correction="none"'. Fixed. o roc.ppp The default plot of the result of roc.ppp did not include the diagonal line 'y=x'. Fixed. CHANGES IN spatstat.core VERSION 2.2-0 OVERVIEW o We thank Abdollah Jalilian, Yongtao Guan and Rasmus Waagepetersen for contributions. o summary method for spatial logistic regression models o estimation of the spatial covariance function of a pixel image o simulation of the product shot noise Cox process. o extensions to rhohat NEW FUNCTIONS o rPSNCP Generate simulated realisations of the product shot noise Cox process. Contributed by Abdollah Jalilian, Yongtao Guan and Rasmus Waagepetersen. o spatcov Estimate the spatial covariance function of a pixel image. o summary.slrm, print.summary.slrm Summary method for spatial logistic regression models o coef.summary.slrm Print the fitted coefficients, confidence interval and p-values for a spatial logistic regression model. o pairMean Compute the mean of a specified function of interpoint distance between random points in a window. SIGNIFICANT USER-VISIBLE CHANGES o rhohat New option (smoother='piecewise') computes a piecewise-constant estimate of rho(z). o rhohat The result now includes the 'average' intensity rho. o distcdf Arguments which are NULL will be treated as missing. o distcdf New argument 'savedenom'. CHANGES IN spatstat.core VERSION 2.1-2 OVERVIEW o Reduced CRAN check time. CHANGES IN spatstat.core VERSION 2.1-1 OVERVIEW o Minor bug fix BUG FIXES o simulate.kppm Conditional simulation crashed on rare occasions, with an error about negative probabilities. Fixed. CHANGES IN spatstat.core VERSION 2.1-0 OVERVIEW o We thank Achmad Choiruddin, Jean-Francois Coeurjolly and Rasmus Waagepetersen for contributions. o Conditional simulation in kppm o Information criteria for model selection in kppm o Modified handling of covariates in slrm o densityfun.ppp handles query points outside original window o Improved output in summary.mppm o Minor improvements and bug fixes. NEW FUNCTIONS o ic Information criteria for model selection in ppm and kppm. Kindly contributed by Achmad Choiruddin, Jean-Francois Coeurjolly and Rasmus Waagepetersen. SIGNIFICANT USER-VISIBLE CHANGES o simulate.kppm Conditional simulation of the model, given a fixed number of points, is now supported using the new arguments 'n.cond' and 'w.cond'. o densityfun.ppp The resulting function can now handle query points which lie outside the window of the original data, and has argument 'drop=TRUE' which specifies how to handle them. o rpoint New argument 'forcewin' forces the code to use the window 'win' when 'f' is a pixel image. o slrm In the default case (where dataAtPoints is not given) all spatial covariates, including the spatial coordinates x and y, are now evaluated at the centre of each pixel. This improves consistency with other implementations of spatial logistic regression. o slrm Silently ignores any arguments '...' that are not recognised by 'as.mask' o summary.mppm Improved summary of the dependence of the interpoint interaction on the covariates. o pairs.im New argument 'drop'. BUG FIXES o model.matrix.mppm If the model was fitted using 'gam', the resulting matrix did not have an 'assign' attribute. Fixed. o model.depends Crashed for models fitted using 'gam'. Fixed. o predict.slrm, fitted.slrm Crashed if the model was fitted using split pixels (argument 'splitby'). Fixed. o predict.slrm, fitted.slrm Crashed in some cases when 'window' was given. Fixed. o update.slrm Failed to find covariates that were provided in 'env'. Fixed. o cdf.test Crashed if the covariate was constant. Fixed. CHANGES IN spatstat.core VERSION 2.0-0 OVERVIEW o We thank Tilman Davies, Greg McSwiggan and Suman Rakshit for contributions. o We thank Corey Anderson, Michael Chirico, Andy Craig, Marcelino de la Cruz, Tilman Davies, Pavel Fibich, Kurt Hornik, Gopalan Nair, Yonatan Rosen and Rasmus Waagepetersen for contributions. o Diffusion kernel smoothing. o More support for spatial logistic regression models. o predict.mppm now works for multitype point process models. o Improved handling of 'newdata' in predict.mppm. o More support for multi-dimensional patterns. NEW FUNCTIONS o densityHeat New generic function for diffusion kernel estimation of intensity o densityHeat.ppp Diffusion kernel estimation of intensity for point pattern in 2 dimensions. This is an alternative to density.ppp. o intersect.boxx Compute intersection of boxes in multi-dimensional space o scale.boxx, scale.ppx Methods for 'scale' for boxes and patterns in multi-dimensional space o shift.boxx, shift.ppx Methods for 'shift' for boxes and patterns in multi-dimensional space o is.boxx Determine whether an object is a multidimensional box o mincontrast New argument 'action.bad.values' specifies what action is taken when the summary function produces NA or NaN or infinite values. SIGNIFICANT USER-VISIBLE CHANGES o slrm 'step' can now be applied to models fitted using 'slrm'. o predict.mppm Now supports multitype point process models. o predict.mppm Improved handling of argument 'newdata' o rotmean The result now has the same 'unitname' as the input object X. New argument 'adjust' controls the smoothing bandwidth. o rlabel New argument 'group' specifies that the points are divided into several groups, and that relabelling is applied within each group. o simulate.ppm Now recognises the argument 'window' as an alternative to 'w'. o kppm Improved numerical robustness. o Kcross, Gcross, Jcross Function labels (shown on the plot legend) have been improved when i = j. o anova.mppm Issues a warning when applied to random-effects models (models fitted using the argument 'random'). BUG FIXES o Gest If correction="rs" or correction="km", then both the reduced-sample (border correction) and Kaplan-Meier corrected estimates were calculated. [Spotted by Gopalan Nair.] Fixed. o rMatClust, rThomas, rCauchy, rVarGamma If the fitted model was effectively a Poisson process, the result did not have attributes 'Lambda' and 'parents' even when the user requested them. Fixed. o model.matrix.mppm Crashed with random-effects models. Fixed. o anova.mppm Crashed with random-effects models. Fixed. o simulate.rhohat Crashed when applied to rhohat objects computed from data on a linear network. Fixed. o objsurf.kppm Crashed if the model was fitted by Palm likelihood (method="palm") or second order composite likelihood (method="clik2"). Fixed. CHANGES IN spatstat.core VERSION 1.65-11 OVERVIEW o Internal tweaks. CHANGES IN spatstat.core VERSION 1.65-10 OVERVIEW o Minor corrections to documentation. CHANGES IN spatstat.core VERSION 1.65-9 OVERVIEW o We thank Ian Buller for a suggestion. o weights permitted in density calculation for line segments. SIGNIFICANT USER-VISIBLE CHANGES o density.psp New argument 'weights'. CHANGES IN spatstat.core VERSION 1.65-8 OVERVIEW o Minor changes to appease the compiler. CHANGES IN spatstat.core VERSION 1.65-7 OVERVIEW o We thank Michael Chirico for a contribution. o Minor changes to appease the compiler. CHANGES IN spatstat.core VERSION 1.65-6 OVERVIEW o We thank Tilman Davies and Pavel Fibich for contributions. o Important bug fix in simulation of log-Gaussian Cox processes. o Increased speed for large datasets. o variance calculations handle larger datasets. SIGNIFICANT USER-VISIBLE CHANGES o vcov.ppm, summary.ppm Variance calculations now handle larger datasets (using sparse arrays). o rSSI Accelerated. o overall speed Changes have been made to the internal code of spatstat which should accelerate computations involving large datasets. o localpcf, localpcfinhom New argument 'rvalue'. BUG FIXES o rLGCP, simulate.kppm Simulation results for log-Gaussian Cox processes were incorrect unless the pixel dimensions and pixel spacings were identical on the horizontal and vertical axes. (If pixel dimensions were not specified, then the results were incorrect whenever the Frame of the simulation window was not a square.) [Spotted by Tilman Davies.] Fixed. o Vmark Crashed if normalise=TRUE when there was only one column of marks. (Spotted by Pavel Fibich.) Fixed. o nnclean Crashed if k >= npoints(X). Fixed. o print.ppm Crashed sometimes when applied to the result of subfits(). Fixed. CHANGES IN spatstat.core VERSION 1.65-5 OVERVIEW o Minor changes required by CRAN. CHANGES IN spatstat.core VERSION 1.65-1 OVERVIEW o Added NEWS file. CHANGES IN spatstat.core VERSION 1.65-0 OVERVIEW o Package initialised at version 1.65-0 SIGNIFICANT USER-VISIBLE CHANGES o spatstat.core The package 'spatstat.core' has been created from a subset of the code in the original 'spatstat' package version 1.65-0. It contains the core functionality for statistical analysis of spatial data. 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plot(Gest(finpines)) ################################################### ### code chunk number 13: Gplot ################################################### getOption("SweaveHooks")[["fig"]]() aa <- plot(Gest(finpines)) ################################################### ### code chunk number 14: fv.Rnw:321-323 (eval = FALSE) ################################################### ## aa <- plot(Gest(finpines)) ## aa ################################################### ### code chunk number 15: fv.Rnw:325-326 ################################################### aa ################################################### ### code chunk number 16: fv.Rnw:384-386 ################################################### G <- Gest(finpines) G ################################################### ### code chunk number 17: Kiso ################################################### getOption("SweaveHooks")[["fig"]]() plot(K, iso ~ r) ################################################### ### code chunk number 18: Kit ################################################### getOption("SweaveHooks")[["fig"]]() plot(K, cbind(iso, theo) ~ r) ################################################### ### code chunk number 19: fv.Rnw:495-497 ################################################### fvnames(K, ".y") fvnames(K, ".") ################################################### ### code chunk number 20: Ksubtheo ################################################### getOption("SweaveHooks")[["fig"]]() plot(K, . - theo ~ r) ################################################### ### code chunk number 21: Ktheo ################################################### getOption("SweaveHooks")[["fig"]]() plot(K, . ~ theo) ################################################### ### code chunk number 22: Kcox ################################################### getOption("SweaveHooks")[["fig"]]() plot(K, . ~ r^2) ################################################### ### code chunk number 23: Kswed ################################################### getOption("SweaveHooks")[["fig"]]() lambda <- intensity(swedishpines) plot(K, lambda * . ~ r) ################################################### ### code chunk number 24: fv.Rnw:548-551 (eval = FALSE) ################################################### ## K <- Kest(cells) ## K/pi ## sqrt(K/pi) ################################################### ### code chunk number 25: fv.Rnw:563-564 (eval = FALSE) ################################################### ## sqrt(Kest(cells)/pi) ################################################### ### code chunk number 26: fv.Rnw:583-584 (eval = FALSE) ################################################### ## Kpos <- eval.fv(pmax(0, K)) ################################################### ### code chunk number 27: fv.Rnw:611-614 ################################################### Kr <- Kest(redwood) z <- with(Kr, iso - theo) x <- with(Kr, r) ################################################### ### code chunk number 28: fv.Rnw:633-634 ################################################### Kcen <- with(Kr, . - theo) ################################################### ### code chunk number 29: fv.Rnw:641-642 ################################################### with(Kr, max(abs(iso-theo))) ################################################### ### code chunk number 30: fv.Rnw:653-654 ################################################### df <- as.data.frame(K) ################################################### ### code chunk number 31: fv.Rnw:679-680 ################################################### Ko <- subset(K, r < 0.1, select= -border) ################################################### ### code chunk number 32: fv.Rnw:690-693 ################################################### Ks <- Kest(swedishpines) kfun <- as.function(Ks) kfun(9) ################################################### ### code chunk number 33: fv.Rnw:705-708 ################################################### kf <- as.function(Ks, value=".") kf(9) kf(9, "trans") ################################################### ### code chunk number 34: fv.Rnw:751-754 (eval = FALSE) ################################################### ## Kcel <- Kest(cells) ## Kred <- Kest(redwood) ## Kdif <- Kcel - Kred ################################################### ### code chunk number 35: fv.Rnw:774-775 (eval = FALSE) ################################################### ## Kest(cells) - Kest(redwood) ################################################### ### code chunk number 36: fv.Rnw:786-789 (eval = FALSE) ################################################### ## Kcel <- Kest(cells) ## Kred <- Kest(redwood) ## Kmax <- eval.fv(pmax(Kcel, Kred)) ################################################### ### code chunk number 37: fv.Rnw:805-807 (eval = FALSE) ################################################### ## Kmax <- eval.fm(pmax(Kcel, Kred), ## envir=list(Kcel=Kest(cells), Kred=Kest(redwood))) ################################################### ### code chunk number 38: fv.Rnw:1024-1028 ################################################### makefvlabel(NULL, NULL, "K", "pois") makefvlabel(NULL, "hat", "K", "bord") makefvlabel(NULL, "hat", c("K", "inhom"), "bord") makefvlabel("var", "hat", c("K", "inhom"), "bord") ################################################### ### code chunk number 39: fv.Rnw:1081-1083 (eval = FALSE) ################################################### ## compileK(D, r, weights = NULL, denom = 1, ...) ## compilepcf(D, r, weights = NULL, denom = 1, ...) ################################################### ### code chunk number 40: fv.Rnw:1118-1126 ################################################### X <- japanesepines D <- pairdist(X) Wt <- edge.Ripley(X, D) lambda <- intensity(X) a <- (npoints(X)-1) * lambda r <- seq(0, 0.25, by=0.01) K <- compileK(D=D, r=r, weights=Wt, denom=a) g <- compilepcf(D=D, r=r, weights=Wt, denom= a * 2 * pi * r) ################################################### ### code chunk number 41: fv.Rnw:1141-1142 (eval = FALSE) ################################################### ## compileCDF(D, B, r, ..., han.denom = NULL) ################################################### ### code chunk number 42: fv.Rnw:1158-1166 ################################################### X <- japanesepines D <- nndist(X) B <- bdist.points(X) r <- seq(0, 1, by=0.01) h <- eroded.areas(Window(X), r) G <- compileCDF(D=D, B=B, r=r, han.denom=h) ## give it a better name G <- rebadge.fv(G, new.fname="G", new.ylab=quote(G(r))) ################################################### ### code chunk number 43: fv.Rnw:1217-1221 (eval = FALSE) ################################################### ## rebadge.fv(x, new.ylab, new.fname, ## tags, new.desc, new.labl, ## new.yexp=new.ylab, new.dotnames, ## new.preferred, new.formula, new.tags) ################################################### ### code chunk number 44: fv.Rnw:1277-1278 (eval = FALSE) ################################################### ## tweak.fv.entry(x, current.tag, new.labl=NULL, new.desc=NULL, new.tag=NULL) ################################################### ### code chunk number 45: fv.Rnw:1294-1296 (eval = FALSE) ################################################### ## prefixfv(x, tagprefix="", descprefix="", lablprefix=tagprefix, ## whichtags=fvnames(x, "*")) ################################################### ### code chunk number 46: fv.Rnw:1309-1310 (eval = FALSE) ################################################### ## rebadge.as.crossfun(x, main, sub=NULL, i, j) ################################################### ### code chunk number 47: fv.Rnw:1316-1317 (eval = FALSE) ################################################### ## rebadge.as.crossfun(x, "L", i="A", j="B") ################################################### ### code chunk number 48: fv.Rnw:1320-1321 (eval = FALSE) ################################################### ## rebadge.as.crossfun(x, "L", "inhom", "A", "B") ################################################### ### code chunk number 49: fv.Rnw:1331-1332 (eval = FALSE) ################################################### ## rebadge.as.dotfun(x, main, sub=NULL, i) ################################################### ### code chunk number 50: fv.Rnw:1368-1370 ################################################### K <- Kest(cells) formula(K) ################################################### ### code chunk number 51: fv.Rnw:1377-1378 ################################################### fvnames(K, ".") ################################################### ### code chunk number 52: fv.Rnw:1387-1388 ################################################### fvnames(K, ".") <- c("iso", "theo") ################################################### ### code chunk number 53: fv.Rnw:1444-1446 ################################################### class(Kest(cells)) class(Kest(cells, ratio=TRUE)) ################################################### ### code chunk number 54: fv.Rnw:1482-1487 ################################################### X1 <- runifpoint(50) X2 <- runifpoint(50) K1 <- Kest(X1, ratio=TRUE) K2 <- Kest(X2, ratio=TRUE) K <- pool(K1, K2) ################################################### ### code chunk number 55: fv.Rnw:1490-1493 ################################################### Xlist <- runifpoint(50, nsim=6) Klist <- lapply(Xlist, Kest, ratio=TRUE) K <- do.call(pool, Klist) ################################################### ### code chunk number 56: fv.Rnw:1509-1510 (eval = FALSE) ################################################### ## ratfv(df, numer, denom, ..., ratio=TRUE) ################################################### ### code chunk number 57: fv.Rnw:1574-1577 ################################################### G <- Gest(finpines) df <- as.data.frame(G) head(df) ################################################### ### code chunk number 58: fv.Rnw:1598-1599 ################################################### G ################################################### ### code chunk number 59: fv.Rnw:1687-1688 (eval = FALSE) ################################################### ## E <- envelope(swp, Kest, nsim=39, fix.n=TRUE) ################################################### ### code chunk number 60: fv.Rnw:1696-1697 ################################################### E ################################################### ### code chunk number 61: fv.Rnw:1716-1719 (eval = FALSE) ################################################### ## E1 <- envelope(redwood, Kest, savepatterns=TRUE) ## E2 <- envelope(E1, Gest, global=TRUE, ## transform=expression(fisher(.))) ################################################### ### code chunk number 62: fv.Rnw:1726-1729 (eval = FALSE) ################################################### ## A1 <- envelope(redwood, Kest, nsim=39, savefuns=TRUE) ## A2 <- envelope(A1, global=TRUE, nsim=19, ## transform=expression(sqrt(./pi))) ################################################### ### code chunk number 63: fv.Rnw:1743-1746 (eval = FALSE) ################################################### ## E1 <- envelope(cells, Kest, nsim=10, savefuns=TRUE) ## E2 <- envelope(cells, Kest, nsim=20, savefuns=TRUE) ## E <- pool(E1, E2) spatstat/inst/doc/Nickname.txt0000644000176200001440000000002314243060071016115 0ustar liggesusers"Watch this space" spatstat/inst/doc/BEGINNER.txt0000644000176200001440000000202314243060071015563 0ustar liggesusers -== Welcome to the 'spatstat' package! ==- For a friendly introduction to spatstat, type the command vignette('getstart') which displays the document "Getting Started with Spatstat". For an overview of all capabilities, type help(spatstat) View the documentation for any command/function 'foo' by typing help(foo) Activate the graphical help interface by typing help.start() To handle spatial data in the 'shapefile' format, see the document "Handling shapefiles in the spatstat package", by typing vignette('shapefiles') For a complete course on spatstat, see the book "Spatial Point Patterns: Methodology and Applications with R" by Baddeley, Rubak and Turner, Chapman and Hall/CRC Press, December 2015. For a summary of changes to spatstat since the book was finished, type vignette('updates') Visit the website www.spatstat.org for updates and free chapters. For news about the very latest version of spatstat, type latest.news [[[Press 'Q' to exit, on some computers]]] spatstat/inst/doc/replicated.Rnw0000644000176200001440000014174114243060071016450 0ustar liggesusers\documentclass[11pt]{article} % \VignetteIndexEntry{Analysing Replicated Point Patterns in Spatstat} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\indicate}[1]{\boldmaths{1}\{ {#1} \}} \newcommand{\dee}[1]{\, {\rm d}{#1}} \newcommand{\boldmaths}[1]{{\ensuremath\boldsymbol{#1}}} \newcommand{\xx}{\boldmaths{x}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) @ \title{Analysing replicated point patterns in \texttt{spatstat}} \author{Adrian Baddeley} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle \begin{abstract} This document describes \spst's capabilities for fitting models to replicated point patterns. More generally it applies to data from a designed experiment in which the response from each unit is a spatial point pattern. \end{abstract} \tableofcontents \newpage %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} `Replicated point patterns' are datasets consisting of several point patterns which can be regarded as independent repetitions of the same experiment. For example, three point patterns taken from micrographs of three pipette samples of the same jug of milk, could be assumed to be replicated observations. More generally we could have several experimental groups, with replicated point pattern data in each group. For example there may be two jugs of milk that were treated differently, and we take three pipette samples from each jug. Even more generally our point patterns could be the result of a designed experiment involving control and treatment groups, covariates such as temperature, and even spatial covariates (such as image data). This document describes some capabilities available in the \spst\ package for analysing such data. \textbf{For further detail, see Chapter 16 of the spatstat book \cite{TheBook}.} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Overview of software} The main components needed are: \begin{itemize} \item the model-fitting function \texttt{mppm}, an extension of the \texttt{spatstat} function \texttt{ppm}, that will fit Gibbs point process models to multiple point pattern datasets; \item support for the class \texttt{"mppm"} of point process models fitted by \texttt{mppm} (e.g. functions to print and plot the fitted model, analysis of deviance for Poisson models) \item some tools for exploratory data analysis; \item basic support for the data from such experiments by storing the data in a \emph{``hyperframe''}. A hyperframe is like a data frame, except that each entry in a column can be a point pattern or a pixel image, as well as a single number or categorical value. \item four example datasets. \end{itemize} \section{Formulating the problem} We view the experiment as involving a series of {\em `units'\/}. Each unit is subjected to a known set of experimental conditions (described by the values of the {\em covariates\/}), and each unit yields a {\em response\/} which is a spatial point pattern. The value of a particular covariate for each unit can be either a single value (numerical, logical or factor), or a pixel image. Three important cases are: \begin{description} \item[independent replicates:] We observe $n$ different point patterns that can be regarded as independent replicates, i.e.\ independent realisations of the same point process. The `responses' are the point patterns; there are no covariates. \item[replication in groups:] there are $K$ different experimental groups (e.g. control, aspirin, nurofen). In group $k$ ($k=1,\ldots,K$) we observe $n_k$ point patterns which can be regarded as independent replicates within this group. We regard this as an experiment with $n = \sum_k n_k$ units. The responses are the point patterns; there is one covariate which is a factor (categorical variable) identifying which group each point pattern belongs to. \item[general case:] there are covariates other than factors that influence the response. The point patterns are assumed to be independent, but no two patterns have the same distribution. \end{description} Examples of these three cases are given in the datasets \texttt{waterstriders}, \texttt{pyramidal} and \texttt{demohyper} respectively, which are installed in \spst. \section{Installed datasets} The following datasets are currently installed in \spst. \begin{itemize} \item \texttt{waterstriders}: Penttinen's \cite{pent84} waterstriders data recording the locations of insect larvae on a pond in 3 independent experiments. \item \texttt{pyramidal}: data from Diggle, Lange and Benes \cite{digglangbene91} on the locations of pyramidal neurons in human brain, 31 human subjects grouped into 3 groups (controls, schizoaffective and schizophrenic). \item \texttt{flu}: data from Chen et al \cite{chenetal08} giving the locations of two different virus proteins on the membranes of cells infected with influenza virus; 41 multitype point patterns divided into two virus types (wild and mutant) and two stain types. \item \texttt{simba}: simulated data from an experiment with two groups and 5 replicate point patterns per group. \item \texttt{demohyper}: simulated data from an experiment with two groups in which each experimental unit has a point pattern response and a pixel image covariate. \end{itemize} \section{Lists of point patterns} First we need a convenient way to store the \emph{responses} from all the units in an experiment. An individual point pattern is stored as an object of class \verb!"ppp"!. The easiest way to store all the responses is to form a list of \verb!"ppp"! objects. \subsection{Waterstriders data} The \texttt{waterstriders} data are an example of this type. The data consist of 3 independent point patterns representing the locations of insect larvae on a pond. See \texttt{help(waterstriders)}. <<>>= waterstriders @ The \texttt{waterstriders} dataset is a list of point patterns. It is a list, each of whose entries is a point pattern (object of class \verb!"ppp"!). Note that the observation windows of the three point patterns are {\tt not\/} identical. \subsection{The class \texttt{listof}} For convenience, the \texttt{waterstriders} dataset also belongs to the class \verb!"listof"!. This is a simple mechanism to allow us to handle the list neatly --- for example, we can provide special methods for printing, plotting and summarising the list. \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= plot(waterstriders, main="") @ Notice that the plot method displays each entry of the list in a separate panel. There's also the summary method: <<>>= summary(waterstriders) @ \subsection{Creating a \texttt{listof} object} For example, here is a simulated dataset containing three independent realisations of the Poisson process with intensity 100. <<>>= X <- listof(rpoispp(100), rpoispp(100), rpoispp(100)) @ Then it can be printed and plotted. <>= plot(X) X @ To convert an existing list to the class \code{listof}, use \code{as.listof}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Hyperframes} A \emph{hyperframe} is like a data frame, except that its entries can be objects of any kind. A hyperframe is effectively a two-dimensional array in which each column consists of values of one type (as in a data frame) or consists of objects of one class. The entries in a hyperframe can be point patterns, pixel images, windows, or any other objects. To analyse an experiment, we will store {\bf all} the data from the experiment in a single hyperframe. The rows of the hyperframe will correspond to different experimental units, while the columns represent different variables (response variables or covariates). \subsection{Creating hyperframes} The function \texttt{hyperframe} will create a hyperframe. <>= hyperframe(...) @ The arguments \verb!...! are any number of arguments of the form \texttt{tag=value}. Each \texttt{value} will become a column of the array. The \texttt{tag} determines the name of the column. Each \texttt{value} can be either \begin{itemize} \item an atomic vector or factor (i.e. numeric vector, integer vector, character vector, logical vector, complex vector or factor) \item a list of objects which are all of the same class \item one atomic value, which will be replicated to make an atomic vector or factor \item one object, which will be replicated to make a list of identical objects. \end{itemize} All columns (vectors, factors and lists) must be of the same length, if their length is greater than 1. For example, here is a hyperframe containing a column of numbers and a column of \emph{functions}: <<>>= H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) H @ Note that a column of character strings will be converted to a factor, unless you set \texttt{stringsAsFactors=FALSE} in the call to \code{hyperframe}. This is the same behaviour as for the function \code{data.frame}. <<>>= G <- hyperframe(X=1:3, Y=letters[1:3], Z=factor(letters[1:3]), W=list(rpoispp(100),rpoispp(100), rpoispp(100)), U=42, V=rpoispp(100), stringsAsFactors=FALSE) G @ This hyperframe has 3 rows and 6 columns. The columns named \texttt{U} and \texttt{V} are constant (all entries in a column are the same). The column named \texttt{Y} is a character vector while \texttt{Z} is a factor. \subsection{Hyperframes of data} To analyse an experiment, we will store {\bf all} the data from the experiment in a single hyperframe. The rows of the hyperframe will correspond to different experimental units, while the columns represent different variables (response variables or covariates). Several examples of hyperframes are provided with the package, including \texttt{demohyper}, \texttt{flu}, \texttt{simba} and \texttt{pyramidal}, described above. The \texttt{simba} dataset contains simulated data from an experiment with a `control' group and a `treatment' group, each group containing 5 experimental units. The responses in the control group are independent Poisson point patterns with intensity 80. The responses in the treatment group are independent realisations of a Strauss process (see \texttt{help(simba)} for details). The \texttt{simba} dataset is a hyperframe with 10 rows and 2 columns: \texttt{Points} (the point patterns) and \texttt{group} (a factor with levels \texttt{control} and \texttt{treatment}). <<>>= simba @ The \texttt{pyramidal} dataset contains data from Diggle, Lange and Benes \cite{digglangbene91} on the locations of pyramidal neurons in human brain. One point pattern was observed in each of 31 human subjects. The subjects were classified into 3 groups (controls, schizoaffective and schizophrenic). The \texttt{pyramidal} dataset is a hyperframe with 31 rows and 2 columns: \code{Neurons} (the point patterns) and \code{group} (a factor with levels \texttt{control}, \texttt{schizoaffective} and \texttt{schizophrenic}). <<>>= pyramidal @ The \texttt{waterstriders} dataset is not a hyperframe; it's just a list of point patterns. It can easily be converted into a hyperframe: <<>>= ws <- hyperframe(Striders=waterstriders) @ \subsection{Columns of a hyperframe} Individual columns of a hyperframe can be extracted using \verb!$!: <<>>= H$X H$Y @ The result of \verb!$! is a vector or factor if the column contains atomic values; otherwise it is a list of objects (with class \texttt{"listof"} to make it easier to print and plot). Individual columns can also be assigned (overwritten or created) using \verb!$<-!: <<>>= H$U <- letters[1:3] H @ This can be used to build up a hyperframe column-by-column: <<>>= G <- hyperframe() G$X <- waterstriders G$Y <- 1:3 G @ \subsection{Subsets of a hyperframe} Other subsets of a hyperframe can be extracted with \verb![!: <<>>= H[,1] H[2,] H[2:3, ] H[1,1] @ The result of \verb![! is a hyperframe, unless you set \verb!drop=TRUE! and the subset consists of only one element or one column: <<>>= H[,1,drop=TRUE] H[1,1,drop=TRUE] H[1,2,drop=TRUE] @ There is also a method for \verb![<-! that allows you to assign values to a subset of a hyperframe. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Plotting} \subsection{Plotting a \code{listof} object} The plot method for \code{listof} objects has formal arguments <>= plot.listof(x, ..., main, arrange = TRUE, nrows = NULL, ncols = NULL) @ where \code{main} is a title for the entire page. If \code{arrange=TRUE} then the entries of the list are displayed in separate panels on the same page (with \code{nrows} rows and \code{ncols} columns of panels), while if \code{arrange=FALSE} then the entries are just plotted as a series of plot frames. The extra arguments \verb!...! control the individual plot panels. These arguments will be passed to the plot method that displays each entry of the list. Suitable arguments depend on the type of entries. <>= plot(waterstriders, pch=16, nrows=1) @ \subsection{Plotting a hyperframe} \subsubsection{Plotting one column} If \code{h} is a hyperframe, then the default action of \code{plot(h)} is to extract the first column of \code{h} and plot each of the entries in a separate panel on one page (actually using the plot method for class \verb!"listof"!). \SweaveOpts{width=7,height=5} \setkeys{Gin}{width=0.9\textwidth} <>= plot(simba) @ This only works if the entries in the first column are objects for which a plot method is defined (for example, point patterns, images, windows). To select a different column, use \verb!