pax_global_header00006660000000000000000000000064141266033170014515gustar00rootroot0000000000000052 comment=d300156edb2c4ecadb73c45b0df281eadc371566 r-cran-shapes-1.2.6/000077500000000000000000000000001412660331700141665ustar00rootroot00000000000000r-cran-shapes-1.2.6/DESCRIPTION000077500000000000000000000014701412660331700157010ustar00rootroot00000000000000Package: shapes Title: Statistical Shape Analysis Date: 2021-03-30 Version: 1.2.6 Author: Ian L. Dryden Description: Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons. 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r-cran-shapes-1.2.6/NAMESPACE000077500000000000000000000012411412660331700154060ustar00rootroot00000000000000# Export all names exportPattern(".") # Import all packages listed as Imports or Depends import( minpack.lm, scatterplot3d, rgl, MASS ) importFrom("graphics", "arrows", "lines", "par", "persp", "plot", "points", "text", "title") importFrom("stats", "cmdscale", "cor", "cov", "nlm", "pchisq", "pf", "prcomp", "predict", "qchisq", "rnorm", "var", "lm", "optim", "qnorm", "sd") importFrom("grDevices", "col2rgb", "rgb") importFrom("graphics", "abline", "legend") importFrom("stats", "coef", "princomp", "rbeta", "runif") importFrom("utils", "tail") importFrom("grDevices", "rainbow") importFrom("utils", "head")r-cran-shapes-1.2.6/NEWS.md000077500000000000000000000041151412660331700152700ustar00rootroot000000000000001.2.6 Added in pnss3d and plot3darcs for displaying the PNSS modes of variation 1.2.5 Corrected bug in estcov for method="Power" when exit occurred in some zero eigenvalue cases, by including abs(eigenvalue) Faster versions of some functions kindly supplied by Gregorio Quintana-Orti and Amelia Simo, University Jaume I, Spain. 1.2.4 Added Principal Nested Spheres (pns) Added Principal Nested Shape Spaces using PCA (pns4pc) Updated some references in help files 1.2.3 Minor adjustment to Penalized Euclidean Distance regression function, including a different name ped() 1.2.2 Added in a function to carry out Penalized Euclidean Distance regression, which is a sparse regression method (Vasiliu et al., 2017, arxiv). Renamed the function sigma() to sigmacov() prevent a warning that the same function name is used in the `stats' package. 1.2.1 corrected a bug in the calculation of principal warp eigenvectors in the function shaperw, which in turn is used by procGPA (thanks to Paolo Piras) 1.2.0 corrected an error in apes$x[,,60] data, which should have been the same as panf.dat[,,1] (thanks to Katie Severn) 1.1-13 Corrected a bug in shaperw for the m=3 case (transposes needed) (thanks to Valerio Varano and Paulo Piras) internal expression of bendingenergy (benergy in TPSgrid) has correct constant now. (thanks to Valerio Varano) 1.1-12 procdist - function added to compute different types of Procrustes shape distances 1.1-11 MDSshape - function added to compute MDS mean shape Several new datasets added 1.1-10 procGPA fixed recently introduced error in reading in complex matrices procGPA( , scale=FALSE,pcaoutput=FALSE) was still calculating PCA, so this has now been fixed. The internal function prcomp1 now uses eigen() rather than svd(), due to some convergence issues in LAPACK for some singular matrices. transformations() :relative translations between centroids now given, rather than just translating the original to have centroid at the origin. r-cran-shapes-1.2.6/R/000077500000000000000000000000001412660331700143675ustar00rootroot00000000000000r-cran-shapes-1.2.6/R/shapes.R000077500000000000000000013120421412660331700160030ustar00rootroot00000000000000#----------------------------------------------------------------------- # # Statistical shape analysis routines # written by Ian Dryden in R (see http://cran.r-project.org) # (c) Ian Dryden # University of Nottingham. version 1.2.6 # 2003-2021 # # Includes contributions by many other authors, including # Mohammad Faghihi, Kwang-Rae Kim, Alfred Kume, # Gregorio Quintana-Orti, Amelia Simo. # ########################################################################### tangentcoords.partial.inv = function(v, p, R) { return(matrix(sqrt(1 - sum(v^2)) * c(p) + v, nrow = nrow(p)) %*% t(R)) } preshape2shape = function(z) { k = nrow(z) + 1 H = defh(k - 1) return(t(H) %*% z) } plot3darcs<-function(x,pcno=1,c=1,nn=100,boundary.data=TRUE,view.theta=0,view.phi=0,type="pnss"){ # points along principal arcs pns.out <- x k<-pns.out$GPAout$k m<-pns.out$GPAout$m n.pc<-dim(pns.out$resmat)[1] npts = 100 arc1 = t(get.prinarc(resmat = pns.out$resmat, PNS = pns.out$PNS, arc = 1, n = npts, boundary.data = boundary.data)) arc2 = t(get.prinarc(resmat = pns.out$resmat, PNS = pns.out$PNS, arc = 2, n = npts, boundary.data = boundary.data)) arc3 = t(get.prinarc(resmat = pns.out$resmat, PNS = pns.out$PNS, arc = 3, n = npts, boundary.data = boundary.data)) # PNS mean PNSmean = pns.out$PNS$mean GPAout = pns.out$GPAout # c * sd : lower and upper along principal arcs. lower -> 0 -> upper # total 2 * nn + 1 configurations # : nn configurations from negative to 0 # 1 configuration at 0 # nn configurations from 0 to positive #default c = 1 { cat("stdev of PNS1 score:", round(sd(pns.out$resmat[ 1, ]), 4), "\n") cat("stdev of PNS2 score:", round(sd(pns.out$resmat[ 2, ]), 4), "\n") cat("stdev of PNS3 score:", round(sd(pns.out$resmat[ 3, ]), 4), "\n") } rng = c * sd(pns.out$resmat[1,]) val = c(seq(-rng, 0, length = nn + 1)[-(nn + 1)], 0, seq(0, rng, length = nn + 1)[-1]) lu.arc1 = t(get.prinarc.value(PNS = pns.out$PNS, arc = 1, res = val)) rng = c * sd(pns.out$resmat[2,]) val = c(seq(-rng, 0, length = nn + 1)[-(nn + 1)], 0, seq(0, rng, length = nn + 1)[-1]) lu.arc2 = t(get.prinarc.value(PNS = pns.out$PNS, arc = 2, res = val)) rng = c * sd(pns.out$resmat[3,]) val = c(seq(-rng, 0, length = nn + 1)[-(nn + 1)], 0, seq(0, rng, length = nn + 1)[-1]) lu.arc3 = t(get.prinarc.value(PNS = pns.out$PNS, arc = 3, res = val)) # spherical data to PC scores scores.arc1 = sphere2pcscore(x = arc1) scores.arc2 = sphere2pcscore(x = arc2) scores.arc3 = sphere2pcscore(x = arc3) scores.PNSmean = sphere2pcscore(x = t(PNSmean)) scores.lu.arc1 = sphere2pcscore(x = lu.arc1) scores.lu.arc2 = sphere2pcscore(x = lu.arc2) scores.lu.arc3 = sphere2pcscore(x = lu.arc3) # PC scores to tangent coordinates U1 = matrix(0, npts, ncol(GPAout$pcar)) U2 = matrix(0, npts, ncol(GPAout$pcar)) U3 = matrix(0, npts, ncol(GPAout$pcar)) for (i in 1:npts) { for (j in 1:n.pc) { U1[i, ] = U1[i, ] + scores.arc1[i, j] * GPAout$pcar[ , j] U2[i, ] = U2[i, ] + scores.arc2[i, j] * GPAout$pcar[ , j] U3[i, ] = U3[i, ] + scores.arc3[i, j] * GPAout$pcar[ , j] } } U.mean = matrix(0, 1, ncol(GPAout$pcar)) for (j in 1:n.pc) { U.mean = U.mean + scores.PNSmean[j] * GPAout$pcar[ , j] } tan.lu.arc1 = matrix(0, nrow(lu.arc1), ncol(GPAout$pcar)) tan.lu.arc2 = matrix(0, nrow(lu.arc2), ncol(GPAout$pcar)) tan.lu.arc3 = matrix(0, nrow(lu.arc3), ncol(GPAout$pcar)) for (i in 1:nrow(lu.arc1)) { for (j in 1:n.pc) { tan.lu.arc1[i, ] = tan.lu.arc1[i, ] + scores.lu.arc1[i, j] * GPAout$pcar[ , j] tan.lu.arc2[i, ] = tan.lu.arc2[i, ] + scores.lu.arc2[i, j] * GPAout$pcar[ , j] tan.lu.arc3[i, ] = tan.lu.arc3[i, ] + scores.lu.arc3[i, j] * GPAout$pcar[ , j] } } # tangent coordinates to preshapes # preshapes to shapes shapes.arc1 = array(NA, c(k, m, npts)) shapes.arc2 = array(NA, c(k, m, npts)) shapes.arc3 = array(NA, c(k, m, npts)) H = defh(k - 1) for (i in 1:npts) { shapes.arc1[ , , i] = preshape2shape(tangentcoords.partial.inv(v = U1[i, ], p = H %*% GPAout$mshape, R = diag(m))) shapes.arc2[ , , i] = preshape2shape(tangentcoords.partial.inv(v = U2[i, ], p = H %*% GPAout$mshape, R = diag(m))) shapes.arc3[ , , i] = preshape2shape(tangentcoords.partial.inv(v = U3[i, ], p = H %*% GPAout$mshape, R = diag(m))) } shapes.PNSmean = preshape2shape(tangentcoords.partial.inv(v = U.mean, p = H %*% GPAout$mshape, R = diag(m))) shapes.lu.arc1 = array(NA, c(k, m, nrow(lu.arc1))) shapes.lu.arc2 = array(NA, c(k, m, nrow(lu.arc2))) shapes.lu.arc3 = array(NA, c(k, m, nrow(lu.arc3))) for (i in 1:nrow(lu.arc1)) { shapes.lu.arc1[ , , i] = preshape2shape(tangentcoords.partial.inv(v = tan.lu.arc1[i, ], p = H %*% GPAout$mshape, R = diag(m))) shapes.lu.arc2[ , , i] = preshape2shape(tangentcoords.partial.inv(v = tan.lu.arc2[i, ], p = H %*% GPAout$mshape, R = diag(m))) shapes.lu.arc3[ , , i] = preshape2shape(tangentcoords.partial.inv(v = tan.lu.arc3[i, ], p = H %*% GPAout$mshape, R = diag(m))) } #convert PCs to configuration space if not already in place h <- defh(k - 1) zero <- matrix(0, k - 1, k) H <- cbind(h, zero, zero) H1 <- cbind(zero, h, zero) H2 <- cbind(zero, zero, h) H <- rbind(H, H1, H2) if (dim(GPAout$pcar)[1] == (3 * (k - 1))) { pcarot <- (t(H) %*% GPAout$pcar) GPAout$pcar <- pcarot } #------------------------# # # # PLOT TYPE I # # : full plot # # # #------------------------# if (pcno==1){ shapes.lu.arc<-shapes.lu.arc1 } if (pcno==2){ shapes.lu.arc<-shapes.lu.arc2 } if (pcno==3){ shapes.lu.arc<-shapes.lu.arc3 } if (type=="pca"){ open3d() par3d(windowRect = c(20, 30, 800, 800)) view3d(view.theta,view.phi) # GPA mean + PC plot3d(GPAout$mshape, type = "s", col = rainbow(k), size = 1, add = TRUE) lines3d(GPAout$mshape, col = rainbow(k), lwd = 5) pcu <- GPAout$mshape + c * GPAout$pcasd[pcno] * cbind(GPAout$pcar[1:k, pcno], GPAout$pcar[(k + 1):(2 * k), pcno], GPAout$pcar[(2 * k + 1):(3 * k), pcno]) pcl <- GPAout$mshape - c * GPAout$pcasd[pcno] * cbind(GPAout$pcar[1:k, pcno], GPAout$pcar[(k + 1):(2 * k), pcno], GPAout$pcar[(2 * k + 1):(3 * k), pcno]) spheres3d( pcu, radius = 0.004, color = "black") spheres3d( pcl, radius = 0.004, color = "grey") for (j in 1:k) { lines3d(rbind(pcl[j, ], pcu[j, ]), col = rainbow(k)[j] ) if (j>1){ lines3d(rbind(pcu[j-1, ],pcu[j, ]) ,col="black" ) lines3d(rbind(pcl[j-1, ],pcl[j, ]) ,col="grey" ) } } } if (type=="pnss"){ open3d() par3d(windowRect = c(20, 30, 800, 800)) view3d(view.theta,view.phi) # principal arcs in landmark space # PNS mean in landmark space plot3d(shapes.PNSmean, type = "s", col = rainbow(k), size = 1.3, add = TRUE) lines3d(shapes.PNSmean,lwd=5,col=rainbow(k)) # lower and upper for (i in 1:k) { lines3d(t(shapes.lu.arc[i, , ]), col = rainbow(k)[i], lwd = 1,lty=2) # farthest negative from 0 spheres3d(head(t(shapes.lu.arc[i, , ]), 1), radius = 0.004, color = "black") if (i>1){ lines3d( (shapes.lu.arc[(i-1):i,,1]) ,col="black" ) } # # farthest positive from 0 spheres3d(tail(t(shapes.lu.arc[i, , ]), 1), radius = 0.004, color = "grey") if (i>1){ lines3d( (shapes.lu.arc[(i-1):i,,201]) ,col="grey" ) } } } out<-list(PNSmean=0,lu.arc=0) out$PNSmean<-shapes.PNSmean out$lu.arc<-shapes.lu.arc out } ######## pnss3d<- function (x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0, n.pc = 3) { k = dim(x)[1] m = dim(x)[2] n = dim(x)[3] if (n.pc =="Full" ) { n.pc=m*k-m*(m-1)/2-m } if (m==2){ tem1 <- array( 0, c(k,3,n) ) tem1[,1:2,]<-x x<-tem1 m<-3 } if (n < ((k - 1) * m)) { print("Note: n < (k - 1) * m.") jj<- round( (k-1)*m/n + 0.5) print("Adding extra copies of the data") tem<- array(0,c(k,m,jj*n)) tem[,,1:n]<-x for (i in 2:jj){ for (j in 1:n){ tem[,,(i-1)*n+ j ]<-x[,,j] + 0*matrix( rnorm(k*m), k,m) } } x<-tem } k = dim(x)[1] m = dim(x)[2] n = dim(x)[3] out = pc2sphere2(x = x, n.pc = n.pc) spheredata = t(out$spheredata) GPAout = out$GPAout pns.out = pns(x = spheredata, sphere.type = sphere.type, alpha = alpha, R = R, nlast.small.sphere = nlast.small.sphere) print("Radii of spheres") print(pns.out$PNS$radii) pns.out$percent = pns.out$percent * sum(GPAout$percent[1:n.pc])/100 pns.out$GPAout = GPAout pns.out$spheredata = spheredata return(pns.out) } pc2sphere2<-function (x, n.pc) { k = dim(x)[1] m = dim(x)[2] n = dim(x)[3] GPAout = procGPA(x = x, scale = TRUE, reflect = FALSE, tol1=1e-5,tangentcoords = "partial", distances = FALSE) cat("First ", n.pc, " principal components explain ", round(sum(GPAout$percent[1:n.pc])), "% of total variance. \n", sep = "") H = defh(k - 1) X.hat = H %*% GPAout$mshape S = array(NA, c(k - 1, m, n)) for (i in 1:n) { S[, , i] = H %*% GPAout$rotated[, , i] } T.c = GPAout$tan - apply(GPAout$tan, 1, mean) out = pcscore2sphere(n.pc = n.pc, X.hat = X.hat, S = S, Tan = T.c, V = GPAout$pcar) return(list(spheredata = out, GPAout = GPAout)) } #================================================================================== # PNS The Principal Nested Spheres code (PNS) for spheres and shapes has # been written by Kwang-Rae Kim, and builds closely on the original matlab # code for PNS by Sungkyu Jung #================================================================================== #================================================================================== pns = function(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0) { n = ncol(x) k = nrow(x) if (abs(sum(apply(x ^ 2, 2, sum)) - n) > 1e-8) { stop("Error: Each column of x should be a unit vector, ||x[ , i]|| = 1.") } svd.x = svd(x, nu = nrow(x)) uu = svd.x$u maxd = which(svd.x$d < 1e-15)[1] if (is.na(maxd) | k > n) { maxd = min(k, n) + 1 } nullspdim = k - maxd + 1 d = k - 1 cat("Message from pns() : dataset is on ", d, "-sphere. \n", sep = "") if (nullspdim > 0) { cat(" .. found null space of dimension ", nullspdim, ", to be trivially reduced. \n", sep = "") } resmat = matrix(NA, d, n) orthaxis = list() orthaxis[[d - 1]] = NA dist = rep(NA, d - 1) pvalues = matrix(NA, d - 1, 2) ratio = rep(NA, d - 1) currentSphere = x if (nullspdim > 0) { for (i in 1:nullspdim) { oaxis = uu[, ncol(uu) - i + 1] r = pi / 2 pvalues[i,] = c(NaN, NaN) res = acos(t(oaxis) %*% currentSphere) - r orthaxis[[i]] = oaxis dist[i] = r resmat[i,] = res NestedSphere = rotMat(oaxis) %*% currentSphere currentSphere = NestedSphere[1:(k - i),] / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), k - i, 1) uu = rotMat(oaxis) %*% uu uu = uu[1:(k - i),] / repmat(matrix(sqrt(1 - uu[nrow(uu),] ^ 2), nrow = 1), k - i, 1) cat(d - i + 1, "-sphere to ", d - i, "-sphere, by ", "NULL space \n", sep = "") } } if (sphere.type == "seq.test") { cat(" .. sequential tests with significance level ", alpha, "\n", sep = "") isIsotropic = FALSE for (i in (nullspdim + 1):(d - 1)) { if (!isIsotropic) { sp = getSubSphere(x = currentSphere, geodesic = "small") center.s = sp$center r.s = sp$r resSMALL = acos(t(center.s) %*% currentSphere) - r.s sp = getSubSphere(x = currentSphere, geodesic = "great") center.g = sp$center r.g = sp$r resGREAT = acos(t(center.g) %*% currentSphere) - r.g pval1 = LRTpval(resGREAT, resSMALL, n) pvalues[i, 1] = pval1 if (pval1 > alpha) { center = center.g r = r.g pvalues[i, 2] = NA cat( d - i + 1, "-sphere to ", d - i, "-sphere, by GREAT sphere, p(LRT) = ", pval1, "\n", sep = "" ) } else { pval2 = vMFtest(currentSphere, R) pvalues[i, 2] = pval2 if (pval2 > alpha) { center = center.g r = r.g cat( d - i + 1, "-sphere to ", d - i, "-sphere, by GREAT sphere, p(LRT) = ", pval1, ", p(vMF) = ", pval2, "\n", sep = "" ) isIsotropic = TRUE } else { center = center.s r = r.s cat( d - i + 1, "-sphere to ", d - i, "-sphere, by SMALL sphere, p(LRT) = ", pval1, ", p(vMF) = ", pval2, "\n", sep = "" ) } } } else if (isIsotropic) { sp = getSubSphere(x = currentSphere, geodesic = "great") center = sp$center r = sp$r cat( d - i + 1, "-sphere to ", d - i, "-sphere, by GREAT sphere, restricted by testing vMF distn", "\n", sep = "" ) pvalues[i, 1] = NA pvalues[i, 2] = NA } res = acos(t(center) %*% currentSphere) - r orthaxis[[i]] = center dist[i] = r resmat[i,] = res cur.proj = project.subsphere(x = currentSphere, center = center, r = r) NestedSphere = rotMat(center) %*% currentSphere currentSphere = NestedSphere[1:(k - i),] / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), k - i, 1) if (nrow(currentSphere) == 3) { plot3d( x = t(currentSphere), xlab = "", ylab = "", zlab = "", xlim = c(-1, 1), ylim = c(-1, 1), zlim = c(-1, 1), box = FALSE, axes = FALSE, aspect = "iso" ) axis3d( edge = 'x', labels = TRUE, tick = TRUE, at = c(-1, 1), pos = c(NA, 0, 0) ) axis3d( edge = 'y', labels = TRUE, tick = TRUE, at = c(-1, 1), pos = c(0, NA, 0) ) axis3d( edge = 'z', labels = TRUE, tick = TRUE, at = c(-1, 1), pos = c(0, 0, NA) ) } if (nrow(currentSphere) == 2) { points3d(t(cur.proj), col = "red", size = 2) } } } else if (sphere.type == "BIC") { cat(" .. with BIC \n") for (i in (nullspdim + 1):(d - 1)) { sp = getSubSphere(x = currentSphere, geodesic = "small") center.s = sp$center r.s = sp$r resSMALL = acos(t(center.s) %*% currentSphere) - r.s sp = getSubSphere(x = currentSphere, geodesic = "great") center.g = sp$center r.g = sp$r resGREAT = acos(t(center.g) %*% currentSphere) - r.g BICsmall = n * log(mean(resSMALL ^ 2)) + (d - i + 1 + 1) * log(n) BICgreat = n * log(mean(resGREAT ^ 2)) + (d - i + 1) * log(n) cat("BICsm: ", BICsmall, ", BICgr: ", BICgreat, "\n", sep = "") if (BICsmall > BICgreat) { center = center.g r = r.g cat(d - i + 1, "-sphere to ", d - i, "-sphere, by ", "GREAT sphere, BIC \n", sep = "") } else { center = center.s r = r.s cat(d - i + 1, "-sphere to ", d - i, "-sphere, by ", "SMALL sphere, BIC \n", sep = "") } res = acos(t(center) %*% currentSphere) - r orthaxis[[i]] = center dist[i] = r resmat[i,] = res NestedSphere = rotMat(center) %*% currentSphere currentSphere = NestedSphere[1:(k - i),] / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), k - i, 1) } } else if (sphere.type == "small" | sphere.type == "great") { pvalues = NaN for (i in (nullspdim + 1):(d - 1)) { sp = getSubSphere(x = currentSphere, geodesic = sphere.type) center = sp$center r = sp$r res = acos(t(center) %*% currentSphere) - r orthaxis[[i]] = center dist[i] = r resmat[i,] = res NestedSphere = rotMat(center) %*% currentSphere currentSphere = NestedSphere[1:(k - i),] / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), k - i, 1) } } else if (sphere.type == "bi.sphere") { if (nlast.small.sphere < 0) { cat("!!! Error from pns(): \n") cat("!!! nlast.small.sphere should be >= 0. \n") return(NULL) } mx = (d - 1) - nullspdim if (nlast.small.sphere > mx) { cat("!!! Error from pns(): \n") cat("!!! nlast.small.sphere should be <= ", mx, " for this data. \n", sep = "") return(NULL) } pvalues = NaN if (nlast.small.sphere != mx) { for (i in (nullspdim + 1):(d - 1 - nlast.small.sphere)) { sp = getSubSphere(x = currentSphere, geodesic = "great") center = sp$center r = sp$r res = acos(t(center) %*% currentSphere) - r orthaxis[[i]] = center dist[i] = r resmat[i,] = res NestedSphere = rotMat(center) %*% currentSphere currentSphere = NestedSphere[1:(k - i),] / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), k - i, 1) } } if (nlast.small.sphere != 0) { for (i in (d - nlast.small.sphere):(d - 1)) { sp = getSubSphere(x = currentSphere, geodesic = "small") center = sp$center r = sp$r res = acos(t(center) %*% currentSphere) - r orthaxis[[i]] = center dist[i] = r resmat[i,] = res NestedSphere = rotMat(center) %*% currentSphere currentSphere = NestedSphere[1:(k - i),] / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), k - i, 1) } } } else { print("!!! Error from pns():") print("!!! sphere.type must be 'seq.test', 'small', 'great', 'BIC', or 'bi.sphere'") print("!!! Terminating execution ") return(NULL) } S1toRadian = atan2(currentSphere[2,], currentSphere[1,]) meantheta = geodmeanS1(S1toRadian)$geodmean orthaxis[[d]] = meantheta resmat[d,] = mod(S1toRadian - meantheta + pi, 2 * pi) - pi par( mfrow = c(1, 1), mar = c(4, 4, 1, 1), mgp = c(2.5, 1, 0), cex = 0.8 ) plot( currentSphere[1,], currentSphere[2,], xlab = "", ylab = "", xlim = c(-1, 1), ylim = c(-1, 1), asp = 1 ) abline(h = 0, v = 0) points( cos(meantheta), sin(meantheta), pch = 1, cex = 3, col = "black", lwd = 5 ) abline( a = 0, b = sin(meantheta) / cos(meantheta), lty = 3 ) l = mod(S1toRadian - meantheta + pi, 2 * pi) - pi points( cos(S1toRadian[which.max(l)]), sin(S1toRadian[which.max(l)]), pch = 4, cex = 3, col = "blue" ) points( cos(S1toRadian[which.min(l)]), sin(S1toRadian[which.min(l)]), pch = 4, cex = 3, col = "red" ) legend( "topright", legend = c("Geodesic mean", "Max (+)ve from mean", "Min (-)ve from mean"), col = c("black", "blue", "red"), pch = c(1, 4, 4) ) { cat("\n") cat( "length of BLUE from geodesic mean : ", max(l), " (", round(max(l) * 180 / pi), " degree)", "\n", sep = "" ) cat( "length of RED from geodesic mean : ", min(l), " (", round(min(l) * 180 / pi), " degree)", "\n", sep = "" ) cat("\n") } radii = 1 for (i in 1:(d - 1)) { radii = c(radii, prod(sin(dist[1:i]))) } resmat = flipud0(repmat(matrix(radii, ncol = 1), 1, n) * resmat) PNS = list() PNS$radii = radii PNS$orthaxis = orthaxis PNS$dist = dist PNS$pvalues = pvalues PNS$ratio = ratio PNS$basisu = NULL PNS$mean = c(PNSe2s(matrix(0, d, 1), PNS)) if (sphere.type == "seq.test") { PNS$sphere.type = "seq.test" } else if (sphere.type == "small") { PNS$sphere.type = "small" } else if (sphere.type == "great") { PNS$sphere.type = "great" } else if (sphere.type == "BIC") { PNS$sphere.type = "BIC" } varPNS = apply(abs(resmat) ^ 2, 1, sum) / n total = sum(varPNS) propPNS = varPNS / total * 100 return(list( resmat = resmat, PNS = PNS, percent = propPNS )) } #================================================================================== pns4pc = function(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0, n.pc = 2) { if (n.pc < 2) { stop("Error: n.pc should be >= 2.") } out = pc2sphere(x = x, n.pc = n.pc) spheredata = t(out$spheredata) GPAout = out$GPAout pns.out = pns( x = spheredata, sphere.type = sphere.type, alpha = alpha, R = R, nlast.small.sphere = nlast.small.sphere ) pns.out$percent = pns.out$percent * sum(GPAout$percent[1:n.pc]) / 100 pns.out$GPAout = GPAout pns.out$spheredata = spheredata return(pns.out) } pns.pc = function(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0, n.pc = 0) { k = dim(x)[1] m = dim(x)[2] n = dim(x)[3] if (n.pc == 0) { GPAout = procGPA( x = x, scale = TRUE, reflect = FALSE, tangentcoords = "partial", distances = FALSE ) spheredata = matrix(NA, k * m, n) for (i in 1:n) { spheredata[, i] = c(GPAout$rotated[, , i]) } pns.out = pns( x = spheredata, sphere.type = sphere.type, alpha = alpha, R = R, nlast.small.sphere = nlast.small.sphere ) resmat = pns.out$resmat PNS = pns.out$PNS npts = 200 prinarc1 = get.prinarc( resmat, PNS, arc = 1, n = npts, boundary.data = FALSE ) prinarc2 = get.prinarc( resmat, PNS, arc = 2, n = npts, boundary.data = FALSE ) prinarc1.ar = array(NA, c(k, m, npts)) prinarc2.ar = array(NA, c(k, m, npts)) for (i in 1:npts) { prinarc1.ar[, , i] = matrix(prinarc1[, i], nrow = k) prinarc2.ar[, , i] = matrix(prinarc2[, i], nrow = k) } scores.prinarc1 = shape.pcscores.partial(PCAout = GPAout, x = prinarc1.ar) scores.prinarc2 = shape.pcscores.partial(PCAout = GPAout, x = prinarc2.ar) out = pns.out out$GPAout = GPAout out$scores.prinarc1 = scores.prinarc1 out$scores.prinarc2 = scores.prinarc2 } else { pns.out = pns4pc( x = x, sphere.type = sphere.type, alpha = alpha, R = R, nlast.small.sphere = nlast.small.sphere, n.pc = n.pc ) GPAout = pns.out$GPAout resmat = pns.out$resmat PNS = pns.out$PNS npts = 200 prinarc1 = get.prinarc( resmat, PNS, arc = 1, n = npts, boundary.data = FALSE ) prinarc2 = get.prinarc( resmat, PNS, arc = 2, n = npts, boundary.data = FALSE ) scores.prinarc1 = matrix(NA, npts, n.pc) scores.prinarc2 = matrix(NA, npts, n.pc) for (g in 1:npts) { size1 = acos(prinarc1[1, g]) size2 = acos(prinarc2[1, g]) scores.prinarc1[g,] = prinarc1[2:(n.pc + 1), g] / (sin(size1) / size1) scores.prinarc2[g,] = prinarc2[2:(n.pc + 1), g] / (sin(size2) / size2) } out = pns.out out$scores.prinarc1 = scores.prinarc1 out$scores.prinarc2 = scores.prinarc2 } return(out) } #================================================================================== rotMat = function(b, a = NULL, alpha = NULL) { if (is.matrix(b)) { if (min(dim(b)) == 1) { b = c(b) } else { stop("Error: b should be a unit vector.") } } d = length(b) b = b / norm(b, type = "2") if (is.null(a) & is.null(alpha)) { a = c(rep(0, d - 1), 1) alpha = acos(sum(a * b)) } else if (!is.null(a) & is.null(alpha)) { alpha = acos(sum(a * b)) } else if (is.null(a) & !is.null(alpha)) { a = c(rep(0, d - 1), 1) } if (abs(sum(a * b) - 1) < 1e-15) { rot = diag(d) return(rot) } if (abs(sum(a * b) + 1) < 1e-15) { rot = -diag(d) return(rot) } c = b - a * sum(a * b) c = c / norm(c, type = "2") A = a %*% t(c) - c %*% t(a) rot = diag(d) + sin(alpha) * A + (cos(alpha) - 1) * (a %*% t(a) + c %*% t(c)) return(rot) } #================================================================================== ExpNPd = function(x) { if (is.vector(x)) { x = as.matrix(x) } d = nrow(x) nv = sqrt(apply(x ^ 2, 2, sum)) Exppx = rbind(matrix(rep(sin(nv) / nv, d), nrow = d, byrow = T) * x, cos(nv)) Exppx[, nv < 1e-16] = repmat(matrix(c(rep(0, d), 1)), 1, sum(nv < 1e-16)) return(Exppx) } #================================================================================== LogNPd = function(x) { n = ncol(x) d = nrow(x) scale = acos(x[d,]) / sqrt(1 - x[d,] ^ 2) scale[is.nan(scale)] = 1 Logpx = repmat(t(scale), d - 1, 1) * x[-d,] return(Logpx) } #================================================================================== objfn = function(center, r, x) { return(mean((acos(t( center ) %*% x) - r) ^ 2)) } #================================================================================== getSubSphere = function(x, geodesic = "small") { svd.x = svd(x) initialCenter = svd.x$u[, ncol(svd.x$u)] c0 = initialCenter TOL = 1e-10 cnt = 0 err = 1 n = ncol(x) d = nrow(x) Gnow = 1e+10 while (err > TOL) { c0 = c0 / norm(c0, type = "2") rot = rotMat(c0) TpX = LogNPd(rot %*% x) fit = sphereFit( x = TpX, initialCenter = rep(0, d - 1), geodesic = geodesic ) newCenterTp = fit$center r = fit$r if (r > pi) { r = pi / 2 svd.TpX = svd(TpX) newCenterTp = svd.TpX$u[, ncol(svd.TpX$u)] * pi / 2 } newCenter = ExpNPd(newCenterTp) center = solve(rot, newCenter) Gnext = objfn(center, r, x) err = abs(Gnow - Gnext) Gnow = Gnext c0 = center cnt = cnt + 1 if (cnt > 30) { break } } i1save = list() i1save$Gnow = Gnow i1save$center = center i1save$r = r U = princomp(t(x))$loadings[,] initialCenter = U[, ncol(U)] c0 = initialCenter TOL = 1e-10 cnt = 0 err = 1 n = ncol(x) d = nrow(x) Gnow = 1e+10 while (err > TOL) { c0 = c0 / norm(c0, type = "2") rot = rotMat(c0) TpX = LogNPd(rot %*% x) fit = sphereFit( x = TpX, initialCenter = rep(0, d - 1), geodesic = geodesic ) newCenterTp = fit$center r = fit$r if (r > pi) { r = pi / 2 svd.TpX = svd(TpX) newCenterTp = svd.TpX$u[, ncol(svd.TpX$u)] * pi / 2 } newCenter = ExpNPd(newCenterTp) center = solve(rot, newCenter) Gnext = objfn(center, r, x) err = abs(Gnow - Gnext) Gnow = Gnext c0 = center cnt = cnt + 1 if (cnt > 30) { break } } if (i1save$Gnow == min(Gnow, i1save$Gnow)) { center = i1save$center r = i1save$r } if (r > pi / 2) { center = -center r = pi - r } return(list(center = c(center), r = r)) } #================================================================================== LRTpval = function(resGREAT, resSMALL, n) { chi2 = max(n * log(sum(resGREAT ^ 2) / sum(resSMALL ^ 2)), 0) return(pchisq( q = chi2, df = 1, lower.tail = FALSE )) } #================================================================================== vMFtest = function(x, R = 100) { d = nrow(x) n = ncol(x) sumx = apply(x, 1, sum) rbar = norm(sumx, "2") / n muMLE = sumx / norm(sumx, "2") kappaMLE = (rbar * d - rbar ^ 3) / (1 - rbar ^ 2) sp = getSubSphere(x = x, geodesic = "small") center.s = sp$center r.s = sp$r radialdistances = acos(t(center.s) %*% x) xi_sample = mean(radialdistances) / sd(radialdistances) xi_vec = rep(0, R) for (r in 1:R) { rdata = randvonMisesFisherm(d, n, kappaMLE) sp = getSubSphere(x = rdata, geodesic = "small") center.s = sp$center r.s = sp$r radialdistances = acos(t(center.s) %*% rdata) xi_vec[r] = mean(radialdistances) / sd(radialdistances) } pvalue = mean(xi_vec > xi_sample) return(pvalue) } #================================================================================== geodmeanS1 = function(theta) { n = length(theta) meancandi = mod(mean(theta) + 2 * pi * (0:(n - 1)) / n, 2 * pi) theta = mod(theta, 2 * pi) geodvar = rep(0, n) for (i in 1:n) { v = meancandi[i] dist2 = apply(cbind((theta - v) ^ 2, (theta - v + 2 * pi) ^ 2, (v - theta + 2 * pi) ^ 2), 1, min) geodvar[i] = sum(dist2) } m = min(geodvar) ind = which.min(geodvar) geodmean = mod(meancandi[ind], 2 * pi) geodvar = geodvar[ind] / n return(list(geodmean = geodmean, geodvar = geodvar)) } #================================================================================== PNSe2s = function(resmat, PNS) { dm = nrow(resmat) n = ncol(resmat) NSOrthaxis = rev(PNS$orthaxis[1:(dm - 1)]) NSradius = flipud0(matrix(PNS$dist, ncol = 1)) geodmean = PNS$orthaxis[[dm]] res = resmat / repmat(flipud0(matrix(PNS$radii, ncol = 1)), 1, n) T = t(rotMat(NSOrthaxis[[1]])) %*% rbind(repmat(sin(NSradius[1] + matrix(res[2,], nrow = 1)), 2, 1) * rbind(cos(geodmean + res[1,]), sin(geodmean + res[1,])), cos(NSradius[1] + res[2,])) if (dm > 2) { for (i in 1:(dm - 2)) { T = t(rotMat(NSOrthaxis[[i + 1]])) %*% rbind(repmat(sin(NSradius[i + 1] + matrix( res[i + 2,], nrow = 1 )), 2 + i, 1) * T, cos(NSradius[i + 1] + res[i + 2,])) } } if (!is.null(PNS$basisu)) { T = PNS$basisu %*% T } return(T) } #================================================================================== PNSs2e = function(spheredata, PNS) { if (nrow(spheredata) != length(PNS$mean)) { cat(" Error from PNSs2e() \n") cat(" Dimensions of the sphere and PNS decomposition do not match") return(NULL) } if (!is.null(PNS$basisu)) { spheredata = t(PNS$basisu) %*% spheredata } kk = nrow(spheredata) n = ncol(spheredata) Res = matrix(0, kk - 1, n) currentSphere = spheredata for (i in 1:(kk - 2)) { v = PNS$orthaxis[[i]] r = PNS$dist[i] res = acos(t(v) %*% currentSphere) - r Res[i,] = res NestedSphere = rotMat(v) %*% currentSphere currentSphere = as.matrix(NestedSphere[1:(kk - i),]) / repmat(matrix(sqrt(1 - NestedSphere[nrow(NestedSphere),] ^ 2), nrow = 1), kk - i, 1) } S1toRadian = atan2(currentSphere[2,], currentSphere[1,]) devS1 = mod(S1toRadian - rev(PNS$orthaxis)[[1]] + pi, 2 * pi) - pi Res[kk - 1,] = devS1 EuclidData = flipud0(repmat(PNS$radii, 1, n) * Res) return(EuclidData) } #================================================================================== randvonMisesFisherm = function(m, n, kappa, mu = NULL) { if (is.null(mu)) { muflag = FALSE } else { muflag = TRUE } if (m < 2) { print("Message from randvonMisesFisherm(): dimension m must be > 2") print("Message from randvonMisesFisherm(): Set m to be 2") m = 2 } if (kappa < 0) { print("Message from randvonMisesFisherm(): kappa must be >= 0") print("Message from randvonMisesFisherm(): Set kappa to be 0") kappa = 0 } b = (-2 * kappa + sqrt(4 * kappa ^ 2 + (m - 1) ^ 2)) / (m - 1) x0 = (1 - b) / (1 + b) c = kappa * x0 + (m - 1) * log(1 - x0 ^ 2) nnow = n w = c() while (TRUE) { ntrial = max(round(nnow * 1.2), nnow + 10) Z = rbeta(n = ntrial, shape1 = (m - 1) / 2, shape2 = (m - 1) / 2) U = runif(ntrial) W = (1 - (1 + b) * Z) / (1 - (1 - b) * Z) indicator = kappa * W + (m - 1) * log(1 - x0 * W) - c >= log(U) if (sum(indicator) >= nnow) { w1 = W[indicator] w = c(w, w1[1:nnow]) break } else { w = c(w, W[indicator]) nnow = nnow - sum(indicator) } } V = UNIFORMdirections(m - 1, n) X = rbind(repmat(sqrt(1 - matrix(w, nrow = 1) ^ 2), m - 1, 1) * V, matrix(w, nrow = 1)) if (muflag) { mu = mu / norm(mu, "2") X = t(rotMat(mu)) %*% X } return(X) } #================================================================================== UNIFORMdirections = function(m, n) { V = matrix(0, m, n) nr = matrix(rnorm(m * n), nrow = m) for (i in 1:n) { while (TRUE) { ni = sum(nr[, i] ^ 2) if (ni < 1e-10) { nr[, i] = rnorm(m) } else { V[, i] = nr[, i] / sqrt(ni) break } } } return(V) } #================================================================================== trans.subsphere = function(x, center) { return(repmat(1 / sqrt(1 - (t( center ) %*% x) ^ 2), length(center) - 1, 1) * (rotMat(center)[-length(center),] %*% x)) } #================================================================================== get.prinarc.value = function(PNS, arc, res) { d = length(PNS$orthaxis) n = length(res) prinarc = matrix(NA, d + 1, n) for (g in 1:n) { newres = matrix(0, d, 1) newres[arc] = res[g] T = PNSe2s(newres, PNS) prinarc[, g] = T } return(prinarc) } #================================================================================== get.prinarc = function(resmat, PNS, arc, n, boundary.data = FALSE) { d = nrow(resmat) if (boundary.data) { mn = min(resmat[arc,]) mx = max(resmat[arc,]) } else { mn = -pi * tail(PNS$radii, arc)[1] mx = pi * tail(PNS$radii, arc)[1] } prinarcgrid = seq(mn, mx, length = n) prinarc = matrix(NA, d + 1, n) for (g in 1:n) { newres = matrix(0, d, 1) newres[arc] = prinarcgrid[g] T = PNSe2s(newres, PNS) prinarc[, g] = T } return(prinarc) } #================================================================================== get.prinarc.subsphere = function(resmat, PNS, arc, n, subsphere = arc, boundary.data = FALSE) { if (subsphere < arc) { stop("Error: subsphere >= arc.") } if (subsphere < 1) { stop("Error: subsphere >= 1.") } prinarc = get.prinarc( resmat = resmat, PNS = PNS, arc = arc, n = n, boundary.data = boundary.data ) d = nrow(resmat) prinarc.sub = prinarc if (subsphere < d) { for (i in 1:(d - subsphere)) { prinarc.sub = trans.subsphere(x = prinarc.sub, center = PNS$orthaxis[[i]]) } } return(prinarc.sub) } #================================================================================== get.data.subsphere = function(resmat, PNS, x, subsphere) { if (subsphere < 1) { stop("Error: subsphere >= 1.") } d = nrow(resmat) x.sub = x if (subsphere < d) { for (i in 1:(d - subsphere)) { x.sub = trans.subsphere(x = x.sub, center = PNS$orthaxis[[i]]) } } return(x.sub) } #================================================================================== mod = function(x, y) { return(x %% y) } #================================================================================== repmat = function(x, m, n) { return(kronecker(matrix(1, m, n), x)) } #================================================================================== flipud0 = function(x) { return(apply(x, 2, rev)) } #================================================================================== sphere.obj = function(center, x, is.greatCircle) { di = sqrt(apply((x - repmat( matrix(center, ncol = 1), 1, ncol(x) )) ^ 2, 2, sum)) if (is.greatCircle) { r = pi / 2 } else { r = mean(di) } sum((di - r) ^ 2) } #================================================================================== sphere.res = function(center, x, is.greatCircle) { center = c(center) xmc = x - center di = sqrt(apply(xmc ^ 2, 2, sum)) if (is.greatCircle) { r = pi / 2 } else { r = mean(di) } (di - r) } #================================================================================== sphere.jac = function(center, x, is.greatCircle) { center = c(center) xmc = x - center di = sqrt(apply(xmc ^ 2, 2, sum)) di.vj = -xmc / repmat(matrix(di, nrow = 1), length(center), 1) if (is.greatCircle) { c(t(di.vj)) } else { r.vj = apply(di.vj, 1, mean) c(t(di.vj - repmat(matrix(r.vj, ncol = 1), 1, ncol(x)))) } } #================================================================================== sphereFit = function(x, initialCenter = NULL, geodesic = "small") { if (is.null(initialCenter)) { initialCenter = apply(x, 1, mean) } op = nls.lm( par = initialCenter, fn = sphere.res, jac = sphere.jac, x = x, is.greatCircle = ifelse(geodesic == "great", TRUE, FALSE), control = nls.lm.control(maxiter = 300) ) center = coef(op) di = sqrt(apply((x - repmat( matrix(center, ncol = 1), 1, ncol(x) )) ^ 2, 2, sum)) if (geodesic == "great") { r = pi / 2 } else { r = mean(di) } list(center = center, r = r) } #================================================================================== tr = function(x) { return(sum(diag(x))) } #================================================================================== Enormalize = function(x) { return(x / Enorm(x)) } #================================================================================== sphere2pcscore = function(x) { n = nrow(x) p = ncol(x) scores = matrix(NA, n, p - 1) for (i in 1:n) { size = acos(x[i, 1]) scores[i,] = (size / sin(size)) * x[i, 2:p] } return(scores) } #================================================================================== pcscore2sphere = function(n.pc, X.hat, S, Tan, V) { d = nrow(Tan) n = ncol(Tan) W = matrix(NA, d, n) for (i in 1:n) { W[, i] = acos(tr(S[, , i] %*% t(X.hat))) * Tan[, i] / sqrt(sum(Tan[, i] ^ 2)) } lambda = matrix(NA, n, d) for (i in 1:n) { for (j in 1:d) { lambda[i, j] = sum(W[, i] * V[, j]) } } U = matrix(0, n, d) for (i in 1:n) { for (j in 1:n.pc) { U[i,] = U[i,] + lambda[i, j] * V[, j] } } S.star = matrix(NA, n, n.pc + 1) for (i in 1:n) { U.norm = sqrt(sum(U[i,] ^ 2)) S.star[i,] = c(cos(U.norm), sin(U.norm) / U.norm * lambda[i, 1:n.pc]) } return(S.star) } #================================================================================== pc2sphere = function(x, n.pc) { k = dim(x)[1] m = dim(x)[2] n = dim(x)[3] if (n < ((k - 1) * m)) { stop("Error: n must be >= (k - 1) * m.") } GPAout = procGPA( x = x, scale = TRUE, reflect = FALSE, tangentcoords = "partial", distances = FALSE ) cat( "First ", n.pc, " principal components explain ", round(sum(GPAout$percent[1:n.pc])), "% of total variance. \n", sep = "" ) H = defh(k - 1) X.hat = H %*% GPAout$mshape S = array(NA, c(k - 1, m, n)) for (i in 1:n) { S[, , i] = H %*% GPAout$rotated[, , i] } T.c = GPAout$tan - apply(GPAout$tan, 1, mean) out = pcscore2sphere( n.pc = n.pc, X.hat = X.hat, S = S, Tan = T.c, V = GPAout$pcar ) return(list(spheredata = out, GPAout = GPAout)) } #================================================================================== rot.mat = function(Y, X, reflect = FALSE, center = TRUE) { svd.out = svd(t(X) %*% Y) R = svd.out$u %*% t(svd.out$v) if (!reflect) { if (det(R) < 0) { u = svd.out$u v = svd.out$v if (det(u) < 0) { u[, dim(u)[2]] = -u[, dim(u)[2]] } else if (det(v) < 0) { v[, dim(v)[2]] = -v[, dim(v)[2]] } R = u %*% t(v) } } return(R) } #================================================================================== Procrustes.dist.full = function(x1, x2) { m = ncol(x1) z1 = preshape(x1) z2 = preshape(x2) Q = t(z1) %*% z2 %*% t(z2) %*% z1 ev = eigen(Q)$values sign = ifelse(det(t(z1) %*% z2) >= 0, 1,-1) dF = sqrt(abs(1 - sum(sqrt(abs( ev[1:(m - 1)] )), sign * sqrt(abs( ev[m] ))) ^ 2)) R = rot.mat( Y = z2, X = z1, reflect = FALSE, center = FALSE ) scale = sum(svd(t(z1) %*% z2)$d) return(list(dF = dF, R = R, scale = scale)) } #================================================================================== tangent.coords.partial = function(x, p) { k = nrow(x) m = ncol(x) if (abs(norm(p, "F") - 1) > 1e-15) { print("||p|| is not 1. Normalised one is used.") p = Enormalize(p) } tmp = Procrustes.dist.full(x, p) R = tmp$R scale = tmp$scale pre.p = preshape(p) pre.x = preshape(x) ident = diag(k * m - m) tan = (ident - matrix(pre.p) %*% t(c(pre.p))) %*% c(pre.x %*% R) tan.scale = (ident - matrix(pre.p) %*% t(c(pre.p))) %*% c(pre.x %*% R * scale) return(list( tan = c(tan), tan.scale = c(tan.scale), R = R, scale = scale )) } #================================================================================== shape.pcscores = function(PCAout, x, tangentcoords = "partial") { if (tangentcoords == "partial") { if (abs(norm(PCAout$mshape, "F") - 1) > 1e-15) { print("||PCAout$mshape|| is not 1. Normalised one is used.") mshape = Enormalize(PCAout$mshape) } else { mshape = PCAout$mshape } if (abs(norm(x, "F") - 1) > 1e-15) { print("||x|| is not 1. Normalised one is used.") x = Enormalize(x) } opa.out = procOPA(mshape, x, scale = FALSE) matched = opa.out$Bhat tan.out = tangent.coords.partial(matched, mshape) mean.tan = apply(PCAout$tan, 1, mean) scores = t(tan.out$tan - mean.tan) %*% PCAout$pcar scores.scale = t(tan.out$tan.scale - mean.tan) %*% PCAout$pcar return( list( rotated = matched, tan = tan.out$tan, tan.scale = tan.out$tan.scale, scores = c(scores), scores.scale = c(scores.scale) ) ) } } #================================================================================== shape.pcscores.partial = function(PCAout, x) { n = dim(x)[3] scores = c() for (i in 1:n) { s = shape.pcscores(PCAout, x[, , i], tangentcoords = "partial") scores = rbind(scores, s$scores) } return(scores) } #================================================================================== plotshapes3d.pns = function(x, type = "p", col = "black", size = 5, aspect = "iso", joinline = TRUE, col.joinline = "#d4d2d2", lwd.joinline = 0.5, tick = FALSE, labels.tick = FALSE, xlab = "", ylab = "", zlab = "") { k = dim(x)[1] n = dim(x)[3] aa = c() bb = c() cc = c() for (i in 1:n) { aa = c(aa, x[, 1, i]) bb = c(bb, x[, 2, i]) cc = c(cc, x[, 3, i]) } xlim = range(aa) ylim = range(bb) zlim = range(cc) plot3d( x[, , 1], type = "n", xlab = "", ylab = "", zlab = "", box = FALSE, axes = FALSE, aspect = aspect, xlim = xlim, ylim = ylim, zlim = zlim ) for (i in 1:n) { plot3d( x[, , i], type = type, col = col, size = size, add = TRUE ) } if (tick) { axis3d( edge = 'x', labels = labels.tick, tick = TRUE, pos = c(NA, 0, 0), cex = 0.6, lwd = 0.5 ) axis3d( edge = 'y', labels = labels.tick, tick = TRUE, pos = c(0, NA, 0), cex = 0.6, lwd = 0.5 ) axis3d( edge = 'z', labels = labels.tick, tick = TRUE, pos = c(0, 0, NA), cex = 0.6, lwd = 0.5 ) } else { } r = cbind(xlim, ylim, zlim) pos = r[2,] + apply(r, 2, diff) / 20 text3d(pos[1], 0, 0, texts = xlab, cex = 0.8) text3d(0, pos[2], 0, texts = ylab, cex = 0.8) text3d(0, 0, pos[3], texts = zlab, cex = 0.8) if (joinline) { for (i in 1:n) { lines3d(x[, , i], col = col.joinline, lwd = lwd.joinline) } } } #================================================================================== Plot3D = function(x, type = "s", col = "black", size = 1.2, aspect = "iso", joinline = FALSE, col.joinline = "#d4d2d2", lwd.joinline = 0.5, tick = TRUE, tick.boundary = FALSE, labels.tick = TRUE, xlab = "", ylab = "", zlab = "") { n = nrow(x) plot3d( x, type = "n", xlab = "", ylab = "", zlab = "", box = FALSE, axes = FALSE, aspect = aspect ) plot3d( x, type = type, col = col, size = size, add = TRUE ) if (tick) { axis3d( edge = 'x', labels = labels.tick, tick = TRUE, pos = c(NA, 0, 0), cex = 0.6, lwd = 0.5 ) axis3d( edge = 'y', labels = labels.tick, tick = TRUE, pos = c(0, NA, 0), cex = 0.6, lwd = 0.5 ) axis3d( edge = 'z', labels = labels.tick, tick = TRUE, pos = c(0, 0, NA), cex = 0.6, lwd = 0.5 ) } if (tick.boundary) { tks = pretty(x[, 1], n = 10) axis3d( edge = 'x', labels = labels.tick, tick = TRUE, at = c(tks[1], tks[length(tks)]), pos = c(NA, 0, 0), cex = 0.6, lwd = 0.5 ) tks = pretty(x[, 2], n = 10) axis3d( edge = 'y', labels = labels.tick, tick = TRUE, at = c(tks[1], tks[length(tks)]), pos = c(0, NA, 0), cex = 0.6, lwd = 0.5 ) tks = pretty(x[, 3], n = 10) axis3d( edge = 'z', labels = labels.tick, tick = TRUE, at = c(tks[1], tks[length(tks)]), pos = c(0, 0, NA), cex = 0.6, lwd = 0.5 ) } r = apply(x, 2, range) pos = r[2,] + apply(r, 2, diff) / 20 text3d(pos[1], 0, 0, texts = xlab, cex = 0.8) text3d(0, pos[2], 0, texts = ylab, cex = 0.8) text3d(0, 0, pos[3], texts = zlab, cex = 0.8) if (joinline) { lines3d(x, col = col.joinline, lwd = lwd.joinline) } } #================================================================================== col2RGB = function(col, alpha = 255) { n = length(col) out = c() for (i in 1:n) { out[i] = rgb( red = col2rgb(col[i])[1], green = col2rgb(col[i])[2], blue = col2rgb(col[i])[3], alpha = alpha, maxColorValue = 255 ) } return(out) } #================================================================================== project.subsphere = function(x, center, r) { n = ncol(x) d = nrow(x) x.proj = matrix(NA, d, n) for (i in 1:n) { rho = acos(sum(x[, i] * center)) x.proj[, i] = (sin(r) * x[, i] + sin(rho - r) * center) / sin(rho) } return(x.proj) } ##############################################################end of PNS########### ##### Penalised Euclidean Distance Regression #================================================================================== ped <- function(X, Y, method = c("AIC")) { if (method == "AIC") { aicmin <- 999999999 for (lam in c(0.2, 0.5, 1.0)) { for (cofp in c(0.75, 1, 1.35, 1.5)) { out <- pedreg( X, Y, nlambda = 1, constc0 = 1.1, constc1 = cofp, lambdainit = lam ) if (out$aic < aicmin) { minout <- out mincofp <- cofp aicmin <- out$aic } } } out <- minout } if (method == "BIC") { bicmin <- 999999999 for (lam in c(0.2, 0.5, 1.0)) { for (cofp in c(0.75, 1, 1.35, 1.5)) { out <- pedreg( X, Y, nlambda = 1, constc0 = 1.1, constc1 = cofp, lambdainit = lam ) if (out$bic < bicmin) { minout <- out mincofp <- cofp bicmin <- out$bic } } } out <- minout } if (method == "khat") { aicmin <- 999999999 for (lam in c(0.2, 0.5, 1.0)) { for (cofp in c(0.75, 1, 1.25, 1.5)) { out <- pedreg( X, Y, nlambda = 1, constc0 = 1.1, constc1 = cofp, lambdainit = lam ) if (-out$khat < aicmin) { minout <- out mincofp <- cofp aicmin <- -out$khat } } } out <- minout } if (method == "CV") { n <- length(Y) cvmin <- 999999999 for (lam in c(0.2, 0.5, 1.0)) { for (cofp in c(0.75, 1, 1.25, 1.5)) { cverr <- 0 for (jj in 1:10) { subsample <- ((jj - 1) * 10 + 1):((jj - 1) * 10 + 10) out <- pedreg( X[-subsample, ], Y[-subsample], nlambda = 1, constc0 = 1.1, constc1 = cofp, lambdainit = lam ) cverr <- cverr + Enorm(Y[subsample] - out$intercept - X[subsample, ] %*% out$betahat) ** 2 } if (cverr < cvmin) { minout <- out minlam <- lam mincofp <- cofp cvmin <- cverr } } } out <- pedreg( X, Y, nlambda = 1, constc0 = 1.1, constc1 = mincofp, lambdainit = minlam ) } out1 <- list( betahat = 0, yhat = 0, lambda = 0, coef = 0, resid = 0 ) out1$intercept <- out$intercept out1$coef <- c(out$intercept, out$betahat) out1$betahat <- out$betahat out1$lambda <- out$lambda out1$delta <- mincofp out1$yhat <- out$yhat out1$resid <- Y - out$yhat out1 } ###########################function for PED##################### #================================================================================== pedreg <- function(X, Y, constc0 = 1.1, constc1 = 1.35, alpha = 0.05, LMM = 50, MIT = 10000, NUM_METHOD = 1, nlambda = 1, lambdamax = 1, PLOT = TRUE, BIC = FALSE, lambdainit = 1) { # NUM_METHOD = 1 = L-BFGS-B # LMM = Parameter M in L-BFGS method 1 # MIT = Max iterations for optimization p <- dim(X)[2] n <- dim(X)[1] constc <- constc0 Ymean <- mean(Y) Ysd <- sd(Y) Yinit <- Y pinit <- p ans0 <- rep(0, times = pinit) Xorig <- X Yorig <- Y vm <- rep(0, times = ncol(X)) vsd <- rep(0, times = ncol(X)) for (i in 1:ncol(X)) { vm[i] <- mean(X[, i]) } for (i in 1:ncol(X)) { vsd[i] <- sd(X[, i]) } #standardize to sphere X <- scale(X) / sqrt(n - 1) Y <- scale(Y) / sqrt(n - 1) X0 <- X Y0 <- Y lambdainit1 <- constc / sqrt(n - 1) * sqrt(sqrt(p)) / n * qnorm(1 - alpha / (2 * p)) if (nlambda == 1) { lambdainit1 <- lambdainit } METHOD1 <- "L-BFGS-B" xi <- -9999999 c1 <- 1 nlam <- nlambda betamat <- matrix(0, p, nlam) betamat.sparse <- betamat lambdamat <- rep(0, times = nlam) ximat <- rep(0, times = nlam) aic <- rep(0, times = nlam) bic <- rep(0, times = nlam) npar <- rep(0, times = nlam) selectmat <- betamat #cat(c("Lambda iteration (out of ",nlam,"):")) for (ilam in (nlam:1)) { #cat(c(ilam," ")) if (nlam == 1) { lambda <- lambdainit1 } if (nlam > 1) { c1 <- sqrt(n) + (ilam - 1) / (nlam - 1) * 1 / lambdainit1 c1 <- sqrt(n) + ((ilam - 1) / (nlam - 1)) * 1 / lambdainit1 * (lambdamax - lambdainit1 * sqrt(n)) lambda <- lambdainit1 * c1 } if (ilam == nlam) { x0 <- rep(1 / sqrt(p), times = p) } if (ilam != nlam) { x0 <- betahat + rnorm(p) / sqrt(p) } #x0<-rnorm(p)/sqrt(p) pedfun <- function(pars, Y = 0, X = 0) { p <- length(pars) pars <- matrix(pars, p, 1) ped <- Enorm(Y - X %*% pars) + lambda * sqrt(Enorm(pars) * sum(abs(pars))) ped } pedgrad <- function(pars, X = 0, Y = 0) { GM <- sqrt(Enorm(pars) * sum(abs(pars))) gradL <- rep(0, times = p) gradL <- -t(X) %*% (Y - X %*% pars) / Enorm(Y - X %*% pars) gradL <- gradL + matrix( lambda / 2 * pars / Enorm(pars) * sum(abs(pars)) / GM + lambda / 2 * sign(pars) * Enorm(pars) / GM , p, 1 ) c(gradL) } if (NUM_METHOD == 1) { repeat { #,ndeps=1e-3,factr=1e-5,pgtol=1e-5 res2 <- optim( par = x0, fn = pedfun, gr = pedgrad, method = METHOD1, control = list(lmm = LMM, maxit = MIT), X = X, Y = Y ) betahat <- res2$par if (res2$convergence == 0) { break } x0 <- rnorm(p) / sqrt(p) } } oldxi <- xi xi <- sqrt(Enorm(betahat) / sum(abs(betahat))) - sqrt(n) / (constc * c1 * p ^ (1 / 4)) dif <- (xi - oldxi) REGC <- 0.0001 betamat[, ilam] <- betahat / (Enorm(betahat) + REGC) lambdamat[ilam] <- lambda ximat[ilam] <- xi ximat[ilam] <- sqrt(Enorm(betahat) / sum(abs(betahat))) MM <- constc1 / (sqrt(n)) select <- (abs(betahat) / (Enorm(betahat) + REGC) > MM) selectmat[, ilam] <- select betamat.sparse[, ilam] <- betamat[, ilam] betamat.sparse[select == FALSE, ilam] <- 0 * betamat[select == FALSE, ilam] pp <- sum(select) npar[ilam] <- pp bic[ilam] <- log(Enorm(Y) ** 2 / n) * (n) + log(n) * (1) aic[ilam] <- log(Enorm(Y) ** 2 / n) * (n) + 2 * (1) if (sum(select) > 0) { aa <- lm(Y ~ X[, c(1:p)[select]] - 1) pred <- predict(aa) # Use AIC with finite sample correction aic[ilam] <- log(Enorm(Y - pred) ** 2 / n) * (n) + 2 * (pp + 1) + 2 * (pp + 1) * (pp + 2) / (n - pp - 2) # Use AIC/BIC bic[ilam] <- log(Enorm(Y - pred) ** 2 / n) * (n) + log(n) * (pp + 1) aic[ilam] <- log(Enorm(Y - pred) ** 2 / n) * (n) + 2 * (pp + 1) } } best <- 1 if (nlam > 1) { ###########################################choose best via AIC best <- c(1:nlam)[aic == min(aic)][1] select <- as.logical(selectmat[, best]) lambdaaic <- lambdamat[best] ###########################################choose best via Corollary 1 with khat best2 <- nlam if ((sum(ximat > 0.25)) > 0) { best2 <- c(1:nlam)[(ximat) > 0.25][1] } #################### biggest xi from Corollary 1 xism <- (ximat) if (sum(diff(xism) < 0.01) > 0) { best2 <- c(2:nlam)[diff(xism) < 0.01][1] - 1 } ############## biggest sqrt( Enorm(beta) / norm(beta)_1 ) best2 <- c(1:nlam)[ximat == max(ximat)] selectcor <- as.logical(selectmat[, best2]) lambdacor1 <- lambdamat[best2] if (BIC == FALSE) { best <- best2 select <- selectcor } } ################last part######estimate with reduced p########### if (sum(select) > 0) { X <- as.matrix(X[, select]) p <- sum(select) p14 <- sqrt(sqrt(p)) final <- c(1:pinit)[select] } ####### lambda <- constc / sqrt(sqrt(p)) / sqrt(n) * qnorm(1 - alpha / (2 * p)) if (sum(select) > 0) { x0 <- rep(1 / p, times = p) if (NUM_METHOD == 1) { repeat { res2 <- optim( par = x0, fn = pedfun, gr = pedgrad, method = METHOD1, control = list(lmm = LMM, maxit = MIT), X = X, Y = Y ) betahat <- res2$par if (res2$convergence == 0) { break } x0 <- rnorm(p) / p } } ans0[final] <- betahat } #ind<-which(abs(ans0/Enorm(ans0))<10^(-5)) #ans0[ind]<-0 out <- list(betahat = 0, yhat = 0, lambda = 0) out$betahatscale <- ans0 out$yhatscale <- c(X0 %*% ans0) if (nlam > 1) { out$lambdacor1 <- lambdacor1 out$lambdaaic <- lambdaaic out$betamat.sparse <- betamat.sparse out$betamat.rescale <- betamat out$betamat <- betamat for (i in 1:nlam) { out$betamat.rescale[, i] <- c(out$betamat[, i] / vsd) * sd(Yorig) } out$lambdamat <- lambdamat out$ximat <- ximat out$MM <- MM out$fmax <- res2$value out$npar <- npar out$selectmat <- selectmat } #use AIC out$aic <- aic #use BIC out$bic <- bic #use khat out$khat <- ximat out$lambdath3 <- lambdainit1 * sqrt(n) out$lambda <- out$lambdacor1 if (BIC == TRUE) { out$lambda <- out$lambdaaic } if (nlam == 1) { out$lambda <- lambdainit1 out$constc1 <- constc1 } sol <- sd(Yorig) * c(ans0 / vsd) inter <- drop(mean(Yorig) - sd(Yorig) * (vm / vsd) %*% ans0) out$intercept <- drop(mean(Yorig) - sd(Yorig) * (vm / vsd) %*% ans0) out$betahat <- sd(Yorig) * c(ans0 / vsd) out$best <- best out$Yinit <- Yinit out$yhat <- Xorig %*% sol + inter out } ############################################################ # # FUNCTIONS FOR CALCULATING NON-EUCLIDEAN MEANS AND DISTANCES # OF COVARIANCE MATRICES # ############################################################ # Log Euclidean mean: Sigma_L #================================================================================== estLogEuclid <- function(S, weights = 1) { M <- dim(S)[3] if (length(weights) == 1) { weights <- rep(1, times = M) } sum <- S[, , 1] * 0 for (j in 1:M) { eS <- eigen(S[, , j], symmetric = TRUE) sum <- sum + weights[j] * eS$vectors %*% diag(log(eS$values)) %*% t(eS$vectors) / sum(weights) } ans <- sum eL <- eigen(ans, symmetric = TRUE) eL$vectors %*% diag(exp(eL$values)) %*% t(eL$vectors) } #================================================================================== estPowerEuclid <- function(S, weights = 1, alpha = 0.5) { M <- dim(S)[3] if (length(weights) == 1) { weights <- rep(1, times = M) } sum <- S[, , 1] * 0 for (j in 1:M) { eS <- eigen(S[, , j], symmetric = TRUE) sum <- sum + weights[j] * eS$vectors %*% diag(abs(eS$values) ** alpha) %*% t(eS$vectors) / sum(weights) } ans <- sum eL <- eigen(ans, symmetric = TRUE) eL$vectors %*% diag(abs(eL$values) ** (1 / alpha)) %*% t(eL$vectors) } # Riemannian (weighted mean) : Sigma_R #================================================================================== estLogRiem2 <- function(S, weights = 1) { M <- dim(S)[3] if (length(weights) == 1) { weights <- rep(1, times = M) } check <- 9 tau <- 1 Hold <- 99999 mu <- estLogEuclid(S, weights) while (check > 0.0000000001) { ev <- eigen(mu, symmetric = TRUE) logmu <- ev$vectors %*% diag(log(ev$values)) %*% t(ev$vectors) Hnew <- Re(Hessian2(S, mu, weights)) logmunew <- logmu + tau * Hnew ev <- eigen(logmunew, symmetric = TRUE) mu <- ev$vectors %*% diag(exp(ev$values)) %*% t(ev$vectors) check <- Re(Enorm(Hold) - Enorm(Hnew)) if (check < 0) { tau <- tau / 2 check <- 999999 } Hold <- Hnew } mu } # Hessian used in calculating Sigma_R #================================================================================== Hessian2 <- function(S, Sigma, weights = 1) { M <- dim(S)[3] k <- dim(S)[1] if (length(weights) == 1) { weights <- rep(1, times = M) } ev0 <- eigen(Sigma, symmetric = TRUE) shalf <- ev0$vectors %*% (diag(sqrt((ev0$values)))) %*% t(ev0$vectors) sumit <- matrix(0, k, k) for (i in 1:(M)) { ev2 <- eigen(shalf %*% solve(S[, , i]) %*% shalf, symmetric = TRUE) sumit <- sumit + weights[i] * ev2$vectors %*% diag (log((ev2$values))) %*% t(ev2$vectors) / sum(weights) } - sumit } # Euclidean : Sigma_E #================================================================================== estEuclid <- function(S, weights = 1) { M <- dim(S)[3] if (length(weights) == 1) { weights <- rep(1, times = M) } sum <- S[, , 1] * 0 for (j in 1:M) { sum <- sum + S[, , j] * weights[j] / sum(weights) } sum } # Cholesky mean : Sigma_C #================================================================================== estCholesky <- function(S, weights = 1) { M <- dim(S)[3] if (length(weights) == 1) { weights <- rep(1, times = M) } sum <- S[, , 1] * 0 for (j in 1:M) { sum <- sum + t(chol(S[, , j])) * weights[j] / sum(weights) } cc <- sum cc %*% t(cc) } #================================================================================== ild_estSS <- function(S, weights = 1) { M <- dim(S)[3] k <- dim(S)[1] H <- defh(k) if (length(weights) == 1) { weights <- rep(1, times = M) } Q <- array(0, c(k + 1, k, M)) for (j in 1:M) { Q[, , j] <- t(H) %*% (rootmat(S[, , j])) } ans <- procWGPA( Q, fixcovmatrix = diag(k + 1), scale = FALSE, reflect = TRUE, sampleweights = weights ) H %*% ans$mshape %*% t(H %*% ans$mshape) } #================================================================================== ild_estShape <- function(S, weights = 1) { M <- dim(S)[3] k <- dim(S)[1] H <- defh(k) if (length(weights) == 1) { weights <- rep(1, times = M) } Q <- array(0, c(k + 1, k, M)) for (j in 1:M) { Q[, , j] <- t(H) %*% (rootmat(S[, , j])) } ans <- procWGPA( Q, fixcovmatrix = diag(k + 1), scale = TRUE, reflect = TRUE, sampleweights = weights ) H %*% ans$mshape %*% t(H %*% ans$mshape) } #================================================================================== estRiemLe <- function(S, weights) { M <- dim(S)[3] k <- dim(S)[1] if (M != 2) print("Sorry - Calculation not implemented for M>2 yet") if (M == 2) { P1 <- S[, , 1] P2 <- S[, , 2] detP1 <- prod(eigen(P1)$values) detP2 <- prod(eigen(P2)$values) P1 <- P1 / (detP1) ^ (1 / k) P2 <- P2 / (detP2) ^ (1 / k) P1inv <- solve(P1) P12sq <- P1inv %*% P2 %*% P2 %*% P1inv tem <- eigen(P12sq, symmetric = TRUE) A2 <- tem$vectors %*% diag(log(tem$values)) %*% t(tem$vectors) logPs2 <- weights[2] * A2 tem2 <- eigen(logPs2, symmetric = TRUE) Ps2 <- tem2$vectors %*% diag(exp(tem2$values)) %*% t(tem2$vectors) P12s <- P1 %*% Ps2 %*% P1 tem3 <- eigen(P12s, symmetric = TRUE) P12sA <- tem3$vectors %*% diag(sqrt(tem3$values)) %*% t(tem3$vectors) Ptildes <- (detP1 * (detP2 / detP1) ^ weights[2]) ^ (1 / k) * P12sA Ptildes } } ##########distances################################# #================================================================================== distRiemPennec <- function(P1, P2) { eig <- eigen(P1, symmetric = TRUE) P1half <- eig$vectors %*% diag(sqrt(eig$values)) %*% t(eig$vectors) P1halfinv <- solve(P1half) AA <- P1halfinv %*% P2 %*% P1halfinv tem <- eigen(AA, symmetric = TRUE) A2 <- tem$vectors %*% diag(log(tem$values)) %*% t(tem$vectors) dd <- Enorm(A2) dd } #================================================================================== distLogEuclidean <- function(P1, P2) { eig <- eigen(P1, symmetric = TRUE) logP1 <- eig$vectors %*% diag(log(eig$values)) %*% t(eig$vectors) tem <- eigen(P2, symmetric = TRUE) logP2 <- tem$vectors %*% diag(log(tem$values)) %*% t(tem$vectors) dd <- Enorm(logP1 - logP2) dd } #================================================================================== distRiemannianLe <- function(P1, P2) { dd <- distRiemPennec(P1 %*% t(P1), P2 %*% t(P2)) / 2 dd } #================================================================================== ild_distProcrustesSizeShape <- function (P1, P2) { H <- defh(dim(P1)[1]) Q1 <- t(H) %*% rootmat(P1) Q2 <- t(H) %*% rootmat(P2) ans <- sqrt( centroid.size(Q1) ^ 2 + centroid.size(Q2) ^ 2 - 2 * centroid.size(Q1) * centroid.size(Q2) * cos(riemdist(Q1, Q2, reflect = TRUE)) ) ans } #================================================================================== ild_distProcrustesFull <- function(P1, P2) { H <- defh(dim(P1)[1]) Q1 <- t(H) %*% rootmat(P1) Q2 <- t(H) %*% rootmat(P2) ans <- riemdist(Q1, Q2, reflect = TRUE) ans } #================================================================================== distPowerEuclidean <- function(P1, P2, alpha = 1 / 2) { if (alpha != 0) { eS <- eigen(P1, symmetric = TRUE) Q1 <- eS$vectors %*% diag(abs(eS$values) ^ alpha) %*% t(eS$vectors) eS <- eigen(P2, symmetric = TRUE) Q2 <- eS$vectors %*% diag(abs(eS$values) ^ alpha) %*% t(eS$vectors) dd <- Enorm(Q1 - Q2) / abs(alpha) } if (alpha == 0) { dd <- distLogEuclidean(P1, P2) } dd } #================================================================================== ild_distCholesky <- function(P1, P2) { H <- defh(dim(P1)[1]) Q1 <- t(H) %*% t(chol(P1)) Q2 <- t(H) %*% t(chol(P2)) ans <- Enorm(Q1 - Q2) ans } #================================================================================== distEuclidean <- function(P1, P2) { ans <- Enorm(P1 - P2) ans } ################## #================================================================================== distcov <- function(S1, S2 , method = "Riemannian", alpha = 1 / 2) { if (method == "Procrustes") { dd <- distProcrustesSizeShape(S1, S2) } if (method == "ProcrustesShape") { dd <- distProcrustesFull(S1, S2) } if (method == "Riemannian") { dd <- distRiemPennec(S1, S2) } if (method == "Cholesky") { dd <- distCholesky(S1, S2) } if (method == "Power") { dd <- distPowerEuclidean(S1, S2, alpha) } if (method == "Euclidean") { dd <- distEuclidean(S1, S2) } if (method == "LogEuclidean") { dd <- distLogEuclidean(S1, S2) } if (method == "RiemannianLe") { dd <- distRiemannianLe(S1, S2) } dd } #================================================================================== estcov <- function (S, method = "Riemannian", weights = 1, alpha = 1 / 2, MDSk = 2) { out <- list( mean = 0, sd = 0, pco = 0, eig = 0, dist = 0 ) M <- dim(S)[3] if (length(weights) == 1) { weights <- rep(1, times = M) } if (method == "Procrustes") { dd <- estSS(S, weights) } if (method == "ProcrustesShape") { dd <- estShape(S, weights) } if (method == "Riemannian") { dd <- estLogRiem2(S, weights) } if (method == "Cholesky") { dd <- estCholesky(S, weights) } if (method == "Power") { dd <- estPowerEuclid(S, weights, alpha) } if (method == "Euclidean") { dd <- estEuclid(S, weights) } if (method == "LogEuclidean") { dd <- estLogEuclid(S, weights) } if (method == "RiemannianLe") { dd <- estRiemLe(S, weights) } out$mean <- dd sum <- 0 for (i in 1:M) { sum <- sum + weights[i] * distcov(S[, , i], dd, method = method) ^ 2 / sum(weights) } out$sd <- sqrt(sum) dist <- matrix(0, M, M) for (i in 2:M) { for (j in 1:(i - 1)) { dist[i, j] <- distcov(S[, , i], S[, , j], method = method) dist[j, i] <- dist[i, j] } } out$dist <- dist if (M > MDSk) { ans <- cmdscale( dist, k = MDSk, eig = TRUE, add = TRUE, x.ret = TRUE ) out$pco <- ans$points out$eig <- ans$eig if (MDSk > 2) { shapes3d(out$pco[, 1:min(MDSk, 3)], axes3 = TRUE) } if (MDSk == 2) { plot(out$pco, type = "n", xlab = "MDS1", ylab = "MDS2") text(out$pco[, 1], out$pco[, 2], 1:length(out$pco[, 1])) } } out } rootmat <- function(P1) { eS <- eigen(P1, symmetric = TRUE) if (min(eS$values) < -0.001) { print("Not positive-semi definite") } else{ Q1 <- eS$vectors %*% diag(sqrt(abs(eS$values))) %*% t(eS$vectors) Q1 } } ########################## #================================================================================== shapes.cva <- function(X , groups , scale = TRUE, ncv = 2) { g <- dim(table (groups)) ans <- procGPA(X , scale = scale) if (scale == TRUE) pp <- (ans$k - 1) * ans$m - (ans$m * (ans$m - 1) / 2) - 1 if (scale == FALSE) pp <- (ans$k - 1) * ans$m - (ans$m * (ans$m - 1) / 2) pracdim <- min(pp, ans$n - g) out <- lda(ans$scores[, 1:pracdim] , groups) print((out)) cv <- predict(out, dimen = 3)$x if (dim(cv)[2] == 1) { cv <- cbind(cv, rnorm(dim(cv)[1]) / 1000) } if (ncv == 2) { eqscplot(cv, type = "n", xlab = "CV1", ylab = "CV2") text(cv, labels = groups) } if (ncv == 3) { shapes3d(cv, color = groups, axes3 = TRUE) } cv } #================================================================================== groupstack <- function(A1, A2, A3 = 0, A4 = 0, A5 = 0, A6 = 0, A7 = 0, A8 = 0) { out <- list(x = 0, groups = "") dat <- abind(A1, A2) group <- c(rep(1, times = dim(A1)[3]), rep(2, times = dim(A2)[3])) if (is.array(A3)) { dat <- abind(dat, A3) group <- c(group, rep(3, times = dim(A3)[3])) if (is.array(A4)) { dat <- abind(dat, A4) group <- c(group, rep(4, times = dim(A4)[3])) if (is.array(A5)) { dat <- abind(dat, A5) group <- c(group, rep(5, times = dim(A5)[3])) if (is.array(A6)) { dat <- abind(dat, A6) group <- c(group, rep(6, times = dim(A6)[3])) if (is.array(A7)) { dat <- abind(dat, A7) group <- c(group, rep(7, times = dim(A7)[3])) if (is.array(A8)) { dat <- abind(dat, A8) group <- c(group, rep(8, times = dim(A8)[3])) } } } } } } out$x <- dat out$groups <- group out } ########################### #================================================================================== procdist <- function(x, y, type = "full", reflect = FALSE) { if (type == "full") { out <- sin(riemdist(x, y, reflect = reflect)) } if (type == "partial") { out <- sqrt(2) * sqrt(abs(1 - cos(riemdist(x, y, reflect = reflect)))) } if (type == "Riemannian") { out <- riemdist(x, y, reflect = reflect) } if (type == "sizeandshape") { out <- ssriemdist(x, y, reflect = reflect) } out } #================================================================================== transformations <- function(Xrotated, Xoriginal) { # outputs the translations, rotations and # scalings for ordinary Procrustes rotation # of each individual in Xoriginal to the # Procrustes rotated individuals in Xrotated X1 <- Xrotated X2 <- Xoriginal n <- dim(X1)[3] m <- dim(X1)[2] translation <- matrix(0, m, n) scale <- rep(0, times = n) rotation <- array(0, c(m, m, n)) for (i in 1:n) { translation[, i] <- -apply(X2[, , i] - X1[, , i], 2, mean) ans <- procOPA(X1[, , i], X2[, , i]) scale[i] <- ans$s rotation[, , i] <- ans$R } out <- list(translation = 0, scale = 0, rotation = 0) out$translation <- translation out$scale <- scale out$rotation <- rotation out } #================================================================================== iglogl <- function(x , lam, nlam) { gamma <- abs(x[1]) alpha <- gamma / mean(1 / lam[1:nlam]) ll <- -(gamma + 1) * sum(log(lam[1:nlam])) - alpha * sum (1 / lam[1:nlam]) + nlam * gamma * log(alpha) - nlam * lgamma (gamma) - ll } #================================================================================== procWGPA <- function(x, fixcovmatrix = FALSE, initial = "Identity", maxiterations = 10, scale = TRUE, reflect = FALSE, prior = "Exponential", diagonal = TRUE, sampleweights = "Equal") { X <- x priorargument <- prior alpha <- "not estimated" gamma <- "not estimated" k <- dim(X)[1] n <- dim(X)[3] m <- dim(X)[2] if (initial[1] == "Identity") { Sigmak <- diag(k) } else{ if (initial[1] == "Rawdata") { tol <- 0.0000000001 if (m == 2) { Sigmak <- diag(diag(var(t(X[, 1, ]))) + diag(var(t(X[, 2, ])))) / 2 + tol } if (m == 3) { Sigmak <- diag(diag(var(t(X[, 1, ]))) + diag(var(t(X[, 2, ]))) + diag(var(t(X[, 3, ])))) / 3 + tol } } else { Sigmak <- initial } } mu <- procGPA(X, scale = scale)$mshape #cat("Iteration 1 \n") if (fixcovmatrix[1] != FALSE) { Sigmak <- fixcovmatrix } ans <- procWGPA1( X, mu, metric = Sigmak, scale = scale, reflect = reflect, sampleweights = sampleweights ) if ((maxiterations > 1) && (fixcovmatrix[1] == FALSE)) { ans0 <- ans dif <- 999999 it <- 1 while ((dif > 0.00001) && (it < maxiterations)) { it <- it + 1 if (it == 2) { cat("Differences in norm of Sigma estimates... \n ") } if (prior[1] == "Identity") { prior <- diag(k) } if (prior[1] == "Inversegamma") { lam <- eigen(ans$Sigmak)$values nlam <- min(c(n * m - m - 3, k - 3)) mu <- mean(1 / lam[1:(nlam)]) alpha <- 1 / mu out <- nlm(iglogl, p = c(1) , lam = lam, nlam = nlam) #print(out) gamma <- abs(out$estimate[1]) alpha <- gamma / mean(1 / lam[1:nlam]) newmetric <- n * m / (n * m + 2 * (1 + gamma)) * (ans$Sigmak + (2 * alpha / (n * m)) * diag(k)) #dif2<-999999 #while (dif2> 0.000001){ #old<-alpha #lam <- eigen(newmetric)$values #out <- nlm( iglogl, p=c(1) ,lam=lam, nlam=nlam) #gamma <- abs(out$estimate[1]) #alpha<- gamma/ mean(1/lam[1:nlam]) #newmetric <- n*m/(n*m+2*(1+gamma))*( ans$Sigmak + (2*alpha/(n*m))*diag(k) ) #dif2<- abs(alpha- old) #print(dif2) #} } if (prior[1] == "Exponential") { lam <- eigen(ans$Sigmak)$values nlam <- min(c(n * m - m - 2, k - 2)) mu <- mean(1 / lam[1:(nlam)]) alpha <- 1 / mu gamma <- 1 newmetric <- n * m / (n * m + 2 * (2)) * (ans$Sigmak + (2 * alpha / (n * m)) * diag(k)) #dif2<-999999 #while (dif2> 0.000001){ #old<-alpha #newmetric <- n*m/(n*m+2*(2))*( ans$Sigmak + (2*alpha/(n*m))*diag(k) ) #lam <- eigen(newmetric)$values #mu <- mean(1/lam[1:( nlam)]) #alpha <- 1/mu #newmetric <- n*m/(n*m+2*(2))*( ans$Sigmak + (2*alpha/(n*m))*diag(k) ) #dif2<- abs(alpha- old) #} } if (is.double(prior[1])) { newmetric <- (ans$Sigmak + prior) } if (diagonal == TRUE) { newmetric <- diag(diag(newmetric)) } if (fixcovmatrix[1] != FALSE) { newmetric <- fixcovmatrix } ans2 <- procWGPA1( X, ans$mshape, metric = newmetric , scale = scale, sampleweights = sampleweights ) plotshapes(ans2$rotated) dif <- Enorm((ans$Sigmak - ans2$Sigmak)) ans <- ans2 cat(c(it, " ", dif, " \n")) } } if ((priorargument[1] == "Exponential") || (priorargument[1] == "Inversegamma")) { ans$alpha <- alpha ans$gamma <- gamma } cat(" \n") ans } #================================================================================== procWGPA1 <- function(X, mu, metric = "Identity", scale = TRUE, reflect = FALSE, sampleweights = "Equal") { k <- dim(X)[1] n <- dim(X)[3] m <- dim(X)[2] sum <- 0 for (i in 1:n) { sum <- sum + centroid.size(X[, , i]) ** 2 } size1 <- sqrt(sum) if (sampleweights[1] == "Equal") { sampleweights <- rep(1 / n, times = n) } if (length(sampleweights) != n) { cat("Sample weight vector not of correct length \n") } if (metric[1] == "Identity") { Sigmak <- diag(k) } else{ Sigmak <- metric } eig <- eigen(Sigmak, symmetric = TRUE) Sighalf <- eig$vectors %*% diag (sqrt(abs(eig$values))) %*% t(eig$vectors) Siginvhalf <- eig$vectors %*% diag(1 / sqrt(abs(eig$values))) %*% t(eig$vectors) Siginv <- eig$vectors %*% diag (1 / (eig$values)) %*% t(eig$vectors) one <- matrix(rep(1, times = k), k, 1) Xstar <- X for (i in 1:n) { Xstar[, , i] <- Xstar[, , i] - one %*% t(one) %*% Siginv %*% Xstar[, , i] / c(t(one) %*% Siginv %*% one) Xstar[, , i] <- Siginvhalf %*% Xstar[, , i] } mu <- mu - one %*% t(one) %*% Siginv %*% mu / c(t(one) %*% Siginv %*% one) ans <- procGPA(Xstar, eigen2d = FALSE) ans2 <- ans dif3 <- 99999999 while (dif3 > 0.00001) { for (i in 1:n) { old <- mu tem <- procOPA(Siginvhalf %*% mu , Xstar[, , i], scale = scale, reflect = reflect) Gammai <- tem$R betai <- tem$s #ci <- t(one)%*% Siginvhalf %*% X[,,i] %*% Gammai*betai/k #Yi <- Sighalf%*% ans$rotated[,,i] + Sighalf%*%one%*% ci #Zi <- Yi - one %*% t(one)%*% Siginv %*% Yi / c( t(one)%*%Siginv%*%one ) Zi <- Sighalf %*% Xstar[, , i] %*% Gammai * betai ans2$rotated[, , i] <- Zi } sum2 <- 0 for (i in 1:n) { sum2 <- sum2 + centroid.size(ans2$rotated[, , i]) ** 2 } size2 <- sqrt(sum2) tem <- ans2$mshape * 0 for (i in 1:n) { ans2$rotated[, , i] <- ans2$rotated[, , i] * size1 / size2 tem <- tem + ans2$rotated[, , i] * sampleweights[i] / sum(sampleweights) } mu <- tem dif3 <- riemdist(old, mu) } z <- ans2 z$mshape <- tem tan <- z$rotated[, 1,] - z$mshape[, 1] for (i in 2:m) { tan <- rbind(tan, z$rotated[, i,] - z$mshape[, i]) } pca <- prcomp1(t(tan)) z$tan <- tan npc <- 0 for (i in 1:length(pca$sdev)) { if (pca$sdev[i] > 1e-07) { npc <- npc + 1 } } z$scores <- pca$x z$rawscores <- pca$x z$stdscores <- pca$x for (i in 1:npc) { z$stdscores[, i] <- pca$x[, i] / pca$sdev[i] } z$pcar <- pca$rotation z$pcasd <- pca$sdev z$percent <- z$pcasd ^ 2 / sum(z$pcasd ^ 2) * 100 size <- rep(0, times = n) rho <- rep(0, times = n) x <- X size <- apply(x, 3, centroid.size) rho <- apply(x, 3, y <- function(x) { riemdist(x, z$mshape) }) z$rho <- rho z$size <- size z$rmsrho <- sqrt(mean(rho ^ 2)) z$rmsd1 <- sqrt(mean(sin(rho) ^ 2)) z$k <- k z$m <- m z$n <- n tem <- matrix(0, k, k) for (i in 1:n) { tem <- tem + (z$rotated[, , i] - z$mshape) %*% t((z$rotated[, , i] - z$mshape)) } tem <- tem / (n * m) z$Sigmak <- tem return(z) } #================================================================================== testshapes <- function(A, B, resamples = 1000, replace = TRUE, scale = TRUE) { if (replace == TRUE) { out <- bootstraptest(A, B, resamples = resamples, scale = scale) } if (replace == FALSE) { out <- permutationtest(A, B, nperms = resamples, scale = scale) } out } #================================================================================== testmeanshapes <- function(A, B, resamples = 1000, replace = FALSE, scale = TRUE) { if (replace == TRUE) { out <- bootstraptest(A, B, resamples = resamples, scale = scale) } if (replace == FALSE) { out <- permutationtest(A, B, nperms = resamples, scale = scale) } if (resamples > 0) { aa <- list( H = 0, H.pvalue = 0, H.table.pvalue = 0, G = 0, G.pvalue = 0, G.table.pvalue = 0, J = 0, J.pvalue = 0, J.table.pvalue = 0 ) aa$H <- out$H aa$H.pvalue <- out$H.pvalue aa$H.table.pvalue <- out$H.table.pvalue aa$G <- out$G aa$G.pvalue <- out$G.pvalue aa$G.table.pvalue <- out$G.table.pvalue aa$J <- out$J aa$J.pvalue <- out$J.pvalue aa$J.table.pvalue <- out$J.table.pvalue } if (resamples == 0) { aa <- list( H = 0, H.table.pvalue = 0, G = 0, G.table.pvalue = 0, J = 0, J.table.pvalue = 0 ) aa$H <- out$H aa$H.table.pvalue <- out$H.table.pvalue aa$G <- out$G aa$G.table.pvalue <- out$G.table.pvalue aa$J <- out$J aa$J.table.pvalue <- out$J.table.pvalue } aa } #================================================================================== permutationtest2 <- function (A, B, nperms = 1000, scale = scale) { A1 <- A A2 <- B mdim <- dim(A1)[2] B <- nperms nsam1 <- dim(A1)[3] nsam2 <- dim(A2)[3] pool <- procGPA( abind (A1, A2) , scale = scale, tangentcoords = "partial", pcaoutput = FALSE ) tempool <- pool for (i in 1:(nsam1 + nsam2)) { tempool$tan[, i] <- pool$tan[, i] / Enorm(pool$tan[, i]) * pool$rho[i] } pool <- tempool permpool <- pool Gtem <- Goodall(pool, nsam1, nsam2) Htem <- Hotelling(pool, nsam1, nsam2) Jtem <- James(pool, nsam1, nsam2, table = TRUE) Ltem <- Lambdamin(pool, nsam1, nsam2) Gumc <- Gtem$F Humc <- Htem$F Jumc <- Jtem$Tsq Lumc <- Ltem$lambdamin Gtabpval <- Gtem$pval Htabpval <- Htem$pval Jtabpval <- Jtem$pval Ltabpval <- Ltem$pval if (B > 0) { Apool <- array(0, c(dim(A1)[1], dim(A1)[2], dim(A1)[3] + dim(A2)[3])) Apool[, , 1:nsam1] <- A1 Apool[, , (nsam1 + 1):(nsam1 + nsam2)] <- A2 out <- list( H = 0, H.pvalue = 0, H.table.pvalue = 0, J = 0, J.pvalue = 0, J.table.pvalue = 0, G = 0, G.pvalue = 0, G.table.pvalue = 0 ) Gu <- rep(0, times = B) Hu <- rep(0, times = B) Ju <- rep(0, times = B) Lu <- rep(0, times = B) cat("Permutations - sampling without replacement: ") cat(c("No of permutations = ", B, "\n")) for (i in 1:B) { if (i / 100 == trunc(i / 100)) { cat(c(i, " ")) } select <- sample(1:(nsam1 + nsam2)) permpool$tan <- pool$tan[, select] Gu[i] <- Goodall(permpool, nsam1, nsam2)$F Hu[i] <- Hotelling(permpool, nsam1, nsam2)$F Ju[i] <- James(permpool, nsam1, nsam2)$Tsq Lu[i] <- Lambdamin(permpool, nsam1, nsam2)$lambdamin } Gu <- sort(Gu) numbig <- length(Gu[Gumc < Gu]) pvalG <- (1 + numbig) / (B + 1) Lu <- sort(Lu) numbig <- length(Lu[Lumc < Lu]) pvalL <- (1 + numbig) / (B + 1) Ju <- sort(Ju) numbig <- length(Ju[Jumc < Ju]) pvalJ <- (1 + numbig) / (B + 1) Hu <- sort(Hu) numbig <- length(Hu[Humc < Hu]) pvalH <- (1 + numbig) / (B + 1) cat(" \n") out$Hu <- Hu out$Ju <- Ju out$Gu <- Gu out$Lu <- Lu out$H <- Humc out$H.pvalue <- pvalH out$H.table.pvalue <- Htabpval out$J <- Jumc out$J.pvalue <- pvalJ out$J.table.pvalue <- Jtabpval out$G <- Gumc out$G.pvalue <- pvalG out$G.table.pvalue <- Gtabpval out$L <- Lumc out$L.pvalue <- pvalL out$L.table.pvalue <- Ltabpval } if (B == 0) { out <- list( H = 0, H.table.pvalue = 0, G = 0, G.table.pvalue = 0 ) out$H <- Humc out$H.table.pvalue <- Htabpval out$J <- Jumc out$J.table.pvalue <- Jtabpval out$G <- Gumc out$G.table.pvalue <- Gtabpval out$L <- Lumc out$L.table.pvalue <- Ltabpval } out } #================================================================================== bootstraptest <- function (A, B, resamples = 200, scale = TRUE) { A1 <- A A2 <- B mdim <- dim(A1)[2] B <- resamples nsam1 <- dim(A1)[3] nsam2 <- dim(A2)[3] pool <- procGPA( abind (A1, A2) , scale = scale , tangentcoords = "partial", pcaoutput = FALSE ) tempool <- pool for (i in 1:(nsam1 + nsam2)) { tempool$tan[, i] <- pool$tan[, i] / Enorm(pool$tan[, i]) * pool$rho[i] } pool <- tempool bootpool <- pool Gtem <- Goodall(pool, nsam1, nsam2) Htem <- Hotelling(pool, nsam1, nsam2) Jtem <- James(pool, nsam1, nsam2, table = TRUE) Ltem <- Lambdamin(pool, nsam1, nsam2) Gumc <- Gtem$F Humc <- Htem$F Jumc <- Jtem$Tsq Lumc <- Ltem$lambdamin Gtabpval <- Gtem$pval Htabpval <- Htem$pval Jtabpval <- Jtem$pval Ltabpval <- Ltem$pval if (B > 0) { Apool <- array(0, c(dim(A1)[1], dim(A1)[2], dim(A1)[3] + dim(A2)[3])) Apool[, , 1:nsam1] <- A1 Apool[, , (nsam1 + 1):(nsam1 + nsam2)] <- A2 out <- list( H = 0, H.pvalue = 0, H.table.pvalue = 0, J = 0, J.pvalue = 0, J.table.pvalue = 0, G = 0, G.pvalue = 0, G.table.pvalue = 0 ) Gu <- rep(0, times = B) Hu <- rep(0, times = B) Ju <- rep(0, times = B) Lu <- rep(0, times = B) pool2 <- pool pool2$tan[, 1:nsam1] <- pool$tan[, 1:nsam1] - apply(pool$tan[, 1:nsam1], 1, mean) pool2$tan[, (nsam1 + 1):(nsam1 + nsam2)] <- pool$tan[, (nsam1 + 1):(nsam1 + nsam2)] - apply(pool$tan[, (nsam1 + 1):(nsam1 + nsam2)], 1, mean) cat("Bootstrap - sampling with replacement within each group under H0: ") cat(c("No of resamples = ", B, "\n")) for (i in 1:B) { if (i / 100 == trunc(i / 100)) { cat(c(i, " ")) } select1 <- sample(1:nsam1, replace = TRUE) select2 <- sample((nsam1 + 1):(nsam1 + nsam2), replace = TRUE) bootpool$tan <- pool2$tan[, c(select1, select2)] Gu[i] <- Goodall(bootpool, nsam1, nsam2)$F Hu[i] <- Hotelling(bootpool, nsam1, nsam2)$F Ju[i] <- James(bootpool, nsam1, nsam2)$Tsq Lu[i] <- Lambdamin(bootpool, nsam1, nsam2)$lambdamin } Gu <- sort(Gu) numbig <- length(Gu[Gumc < Gu]) pvalG <- (1 + numbig) / (B + 1) Ju <- sort(Ju) numbig <- length(Ju[Jumc < Ju]) pvalJ <- (1 + numbig) / (B + 1) Hu <- sort(Hu) numbig <- length(Hu[Humc < Hu]) pvalH <- (1 + numbig) / (B + 1) numbig <- length(Lu[Lumc < Lu]) pvalL <- (1 + numbig) / (B + 1) cat(" \n") out$Hu <- Hu out$Ju <- Ju out$Gu <- Gu out$Lu <- Lu out$H <- Humc out$H.pvalue <- pvalH out$H.table.pvalue <- Htabpval out$J <- Jumc out$J.pvalue <- pvalJ out$J.table.pvalue <- Jtabpval out$G <- Gumc out$G.pvalue <- pvalG out$G.table.pvalue <- Gtabpval out$L <- Lumc out$L.pvalue <- pvalL out$L.table.pvalue <- Ltabpval } if (B == 0) { out <- list( H = 0, H.table.pvalue = 0, G = 0, G.table.pvalue = 0, J = 0, J.table.pvalue = 0 ) out$H <- Humc out$H.table.pvalue <- Htabpval out$J <- Jumc out$J.table.pvalue <- Jtabpval out$G <- Gumc out$G.table.pvalue <- Gtabpval out$L <- Lumc out$L.table.pvalue <- Ltabpval } out } #================================================================================== Lambdamin <- function (pool, n1, n2, p = 0) { censiz <- centroid.size(pool$mshape) tan1 <- pool$tan[, 1:n1] tan2 <- pool$tan[, (n1 + 1):(n1 + n2)] kt <- dim(tan1)[1] n <- n1 + n2 k <- pool$k m <- pool$m if (p == 0) { p <- min(k * m - (m * (m - 1)) / 2 - 1 - m, n1 + n2 - 2) } HH <- diag(k) mu1 <- pool$mshape if (dim(tan1)[1] == k * m - m) { HH <- defh(k - 1) mu1 <- preshape(pool$mshape) } if (m == 2) { mu <- c(mu1[, 1], mu1[, 2]) } if (m == 3) { mu <- c(mu1[, 1], mu1[, 2], mu1[, 3]) } dd <- kt X1 <- tan1 * 0 X2 <- tan2 * 0 S1 <- matrix(0, dd, dd) S2 <- matrix(0, dd, dd) for (i in 1:n1) { X1[, i] <- (mu + tan1[, i]) / Enorm(mu + tan1[, i]) S1 <- S1 + X1[, i] %*% t(X1[, i]) } for (i in 1:n2) { X2[, i] <- (mu + tan2[, i]) / Enorm(mu + tan2[, i]) S2 <- S2 + X2[, i] %*% t(X2[, i]) } sumx1 <- 0 sumx2 <- 0 for (i in 1:n1) { sumx1 <- sumx1 + X1[, i] } for (i in 1:n2) { sumx2 <- sumx2 + X2[, i] } sum1 <- apply(X1, 1, sum) sum2 <- apply(X2, 1, sum) mean1 <- sum1 / Enorm(sum1) mean2 <- sum2 / Enorm(sum2) bb1 <- mean1[1:(dd - 1)] cc1 <- mean1[dd] bb2 <- mean2[1:(dd - 1)] cc2 <- mean2[dd] A1 <- cc1 / abs(cc1) * diag(dd - 1) - cc1 / (abs(cc1) + cc1 ^ 2) * bb1 %*% t(bb1) M1 <- cbind(A1,-bb1) A1 <- cc2 / abs(cc2) * diag(dd - 1) - cc2 / (abs(cc2) + cc2 ^ 2) * bb2 %*% t(bb2) M2 <- cbind(A1,-bb2) G1 <- matrix(0, dd - 1, dd - 1) G2 <- matrix(0, dd - 1, dd - 1) for (iu in 1:(dd - 1)) { for (iv in iu:(dd - 1)) { G1[iu, iv] <- G1[iu, iv] + t((t(M1))[, iu]) %*% S1 %*% (t(M1))[, iv] G1[iv, iu] <- G1[iu, iv] G2[iu, iv] <- G2[iu, iv] + t((t(M2))[, iu]) %*% S2 %*% (t(M2))[, iv] G2[iv, iu] <- G2[iu, iv] } } G1 <- G1 / n1 / Enorm(sumx1 / n1) ^ 2 G2 <- G2 / n2 / Enorm(sumx2 / n2) ^ 2 # eva1 <- eigen(G1, symmetric = TRUE, EISPACK = TRUE) eva1 <- eigen(G1, symmetric = TRUE) pcar1 <- eva1$vectors[, 1:p] pcasd1 <- sqrt(abs(eva1$values[1:p])) # eva2 <- eigen(G2, symmetric = TRUE, EISPACK = TRUE) eva2 <- eigen(G2, symmetric = TRUE) pcar2 <- eva2$vectors[, 1:p] pcasd2 <- sqrt(abs(eva2$values[1:p])) if ((pcasd1[p] < 1e-06) || (pcasd2[p] < 1e-06)) { offset <- 1e-06 cat("*") pcasd1 <- sqrt(pcasd1 ^ 2 + offset) pcasd2 <- sqrt(pcasd2 ^ 2 + offset) } Ahat1 <- n1 * t(M1) %*% (pcar1 %*% diag(1 / pcasd1 ^ 2) %*% t(pcar1)) %*% M1 Ahat2 <- n2 * t(M2) %*% (pcar2 %*% diag(1 / pcasd2 ^ 2) %*% t(pcar2)) %*% M2 Ahat <- (Ahat1 + Ahat2) # eva <- eigen(Ahat, symmetric = TRUE, EISPACK = TRUE) eva <- eigen(Ahat, symmetric = TRUE) lambdamin <- eva$values[p + 1] pval <- 1 - pchisq(lambdamin, p) #print(lambdamin) #print(pval) z <- list() z$pval <- pval z$df <- p z$lambdamin <- lambdamin return(z) } #================================================================================== Goodall <- function(pool , n1, n2, p = 0) { tan1 <- pool$tan[, 1:n1] tan2 <- pool$tan[, (n1 + 1):(n1 + n2)] kt <- dim(tan1)[1] n <- n1 + n2 k <- pool$k m <- pool$m if (p == 0) { p <- min(k * m - (m * (m - 1)) / 2 - 1 - m, n1 + n2 - 2) } top <- Enorm(apply(tan1, 1, mean) - apply(tan2, 1, mean)) ** 2 bot <- sum(diag(var(t(tan1)))) * (n1 - 1) + sum(diag(var(t(tan2)))) * (n2 - 1) Fstat <- ((n1 + n2 - 2) / (1 / n1 + 1 / n2) * top) / bot pval <- 1 - pf(Fstat, p, (n1 + n2 - 2) * p) z <- list() z$F <- Fstat z$pval <- pval z$df1 <- p z$df2 <- (n1 + n2 - 2) * p return(z) } #================================================================================== Hotelling <- function(pool , n1, n2, p = 0) { tan1 <- pool$tan[, 1:n1] tan2 <- pool$tan[, (n1 + 1):(n1 + n2)] kt <- dim(tan1)[1] n <- n1 + n2 k <- pool$k m <- pool$m S1 <- var(t(tan1)) S2 <- var(t(tan2)) Sw <- ((n1 - 1) * S1 + (n2 - 1) * S2) / (n1 + n2 - 2) if (p == 0) { p <- min(k * m - (m * (m - 1)) / 2 - 1 - m, n1 + n2 - 2) } # eva <- eigen(Sw, symmetric = TRUE,EISPACK=TRUE) eva <- eigen(Sw, symmetric = TRUE) pcar <- eva$vectors[, 1:p] pcasd <- sqrt(abs(eva$values[1:p])) if (pcasd[p] < 1e-06) { offset <- 1e-06 cat("*") pcasd <- sqrt(pcasd ^ 2 + offset) } lam <- rep(0, times = kt) lam[1:p] <- 1 / pcasd ^ 2 Suinv <- eva$vectors %*% diag(lam) %*% t(eva$vectors) pcax <- t(pool$tan) %*% pcar one1 <- matrix(1 / n1, n1, 1) one2 <- matrix(1 / n2, n2, 1) oneone <- rbind(one1,-one2) vbar <- pool$tan %*% oneone scores1 <- matrix(vbar, 1, kt) %*% pcar scores <- scores1 / pcasd F.partition <- ((scores[1:p] ^ 2) * (n1 * n2 * (n1 + n2 - p - 1))) / ((n1 + n2) * (n1 + n2 - 2) * p) FF <- sum(F.partition) pval <- 1 - pf(FF, p, (n1 + n2 - p - 1)) z <- list() z$F.partition <- F.partition z$F <- FF z$pval <- pval z$df1 <- p z$T.df1 <- p z$df2 <- (n1 + n2 - p - 1) mm <- n - 2 z$T.df2 <- mm z$Tsq <- FF * (n1 + n2) * (n1 + n2 - 2) * p / (n1 * n2) / (n1 + n2 - p - 1) z$Tsq.partition <- F.partition * (n1 + n2) * (n1 + n2 - 2) * p / (n1 * n2) / (n1 + n2 - p - 1) return(z) } James <- function(pool , n1, n2, p = 0, table = FALSE) { tan1 <- pool$tan[, 1:n1] tan2 <- pool$tan[, (n1 + 1):(n1 + n2)] kt <- dim(tan1)[1] n <- n1 + n2 k <- pool$k m <- pool$m S1 <- var(t(tan1)) S2 <- var(t(tan2)) Sw <- S1 / n1 + S2 / n2 if (p == 0) { p <- min(k * m - (m * (m - 1)) / 2 - 1 - m, n1 + n2 - 2) } # eva <- eigen(Sw, symmetric = TRUE,EISPACK=TRUE) eva <- eigen(Sw, symmetric = TRUE) pcar <- eva$vectors[, 1:p] pcasd <- sqrt(abs(eva$values[1:p])) if (pcasd[p] < 1e-06) { offset <- 1e-06 cat("*") pcasd <- sqrt(pcasd ^ 2 + offset) } lam <- rep(0, times = kt) lam[1:p] <- 1 / pcasd ^ 2 Suinv <- eva$vectors %*% diag(lam) %*% t(eva$vectors) pcax <- t(pool$tan) %*% pcar one1 <- matrix(1 / n1, n1, 1) one2 <- matrix(1 / n2, n2, 1) oneone <- rbind(one1,-one2) vbar <- pool$tan %*% oneone # scores1 <- matrix(vbar, 1, kt ) %*% pcar # scores <- scores1/pcasd # F.partition <- ((scores[1:p]^2) * (n1 * n2 * (n1 + n2 - p - # 1)))/((n1 + n2) * (n1 + n2 - 2) * p) # FF <- sum(F.partition) # pval <- 1 - pf(FF, p, (n1 + n2 - p - 1)) ######### # ginvSw<- pcar%*%diag(1/pcasd**2)%*%t(pcar) ginvSw <- Suinv pval = 0 T1 <- sum(diag((ginvSw %*% S1 / n1))) T2 <- sum(diag((ginvSw %*% S2 / n2))) T1sq <- sum(diag(((ginvSw %*% S1 / n1) %*% ginvSw %*% S1 / n1))) T2sq <- sum(diag(((ginvSw %*% S2 / n2) %*% ginvSw %*% S2 / n2))) Tsq <- (t(vbar) %*% (ginvSw) %*% vbar)[1, 1] if (table == TRUE) { AA <- 1 + 1 / (2 * p) * (T1 ** 2 / (n1 - 1) + T2 ** 2 / (n2 - 1)) BB <- 1 / (p * (p + 2)) * ((T1 ** 2 / 2 + T1sq) / (n1 - 1) + (T2 ** 2 / 2 + T2sq) / (n2 - 1)) kk <- rep(0, times = 1000) for (i in 0:999) { alphai <- i / 1000 kk[i + 1] <- qchisq(alphai, df = p) * (AA + BB * qchisq(alphai, df = p)) } pval <- 1 - max(c(1:1000)[kk < Tsq]) / 1000 } ####### z <- list() z$pval <- pval z$Tsq <- Tsq return(z) } #================================================================================== tpsgrid <- function (TT, YY, xbegin = -999, ybegin = -999, xwidth = -999, opt = 1, ext = 0.1, ngrid = 22, cex = 1, pch = 20, col = 2, zslice = 0, mag = 1, axes3 = FALSE) { k <- nrow(TT) m <- dim(TT)[2] YY <- TT + (YY - TT) * mag bb <- array(TT, c(dim(TT), 1)) aa <- defplotsize2(bb) if (xwidth == -999) { xwidth <- aa$width } if (xbegin == -999) { xbegin <- aa$xl } if (ybegin == -999) { ybegin <- aa$yl } if (m == 3) { zup <- max(TT[, 3]) zlo <- min(TT[, 3]) zpos <- zslice for (ii in 1:length(zslice)) { zpos[ii] <- (zup + zlo) / 2 + (zup - zlo) / 2 * zslice[ii] } } xstart <- xbegin ystart <- ybegin ngrid <- trunc(ngrid / 2) * 2 kx <- ngrid ky <- ngrid - 1 l <- kx * ky step <- xwidth / (kx - 1) r <- 0 X <- rep(0, times = kx) Y2 <- rep(0, times = ky) for (p in 1:kx) { ystart <- ybegin xstart <- xstart + step for (q in 1:ky) { ystart <- ystart + step r <- r + 1 X[r] <- xstart Y2[r] <- ystart } } TPS <- bendingenergy(TT) gamma11 <- TPS$gamma11 gamma21 <- TPS$gamma21 gamma31 <- TPS$gamma31 W <- gamma11 %*% YY ta <- t(gamma21 %*% YY) B <- gamma31 %*% YY WtY <- t(W) %*% YY trace <- c(0) for (i in 1:m) { trace <- trace + WtY[i, i] } benergy <- 16 * pi * trace if (m == 3) { benergy <- 8 * pi * trace } l <- kx * ky phi <- matrix(0, l, m) s <- matrix(0, k, 1) for (islice in 1:length(zslice)) { if (m == 3) { refc <- matrix(c(X, Y2, rep(zpos[islice], times = kx * ky)), kx * ky, m) } if (m == 2) { refc <- matrix(c(X, Y2), kx * ky, m) } for (i in 1:l) { s <- matrix(0, k, 1) for (im in 1:k) { s[im,] <- sigmacov(refc[i,] - TT[im,]) } phi[i,] <- ta + t(B) %*% refc[i,] + t(W) %*% s } if (m == 3) { if (opt == 2) { shapes3d(TT, color = 2, axes3 = axes3, rglopen = FALSE) shapes3d(YY, color = 4, rglopen = FALSE) for (i in 1:k) { lines3d(rbind(TT[i, ], YY[i, ]), col = 1) } for (j in 1:kx) { lines3d(refc[((j - 1) * ky + 1):(ky * j) , ], color = 6) } for (j in 1:ky) { lines3d(refc[(0:(kx - 1) * ky) + j , ], color = 6) } } shapes3d(TT, color = 2, axes3 = axes3, rglopen = FALSE) shapes3d(YY, color = 4, rglopen = FALSE) for (i in 1:k) { lines3d(rbind(TT[i, ], YY[i, ]), col = 1) } for (j in 1:kx) { lines3d(phi[((j - 1) * ky + 1):(ky * j) , ], color = 3) } for (j in 1:ky) { lines3d(phi[(0:(kx - 1) * ky) + j , ], color = 3) } } } if (m == 2) { par(pty = "s") if (opt == 2) { par(mfrow = c(1, 2)) order <- linegrid(refc, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth * ext, xbegin + xwidth * (1 + ext)), ylim = c( ybegin - (xwidth * ky) / kx * ext, ybegin + (xwidth * ky) / kx * (1 + ext) ), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(TT, cex = cex, pch = pch, col = col) } order <- linegrid(phi, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth * ext, xbegin + xwidth * (1 + ext)), ylim = c(ybegin - (xwidth * ext * ky) / kx, ybegin + (xwidth * (1 + ext) * ky) / kx), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(YY, cex = cex, pch = pch, col = col + 1) points(TT, cex = cex, pch = pch, col = col) for (i in 1:(k)) { arrows( TT[i, 1], TT[i, 2] , YY[i, 1], YY[i, 2] , col = col + 2, length = 0.1, angle = 20 ) } } } # #================================================================================== rotatexyz <- function(x, thetax, thetay, thetaz) { thetax <- thetax / 180 * pi thetay <- thetay / 180 * pi thetaz <- thetaz / 180 * pi Rx <- matrix(c( 1, 0, 0, 0, cos(thetax), sin(thetax), 0, -sin(thetax), cos(thetax) ), 3, 3) Ry <- matrix(c( cos(thetay), 0, sin(thetay), 0, 1, 0, -sin(thetay), 0, cos(thetay) ), 3, 3) Rz <- matrix(c( cos(thetaz), sin(thetaz), 0, -sin(thetaz), cos(thetaz), 0, 0, 0, 1 ), 3, 3) y <- x n <- dim(x)[3] for (i in 1:n) { y[, , i] <- x[, , i] %*% Rx %*% Ry %*% Rz } y } #================================================================================== rigidbody <- function(X, transx = 0, transy = 0, transz = 0, thetax = 0, thetay = 0, thetaz = 0) { if (is.matrix(X)) { X <- array(X, c(dim(X), 1)) } m <- dim(X)[2] n <- dim(X)[3] Y <- X if (m == 2) { #xx<-as.3d(X) if (dim(X)[3] < 2) { xx <- array(as.3d(X), dim = c(nrow(X), 3, 1)) } else{ xx <- as.3d(X) } for (i in 1:n) { for (j in 1:m) { xx[j, , i] <- xx[j, , i] - c(transx, transy, transz) } } yy <- rotatexyz(xx, thetax, thetay, thetaz) Y <- yy if ((sum(abs(yy[, 