$! or \verb![!: \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) plot(H$Y) @ The plot can be controlled using the arguments for \code{plot.listof} (and, in this case, \code{plot.function}, since \verb!H$Y! consists of functions). \subsubsection{Complex plots} More generally, we can display any kind of higher-order plot involving one or more columns of a hyperframe: <>= plot(h, e) @ where \code{h} is a hyperframe and \code{e} is an \R\ language call or expression that must be evaluated in each row to generate each plot panel. \SweaveOpts{width=9,height=5} \setkeys{Gin}{width=0.9\textwidth} <>= plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) })) @ Note the use of \code{quote}, which prevents the code inside the braces from being evaluated immediately. To plot the $K$-functions of each of the patterns in the \code{waterstriders} dataset, \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= H <- hyperframe(Bugs=waterstriders) plot(H, quote(plot(Kest(Bugs))), marsize=1) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Data analysis} \subsection{Computing with hyperframes} Often we want to perform some computation on each row of a hyperframe. In a data frame, this can be done using the command \code{with}: <<>>= df <- data.frame(A=1:10, B=10:1) with(df, A-B) @ In this example, the expression \code{A-B} is evaluated in each row of the data frame, and the result is a vector containing the computed values for each row. The function \code{with} is generic, and has a method for data frames, \code{with.data.frame}. The computation above was executed by \code{with.data.frame}. The same syntax is available for hyperframes using the method \code{with.hyperframe}: <>= with(h,e) @ Here \code{h} is a hyperframe, and \code{e} is an {\sf R} language construct involving the names of columns in \code{h}. For each row of \code{h}, the expression \code{e} will be evaluated in such a way that each entry in the row is identified by its column name. <<>>= H <- hyperframe(Bugs=waterstriders) with(H, npoints(Bugs)) with(H, distmap(Bugs)) @ The result of \code{with.hyperframe} is a list of objects (of class \verb!"listof"!), or a vector or factor if appropriate. Notice that (unlike the situation for data frames) the operations in the expression \code{e} do not have to be vectorised. For example, \code{distmap} expects a single point pattern, and is not vectorised to deal with a list of point patterns. Instead, the expression \code{distmap(Bugs)} is evaluated separately in each row of the hyperframe. \subsection{Summary statistics} One application of \code{with.hyperframe} is to calculate summary statistics for each row of a hyperframe. For example, the number of points in a point pattern \code{X} is returned by \code{npoints(X)}. To calculate this for each of the responses in the \code{simba} dataset, <<>>= with(simba, npoints(Points)) @ The summary statistic can be any kind of object. For example, to compute the empirical $K$-functions for each of the patterns in the \code{waterstriders} dataset, <<>>= H <- hyperframe(Bugs=waterstriders) K <- with(H, Kest(Bugs)) @ To plot these $K$-functions you can then just type \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= plot(K) @ The summary statistic for each row could be a numeric vector: <<>>= H <- hyperframe(Bugs=waterstriders) with(H, nndist(Bugs)) @ The result is a list, each entry being a vector of nearest neighbour distances. To find the minimum interpoint distance in each pattern: <<>>= with(H, min(nndist(Bugs))) @ \subsection{Generating new columns} New columns of a hyperframe can be created by computation from the existing columns. For example, I can add a new column to the \code{simba} dataset that contains pixel images of the distance maps for each of the point pattern responses. <>= simba$Dist <- with(simba, distmap(Points)) @ \subsection{Simulation} This can be useful for simulation. For example, to generate Poisson point patterns with different intensities, where the intensities are given by a numeric vector \code{lambda}: \SweaveOpts{width=6,height=6} \setkeys{Gin}{width=0.7\textwidth} <>= lambda <- rexp(6, rate=1/50) H <- hyperframe(lambda=lambda) H$Points <- with(H, rpoispp(lambda)) plot(H, quote(plot(Points, main=paste("lambda=", signif(lambda, 4))))) @ It's even simpler to generate 10 independent Poisson point patterns with the \emph{same} intensity 50, say: <>= H$X <- with(H, rpoispp(50)) @ \noindent The expression \code{rpoispp(50)} is evaluated once in each row, yielding a different point pattern in each row because of the randomness. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Exploratory data analysis} Before fitting models to the data, it is prudent to explore the data to detect unusual features and to suggest appropriate models. \subsection{Exploring spatial trend and covariate effects} Points may be distributed non-uniformly either because they are intrinsically non-uniform (``spatial trend'') or because their abundance depends on a spatial covariate (``covariate effects''). Non-uniformity of a point pattern can be investigated using the kernel smoothed intensity. This is the convolution of the point pattern with a smooth density called the kernel. Effectively each point in the pattern is replaced by a copy of the kernel, and the sum of all copies of the kernel is the kernel-smoothed intensity function. It is computed by \texttt{density.ppp} separately for each point pattern. <>= plot(simba, quote(plot(density(Points), main="")), nrows=2) @ Covariate effects due to a real-valued spatial covariate (a real-valued pixel image) can be investigated using the command \code{rhohat}. This uses a kernel smoothing technique to fit a model of the form \[ \lambda(u) = \rho(Z(u)) \] where $\lambda(u)$ is the point process intensity at a location $u$, and $Z(u)$ is the value of the spatial covariate at that location. Here $\rho$ is an unknown, smooth function which is to be estimated. The function $\rho$ expresses the effect of the spatial covariate on the point process intensity. If $\rho$ turns out to be constant, then the covariate has no effect on point process intensity (and the constant value of $\rho$ is the constant intensity of the point process). <>= rhos <- with(demohyper, rhohat(Points, Image)) plot(rhos) @ \SweaveOpts{width=6,height=4} \setkeys{Gin}{width=0.9\textwidth} \subsection{Exploring interpoint interaction} Still to be written. See Chapter 16 of \cite{baddrubaturn15}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Fitting models of spatial trend} The command \code{mppm} fits models to multiple point patterns. Its syntax is very similar to that of \code{lm} and \code{glm}: <>= mppm(formula, data, interaction, ...) @ where \code{formula} is a formula describing the systematic trend part of the model, \code{data} is a hyperframe containing all the data (responses and covariates), and \code{interaction} determines the stochastic interpoint interaction part of the model. For example: <>= mppm(Points ~ group, simba, Poisson()) @ Note that the formula has a left hand side, which identifies the response. This should be the name of a column of \code{data}. \subsection{Trend formula} The right side of \code{formula} is an expression for the linear predictor (effectively the {\bf logarithm} of the spatial trend). The variables appearing in the right hand side of \code{formula} should be either \begin{itemize} \item names of columns in \code{data} \item objects in the {\sf R} global environment (such as \code{pi} and \code{log}) \item the reserved names \code{x}, \code{y} (representing Cartesian coordinates), \code{marks} (representing mark values attached to points) or \code{id} (a factor representing the row number in the hyperframe). \end{itemize} \subsubsection{Design covariates} The variables in the trend could be `design covariates'. For example, to fit a model to the \code{simba} dataset in which all patterns are independent replicates of the same uniform Poisson process, with the same constant intensity: <<>>= mppm(Points ~ 1, simba) @ To fit a model in which the two groups of patterns (control and treatment groups) each consist of independent replicates of a uniform Poisson process, but with possibly different intensity in each group: <<>>= mppm(Points ~ group, simba) @ To fit a uniform Poisson process to each pattern, with different intensity for each pattern: <<>>= mppm(Points ~ id, simba) @ \subsubsection{Spatial covariates} The variables in the trend could be `spatial covariates'. For example, the \code{demohyper} dataset has a column \code{Image} containing pixel images. <<>>= mppm(Points ~ Image, data=demohyper) @ This model postulates that each pattern is a Poisson process with intensity of the form \[ \lambda(u) = \exp(\beta_0 + \beta_1 Z(u)) \] at location $u$, where $\beta_0, \beta_1$ are coefficients to be estimated, and $Z(u)$ is the value of the pixel image \code{Image} at location $u$. It may or may not be appropriate to assume that the intensity of the points is an exponential function of the image pixel value $Z$. If instead we wanted the intensity $\lambda(u)$ to be \emph{proportional} to $Z(u)$, the appropriate model is <>= mppm(Points ~ offset(log(Image)), data=demohyper) @ which corresponds to an intensity proportional to \code{Image}, \[ \lambda(u) = \exp(\beta_0 + \log Z(u)) = e^{\beta_0} \; Z(u). \] The \code{offset} indicates that there is no coefficient in front of $\log Z(u)$. Alternatively we could allow a coefficient: <>= mppm(Points ~ log(Image), data=demop) @ which corresponds to a gamma transformation of \code{Image}, \[ \lambda(u) = \exp(\beta_0 + \beta_1 \log Z(u)) = e^{\beta_0} \; Z(u)^{\beta_1}. \] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Interpoint interaction} The stochastic interpoint interaction in a point process model is specified by the arguments \code{interaction} and (optionally) \code{iformula} in <>= mppm(formula, data, interaction, ..., iformula=NULL) @ \subsection{Same interaction for all patterns} In the simplest case, the argument \texttt{interaction} is one of the familiar objects that describe the point process interaction structure. It is an object of class \texttt{"interact"} created by calling one of the functions \begin{center} \begin{tabular}{rl} \texttt{Poisson()} & the Poisson point process\\ \texttt{Hardcore()} & the hard core process \\ \texttt{Strauss()} & the Strauss process \\ \texttt{StraussHard()} & the Strauss/hard core point process\\ \texttt{Softcore()} & pairwise interaction, soft core potential\\ \texttt{PairPiece()} & pairwise interaction, piecewise constant \\ \texttt{DiggleGatesStibbard() } & Diggle-Gates-Stibbard pair potential \\ \texttt{DiggleGratton() } & Diggle-Gratton pair potential \\ \texttt{Fiksel() } & Fiksel pair potential \\ \texttt{LennardJones() } & Lennard-Jones pair potential \\ \texttt{Pairwise()} & pairwise interaction, user-supplied potential\\ \texttt{AreaInter()} & area-interaction potential\\ \texttt{Geyer()} & Geyer's saturation process\\ \texttt{BadGey()} & multiscale Geyer saturation process\\ \texttt{Saturated()} & Saturated pair model, user-supplied potential\\ \texttt{OrdThresh()} & Ord process, threshold potential\\ \texttt{Ord()} & Ord model, user-supplied potential \\ \texttt{MultiStrauss()} & multitype Strauss process \\ \texttt{MultiStraussHard()} & multitype Strauss/hard core process \\ \texttt{Concom()} & connected component interaction \\ \texttt{Hybrid()} & hybrid of several interactions \\ \end{tabular} \end{center} In this `simple' usage of \texttt{mppm}, the point process model assumes that all point patterns have exactly the same interpoint interaction, (with the same interaction parameters), and only differ in their spatial trend. \subsection{Hyperframe of interactions} More generally the argument \code{interaction} can be a hyperframe containing objects of class \texttt{"interact"}. For example, we might want to fit a Strauss process to each point pattern, but with a different Strauss interaction radius for each pattern. <>= radii <- with(simba, mean(nndist(Points))) @ Then \code{radii} is a vector of numbers which we could use as the values of the interaction radius for each case. First we need to make the interaction objects: <<>>= Rad <- hyperframe(R=radii) Str <- with(Rad, Strauss(R)) @ Then we put them into a hyperframe and fit the model: <<>>= Int <- hyperframe(str=Str) mppm(Points ~ 1, simba, interaction=Int) @ An important constraint is that all of the interaction objects in one column must be \emph{instances of the same process} (e.g. Strauss) albeit possibly having different parameter values. For example, you cannot put Poisson and Strauss processes in the same column. \subsection{Interaction formula} If \code{interaction} is a hyperframe, then the additional argument \code{iformula} may be used to fully specify the interaction. (An \code{iformula} is also required if \code{interaction} has more than one column.) The \code{iformula} should be a formula without a left hand side. Variables on the right hand side are typically the names of columns in \code{interaction}. \subsubsection{Selecting one column} If the right hand side of \code{iformula} is a single name, then this identifies the column in \code{interaction} to be used as the interpoint interaction structure. <<>>= h <- hyperframe(Y=waterstriders) g <- hyperframe(po=Poisson(), str4 = Strauss(4), str7= Strauss(7)) mppm(Y ~ 1, data=h, interaction=g, iformula=~str4) @ \subsubsection{Interaction depending on design} The \code{iformula} can also involve columns of \code{data}, but only those columns that are vectors or factors. This allows us to specify an interaction that depends on the experimental design. [This feature is {\bf experimental}.] For example <<>>= fit <- mppm(Points ~ 1, simba, Strauss(0.07), iformula = ~Interaction*group) @ Since \code{Strauss(0.1)} is not a hyperframe, it is first converted to a hyperframe with a single column named \code{Interaction}. The \code{iformula = ~Interaction*group} specifies (since \code{group} is a factor) that the interpoint interaction shall have a different coefficient in each experimental group. That is, we fit a model which has two different values for the Strauss interaction parameter $\gamma$, one for the control group and one for the treatment group. When you print the result of such a fit, the package tries to do `automatic interpretation' of the fitted model (translating the fitted interaction coefficients into meaningful numbers like $\gamma$). This will be successful in \emph{most} cases: <<>>= fit @ <>= co <- coef(fit) si <- function(x) { signif(x, 4) } @ Thus we see that the estimate of the Strauss parameter $\gamma$ for the control group is \Sexpr{si(exp(co[2]))}, and for the treatment group \Sexpr{si(exp(sum(co[c(2,4)])))} (the correct values in this simulated dataset were $1$ and $0.5$). The fitted model can also be interpreted directly from the fitted canonical coefficients: <<>>= coef(fit) @ The last output shows all the coefficients $\beta_j$ in the linear predictor for the (log) conditional intensity. The interpretation of the model coefficients, for any fitted model in \R, depends on the \emph{contrasts} which were applicable when the model was fitted. This is part of the core {\sf R} system: see \code{help(contrasts)} or \code{options(contrasts)}. If you did not specify otherwise, the default is to use \emph{treatment contrasts}. This means that, for an explanatory variable which is a \texttt{factor} with $N$ levels, the first level of the factor is used as a baseline, and the fitted model coefficients represent the factor levels $2, 3, \ldots, N$ relative to this baseline. In the output above, there is a coefficient for \code{(Intercept)} and one for \code{grouptreatment}. These are coefficients related to the \code{group} factor. According to the ``treatment contrasts'' rule, the \code{(Intercept)} coefficient is the estimated effect for the control group, and the \code{grouptreatment} coefficient is the estimated difference between the treatment and control groups. Thus the fitted first order trend is $\exp(\Sexpr{si(co[1])}) = \Sexpr{si(exp(co[1]))}$ for the control group and $\exp(\Sexpr{si(co[1])} + \Sexpr{si(co[3])}) = \Sexpr{si(exp(sum(co[c(1,3)])))}$ for the treatment group. The correct values in this simulated dataset were $80$ and $100$. The remaining coefficients in the output are \code{Interaction} and \code{Interaction:grouptreatment}. Recall that the Strauss process interaction term is $\gamma^{t(u,\xx)} = \exp(t(u,\xx) \log\gamma)$ at a spatial location $u$, for a point pattern $\xx$. Since we're using treatment contrasts, the coefficient \code{Interaction} is the estimate of $\log\gamma$ for the control group. The coefficient \code{Interaction:grouptreatment} is the estimate of the difference in $\log\gamma$ between the treatment and control groups. Thus the estimated Strauss interaction parameter $\gamma$ is $\exp(\Sexpr{si(co[2])}) = \Sexpr{si(exp(co[2]))}$ for the control group and $\exp(\Sexpr{si(co[2])} + (\Sexpr{si(co[4])})) = \Sexpr{si(exp(co[2]+co[4]))}$ for the treatment group. The correct values were $1$ and $0.5$. \subsubsection{Completely different interactions for different cases} In the previous example, when we fitted a Strauss model to all point patterns in the \code{simba} dataset, the fitted model for the patterns in the control group was close to Poisson ($\gamma \approx 1$). Suppose we now want to fit a model which {\it is} Poisson in the control group, and Strauss in the treatment group. The Poisson and Strauss interactions must be given as separate columns in a hyperframe of interactions: <>= interaction=hyperframe(po=Poisson(), str=Strauss(0.07)) @ What do we write for the \code{iformula}? The following \emph{will not} work: <>= iformula=~ifelse(group=="control", po, str) @ This does not work because the Poisson and Strauss models are `incompatible' inside such expressions. The canonical sufficient statistics for the Poisson and Strauss processes do not have the same dimension. Internally in \code{mppm} we translate the symbols \code{po} and \code{str} into matrices; the dimensions of these matrices are different, so the \code{ifelse} expression cannot be evaluated. Instead we need something like the following: <>= iformula=~I((group=="control")*po) + I((group=="treatment") * str) @ The letter \code{I} here is a standard R function that prevents its argument from being interpreted as a formula (thus the \code{*} is interpreted as multiplication instead of a model interaction). The expression \code{(group=="control")} is logical, and when multiplied by the matrix \code{po}, yields a matrix. So the following does work: <<>>= g <- hyperframe(po=Poisson(), str=Strauss(0.07)) fit2 <- mppm(Points ~ 1, simba, g, iformula=~I((group=="control")*po) + I((group=="treatment") * str)) fit2 @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %#%^!ifdef RANDOMEFFECTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Random effects} \subsection{Mixed effects models} It is also possible to fit models that include `random effects'. Effectively, some of the coefficients in the model are assumed to be Normally-distributed random variables instead of constants. \subsubsection{Mixed Poisson model} Consider the simplest model of a uniform Poisson process which we fitted to the 3 point patterns of waterstriders. It might be sensible to assume that each pattern is a realisation of a Poisson process, but with {\em random intensity\/}. In each realisation the intensity $\lambda$ is constant across different locations, but it is a different, random value in different realisations. This example is called a `mixed Poisson process' and belongs to the class of `Cox processes' (Poisson processes with random intensity functions). Let's assume further that the log-intensity is a Normal random variable. Then the model is a (very degenerate) special case of a `log-Gaussian Cox process'. To fit such a model we use the standard techniques of mixed effects models \cite{lairware82,davigilt95,pinhbate00}. The mixed Poisson process which we discussed above would be written in standard form \begin{equation} \label{mixPois} \lambda_i(u) = \exp(\mu + Z_i) \end{equation} for the $i$th point pattern, where $\mu$ is a parameter to be estimated (the `fixed effect') and $Z_i \sim N(0, \sigma^2)$ is a zero-mean Normal random variable (the `random effect' for point pattern $i$). In the simplest case we would assume that $Z_1, \ldots, Z_n$ are independent. The variance $\sigma^2$ of the random effects would be estimated. One can also estimate the individual realised values $z_i$ of the random effects for each point pattern, although these are usually not of such great interest. Since the model includes both fixed and random effects, it is called a ``mixed-effects'' model. \subsubsection{Dependence structure} When we formulate a random-effects or mixed-effects model, we must specify the dependence structure of the random effects. In the model above we assumed that the $Z_i$ are independent for all point patterns $i$. If the experiment consists of two groups, we could alternatively assume that $Z_i = Z_j$ whenever $i$ and $j$ belong to the same group. In other words all the patterns in one group have the same value of the random effect. So the random effect is associated with the group rather than with individual patterns. This could be appropriate if, for example, the groups represent different batches of a chemical. Each batch is prepared under slightly different conditions so we believe that there are random variations between batches, but within a batch we believe that the chemical is well-mixed. \subsubsection{Random effects are coefficients} In the mixed Poisson model (\ref{mixPois}), the random effect is an additive constant (with a random value) in the log-intensity. In general, a random effect is a \emph{coefficient} of one of the covariates. For example if $v$ is a real-valued design covariate (e.g. `temperature'), with value $v_i$ for the $i$th point pattern, then we could assume \begin{equation} \label{ranef2} \lambda_i(u) = \exp(\mu + Z_i v_i) \end{equation} where $Z_i \sim N(0, \sigma^2)$ are independent for different $i$. This model has a random effect in the dependence on $v$. We could also have a random effect for a spatial covariate $V$. Suppose $V_i$ is a real-valued image for the $i$th pattern (so that $V_i(u)$ is the value of some covariate at the location $u$ for the $i$th case). Then we could assume \begin{equation} \label{ranef3} \lambda_i(u) = \exp(\mu + Z_i V_i(u)) \end{equation} where $Z_i \sim N(0, \sigma^2)$ are independent for different $i$. This kind of random effect would be appropriate if, for example, the images $V_i$ are not `normalised' or `standardised' relative to each other (e.g.\ they are images taken under different illumination). Then the coefficients $Z_i$ effectively include the rescaling necessary to standardise the images. \subsection{Fitting a mixed-effects model} The call to \texttt{mppm} can also include the argument \texttt{random}. This should be a formula (with no left-hand side) describing the structure of random effects. The formula for random effects must be recognisable to \texttt{lme}. It is typically of the form \begin{verbatim} ~x1 + ... + xn | g \end{verbatim} or \begin{verbatim} ~x1 + ... + xn | g1/.../gm \end{verbatim} where \verb!x1 + ... + xn! specifies the covariates for the random effects and \texttt{g} or \verb!g1/.../gm! determines the grouping (dependence) structure. Here \code{g} or \code{g1, \ldots, gm} should be factors. To fit the mixed Poisson model (\ref{mixPois}) to the waterstriders, we want to have a random intercept coefficient (so \texttt{x} is \texttt{1}) that varies for different point patterns (so \texttt{g} is \texttt{id}). The reserved name \code{id} is a factor referring to the individual point pattern. Thus <<>>= H <- hyperframe(P=waterstriders) mppm(P ~ 1, H, random=~1|id) @ To fit the mixed effects model (\ref{ranef2}) to the coculture data with the \code{AstroIm} covariate, with a random effect associated with each well, <>= mppm(Neurons ~ AstroIm, random=~AstroIm|WellNumber) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %#%^!endif %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Studying the fitted model} Fitted models produced by \code{mppm} can be examined and validated in many ways. \subsection{Fits for each pattern} \subsubsection{Subfits} The command \code{subfits} takes an \code{mppm} object and extracts, for each individual point pattern, the fitted point process model for that pattern \emph{that is implied by the overall fit}. It returns a list of objects of class \code{ppm}. <<>>= H <- hyperframe(W=waterstriders) fit <- mppm(W ~ 1, H) subfits(fit) @ In this example the result is a list of three \code{ppm} objects representing the implied fits for each of the three point patterns in the \code{waterstriders} dataset. Notice that {\bf the fitted coefficients are the same} in all three models. Note that there are some unresolved difficulties with the implementation of \code{subfits}. Two completely different implementations are supplied in the package; they are called \code{subfits.old} %(used in versions 0.1--1 and earlier) and \code{subfits.new}.% (introduced in 0.1--2). The old version would occasionally crash. Unfortunately the newer version \code{subfits.new} is quite memory-hungry and sometimes causes R to hang. We're still working on this problem. So for the time being, \code{subfits} is the same as \code{subfits.old}. You can change this simply by reassigning, e.g. <>= subfits <- subfits.new @ \subsubsection{Fitting separately to each pattern} For comparison, we could fit a point process model separately to each point pattern dataset using \code{ppm}. The easy way to do this is with \code{with.hyperframe}. To fit a \emph{separate} uniform Poisson point process to each of the three waterstriders patterns, <<>>= H <- hyperframe(W=waterstriders) with(H, ppm(W)) @ The result is again a list of three fitted point process models (objects of class \code{ppm}), but now the fitted coefficients are different. \subsection{Residuals} One standard way to check a fitted model is to examine the residuals. \subsubsection{Point process residuals} Some recent papers \cite{baddetal05,baddmollpake08} have defined residuals for a fitted point process model (fitted to a \emph{single} point pattern). These residuals are implemented in \code{spatstat} as \code{residuals.ppm} and apply to an object of class \code{ppm}, that is, a model fitted to a \emph{single} point pattern. The command \code{residuals.mppm} computes the point process residuals for an \code{mppm} object. <<>>= fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) @ The result is a list, with one entry for each of the point pattern datasets. Each list entry contains the point process residuals for the corresponding point pattern dataset. Each entry in the list is a signed measure (object of class \code{"msr"}) as explained in the help for \code{residuals.ppm}). It can be plotted: <>= plot(res) @ You probably want the smoothed residual field: <>= smor <- with(hyperframe(res=res), Smooth(res, sigma=4)) plot(smor) @ \subsubsection{Sums of residuals} It would be useful to have a residual that is a single value for each point pattern (representing how much that point pattern departs from the model fitted to all the point patterns). That can be computed by \emph{integrating} the residual measures using the function \code{integral.msr}: <<>>= fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) totres <- sapply(res, integral.msr) @ In designed experiments we can plot these total residuals against the design covariates: <>= fit <- mppm(Points~Image, data=demohyper) resids <- residuals(fit, type="Pearson") totres <- sapply(resids, integral.msr) areas <- with(demohyper, area.owin(as.owin(Points))) df <- as.data.frame(demohyper[, "Group"]) df$resids <- totres/areas plot(resids~Group, df) @ \subsubsection{Four-panel diagnostic plots} Sometimes a more useful tool is the function \code{diagnose.ppm} which produces a four-panel diagnostic plot based on the point process residuals. However, it is only available for \code{ppm} objects. To obtain a four-panel diagnostic plot for each of the point patterns, do the following: \begin{enumerate} \item fit a model to multiple point patterns using \code{mppm}. \item extract the individual fits using \code{subfits}. \item plot the residuals of the individual fits. \end{enumerate} For example: <>= fit <- mppm(P ~ 1, hyperframe(P=waterstriders)) sub <- hyperframe(Model=subfits(fit)) plot(sub, quote(diagnose.ppm(Model))) @ (One could also do this for models fitted separately to the individual point patterns.) \subsubsection{Residuals of the parameter estimates} We can also compare the parameter estimates obtained by fitting the model simultaneously to all patterns (using \code{mppm}) with those obtained by fitting the model separately to each pattern (using \code{ppm}). <<>>= H <- hyperframe(P = waterstriders) fitall <- mppm(P ~ 1, H) together <- subfits(fitall) separate <- with(H, ppm(P)) Fits <- hyperframe(Together=together, Separate=separate) dr <- with(Fits, unlist(coef(Separate)) - unlist(coef(Together))) dr exp(dr) @ One could also try deletion residuals, etc. \subsection{Goodness-of-fit tests} \subsubsection{Quadrat count test} The $\chi^2$ goodness-of-fit test based on quadrat counts is implemented for objects of class \code{ppm} (in \code{quadrat.test.ppm}) and also for objects of class \code{mppm} (in \code{quadrat.test.mppm}). This is a goodness-of-fit test for a fitted {\bf Poisson} point process model only. The model could be uniform or non-uniform and the intensity might depend on covariates. <<>>= H <- hyperframe(X=waterstriders) # Poisson with constant intensity for all patterns fit1 <- mppm(X~1, H) quadrat.test(fit1, nx=2) # uniform Poisson with different intensity for each pattern fit2 <- mppm(X ~ id, H) quadrat.test(fit2, nx=2) @ See the help for \code{quadrat.test.ppm} and \code{quadrat.test.mppm} for further details. \subsubsection{Kolmogorov-Smirnov test} The Kolmogorov-Smirnov test of goodness-of-fit of a Poisson point process model compares the observed and predicted distributions of the values of a spatial covariate. We want to test the null hypothesis $H_0$ that the observed point pattern ${\mathbf x}$ is a realisation from the Poisson process with intensity function $\lambda(u)$ (for locations $u$ in the window $W$). Let $Z(u)$ be a given, real-valued covariate defined at each spatial location $u$. Under $H_0$, the \emph{observed} values of $Z$ at the data points, $Z(x_i)$ for each $x_i \in {\mathbf x}$, are independent random variables with common probability distribution function \[ F_0(z) = \frac{\int_W \lambda(u) \indicate{Z(u) \le z} \dee u} {\int_W \lambda(u) \dee u}. \] We can therefore apply the Kolmogorov-Smirnov test of goodness-of-fit. This compares the empirical cumulative distribution of the observed values $Z(x_i)$ to the predicted c.d.f. $F_0$. The test is implemented as \code{kstest.ppm}. The syntax is <>= kstest.mppm(model, covariate) @ where \code{model} is a fitted model (of class \texttt{"mppm"}) and \code{covariate} is either \begin{itemize} \item a \code{function(x,y)} making it possible to compute the value of the covariate at any location \code{(x,y)} \item a pixel image containing the covariate values \item a list of functions, one for each row of the hyperframe of original data \item a list of pixel images, one for each row of the hyperframe of original data \item a hyperframe with one column containing either functions or pixel images. \end{itemize} See Chapter 16 of \cite{baddrubaturn15} for further information. \newpage \addcontentsline{toc}{section}{Bibliography} %\bibliography{% %extra,% %extra2,% %biblio/badd,% %biblio/bioscience,% %biblio/censoring,% %biblio/mcmc,% %biblio/spatstat,% %biblio/stat,% %biblio/stochgeom% %} \begin{thebibliography}{1} \bibitem{baddmollpake08} A. Baddeley, J. M{\o}ller, and A.G. Pakes. \newblock Properties of residuals for spatial point processes. \newblock {\em Annals of the Institute of Statistical Mathematics}, 60:627--649, 2008. \bibitem{TheBook} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with R}. \newblock Chapman \& Hall/CRC Press, 2015. \bibitem{statpaper} A. Baddeley, I. Sintorn, L. Bischof, R. Turner, and S. Heggarty. \newblock Analysing designed experiments where the response is a spatial point pattern. \newblock In preparation. \bibitem{baddetal05} A. Baddeley, R. Turner, J. M{\o}ller, and M. Hazelton. \newblock Residual analysis for spatial point processes (with discussion). \newblock {\em Journal of the Royal Statistical Society, series B}, 67(5):617--666, 2005. \bibitem{chenetal08} B.J. Chen, G.P. Leser, D. Jackson, and R.A. Lamb. \newblock The influenza virus {M2} protein cytoplasmic tail interacts with the {M1} protein and influences virus assembly at the site of virus budding. \newblock {\em Journal of Virology}, 82:10059--10070, 2008. %#%^!ifdef RANDOMEFFECTS \bibitem{davigilt95} M. Davidian and D.M. Giltinan. \newblock {\em Nonlinear Mixed Effects Models for Repeated Measurement Data}. \newblock Chapman and Hall, 1995. %#%^!endif \bibitem{digglangbene91} P.J. Diggle, N. Lange, and F. M. Benes. \newblock Analysis of variance for replicated spatial point patterns in clinical neuroanatomy. \newblock {\em Journal of the {A}merican {S}tatistical {A}ssociation}, 86:618--625, 1991. %#%^!ifdef RANDOMEFFECTS \bibitem{lairware82} N.M. Laird and J.H. Ware. \newblock Random-effects models for longitudinal data. \newblock {\em Biometrics}, 38:963--974, 1982. %#%^!endif \bibitem{pent84} A. Penttinen. \newblock {\em Modelling Interaction in Spatial Point Patterns: Parameter Estimation by the Maximum Likelihood Method}. \newblock Number 7 in {Jyv\"askyl\"a} Studies in Computer Science, Economics and Statistics. University of {Jyv\"askyl\"a}, 1984. %#%^!ifdef RANDOMEFFECTS \bibitem{pinhbate00} J.C. Pinheiro and D.M. Bates. \newblock {\em Mixed-Effects Models in {S} and {S-PLUS}}. \newblock Springer, 2000. %#%^!endif \end{thebibliography} %\addcontentsline{toc}{section}{Index} %\printindex \end{document} spatstat/inst/doc/bugfixes.R0000644000176200001440000000523414517616235015614 0ustar liggesusers### R code from vignette source 'bugfixes.Rnw' ################################################### ### code chunk number 1: bugfixes.Rnw:20-26 ################################################### library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) ################################################### ### code chunk number 2: bugfixes.Rnw:40-42 ################################################### nbugs <- nrow(bugfixes("all", show=FALSE)) nbugssince <- nrow(bugfixes("book", show=FALSE)) ################################################### ### code chunk number 3: bugfixes.Rnw:60-61 (eval = FALSE) ################################################### ## bugfixes ################################################### ### code chunk number 4: bugfixes.Rnw:65-66 (eval = FALSE) ################################################### ## bugfixes(sinceversion="1.50-0") ################################################### ### code chunk number 5: bugfixes.Rnw:70-71 (eval = FALSE) ################################################### ## bugfixes(sincedate="2017-06-30") ################################################### ### code chunk number 6: bugfixes.Rnw:74-75 (eval = FALSE) ################################################### ## bugfixes("book") ################################################### ### code chunk number 7: bugfixes.Rnw:78-79 (eval = FALSE) ################################################### ## bugfixes("all") ################################################### ### code chunk number 8: bugfixes.Rnw:92-110 ################################################### getstuff <- function(pkg) { x <- read.dcf(file=system.file("DESCRIPTION", package=pkg), fields=c("Version", "Date")) xversion <- as.character(x[,"Version"]) xdate <- as.character(x[,"Date"]) data.frame(date=as.Date(xdate), package=pkg, version=xversion) } vtable <- do.call(rbind, lapply(c("spatstat.utils", "spatstat.data", "spatstat.sparse", "spatstat.geom", "spatstat.random", "spatstat.explore", "spatstat.model", "spatstat.linnet", "spatstat"), getstuff)) ################################################### ### code chunk number 9: bugfixes.Rnw:116-117 ################################################### print(vtable, row.names=FALSE) spatstat/inst/doc/fv.Rnw0000644000176200001440000021335314475230742014761 0ustar liggesusers\documentclass[10pt]{article} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage[bottom=0.5cm, right=1.5cm, left=1.5cm, top=1.5cm]{geometry} % \VignetteIndexEntry{Guide to Function Objects in Spatstat} % $Revision: 1.3 $ $Date: 2023/08/16 02:16:49 $ \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\link}[1]{#1} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \newcommand{\fv}{\texttt{"fv"}} \newcommand{\env}{\texttt{"envelope"}} \newcommand{\rat}{\texttt{"rat"}} \newcommand{\obj}[1]{object of class {#1}} \newcommand{\objs}[1]{objects of class {#1}} \newcommand{\objsfvenv}{\objs\fv{} and \env} \newcommand{\fun}[1]{\texttt{#1}} \newcommand{\class}[1]{\texttt{"{#1}"}} \newcommand{\Kfun}{$K$-function} \newcommand{\Lfun}{$L$-function} \newcommand{\pois}[1]{{#1}_{\mbox{\scriptsize pois}}} \newcommand{\isoest}[1]{\widehat{#1}_{\mbox{\scriptsize iso}}} \newcommand{\figref}[1]{Figure~\ref{#1}} \newcommand{\secref}[1]{Section~\ref{#1}} \newcommand{\eqref}[1]{(\ref{#1})} \newcommand{\editedToHere}{% \newpage % \hrule % % \begin{Large} % \begin{center} % NOT EDITED BEYOND HERE % \end{center} % \end{Large} % % \hrule % } \begin{document} \bibliographystyle{plain} <>= library(spatstat) x <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = c("Version", "Date")) sversion <- as.character(x[,"Version"]) sdate <- as.character(x[,"Date"]) options(useFancyQuotes=FALSE) setmargins <- function(...) { options(SweaveHooks=list(fig=function() par(mar=c(...)+0.1))) } @ <>= options(SweaveHooks=list(fig=function() par(mar=c(5,4,2,4)+0.1))) options(width=100) @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.5\textwidth} \title{A guide to function objects (class \fv\ and \env) in \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{{\small Draft of \today}} \maketitle \thispagestyle{empty} \begin{abstract} This vignette explains how to use and manipulate \objsfvenv. \framebox{\textbf{This vignette is under development!}} \end{abstract} \setcounter{tocdepth}{1} \tableofcontents \newpage \section{Introduction} \subsection{Functional summary statistics} An \obj\fv\ (`function value table') is a convenient way of storing several different estimates of the same function. It is common practice to summarise a spatial point pattern dataset using a summary function, such as Ripley's \Kfun\ $K(r)$, rather than a single numerical summary value. Typically, an empirical estimate of the function, obtained from the data, will be compared with the `theoretical' version of the function that would be expected if the point pattern was completely random. There may be several different empirical estimates of the function, based on different estimation techniques, and we also want to compare these estimates with one another. The \spst{} family of packages makes it very easy to compute and handle multiple versions of a summary function. Taking the Finnish Pines data \texttt{finpines} as an example, we can compute and plot estimates of Ripley's \Kfun\ by typing <<>>= K <- Kest(finpines) @ <>= plot(K) @ The plot shows several curves, which represent the different empirical estimates of the \Kfun\ (namely the isotropic correction $\widehat K_{\mbox{\scriptsize iso}}(r)$, translation correction $\widehat K_{\mbox{\scriptsize trans}}(r)$, and border correction $\widehat K_{\mbox{\scriptsize bord}}(r)$) and also the theoretical value $K_{\mbox{\scriptsize pois}}(r)$ that would be expected if the point pattern was completely random. All these functions are plotted against the distance argument $r$. The object \texttt{K} belongs to class \fv{} (``function value table''). It is a data frame (that is, it also belongs to the class \class{data.frame}) with attributes giving extra information such as the recommended way of plotting the function. One column of the data frame contains evenly spaced values of the distance argument $r$, while the other columns contain estimates of the value of the function, or the theoretical value of the function under CSR, corresponding to these distance values. More information is given by the print method \texttt{print.fv:} <<>>= K @ This indicates that the columns in the data frame are named \texttt{r}, \texttt{theo}, \texttt{border}, \texttt{trans}, and \texttt{iso}, and explains their contents. For example, the column \texttt{iso} contains estimates of the \Kfun{} using the isotropic edge correction. This column is labelled in the plot by the \R\ expression \texttt{hat(K)[iso](r)} which is rendered as the mathematical notation $\widehat K_{\mbox{\scriptsize iso}}(r)$. The function argument in an \class{fv} object is usually, but not always, called \texttt{r}. (Counterexamples include \fun{transect.im} which returns an \fv\ object with function argument \texttt{t}, and \fun{roc} which returns an \fv\ object with function argument \texttt{p}.) The command \texttt{plot(K)} is dispatched to the method \texttt{plot.fv} to generate the graphic shown above. The plot method uses the auxiliary information contained in \texttt{K} to attach meaningful labels to the graphic. Stripping off the auxiliary information we can inspect the data frame itself: <<>>= head(as.data.frame(K)) @ This vignette explains how to plot, manipulate and create objects of class \fv. \subsection{Simulation envelopes} Simulation envelopes of summary functions are often used to assess statistical significance in early stages of analysis. The \spst{} command \texttt{envelope} generates simulation envelopes of a summary function: <>= E <- envelope(finpines, Kest, nsim=39) @ <>= plot(E) @ In this example, the command \verb!E <- envelope(finpines, Kest, nsim=39)! generates 39 simulated point patterns according to a completely random process, computes the estimated \Kfun{} for each simulated pattern, and finds the simulation envelopes by identifying the pointwise minimum and maximum of the 39 simulated functions. The result \texttt{E} is again an \obj\fv, but additionally belongs to the class \env, and contains additional information about how the envelopes were computed. In the resulting plot, generated by the method \texttt{plot.envelope}, the region between the upper and lower simulation envelopes is filled in grey shading. The solid black line is the estimated \Kfun{} for the original \texttt{finpines} dataset, and the dashed red line is the theoretical \Kfun{} for a completely random pattern. There is a lot of auxiliary information, displayed by \texttt{print.envelope}: <<>>= E @ This vignette also explains how to plot, manipulate and create \objs\env. Since envelope objects also belong to class \fv, the vignette first focuses on the capabilities of class \fv. \subsection{Why bother?} \label{S:whybother} Any self-respecting programmer would regard it as a trivial task to organise data in a data frame and plot each column of data as a curve in a graph. Although the task is trivial, it can be time-consuming, it is prone to error, and it can take many attempts to get it exactly right. The authors of \spst\ developed the class \fv\ to make this job easier. The class \fv{} is designed to \begin{itemize} \item support \emph{multiple versions of a function}, such as the different estimates of the \Kfun{} obtained using different edge corrections, the theoretical version of the \Kfun{} for a completely random process, the upper and lower simulation envelopes of the \Kfun, and so on. \item do the \emph{``book-keeping''} about the different versions of the function, such as the names of the different columns. \item perform automatic \emph{plotting} of the function, handling all the details of layout and labelling, including generating the mathematical labels for each curve. \item support \emph{calculations} that will be applied automatically to all the versions of the function. \item support \emph{conversion} to other data types in base \R, such as data frames and functions. \end{itemize} For example, Besag's $L$ function is defined as $L(r) = \sqrt{K(r)/\pi}$. Since we have already computed the \Kfun{} in the example above, we can compute and plot the $L$-function just by typing <<>>= L <- sqrt(K/pi) @ <>= plot(L) @ Several kinds of magic have happened here: \begin{itemize} \item The expression \texttt{sqrt(K/pi)}, where \texttt{K} is an \obj\fv, has been evaluated automatically by calculating $\sqrt{K(r)/\pi}$ for each of the versions of the function stored in \texttt{K}; \item The internal data in the object \texttt{K}, which provide mathematical labels for each version of the \Kfun, have been modified according to the algebraic operation that was just performed; \item The result has been saved as a new \obj\fv{} named \texttt{L}; \item The \texttt{plot} method has correctly displayed each version of the modified function using the modified mathematical labels, both on the vertical axis and in the legend box; \item The \texttt{plot} method has \textbf{automatically computed the position of the legend box} to prevent it from overlapping the plotted curves; \item The unit of length for the function argument has been correctly saved in the object \texttt{L} and correctly reported on the horizontal axis label. \end{itemize} The class \env{} extends the class \fv{} to handle additional information about how the envelopes were computed. The code supporting the class \env{} performs many of the ``trivial'' but error-prone calculations involving envelopes. An \obj\env{} can also contain the simulated data (the point patterns and/or the summary functions) that were used to compute the envelopes, which makes it possible to re-use the simulated data to compute a different version of the envelope. \newpage \section{Plotting} \label{S:plot.fv} \subsection{Default plot} If \texttt{f} is an object of class \class{fv}, the command \texttt{plot(f)} is dispatched to the method \fun{plot.fv}. The default behaviour of \texttt{plot(f)} is to generate a plot containing several curves, each representing a different version of the same target function, plotted against the distance argument $r$. <>= plot(Gest(finpines)) @ <>= aa <- plot(Gest(finpines)) @ Here \texttt{Gest} computes estimates of the nearest-neighbour distance distribution function $G(r)$. The plot shows three empirical estimates of $G(r)$ for the \texttt{finpines} dataset, together with the `theoretical' curve $\pois G(r)$ expected for a completely random pattern, all plotted against the distance argument $r$. The legend indicates the meaning of each curve. The main title identifies the object in \R\ that was plotted. The return value from \fun{plot.fv} is a data frame containing more detailed information about the meaning of the curves. For the plot generated above, the return value is <>= aa <- plot(Gest(finpines)) aa @ <>= aa @ Here \texttt{lty} and \texttt{col} are the graphics parameters controlling the line type and line colour, and \texttt{label} is the mathematical notation for each edge-corrected estimate, in the syntax recognised by \R{} graphics functions. The plot generated by \texttt{plot.fv} uses the base \R\ graphics system (not \texttt{lattice} or \texttt{ggplot}). and is affected by graphics parameters specified by \texttt{par()}. \subsection{Modifying parameters of the default plot} The default plot can easily be modified: \begin{description} \item[margin space:] To change the amount of white space around the plot, use \texttt{par('mar')}. \item[main title:] use \texttt{main=""} to suppress the main title. \item[legend:] Set \texttt{legend=FALSE} to suppress the legend. Use the argument \texttt{legendargs} to modify the legend. The legend position is automatically computed to avoid overlap with the plotted curves, but this can be overridden by \texttt{legendpos}. \item[range of values:] Use \texttt{xlim} and \texttt{ylim} to specify the ranges of values on the $x$ and $y$ axes. \textbf{See the note below about the ``recommended range''.} Use \texttt{ylim.covers} to specify a numerical value or values that must be covered by the $y$ axis. For example, \texttt{ylim.covers=0} means that the $y$ axis will always include the origin. \end{description} For further information, see \texttt{help(plot.fv)}. \subsection{Recommended range and recommended columns} The default plot of an \fv\ object does not necessarily display all the data that is contained in the object: \begin{description} \item[shorter range of distances:] the range of values of the distance argument $r$ displayed in the default plot may be shorter than the range of values actually contained in the data frame. \item[not all columns of data:] the plot may not display all the columns of data contained in the data frame. \end{description} This happens because an \obj\fv\ contains ``recommendations'' about the range of distances that should be displayed, and about the columns of data that should be shown. These recommendations are based on standard statistical practice. The recommendations are followed when the default plot is generated, unless they are specifically overridden. Consider this example: <<>>= G <- Gest(finpines) G @ The printout shows the range of values of \texttt{r} that are present in the table as the `\texttt{available range}'. It also gives a `\texttt{recommended range}' which is generally shorter than the available range. \emph{The default plot of the object will only show the function values over the recommended range} and not over the full range of values available. This is done so that the interesting detail is clearly visible in the default plot. Values outside the recommended range may be unreliable due to increased variance or bias, depending on the edge correction. To prevent this behaviour and use the full range of function values available, set \texttt{clip.xlim=FALSE} in the plot command. Alternatively, specify the desired range of \texttt{r} values using the argument \texttt{xlim} in the plot command. The printout also says that the default plot formula is \verb! . ~ r ! where ``\verb!.!'' stands for \texttt{"km", "rs", "han", "theo"}. This means that the default plot will display only the columns named \texttt{"km", "rs", "han"} and \texttt{"theo"} and will \textbf{not} display the columns named \texttt{"hazard"} and \texttt{"theohaz"} which are mentioned in the printout. This is consistent with the graphic shown above. In this example, the column named \texttt{"hazard"} is an estimate of the \emph{hazard rate} $h(r) = G'(r)/(1-G(r))$ of the nearest neighbour distance function, rather than an estimate of $G(r)$ itself. The column named \texttt{"theohaz"} is the corresponding theoretical value of the hazard rate, expected if the point pattern is completely random. It makes sense that the hazard rate $h(r)$ and distribution function $G(r)$ should not normally be plotted together. Therefore when \texttt{Gest} is executed, it designates \texttt{"km", "rs", "han", "theo"} as the ``recommended columns'' that should be displayed by default, and it stores this information in the resulting object \texttt{G}. When \texttt{plot(G)} is executed, \texttt{plot.fv} uses this information to determine which columns are to be plotted. \subsection{Plot specified by a formula} \label{S:plot.formula} Different kinds of plots can be specified using a \texttt{formula} as the second argument to \texttt{plot.fv}. The left side of the formula represents what variables will be plotted on the vertical ($y$) axis, and the right side determines the variable on the horizontal ($x$) axis. For example, in the object \texttt{K <- Kest(finpines)}, the column named \texttt{iso} contains the values of the isotropic correction estimate. To plot the isotropic correction estimate against $r$, simply do <>= plot(K, iso ~ r) @ In \fun{plot.fv}, both sides of the plot formula are interpreted as mathematical expressions, so that operators like `\verb!+!', `\verb!-!', `\verb!*!', `\verb!/!' have their usual meaning in arithmetic. The right-hand side of the formula can be any expression that, when evaluated, yields a numeric vector, and the left-hand side is any expression that evaluates to a vector or matrix of compatible dimensions. If the left-hand side of the formula, when evaluated, yields a matrix, then each column of that matrix is plotted against the specified $x$ variable as a separate curve. In particular the left-hand side of the formula may invoke the function \fun{cbind} to indicate that several different curves should be plotted. For example, to plot only the isotropic correction estimator and the theoretical curve: <>= plot(K, cbind(iso, theo) ~ r) @ Notice that, in this example, \texttt{plot.fv} is clever enough to recognise that \texttt{iso} and \texttt{theo} are both versions of the \Kfun\ $K(r)$, and to decide that the appropriate label for the vertical axis is just $K(r)$. The plot formula may also involve the names of constants like \texttt{pi}, standard functions like \texttt{sqrt}, and some special abbreviations listed in Table~\ref{tab:fvnames}. \begin{table}[!h] \begin{tabular}{ll} \verb!.x! & argument of function \\ \verb!.y! & best estimate of function \\ \verb!.! & all recommended estimates of function \\ \verb!.a! & all columns of function values \\ \verb!.s! & upper and lower limits of shading \end{tabular} \caption{ Recognised abbreviations for columns of an \class{fv} object. } \label{tab:fvnames} \end{table} The symbol \verb!.x! represents the function argument, usually \texttt{"r"}. The symbol \verb!.y! represents one of the columns of function values which has been designated as the `best' estimate, for use by some other commands in \spst. The symbol `\verb!.!' represents the `recommended' estimates. The default plotting formula is \verb!. ~ .x! indicating that each of the recommended estimates will be plotted against the function argument. The formula \verb!.y ~ .x! means that the best estimate of the function will be plotted against the function argument. To expand these abbreviations for a particular \fv\ object, use the function \texttt{fvnames}. <<>>= fvnames(K, ".y") fvnames(K, ".") @ A plot formula can be used to specify a transformation that should be applied to the function values before they are displayed. For example, to subtract the theoretical Poisson value from each of the function estimates: <>= plot(K, . - theo ~ r) @ Alternatively one could plot the function estimates \emph{against} the Poisson value: <>= plot(K, . ~ theo) @ This plot has some theoretical support. In the discussion of Ripley's paper, Cox \cite{cox77discuss} proposed that $\widehat K(r)$ should be plotted against $r^2$, which is almost equivalent. We can follow Cox's recommendation exactly: <>= plot(K, . ~ r^2) @ The mathematical labels for the plot axes, and for the individual curves, are constructed automatically by \spst\ from the plot formula. If the plot formula involves the names of external variables, these will be rendered in Greek where possible. For example, to plot the average number of trees surrounding a typical tree in the Swedish Pines data, <>= lambda <- intensity(swedishpines) plot(K, lambda * . ~ r) @ Here we use the name \texttt{lambda} so that it will be rendered as the Greek letter $\lambda$ in the graphics: the $y$-axis will be labelled $\lambda K(r)$. \section{Calculating with an \fv\ object} This section explains how to do calculations involving a single \obj\fv. The next section covers calculations involving several \objs\fv. \subsection{Arithmetic and mathematical operators} Arithmetic and mathematical operations on an \obj\fv\ can be performed by simply writing the arithmetic expression involving the name of the object. The following are valid: <>= K <- Kest(cells) K/pi sqrt(K/pi) @ These inline calculations are performed by the operators \texttt{Ops.fv} and \texttt{Math.fv}. The operation is applied to each column of \emph{function values}; the function argument \texttt{r} will not be affected. The result is another \obj\fv\ with the same number of columns, with the same column names, but with appropriately adjusted auxiliary information. The expression can involve a command which returns an \obj\fv: <>= sqrt(Kest(cells)/pi) @ The auxiliary information contained in the resulting object will be slightly less elegant in this case. These arithmetic and mathematical operations are applied only to the \emph{recommended} columns of function values identified by \texttt{fvnames(, ".")}. \subsection{Other vectorised operations} Functions such as \texttt{pmax} and \texttt{cumsum} apply to vector data, but are not recognised as arithmetic or mathematical operators, so they are not covered by \texttt{Ops.fv} and \texttt{Math.fv}. For expressions involving \texttt{pmax} and \texttt{cumsum} (or indeed any algebraic expression whatsoever), use the command \texttt{eval.fv} to perform the calculation simultaneously for each column of function values: <>= Kpos <- eval.fv(pmax(0, K)) @ The result \texttt{Kpos} is another \obj\fv\ in which the function values are all non-negative. The first argument of \texttt{eval.fv} should be an expression involving the \textbf{name} of the \obj\fv. By default, the calculation is only applied to the \emph{recommended} columns of function values identified by \texttt{fvnames(, ".")}. This may be overridden by setting \texttt{dotonly=FALSE} in the call to \texttt{eval.fv}. The computations of \texttt{Ops.fv} and \texttt{Math.fv} are implemented using \texttt{eval.fv} but there may be slight differences in the handling of the auxiliary information. \subsection{Calculations involving specific columns} \label{p:with.fv} To manipulate or combine one or more columns of data in an \class{fv} object, it is typically easiest to use \fun{with.fv}, a method for the generic \fun{with}. This behaves in a very similar way to \texttt{with.data.frame}. For example: <<>>= Kr <- Kest(redwood) z <- with(Kr, iso - theo) x <- with(Kr, r) @ The results \texttt{x} and \texttt{z} are numeric vectors, where \texttt{x} contains the values of the distance argument $r$, and \texttt{z} contains the difference between the columns \texttt{iso} (isotropic correction estimate) and \texttt{theo} (theoretical value for CSR) for the \Kfun{} estimate of the redwood seedlings data. For this to work, we have to know that \texttt{Kr} contains columns named \texttt{r}, \texttt{iso} and \texttt{theo}. Printing the object will reveal this information, as would typing \texttt{names(Kr)} or \texttt{colnames(Kr)}. The general syntax is \texttt{with(X, expr)} where \texttt{X} is an \class{fv} object and \texttt{expr} can be any expression involving the names of columns of \texttt{X}. The expression can include functions, so long as they are capable of operating on numeric vectors. The expression can also involve the abbreviations listed in Table~\ref{tab:fvnames}: <<>>= Kcen <- with(Kr, . - theo) @ subtracts the `theoretical' value from all the available edge correction estimates. The result \texttt{Kcen} is another \class{fv} object. You can also get a result which is a vector or single number: <<>>= with(Kr, max(abs(iso-theo))) @ \subsection{Extracting data} An \obj\fv\ is essentially a data frame with additional attributes. It contains the values of the desired function (such as $K(r)$) at a finely spaced grid of values of the function argument $r$. The data frame can be extracted (and the additional attributes removed) using \texttt{as.data.frame.fv}: <<>>= df <- as.data.frame(K) @ A single column of values can be extracted using the \verb!$! operator in the usual way: \verb!K$iso! %$ would extract a vector containing the isotropic correction estimates of $K(r)$. The subset extraction operator `\verb![!' has a method %] for \class{fv} objects. This always returns another \class{fv} object, so it will refuse to remove the column containing values of the function argument \texttt{r}, for example. To override this refusal, convert the object to a data frame using \fun{as.data.frame} and then use `\verb![!': % ] the result will be a data frame or a vector. Commands designed for data frames often work for \class{fv} objects as well. The functions \texttt{head} and \texttt{tail} extract the top (first few rows) and bottom (last few rows) of a data frame. They also work on \class{fv} objects: the result is a new \class{fv} object containing the function values for a short interval of $r$ values at the beginning or end of the range. The function \texttt{subset} selects designated subsets of a data frame using an elegant syntax and this also works on \class{fv} objects. To restrict \texttt{K} to the range $r \le 0.1$ and remove the border correction, <<>>= Ko <- subset(K, r < 0.1, select= -border) @ \subsection{Converting to a true function} An \obj\fv\ is meant to represent a function, but it contains only sample values of the function at a grid of values of the function argument. The table of function values can also be converted to a true function in the \R{} language using \fun{as.function}. This makes it easy to evaluate the function at any desired distance $r$. <<>>= Ks <- Kest(swedishpines) kfun <- as.function(Ks) kfun(9) @ By default, the result \texttt{kfun} is a function in \R, with a single argument \texttt{r} (or whatever the original function argument was called). The new function accepts numeric values or numeric vectors of distance values, and returns the values of the `best' estimate of the function, interpolated linearly between entries in the table. If one of the other function estimates is required, use the argument \texttt{value} to \fun{as.function} to select it. Several estimates can be chosen: <<>>= kf <- as.function(Ks, value=".") kf(9) kf(9, "trans") @ kfun(0.1) @ \subsection{Special operations} \label{S:manip.fv} An \class{fv} object can be manipulated using the operations listed in Table~\ref{tab:fvmethods}. \begin{table}[!h] \begin{tabular}[c]{ll} \texttt{f} & print a description \\ \texttt{print(f)} & print a description \\ \texttt{plot(f)} & plot the function estimates \\ \texttt{as.data.frame(f)} & strip extra information (returns a data frame) \\ \verb!f$iso! & extract column named \texttt{iso} (returns a numeric vector) \\ \verb!f[i,j]! & extract subset (returns an \class{fv} object) \\ \verb!subset(f, ...)! & extract subset (returns an \class{fv} object) \\ \texttt{with(f, expr)} & perform calculations with columns of data frame\\ \texttt{eval.fv(expr)} & perform calculations with several \class{fv} objects \\ \texttt{bind.fv(f, d)} & combine an \class{fv} object \texttt{f} and data frame \texttt{d} \\ \texttt{min(f)}, \texttt{max(f)}, \texttt{range(f)} & range of function values \\ \texttt{Smooth(f)} & apply smoothing to function values \\ \texttt{deriv(f)} & derivative of function\\ \texttt{stieltjes(g,f)} & compute Stieltjes integral with respect to \texttt{f} \\ \texttt{as.function(f)} & convert to a function \end{tabular} \caption{Operations for manipulating an \class{fv} object \code{f}.} \label{tab:fvmethods} \end{table} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Calculating with several \fv\ objects} \subsection{Arithmetic and mathematical operators} Arithmetic and mathematical operations involving several \objs\fv\ can be performed by simply writing the arithmetic expression involving the objects: <>= Kcel <- Kest(cells) Kred <- Kest(redwood) Kdif <- Kcel - Kred @ These inline calculations are performed by the operators \texttt{Ops.fv} and \texttt{Math.fv}. The operation is applied to each column of \emph{function values}; the function argument \texttt{r} will not be affected. The result is another \obj\fv\ with the same number of columns, with the same column names, but with appropriately adjusted auxiliary information. The \fv\ objects should be `compatible' in the sense that they have the same column names, and the same vector of $r$ values. However, \texttt{eval.fv} will attempt to reconcile incompatible objects. (The \spst\ generic function \fun{compatible} determines whether two or more objects are compatible, and the generic function \fun{harmonise} makes them compatible, if possible.) The expression can involve sub-expressions which return \objs\fv: <>= Kest(cells) - Kest(redwood) @ The auxiliary information contained in the resulting object will be slightly less elegant in this case. \subsection{Other vectorised operations} For expressions involving \texttt{pmax} and \texttt{cumsum} (or indeed any algebraic expression whatsoever), use the command \texttt{eval.fv} to perform the calculation simultaneously for each column of function values. <>= Kcel <- Kest(cells) Kred <- Kest(redwood) Kmax <- eval.fv(pmax(Kcel, Kred)) @ The result \texttt{Kmax} is another \obj\fv. The first argument of \texttt{eval.fv} should be an expression involving the \textbf{names} of the \objs\fv. By default, the calculation is only applied to the \emph{recommended} columns of function values identified by \texttt{fvnames(x, ".")} where \texttt{x} is the \obj\fv. This may be overridden by setting \texttt{dotonly=FALSE} in the call to \texttt{eval.fv}. The expression is not permitted to contain sub-expressions that evaluate to \objs\fv. However, you can use the argument \texttt{envir} to supply such sub-expressions: <>= Kmax <- eval.fm(pmax(Kcel, Kred), envir=list(Kcel=Kest(cells), Kred=Kest(redwood))) @ The computations of \texttt{Ops.fv} and \texttt{Math.fv} are implemented using \texttt{eval.fv} but there may be slight differences in the handling of the auxiliary information. \subsection{Combining objects} Several \class{fv} objects can be combined using the operations listed in Table~\ref{tab:fvmethods.multi}. \begin{table}[!h] \begin{tabular}[c]{ll} \texttt{eval.fv(expr)} & perform calculations with several \class{fv} objects \\ \verb!cbind(f1, f2, ...)! & combine \class{fv} objects \texttt{f1, f2, ...} \\ \texttt{bind.fv(f, d)} & combine an \class{fv} object \texttt{f} and data frame \texttt{d} \\ \verb!collapse.fv(f1, f2, ...)! & combine several redundant \class{fv} objects \\ \verb!compatible(f1, f2, ...)! & check whether \class{fv} objects are compatible \\ \verb!harmonise(f1, f2, ...)! & make \class{fv} objects compatible \end{tabular} \caption{Operations for manipulating several \class{fv} objects \code{f1}, \code{f2}.} \label{tab:fvmethods.multi} \end{table} Use \code{\link{cbind.fv}} to combine several \code{"fv"} objects. Use \code{\link{bind.fv}} to glue additional columns onto an existing \code{"fv"} object. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Creating \fv\ objects from raw data} This section explains how to create \objs\fv\ from raw numerical data. This would be useful if you are implementing a completely new kind of summary function. Subsection~\ref{S:creator} explains how to create an \obj\fv\ by providing the numerical data and the required auxiliary information. Section~\ref{S:as.fv} describes an easier way to convert a data frame (or similar object) to an \obj\fv\ without specifying the auxiliary information, using default rules for the auxiliary information. Section~\ref{S:compileK} describes special tools \texttt{compileK, compilepcf, compileCDF} for creating an \obj\fv\ from a numeric vector of distance values, using the rules that apply to the \Kfun, or the pair correlation function, or the nearest-neighbour distance distribution function. \subsection{The creator function \fun{fv}} \label{S:creator} \subsubsection{The creator function} The low-level function \code{fv} is used to create an object of class \code{"fv"} from raw numerical data. It has the following syntax: \begin{verbatim} fv(x, argu = "r", ylab = NULL, valu, fmla = NULL, alim = NULL, labl = names(x), desc = NULL, unitname = NULL, fname = NULL, yexp = ylab) \end{verbatim} The arguments are as follows: \begin{itemize} \item \code{x} contains the numerical data. It should be a data frame, in which one column gives the values of the function argument for which the function has been evaluated, and at least one other column contains the corresponding values of the function. These other columns typically give the values of different versions or estimates of the same function, for example, different estimates of the \Kfun{} obtained using different edge corrections. However they may also contain the values of related functions such as the derivative or hazard rate. \item \code{argu} specifies the name of the column of \code{x} that contains the values of the function argument (typically \code{argu="r"} but this is not compulsory). \item \code{valu} specifies the name of another column that contains the `recommended' estimate of the function. It will be used to provide function values in those situations where a single column of data is required. For example, \code{envelope} computes its simulation envelopes using the recommended value of the summary function. \item \code{fmla} specifies the default plotting behaviour. It should be a formula, or a string that can be converted to a formula. Variables in the formula are names of columns of \code{x}. See \code{plot.fv} for the interpretation of this formula. \item \code{alim} specifies the recommended range of the function argument. This is used in situations where statistical theory or statistical practice indicates that the computed estimates of the function are not trustworthy outside a certain range of values of the function argument. By default, \code{plot.fv} will restrict the plot to this range. \item \code{fname} is a character string (or a vector of 2 character strings) giving the name of the function itself. For example, the \Kfun{} would have \code{fname="K"}, while the inhomogeneous \Kfun\ has \code{fname=c("K", "inhom")}. \item \code{ylab} is a mathematical expression for the function value, used when labelling an axis of the plot, or when printing a description of the function. It should be an \R{} language object. For example the \Kfun's mathematical name $K(r)$ is rendered by \code{ylab=quote(K(r))}. \item \code{yexp} is another mathematical expression for the function value. If \code{yexp} is present, then \code{ylab} will be used only for printing, and \code{yexp} will be used for annotating axes in a plot. (Otherwise \code{yexp} defaults to \code{ylab}). \item \code{labl} is a character vector specifying plot labels for each column of \code{x}. These labels will appear on the plot axes (in non-default plots), legends and printed output. Entries in \code{labl} may contain the string \code{"\%s"} which will be replaced by \code{fname} when plotted or printed. For example the border-corrected estimate of the \Kfun{} has label \code{"\%s[bord](r)"} which becomes \code{"K[bord](r)"} when it is used in \texttt{plot.fv} or \texttt{print.fv}. \item \code{desc} is a character vector containing intelligible explanations of each column of \code{x}. Entries in \code{desc} may contain the string \code{"\%s"} which will be replaced by \code{ylab}. For example the border correction estimate of the \Kfun{} has description \code{"border correction estimate of \%s"}. This will be replaced by \code{"border correction estimate of K(r)"} when it is used in \texttt{print.fv}. \item \code{unitname} is the name of the unit of length for the \underline{function argument}. Typically the function argument \code{"r"} represents distance between points. The distance values are typically expressed in terms of a distance unit, such as metres or feet. This unit will be printed on the horizontal axis. The argument \code{unitname} is an object of class \class{unitname}, or \code{NULL} representing dimensionless values. \end{itemize} \subsubsection{Syntax for \texttt{ylab} and \texttt{yexp}} Mathematical symbols and notation are supported in \R\ base graphics. The labels on the axes of a graph, in the body of the graph, and in graph legends, can all include mathematical notation. The notation has to be encoded as an \R\ language expression. The decoding is slightly idiosyncratic, and this affects the programming of the class \fv. The arguments \code{ylab} and \code{yexp} are mathematical expressions for the function value: \texttt{ylab} is used when printing a description of the function, and \texttt{yexp} is used when labelling an axis. Usually \texttt{ylab} and \texttt{yexp} are the same. For example the \Kfun's mathematical name $K(r)$ is rendered by \code{ylab=quote(K(r))} and \code{yexp=ylab}. An example where they are different is the multitype \Kfun\ $K_{1,2}(r)$ where we set \code{ylab=quote(Kcross[1,2](r))} and \code{yexp=quote(Kcross[list(1,2)](r))} to get the most satisfactory behaviour. A useful programming tip is to use \code{substitute} instead of \code{quote} to insert values of variables into an expression, e.g. \code{substitute(Kcross[i,j](r), list(i=42,j=97))} yields the same as \code{quote(Kcross[42, 97](r))}.) \subsubsection{Syntax for \texttt{labl}} The argument \texttt{labl} is a character vector specifying plot labels for each column of \code{x}. These labels will appear on the plot axes (in non-default plots), legends and printed output. Entries in \code{labl} may contain the string \code{"\%s"} which will be replaced by \code{fname} when plotted or printed. For example the border-corrected estimate of the \Kfun{} has label \code{"\%s[bord](r)"} which becomes \code{"K[bord](r)"} when it is used in \texttt{plot.fv} or \texttt{print.fv}. This mechanism allows the code to adjust the labels when the object is changed --- for example, to produce the correct labels in \code{plot(sqrt(K/pi))} as shown in Section~\ref{S:whybother}. Things become more complicated if \texttt{fname} is a character vector of length 2. In that case the appropriate expression for the border-corrected estimate is \verb!"{hat(%s)[%s]^{bord}}(r)"! which becomes \verb!{hat(K)[inhom]^{bord}}(r)! when it is used in \texttt{plot.fv} or \texttt{print.fv}. We strongly recommend using the function \fun{makefvlabel} to create the appropriate labels. Its syntax is: \begin{verbatim} makefvlabel(op=NULL, accent=NULL, fname, sub=NULL, argname="r") \end{verbatim} where the arguments are character strings: \begin{description} \item[op] is a prefix or operator such as \code{"var"} (rarely used); \item[accent] is an accent that should be applied to the main function symbol, usually \texttt{"hat"} for empirical estimates; \item[fname] is the name of the function (usually a single letter or a character vector of length 2); \item[sub] is an optional subscript, typically used to discriminate between different estimates of the function, such as different edge corrections; \item[argname] is the name of the function argument. \end{description} Examples: <<>>= makefvlabel(NULL, NULL, "K", "pois") makefvlabel(NULL, "hat", "K", "bord") makefvlabel(NULL, "hat", c("K", "inhom"), "bord") makefvlabel("var", "hat", c("K", "inhom"), "bord") @ \subsubsection{Syntax for \texttt{desc}} Each entry of \texttt{desc} is a single character string. It may contain a \underline{single} instance of \code{"\%s"}, which will be replaced by the function name when required. \subsection{Conversion function \fun{as.fv}} \label{S:as.fv} The generic function \texttt{as.fv} converts other kinds of data to an \obj\fv. The methods \fun{as.fv.matrix} and \fun{as.fv.data.frame} provide a lazy way to convert a table of function data to an \obj\fv. The auxiliary information is determined by applying default rules. Other methods apply to classes of objects which intrinsically contain an \obj\fv, and they simply extract the \fv\ object. For example, a fitted model of class \class{kppm} contains the summary function (either the $K$ function or the pair correlation function) that was used to fit the model; so the method \fun{as.fun.kppm} simply extracts this summary function. \subsection{compileK, compilepcf, compileCDF} \label{S:compileK} A shortcut is provided for programmers wishing to implement a summary function that is similar to Ripley's $K$ function, the pair correlation function $g$, the empty space function $F$ or the nearest-neighbour distance distribution function $G$. \subsubsection{$K$ functions and pair correlation functions} Programmers who wish to implement a summary function similar to Ripley's $K$ function or the pair correlation function can use the commands \texttt{compileK} or \texttt{compilepcf}. These low-level functions construct estimates of the $K$ function or pair correlation function, or any similar functions, given only the matrix of pairwise distances and optional weights associated with these distances. These functions are useful for code development and for teaching, because they perform a common task, and do the housekeeping required to make an object of class \fv\ that represents the estimated function. However, they are not very efficient. The basic syntax of \texttt{compileK} and \texttt{compilepcf} is: <>= compileK(D, r, weights = NULL, denom = 1, ...) compilepcf(D, r, weights = NULL, denom = 1, ...) @ where \begin{itemize} \item \texttt{D} is a square matrix giving the distances between all pairs of points; \item \texttt{r} is a vector of distance values, equally spaced, at which the summary function should be calculated; \item \texttt{weights} is an optional matrix of numerical weights for the pairwise distances; \item \texttt{denom} is the denominator for the estimator. It may be a single number, or a numeric vector with the same length as \texttt{r}. \end{itemize} The command \texttt{compileK} calculates the weighted estimate of the $K$ function, \[ K(r) = \frac{1}{v(r)} \sum_i \sum_{j \neq i} w_{i,j} \; 1\{ d_{i,j} \le r\} \] and \texttt{compilepcf} calculates the weighted estimate of the pair correlation function, \[ g(r) = \frac{1}{v(r)} \sum_i \sum_{j \neq i} w_{i,j}\; \kappa ( d_{i,j} - r) \] where $d_{i,j}$ is the distance between spatial points $i$ and $j$, with corresponding weight $w_{i,j}$, and $v(r)$ is the specified denominator. Here $\kappa$ is a fixed-bandwidth smoothing kernel. For a point pattern in two dimensions, the usual denominator $v(r)$ is constant for the $K$ function, and proportional to $r$ for the pair correlation function: <<>>= X <- japanesepines D <- pairdist(X) Wt <- edge.Ripley(X, D) lambda <- intensity(X) a <- (npoints(X)-1) * lambda r <- seq(0, 0.25, by=0.01) K <- compileK(D=D, r=r, weights=Wt, denom=a) g <- compilepcf(D=D, r=r, weights=Wt, denom= a * 2 * pi * r) @ The result of \texttt{compileK} or \texttt{compilepcf} can then be edited (as explained in the next section) to change the function name and other information as desired. \subsubsection{Cumulative distribution functions} Programmers wishing to implement a summary function which is a cumulative distribution function, similar to the functions \texttt{Gest} or \texttt{Fest}, can use the command \texttt{compileCDF}. The basic syntax of \texttt{compileCDF} is: <>= compileCDF(D, B, r, ..., han.denom = NULL) @ where \begin{itemize} \item \texttt{D} is a numeric vector of observed distances (such as the distance from each data point to its nearest neighbour); \item \texttt{B} is a numeric vector of censoring distances (such as the distance from each data point to the boundary of the window); \item \texttt{r} is a vector of distance values, equally spaced, at which the summary function should be calculated; \item \texttt{han.denom} is the denominator for the Hanisch estimator. It is usually a numeric vector with the same length as \texttt{r}. \end{itemize} An example for the nearest-neighbour distance distribution function $G(r)$: <<>>= X <- japanesepines D <- nndist(X) B <- bdist.points(X) r <- seq(0, 1, by=0.01) h <- eroded.areas(Window(X), r) G <- compileCDF(D=D, B=B, r=r, han.denom=h) ## give it a better name G <- rebadge.fv(G, new.fname="G", new.ylab=quote(G(r))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Editing the auxiliary information in \fv\ objects} The ``auxiliary information'' in an \obj\fv\ consists of the function name, a mathematical expression for the function, mathematical expressions for each version of the function contained in a column of data, the choice of which columns will be plotted by default, and other information. A programmer will often wish to create an \fv\ object first, perhaps using some existing code, and then edit the auxiliary information. The safe way to edit the auxiliary information is to \textbf{use the internal functions in \spst} which support the \fv\ class: \begin{itemize} \item \texttt{rebadge.fv} is a low-level function which allows the user to change any of the entries in the auxiliary information as desired. \item \texttt{rebadge.as.crossfun} and \texttt{rebadge.as.dotfun} are wrappers for \texttt{rebadge.fv} which change the auxiliary information into the form expected for a cross-type or dot-type summary function. \item \texttt{fvlabels} extracts the mathematical code for each version of the summary function, and \verb!fvlabels<-! changes the codes. \item \texttt{makefvlabel} creates suitable mathematical code for a version of the summary function. \item The functions \texttt{fvnames} and \verb!fvnames<-! manage the definition of the abbreviations listed in Table~\ref{tab:fvnames}. \item The methods \texttt{formula.fv} and \verb!formula<-.fv! manage the default plotting formula. \item \verb!names<-.fv! changes the names of the columns in the \fv\ object, and adjusts the internal data accordingly. \item \texttt{tweak.fv.entry} is a very low-level function that changes the auxiliary information about one of the columns of data. \item \texttt{prefixfv} is another wrapper for \texttt{rebadge.fv} that adds a prefix to the name of the function. \end{itemize} \subsection{Low-level editing} \texttt{rebadge.fv} is a low-level function which allows the user to change any of the entries in the auxiliary information as desired. It has syntax <>= rebadge.fv(x, new.ylab, new.fname, tags, new.desc, new.labl, new.yexp=new.ylab, new.dotnames, new.preferred, new.formula, new.tags) @ where \texttt{x} is the \obj\fv. The arguments \texttt{new.fname}, \texttt{new.ylab} and \texttt{new.yexp} (if present) specify new values for the function name \texttt{fname} and the mathematical expressions for the function, \texttt{ylab} and \texttt{yexp}, described in Section~\ref{S:creator}. The argument \texttt{new.dotnames} specifies a new value for the selection of columns that are plotted by default. This is a character vector of column names of \texttt{x} and is associated with the abbreviation ``\verb!.!'' in Table~\ref{tab:fvnames}. The argument \texttt{new.preferred} specifies a new value for the choice of column that is designated the ``preferred'' column and is used in calculations which require a single column of data, such as simulation envelopes. This is a single character string which must be a column name of \texttt{x} and is associated with the abbreviation ``\verb!.y!'' in Table~\ref{tab:fvnames}. The argument \texttt{new.formula} specifies a new default plotting formula for the summary function. The argument \texttt{new.desc} specifies new values for the string descriptions of the individual columns, replacing the argument \texttt{desc} described in Section~\ref{S:creator}. It should be a character vector with one entry for every column of \texttt{x} (or see below). The argument \texttt{new.labl} specifies new values for the mathematical labels of the individual columns, replacing the argument \texttt{desc} described in Section~\ref{S:creator}. It should be a character vector with one entry for every column of \texttt{x} (or see below). The argument \texttt{tags} can be used to select some of the columns of data so that only the auxiliary data for the selected columns will be changed. It should be a character vector with entries which match the names of columns of \texttt{x}. In that case, \texttt{new.desc} and \texttt{new.labl} should have the same length as \texttt{tags}, and they will be taken as replacement values for the selected columns only. The optional argument \texttt{new.tags} changes the names of the columns of \texttt{x} (or the columns selected by \texttt{tags}) to new values. \subsection{Changing information about one column} The method \verb!names<-.fv! changes the names of the columns in the \fv\ object, and adjusts the internal data accordingly. The function \texttt{tweak.fv.entry} is a very low-level function that changes the auxiliary information about one of the columns of data. It has syntax <>= tweak.fv.entry(x, current.tag, new.labl=NULL, new.desc=NULL, new.tag=NULL) @ where \texttt{current.tag} is the current name of the column for which the information should be changed, \texttt{new.labl} is the new mathematical label for the column, \texttt{new.desc} is the new text description of the column, and \texttt{new.tag} is the new name of the column. All these arguments are single strings or \texttt{NULL}. \subsection{Special idioms} A few functions are available for performing special idioms. The function \texttt{prefixfv} is a wrapper for \texttt{rebadge.fv} that adds a prefix to the name of the function, and to all the relevant auxiliary information. It has syntax <>= prefixfv(x, tagprefix="", descprefix="", lablprefix=tagprefix, whichtags=fvnames(x, "*")) @ where \texttt{tagprefix}, \texttt{descprefix} and \texttt{lablprefix} are strings that should be added to the beginning of the column name, the text description, and the mathematical expression for each column of data. The argument \texttt{whichtags} specifies which columns of data should be changed; the default is to change all columns. The function \texttt{rebadge.as.crossfun} changes the auxiliary information into the form expected for a bivariate, cross-type summary function, analogous to the bivariate $K$-function $K_{i,j}(r)$ between two types of points labelled $i$ and $j$ that is computed by the \spst\ function \texttt{Kcross}. It has syntax <>= rebadge.as.crossfun(x, main, sub=NULL, i, j) @ where \texttt{main} is the main part of the function name, \texttt{sub} is the subscript part of the function name, and \texttt{i} and \texttt{j} are the type labels. For example <>= rebadge.as.crossfun(x, "L", i="A", j="B") @ would create a function $L_{A,B}(r)$, and <>= rebadge.as.crossfun(x, "L", "inhom", "A", "B") @ would create a function $L_{\mbox{\scriptsize inhom},A,B}(r)$. Similarly the function \texttt{rebadge.as.dotfun} changes the auxiliary information into the form expected for a ``one type-to-any type'' summary function, analogous to the function $K_{i \bullet}(r)$ that is computed by the \spst\ function \texttt{Kdot}. It has syntax <>= rebadge.as.dotfun(x, main, sub=NULL, i) @ \subsection{Handling mathematical labels} The auxiliary information in an \fv\ object includes mathematical labels for the different versions of the function, which are displayed by \texttt{plot.fv}. The function \texttt{fvlabels} extracts the mathematical code for each version of the summary function from the \fv\ object, and \verb!fvlabels<-! changes the codes. The mathematical codes are strings which must be recognisable to the \texttt{plotmath} code in the \R\ base graphics system which is somewhat idiosyncratic. The strings may also (and usually do) include the substring \verb!%s! (appearing once or twice) which will be replaced by the function name. For example, if the function name is \texttt{"K"} and the label is \verb!"hat(%s)[iso](r)"! this will be parsed as \verb!hat(K)[iso](r)! which is rendered as $\widehat K_{\mbox{\scriptsize iso}}(r)$. The function \texttt{makefvlabel} creates suitable mathematical code for a version of the summary function. Programmers are strongly advised to use \texttt{makefvlabel}. \subsection{Changing default behaviour} The default behaviour for plotting an \fv\ object depends on its default \texttt{plot formula} and typically on its \texttt{dot names}. The default plot formula is printed when the object is printed, and can be extracted using \texttt{formula.fv}. <<>>= K <- Kest(cells) formula(K) @ The interpretation of the plot formula is explained in Section~\ref{S:plot.formula}. In the example above, the left hand side of the formula uses the abbreviation ``\verb!.!'' which stands for ``the default list of columns to be plotted''. This abbreviation can be expanded using \texttt{fvnames}: <<>>= fvnames(K, ".") @ which indicates that the columns named \texttt{"iso"}, \texttt{"trans"}, \texttt{"border"} and \texttt{"theo"} will be plotted. The choice of ``dot names'' can be changed using \verb!fvnames<-!: <<>>= fvnames(K, ".") <- c("iso", "theo") @ In general the functions \texttt{fvnames} and \verb!fvnames<-! manage the definition of all the abbreviations listed in Table~\ref{tab:fvnames}. \section{Pooling several function estimates} \subsection{Pooling} ``Pooling'' or combining several datasets into a single dataset is a common statistical procedure. If we are only interested in a summary statistic of the data, then in some special circumstances, the summary statistic of the pooled dataset can be calculated from the summary statistics of the original, separate datasets. For example, if we have a set of $n_1$ observations with sample mean $m_1$, and another set of $n_2$ observations with sample mean $m_2$, then the sample mean of the pooled set of $n_1+n_2$ observations has sample mean $(n_1 m_1 + n_2 m_2)/(n_1+n_2)$, a weighted average of the sample means of the original datasets. This procedure is loosely called ``pooling'' the sample mean. If we have two point pattern datasets, observed in different windows, we can ``pool'' the patterns by simply treating them as a single point pattern observed in the combined window. If we pool two point pattern datasets, the estimated $K$-function of the pooled pattern can be calculated from the estimated $K$-functions $K_1(r)$ and $K_2(r)$ of the original point patterns, if we know the number of points in each of the two original patterns. That is, Ripley's $K$-function can be ``pooled''. The summary functions used in spatial statistics can be pooled, provided they are able to be expressed as a ratio $f(r) = A(r)/B$ or $f(r) = A(r)/B(r)$ where $A(r)$ is the ``numerator'' and $B$ or $B(r)$ is the ``denominator''. The pooled estimate is the ratio of the sum of numerators divided by the sum of denominators. For details, see section 16.8.1 of \cite{baddrubaturn15}. \subsection{Pooling summary functions} The generic function \texttt{pool} performs pooling of summary statistics (including summary functions like the $K$-function). In order for this to work correctly, we must know the numerator and denominator for each of the individual summary statistics or summary functions. For this purpose there is a special class \class{rat} (for ``ratio object''). An \obj\rat\ contains two attributes named \texttt{"numerator"} and \texttt{"denominator"} which contain the numerator and denominator of the ratio. For many of the summary functions provided in \spst, if we set the argument \texttt{ratio=TRUE}, the numerator and denominator will be calculated separately and saved in the resulting object, which will belong to the class \class{rat} (``ratio object'') as well as \fv. <<>>= class(Kest(cells)) class(Kest(cells, ratio=TRUE)) @ This capability is currently available for the functions \texttt{compileK}, \texttt{compilepcf}, \texttt{Finhom}, \texttt{Gcross.inhom}, \texttt{Gdot.inhom}, \texttt{Ginhom}, \texttt{GmultiInhom}, \texttt{Jcross.inhom}, \texttt{Jdot.inhom}, \texttt{Jinhom}, \texttt{Jmulti.inhom}, \texttt{K3est}, \texttt{Kcross}, \texttt{Kdot}, \texttt{Kest}, \texttt{Kinhom}, \texttt{Kmulti}, \texttt{Ksector}, \texttt{linearKinhom}, \texttt{linearK}, \texttt{linearpcfinhom}, \texttt{linearpcf}, \texttt{nnorient}, \texttt{pairorient}, \texttt{pcfcross}, \texttt{pcfdot}, \texttt{pcfmulti}, \texttt{pcf.ppp} and \texttt{Tstat}. The method \texttt{pool.rat} will pool several objects which all belong to the classes \class{fv} and \class{rat}: <<>>= X1 <- runifpoint(50) X2 <- runifpoint(50) K1 <- Kest(X1, ratio=TRUE) K2 <- Kest(X2, ratio=TRUE) K <- pool(K1, K2) @ <<>>= Xlist <- runifpoint(50, nsim=6) Klist <- lapply(Xlist, Kest, ratio=TRUE) K <- do.call(pool, Klist) @ There is also a fallback method \texttt{pool.fv} which is used when some of the objects do not contain ratio information. This method effectively pretends that all the objects have the same denominator. \subsection{Low level utilities} Programmers wishing to implement a summary function with ratio information can use the following low-level utilities: \begin{itemize} \item \texttt{ratfv} is the creator function, analogous to \texttt{fv}. Its syntax is <>= ratfv(df, numer, denom, ..., ratio=TRUE) @ where \texttt{df} is a data frame, \texttt{numer} and \texttt{denom} are \objs\fv, and additional arguments \verb!...! are passed to \texttt{fv}. It is sufficient to specify either \texttt{df} or \texttt{numer}, in addition to \texttt{denom}. \item \texttt{bind.ratfv} glues extra columns onto an existing \obj\fv and \class{rat}. \item \texttt{conform.ratfv} forces the auxiliary information in the numerator and denominator of an \obj\fv and \class{rat} to agree with the auxiliary information of the main object. \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Structure of \objs\fv} This section explains the information contained in \objs\fv. \subsection{Advice} We strongly discourage the user from unpacking the internal contents of \objs\fv{} and manipulating the contents directly. Instead, we recommend using the functions that are available in \spst{} for handling these objects. Although it is easy to extract the internal data contained in an object in \R, the structure of \objs\fv\ is idiosyncratic, and the internal format is variable. Looking at one example of an \obj\fv\ will not tell you how it all works. This is because there are many cases to handle, and many quirks in the formatting of algebraic expressions in \R. Using the functions provided in \spst\ is also more efficient than extracting data yourself, because it avoids creating multiple copies of the data. Most of all, \textbf{do not change the internal contents of \objs\fv}. This can easily violate the internal format and cause errors. Use the functions supplied for handling these objects. \subsection{Objects of class \fv} Objects of class \fv\ are returned by many commands in the \spst\ packages. Usually these objects are obtained by analysing a spatial point pattern dataset. There are also functions to create such objects from raw data. An \obj\fv{} is essentially a data frame with additional attributes containing auxiliary information. \subsubsection*{Data frame structure} The first column of the data frame contains values of the function argument. These values are arranged in increasing order, are usually evenly-spaced, and usually start from zero. The first column usually (but not always) has the column name \texttt{r}. Subsequent columns of the data frame contain the values of different versions of the summary function, corresponding to the values of the function argument. These columns may have any column names. These versions of the function may be referred to by their column names when plotting and manipulating the object. <<>>= G <- Gest(finpines) df <- as.data.frame(G) head(df) @ In this example, the object \texttt{G} contains estimates of the nearest-neighbour distance distribution function $G(r)$ for the \texttt{finpines} dataset. For the distance value $r = $ \Sexpr{round(df[6, "r"], 9)} metres, the estimate of $G(r)$ using the \texttt{han} method is \Sexpr{round(df[6, "han"],8)}. Columns of data can be extracted using the data frame structure. To extract the sequence of \texttt{r} values, use \verb!df$r! or \verb!G$r! or \verb!df[, "r"]!. To extract the corresponding values of \texttt{han}, use \verb!df$han! or \verb!G$han! or \verb!df[, "han"]!. \subsubsection*{Auxiliary information} In the example above, to find out what the column \texttt{han} means, we need the auxiliary information stored in the object \texttt{G}. This can be printed out directly in readable form: <<>>= G @ Thus, \texttt{han} refers to the estimate of $G(r)$ using Hanisch's method. The auxiliary information is stored in attributes of the object. The full list of attributes is as follows: \begin{tabular}{lll} \texttt{argu} & character(1) & Name of function argument (usually \texttt{"r"}) \\ \texttt{valu} & character(1) & Name of preferred function value \\ \texttt{ylab} & language & Mathematical expression for function (for vertical axis of plot) \\ \texttt{yexp} & language & Mathematical expression for function (in algebra) \\ \texttt{fmla} & character(1) & Default plotting formula \\ \texttt{alim} & numeric(2) & Recommended range of function argument \\ \texttt{labl} & character($m$) & Mathematical labels for each column\\ \texttt{desc} & character($m$) & Text descriptions of each column\\ \texttt{units} & unitname & Unit of length (for function argument) \\ \texttt{fname} & character(1 or 2) & Symbol for function only \\ \texttt{dotnames} & character($k \le m$) & Column names of all recommended versions \\ \texttt{shade} & character(0 or 2) & Column names of limits of grey shading\\ \end{tabular} \code{argu} is the name of the column of the data frame that contains the values of the function argument (typically \code{argu="r"} but this is not compulsory). \code{valu} specifies the name of another column that contains the `recommended' estimate of the function. It will be used to provide function values in those situations where a single column of data is required. For example, \code{envelope} computes its simulation envelopes using the recommended value of the summary function. \code{fmla} specifies the default plotting behaviour, as explained in Section~\ref{S:plot.fv}. It is a character string that can be converted to a \texttt{formula} in the \R\ language. \code{alim} specifies the recommended range of the function argument. It is a numeric vector of length 2. This is used in situations where statistical theory or statistical practice indicates that the computed estimates of the function are not trustworthy outside a certain range of values of the function argument. By default, \code{plot.fv} will restrict the plot to this range. \code{fname} gives the name of the function itself. For example, the \Kfun{} would have \code{fname="K"}. It is either a character string, or a vector of two character strings, where the second element is interpreted as a subscript. For example, the inhomogeneous \Kfun{} computed by \code{Kinhom} has \code{fname=c("K", "inhom")}. \code{ylab} is a mathematical expression for the function value, used when printing a description of the function. It is an \R{} language object. For example the \Kfun's mathematical name $K(r)$ is rendered by \code{ylab=quote(K(r))}. \code{yexp} is another mathematical expression for the function value, used for annotating axes in a plot. \code{labl} is a character vector specifying plot labels for each column of the data frame. These labels will appear on the plot axes (in non-default plots), legends and printed output. Entries in \code{labl} may contain the string \code{"\%s"} which will be replaced by \code{fname}. \code{desc} is a character vector containing intelligible explanations of each column of the data frame. Entries in \code{desc} may contain the string \code{"\%s"} which will be replaced by \code{ylab}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Structure of \objs\env} This section explains the information contained in \objs\env. \subsection{The \texttt{envelope} command} The \spst\ function \fun{envelope} performs the calculations required for envelopes. It computes the summary function for a point pattern dataset, generates simulated point patterns, computes the summary functions for the simulated patterns, and computes the envelopes of these summary functions. <>= E <- envelope(swp, Kest, nsim=39, fix.n=TRUE) @ The result is an object of class \class{envelope} and \class{fv} which can be printed and plotted and manipulated using the tools for \class{fv} objects, and by additional tools provided for \class{envelope} objects. The print method gives a lot of detail: <<>>= E @ \subsection{Re-using envelope data} The method \texttt{envelope.envelope} allows new \fun{envelope} commands to be applied to a previously computed \class{envelope} object, provided it contains the necessary data. In the original call to \fun{envelope}, if the argument \texttt{savepatterns=TRUE} was given, the resulting \class{envelope} object contains all the simulated point patterns. Alternatively if the argument \texttt{savefuns=TRUE} was given, the resulting object contains the individual summary functions for each of the simulated patterns. This information is not saved, by default, for efficiency's sake. Envelopes created with \texttt{savepatterns=TRUE} allow any kind of new envelopes to be computed using the same simulated point patterns: <>= E1 <- envelope(redwood, Kest, savepatterns=TRUE) E2 <- envelope(E1, Gest, global=TRUE, transform=expression(fisher(.))) @ Envelopes created with \texttt{savefuns=TRUE} allow the user to switch between pointwise and global envelopes of the same summary function, to apply different transformations of the summary function, and to change some parameters: <>= A1 <- envelope(redwood, Kest, nsim=39, savefuns=TRUE) A2 <- envelope(A1, global=TRUE, nsim=19, transform=expression(sqrt(./pi))) @ \subsection{Pooling several envelopes} It is also possible to combine the simulation data from several envelope objects and to compute envelopes based on the combined data. This is done using \fun{pool.envelope}, a method for the \spst\ generic \fun{pool}. The envelopes must be compatible, in that they are envelopes for the same function, and were computed using the same options. The individual summary functions must have been saved. <>= E1 <- envelope(cells, Kest, nsim=10, savefuns=TRUE) E2 <- envelope(cells, Kest, nsim=20, savefuns=TRUE) E <- pool(E1, E2) @ \subsection{Structure of envelope objects} An \obj\env{} is an \obj\fv{} with additional auxiliary information: \begin{itemize} \item the names of two of the columns of function values, designated as the upper and lower simulation envelopes of the function, saved in \texttt{attr(, "shade")} and retrievable as \texttt{fvnames(, .s)} \item details of how the envelopes were computed, saved in \texttt{attr(, "einfo")} \item optionally, the simulated point patterns used to compute the envelopes, saved in \texttt{attr(, "simpatterns")} \item optionally, the simulated summary functions (the summary functions computed for the simulated point patterns) used to compute the envelopes, saved in \texttt{attr(, "simfuns")} \end{itemize} Objects of class \env\ inherit the class \fv, so they can be manipulated using methods for class \fv, but there are extra methods for the special class \env. \subsection{The \texttt{einfo} list} Additional attribute \texttt{einfo} is a list of: \begin{tabular}{lll} \texttt{call} & character(1) & original function call \\ \texttt{Yname} & character(1) & name of original dataset \\ \texttt{valname} & character(1) & column name of function values used\\ \texttt{csr} & logical(1) & \texttt{TRUE} if simulations based on CSR \\ \texttt{csr.theo} & logical (1) & see below\\ \texttt{use.theory} & logical (1) & see below\\ \texttt{pois} & logical(1) & \texttt{TRUE} if simulations are Poisson process\\ \texttt{simtype} & character(1) & Type of simulation (see below) \\ \texttt{constraints} & character(1) & Additional information (see below) \\ \texttt{nrank} & integer(1) & Rank of envelopes \\ \texttt{nsim} & integer(1) & Number of simulations for envelope \\ \texttt{Nsim} & integer(1) & Total number of simulations\\ \texttt{global} & logical(1) & \texttt{TRUE} if global envelopes\\ \texttt{ginterval} & numeric(0 or 2) & Domain of function argument for global envelopes \\ \texttt{dual} & logical(1) & \texttt{TRUE} if two sets of simulations performed\\ \texttt{nsim2} & integer(1) & Number of simulations in second set \\ \texttt{VARIANCE} & logical(1) & \texttt{TRUE} if limits are based on standard deviation \\ \texttt{nSD} & numeric(1) & Number of standard deviations defining limits \\ \texttt{alternative} & character(1) & \texttt{two.sided}, \texttt{less} or \texttt{greater} \\ \texttt{scale} & \texttt{NULL} or function & Scaling function for function argument \\ \texttt{clamp} & logical(1) & \texttt{TRUE} if one-sided deviations must be positive \\ \texttt{use.weights} & logical(1) & \texttt{TRUE} if sample mean is weighted\\ \texttt{do.pwrong} & logical(1) & \texttt{TRUE} if ``wrong $p$-value'' should be calculated \\ \texttt{gaveup} & logical(1) & \texttt{TRUE} if simulations terminated early \end{tabular} \begin{thebibliography}{1} \bibitem{baddrubaturn15} A. Baddeley, E. Rubak, and R. Turner. \newblock {\em Spatial Point Patterns: Methodology and Applications with {{R}}}. \newblock Chapman \& Hall/CRC Press, 2015. \bibitem{besa77d} J.E. Besag. \newblock Contribution to the discussion of the paper by Ripley (1977). \newblock \emph{Journal of the Royal Statistical Society, Series B} \textbf{39} (1977) 193--195. \bibitem{cox77discuss} D.R. Cox. \newblock Contribution to the discussion of the paper by Ripley (1977). \newblock \emph{Journal of the Royal Statistical Society, Series B} \textbf{39} (1977) 206. \end{thebibliography} \end{document} spatstat/inst/doc/spatstatKnetsize.txt0000644000176200001440000000153614360753512017773 0ustar liggesusersdate version nhelpfiles nobjects ndatasets Rlines srclines "2019-05-22" "1.9-0" 6 3 3 227 3962 "2019-07-29" "1.10-0" 6 3 3 227 3962 "2019-07-29" "1.10-1" 6 3 3 227 3962 "2019-07-29" "1.10-2" 6 3 3 227 3962 "2019-08-08" "1.11-0" 4 3 1 227 3962 "2019-08-08" "1.11-1" 4 3 1 227 3962 "2019-08-09" "1.11-2" 4 3 1 227 3962 "2019-12-13" "1.12-0" 4 3 1 232 3962 "2019-12-15" "1.12-1" 4 3 1 232 4007 "2020-04-27" "1.12-2" 4 3 1 232 4007 "2021-01-03" "1.13-0" 4 3 1 232 4007 "2021-01-10" "1.13-0" 4 3 1 232 4007 "2021-01-10" "1.13-0" 4 3 1 232 4007 "2021-02-15" "1.65-0" 4 3 1 232 4007 "2021-03-31" "2.0-0" 4 3 1 232 4007 "2022-01-12" "2.0-1" 4 3 1 232 4007 "2022-05-24" "2.0-2" 4 3 1 232 4007 "2022-10-30" "3.0" 4 3 1 232 4007 "2022-10-30" "3.0-0" 4 3 1 232 4007 "2022-11-12" "3.0-1" 4 3 1 232 4007 "2022-11-12" "3.0-2" 4 3 1 232 4007 "2022-11-12" "3.0-2" 4 3 1 232 4007 spatstat/inst/doc/replicated.pdf0000644000176200001440000251346714517616256016505 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 6129 /Filter /FlateDecode /N 88 /First 754 >> stream x]YE~_o6Fgfܱ6!V0TH+uiYf~~wUYU݈=Zyg\~}bMFhzHkt37e۰N⡮aZ0+ix7hi$׼QxOLC4ʠ6y5X *aqP#Te3&Qg/M5[<0 \5 @Ito8hndip,9u%K0J:oPG*$$+k%d#P4%^k 8:Q(CX E1h713;0 Ht+gG`0x% BXY54D3,{"1P]cR]PI/hȉF :(ztJdCJwQ4J=ȦL+*J 3mFɆa5J6i@fJdamP83l,Z3ہ J=]!st ek JƲƀd]]xwA#y:/]>6/7 xݴ7ww?m3|{<O}{*ۙto> o~gx~ybW>݋ݻCc>LM?^5R{AArh;뻄j{օ{_"s}4=C]}*砚pno\#ip]s->\!}!B*\:o>;a{sf?^6ӀH+C#+-^r߾@TSFWi?yڷ?P Ix|9FY3ΠHgme:#YOtFtm:#ӥ3)tpM )z06XPNǿyyEx;PI븁:k> GYgQՎ21&G-lcMآUuGk+p|s'qG:tq^d8Xz=BDLZOFd?&Kڂ,x$ Yxmϣ DT/֮}cD"%e%D"Y H-]e!B)* BDb >*VV !y\zR0"+"BjI4*{I+u$ se܁xt(<i콹ݾɿ*I4#)xmRmOd'n'n'=`2cl]j y]VvW'ŶY.6[%P- TzlXNuVq@T?e;ш"te%K`n.êϓpm %1 !wSX0_O@,1**3~dTN|VFSA?y`GN_2THW+ @Cgq%+ڕJwG0OHhA0 wBSb 0@S"?8.f,5ek]E2 Ͳ8emOD2(@ų2:{D-eo£ ojڞ} o- 0!ڕeqYDlL>FRx.wɯ\r]MVUѝh%W -R1T@liIYgKC$ˉG4꓆bqĺP]1)0?!P `nB+G0,i =|S30w`T|+?!3wW]pG0RC8p:* 1Re!%UbBB ɮ jrwQBubԧ>Y: 1՗B@,C~ n%/DE!¦aS)Eؔ"lJ6R_$ I$F= &F] $&j11[qUwSюYU?@n .~QB:e3 N;Z?b /X6a'Cܓ&>k?T<=HhF<3*Ymb,Nh@WDݕ6QףF#O,ҹ ?C4ɝ;Ũ]S#{ rC݄ZBv u }kM=kKaXi ߽xZ"K%A9HۧIĆxxRYkCG <@ۜעa-"#^ؤJEcrpiA)zvճxڹԽd~.{ X䖕rbݜ&RBn?E](+NJv&buҾl/FW'[II$5<3πyThɗB(PDPwGqJ[ RPԾa&BO$tiT5=8;+P~T3}uD(p)Ѽ(y}4/%9tD[G F,Q(5FfF"k[=k[=AIRZqA$)ngBI_i &B!ŝH1ȉ8vQ940hެ&|J)1JGA]rV Kmg7( >/ |@uJ!,@I [H*D B&6ÃH\Jx09c"+.8ãWG2U!.` ,y  SNT9~"X">.su3A~ @爫/KL}lX"cĈTGRNFzX'뒥QԕNf Y b:,!.Y(`!{HHMW>S7H <9Fb-xfV6}L$5"=Z PDw:F2I.cM 0Rc.rsaԜE9Nj4"G'x9^d/HY9^0:xk)V%hd+ f0,&wj+3Y3|D_Ccz&fm`룰cA@]Xţ )Wad4Ivx'[Ń%,^jn铯>귟={fSGyN7OmmVA)6T?))2Au?vJ+=MOo u5ga𞬏Bq(TKjC.J UxܽMLm}ݽzO1}~[i_޵/onmjC]o6oxi޷7n}ovȡojnae揘7WX%=wkR姻7JYҗy{ͫ7[<>݁Wq7M/u7ˍ/zPuſഛOnvB1''w+9ӧwN˨3kas}p* ދRCiLy8Awx%hyyim_oڛͶݷ{lߵ(jfݺ޽k?mo?׷m{=v_q$9B1;Cu~!ď5eGivl C#$}toJ:MV1խoPHE Gjd$))k)Kf6(}V7*.Ĝn\'|eⳚkLjo_>C{1yLh;5-l:`R~{n7o۟=@Y9=M`p#v)4׌&8q>'fpXJc oc srD'8=121tx:QG;bq*Pqِ{ "r YJ͚t/9}AG+y"{T5&}lTO|M)U;Q'c̿R.,3'SYT@,YFbfi-ӞӦQ?4R: Ots)Q(ΪdBߪ]Y/;5XRfjxa(RO-M>XJLдTzRjȪm~ԗaYA٤.RX>ij\ubn i޽"2)=JRJ&M@@+4 Qt2뒊.IM{P[@z$e S&MD(ɀX@EꅙZ#Ǚ;\\P(oʿnFE 5N>1ʲFuO7glI\צ-=B"u!LxJÖK$F _SJ3)GAZ]z>4/==R88}soK7iN{,bbZJ3 80\ 8t\^f|O9 jCG$|hn45o~u(8Ns+h c}z2FC~)˜D DAEsҼUS8p@c D/\TP=V+ 'GV(|#YL4zS :if*״^׼itbqbhدihׯ9x"h@A0-F .E-sgiq äB TU *Iz 4E=dbKJ~sn{$~"ѳu!' 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