3, ]))) < 0.000000001) { Y <- yy[, 1:2, ] } } if (m == 3) { for (i in 1:n) { for (j in 1:m) { X[j, , i] <- X[j, , i] - c(transx, transy, transz) } } Y <- rotatexyz(X, thetax, thetay, thetaz) } Y } #================================================================================== as.3d <- function(X) { k <- dim(X)[1] if (is.matrix(X)) { X <- array(X, c(dim(X), 1)) } n <- dim(X)[3] if (dim(X)[2] != 2) { print("not 2 dimensional!") } Y <- array(0, c(k, 3, n)) Y[, 1:2, ] <- X if (n == 1) { Y <- Y[, , 1] } Y } #================================================================================== abind <- function(X1 , X2) { k <- dim(X1)[1] m <- dim(X1)[2] if (is.matrix(X1)) { tem <- array(0, c(k, m, 1)) tem[, , 1] <- X1 X1 <- tem } if (is.matrix(X2)) { tem <- array(0, c(k, m, 1)) tem[, , 1] <- X2 X2 <- tem } n1 <- dim(X1)[3] n2 <- dim(X2)[3] Y <- array(0, c(k, m, n1 + n2)) Y[, , 1:n1] <- X1 Y[, , (n1 + 1):(n1 + n2)] <- X2 Y } #================================================================================== shapes3d <- function(x, loop = 0, type = "p", color = 2, joinline = c(1:1), axes3 = FALSE, rglopen = TRUE) { if (is.matrix(x)) { xt <- array(0, c(dim(x), 1)) xt[, , 1] <- x x <- xt } if (is.array(x) == FALSE) { cat("Data not in right format : require an array \n") } if (is.array(x) == TRUE) { if (rglopen) { rgl.open() rgl.bg(color = "white") } if (dim(x)[2] == 2) { x <- as.3d(x) } if (loop == 0) { k <- dim(x)[1] sz <- centroid.size(x[, , 1]) / sqrt(k) / 30 plotshapes3d( x, type = type, color = color, size = sz, joinline = joinline ) if (axes3) { axes3d(color = "black") title3d( xlab = "x", ylab = "y", zlab = "z", color = "black" ) } } if (loop > 0) { for (i in 1:loop) { plotshapestime3d(x, type = type) } } } } #================================================================================== plotshapes3d <- function (x, type = "p", rgl = TRUE, color = 2, size = 1, joinline = c(1:1)) { k <- dim(x)[1] n <- dim(x)[3] y <- matrix(0, k * n, 3) for (i in 1:n) { y[(i - 1) * k + (1:k),] <- x[, , i] } if (rgl == FALSE) { par(mfrow = c(1, 1)) out <- defplotsize3(x) xl <- out$xl xu <- out$xu yl <- out$yl yu <- out$yu zl <- out$zl zu <- out$zu scatterplot3d( y, xlim = c(xl, xu), ylim = c(yl, yu), zlim = c(zl, zu), xlab = "x", ylab = "y", zlab = "z", axis = TRUE, type = type, color = color, highlight.3d = TRUE ) } if (rgl == TRUE) { if (type == "l") { points3d(y, col = color, size = size) for (j in 1:n) { lines3d(x[, , j], col = 8) } } if (type == "dots") { points3d(y, col = color, size = size) } if (type == "p") { spheres3d(y, col = color, radius = size) } if (length(joinline) > 1) { for (j in 1:n) { lines3d(x[joinline, , j], col = 8) } } } } #================================================================================== plotshapestime3d <- function (x, type = "p") { par(mfrow = c(1, 1)) out <- defplotsize3(x) xl <- out$xl xu <- out$xu yl <- out$yl yu <- out$yu zl <- out$zl zu <- out$zu n <- dim(x)[3] for (i in 1:n) { scatterplot3d( x[, , i], xlim = c(xl, xu), ylim = c(yl, yu), zlim = c(zl, zu), xlab = "x", ylab = "y", zlab = "z", axis = TRUE, type = type, highlight.3d = TRUE ) title(i) } } #================================================================================== plotPDMnoaxis3 <- function (mean, pc, sd, xl, xu, yl, yu, lineorder, i) { fig <- mean + i * pc * sd k <- length(mean) / 2 figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] plot( figx, figy, axes = FALSE, xlab = " ", ylab = " ", ylim = c(yl, yu), type = "n", xlim = c(xl, xu) ) text(figx, figy, 1:k) lines(figx[lineorder], figy[lineorder]) for (aa in 1:9999) { aaa <- 1 } } ################################# #================================================================================== shapepca <- function (proc, pcno = c(1, 2, 3), type = "r", mag = 1, joinline = c(1, 1), project = c(1, 2), scores3d = FALSE, color = 2, axes3 = FALSE, rglopen = TRUE, zslice = 0) { if (scores3d == TRUE) { axes3 <- TRUE sz <- max(proc$rawscores[, max(pcno)]) - min(proc$rawscores[, max(pcno)]) spheres3d(proc$rawscores[, pcno] , radius = sz / 30, col = color) if (axes3) { axes3d() } } m <- dim(proc$mshape)[2] k <- dim(proc$mshape)[1] if (scores3d == FALSE) { if ((m == 2)) { out <- defplotsize2(proc$rotated, project = project) xl <- out$xl yl <- out$yl width <- out$width plotpca(proc, pcno, type, mag, xl, yl, width, joinline, project) } if ((m == 3) && (type == "m")) { # plot3Dmean(proc) # cat("Mean shape \n") # for (i in 1:length(pcno)) { # cat("PC ", pcno[i], " \n") # plot3Dpca(proc, pcno[i]) # } for (i in 1:length(pcno)) { cat("PC ", pcno[i], " \n") plotpca3d(proc, pcno[i]) } } ## correct length of tangent vector if in Helmertized space h <- defh(k - 1) zero <- matrix(0, k - 1, k) H <- cbind(h, zero, zero) H1 <- cbind(zero, h, zero) H2 <- cbind(zero, zero, h) H <- rbind(H, H1, H2) if (dim(proc$pcar)[1] == (3 * (k - 1))) { pcarot <- (t(H) %*% proc$pcar) proc$pcar <- pcarot } if (((m == 3) && (type != "m")) && (type != "g")) { if (rglopen) { rgl.open() rgl.bg(color = "white") } sz <- centroid.size(proc$mshape) / sqrt(k) / 30 spheres3d(proc$mshape, radius = sz, col = color) if (axes3) { axes3d() } for (i in pcno) { pc <- proc$mshape + 3 * mag * proc$pcasd[i] * cbind(proc$pcar[1:k, i], proc$pcar[(k + 1):(2 * k), i], proc$pcar[(2 * k + 1):(3 * k), i]) for (j in 1:k) { lines3d(rbind(proc$mshape[j, ], pc[j, ]), col = i) } } } } if ((m == 3) && (type == "g")) { if (rglopen) { rgl.open() rgl.bg(color = "white") } for (i in pcno) { pc <- proc$mshape + 3 * mag * proc$pcasd[i] * cbind(proc$pcar[1:k, i], proc$pcar[(k + 1):(2 * k), i], proc$pcar[(2 * k + 1):(3 * k), i]) tpsgrid(proc$mshape, pc, zslice = zslice) } } } #================================================================================== plotpca3d <- function (procreg, pcno = 1) { par(mfrow = c(1, 1)) out <- defplotsize3(procreg$rotated) xl <- out$xl xu <- out$xu yl <- out$yl yu <- out$yu zl <- out$zl zu <- out$zu k <- dim(procreg$mshape)[1] subx <- 1:k suby <- (k + 1):(2 * k) subz <- (2 * k + 1):(3 * k) evec <- cbind(procreg$pcar[subx, pcno], procreg$pcar[suby, pcno], procreg$pcar[subz, pcno]) for (j in 1:10) { for (ii in-12:12) { mag <- ii / 4 scatterplot3d( procreg$mshape + mag * evec * procreg$pcasd[pcno], xlim = c(xl, xu), ylim = c(yl, yu), zlim = c(zl, zu), xlab = "x", ylab = "y", zlab = "z", axis = TRUE, highlight.3d = TRUE ) title(pcno) } for (ii in-11:11) { mag <- -ii / 4 scatterplot3d( procreg$mshape + mag * evec * procreg$pcasd[pcno], xlim = c(xl, xu), ylim = c(yl, yu), zlim = c(zl, zu), xlab = "x", ylab = "y", zlab = "z", axis = TRUE ) title(pcno) } } } ############################## #================================================================================== Hotelling2Djames <- function (A, B) { z <- list(Tsq = 0, pval = 0) n1 <- dim(A)[3] n2 <- dim(B)[3] n <- n1 + n2 k <- dim(A)[1] m <- dim(B)[2] if (m != 2) { print("Data not two dimensional") return(z) } else { pool <- array(0, c(k, m, n)) pool[, , 1:n1] <- A pool[, , (n1 + 1):n] <- B poolpr <- procrustes2d(pool, 1, 2) S1 <- var(t(poolpr$tan[, 1:n1])) S2 <- var(t(poolpr$tan[, (n1 + 1):(n1 + n2)])) gamma <- realtocomplex(preshape(poolpr$mshape)) Sw <- (S1 / n1 + S2 / n2) p <- 2 * k - 4 # TT<-eigen(Sw,symmetric=TRUE,EISPACK=TRUE) TT <- eigen(Sw, symmetric = TRUE) pcar <- TT$vectors[, 1:p] pcasd <- sqrt(abs(TT$values[1:p])) ####### add small offset if defecient in rank if (pcasd[p] < 0.000001) { offset <- 0.000001 cat("*") pcasd <- sqrt(pcasd ** 2 + offset ** 2) } ####################################### pcax <- t(poolpr$tan) %*% pcar h <- defh(k - 1) zero <- matrix(0, k - 1, k) H <- cbind(h, zero) H1 <- cbind(zero, h) H <- rbind(H, H1) meanxy <- t(H) %*% V(gamma) realrot <- t(H) %*% pcar one1 <- matrix(1 / n1, n1, 1) one2 <- matrix(1 / n2, n2, 1) oneone <- rbind(one1,-one2) vbar <- poolpr$tan %*% oneone scores1 <- matrix(vbar, 1, (2 * k - 2)) %*% pcar scores <- scores1 / pcasd F.partition <- ((scores[1:p] ^ 2) * (n1 * n2 * (n1 + n2 - p - 1))) / ((n1 + n2) * (n1 + n2 - 2) * p) FF <- sum(F.partition) pval <- 1 - pf(FF, p, (n1 + n2 - p - 1)) ginvSw <- pcar %*% diag(1 / pcasd ** 2) %*% t(pcar) T1 <- sum(diag((ginvSw %*% S1 / n1))) T2 <- sum(diag((ginvSw %*% S2 / n2))) T1sq <- sum(diag(( (ginvSw %*% S1 / n1) %*% ginvSw %*% S1 / n1 ))) T2sq <- sum(diag(( (ginvSw %*% S2 / n2) %*% ginvSw %*% S2 / n2 ))) Tsq <- (t(vbar) %*% (ginvSw) %*% vbar)[1, 1] AA <- 1 + 1 / (2 * p) * (T1 ** 2 / (n1 - 1) + T2 ** 2 / (n2 - 1)) BB <- 1 / (p * (p + 2)) * ((T1 ** 2 / 2 + T1sq) / (n1 - 1) + (T2 ** 2 / 2 + T2sq) / (n2 - 1)) kk <- rep(0, times = 1000) for (i in 0:999) { alphai <- i / 1000 kk[i + 1] <- qchisq(alphai, df = p) * (AA + BB * qchisq(alphai, df = p)) } pval <- 1 - max(c(1:1000)[kk < Tsq]) / 1000 # z$F.partition <- F.partition # z$F <- FF z$pval <- pval z$Tsq <- Tsq # z$df1 <- p # z$T.df1 <- p # z$df2 <- (n1 + n2 - p - 1) # mm <- n - 2 # z$T.df2 <- mm # z$Tsq <- FF * (n1 + n2) * (n1 + n2 - 2) * p/(n1 * n2)/(n1 + # n2 - p - 1) # z$Tsq.partition <- F.partition * (n1 + n2) * (n1 + n2 - # 2) * p/(n1 * n2)/(n1 + n2 - p - 1) return(z) } } MGM <- function(zst) { nsam <- dim(zst)[2] k <- dim(zst)[1] Mhat <- matrix(0, k - 1, k - 2) lamhat <- rep(0, times = (k - 1)) Sighat <- matrix(0, k - 2, k - 2) kk <- k * 2 - 2 t1 <- reassqpr(preshape(zst)) / nsam # t2 <- eigen(t1,symmetric=TRUE,EISPACK=TRUE) t2 <- eigen(t1, symmetric = TRUE) reagamma <- (t2$vectors[, 1] + t2$vectors[, 2]) / sqrt(2) gamma <- Vinv(reagamma) muhat <- gamma for (i in 1:(k - 2)) { Mhat[, i] <- Vinv(t2$vectors[, 1 + (2 * i)]) } for (i in 1:(k - 1)) { lamhat[i] <- t2$values[(2 * i) - 1] } for (j in 2:(k - 1)) { for (l in 2:(k - 1)) { sum <- 0 for (i in 1:nsam) { zi <- preshape(zst[, i]) sum <- sum + st(Mhat[, j - 1]) %*% zi * st(zi) %*% Mhat[, l - 1] * st(zi) %*% muhat * st(muhat) %*% zi } Sighat[j - 1, l - 1] <- 1 / (lamhat[1] - lamhat[j]) / (lamhat[1] - lamhat[l]) * sum / nsam } } SR <- Re(Sighat) SI <- Im(Sighat) S1 <- cbind(SR, SI) S2 <- cbind(-(SI), SR) S <- rbind(S1, S2) offset <- 0 #es<-eigen(S,symmetric=TRUE,EISPACK=TRUE)$values es <- eigen(S, symmetric = TRUE)$values nn <- length(es) if (es[nn] < 0.000001) { offset <- 0.000001 #cat("Warning: test: small samples, lambda I added to within group covariance matrix \n") cat("*") } invS <- solve(S + offset * diag(nn)) invSR <- invS[1:(k - 2), 1:(k - 2)] invSI <- invS[1:(k - 2), (k - 1):(2 * k - 4)] invS <- invSR + 1i * invSI Mhat <- st(Mhat) MGM <- st(Mhat) %*% invS %*% Mhat MGM } #====================================================================================== resampletest <- function(A, B, resamples = 200, replace = TRUE) { A1 <- A A2 <- B B <- resamples k <- dim(A1)[1] m <- dim(A1)[2] nmin <- min(dim(A1)[3], dim(A2)[3]) ntot <- dim(A1)[3] + dim(A2)[3] M <- (k - 1) * m - m * (m - 1) / 2 - 1 if (M >= ntot) { cat("Warning: Low sample size (n1 + n2 <= p) \n") } if ((M >= nmin) && (replace == TRUE)) { cat( "Warning: Low sample sizes : min(n1,n2)<=p : * indicates some regularization carried out \n" ) } permutation <- !replace if (is.complex(A1)) { tem <- array(0, c(nrow(A1), 2, ncol(A1))) tem[, 1,] <- Re(A1) tem[, 2,] <- Im(A1) A1 <- tem } if (is.complex(A2)) { tem <- array(0, c(nrow(A2), 2, ncol(A2))) tem[, 1,] <- Re(A2) tem[, 2,] <- Im(A2) A2 <- tem } m <- dim(A1)[2] if (m != 2) { print("Data not two dimensional") print("Carrying out tests on Procrustes residuals") out <- testmeanshapes(A1, A2, resamples = resamples, replace = replace) return(out) } zst1 <- A1[, 1, ] + 1i * A1[, 2, ] zst2 <- A2[, 1, ] + 1i * A2[, 2, ] nsam1 <- dim(zst1)[2] nsam2 <- dim(zst2)[2] k <- dim(zst1)[1] LL <- (MGM(zst1) + MGM(zst2)) * (nsam1 + nsam2) LL1 <- cbind(Re(LL), Im(LL)) LL2 <- cbind(-Im(LL), Re(LL)) LL <- rbind(LL1, LL2) #Tumc<-min(eigen(LL,symmetric=TRUE,only.values=TRUE,EISPACK=TRUE)$values) Tumc <- min(eigen(LL, symmetric = TRUE, only.values = TRUE)$values) m1 <- preshape(procrustes2d(zst1)$mshape) m1 <- m1[, 1] + 1i * m1[, 2] m2 <- preshape(procrustes2d(zst2)$mshape) m2 <- m2[, 1] + 1i * m2[, 2] m0 <- preshape(procrustes2d(cbind(zst1, zst2))$mshape) m0 <- m0[, 1] + 1i * m0[, 2] d <- length(m1) H <- defh(k - 1) b <- m1 a <- m0 bt <- b * c((st(b) %*% a) / Mod(st(b) %*% a)) abt <- c(Re(st(bt) %*% a)) ct <- (bt - a * abt) # ct <- ct / sqrt(st(ct) %*% ct) ct <- ct / c(sqrt(st(as.vector(ct)) %*% as.vector(ct))) At <- a %*% st(ct) - ct %*% st(a) salph <- sqrt(1 - abt ** 2) calph <- abt Id <- diag(rep(1, times = d)) U1 <- Id + salph * At + (calph - 1) * (a %*% st(a) + ct %*% st(ct)) b <- m2 a <- m0 bt <- b * c((st(b) %*% a) / Mod(st(b) %*% a)) abt <- c(Re(st(bt) %*% a)) ct <- (bt - a * abt) # ct <- ct / sqrt(st(ct) %*% ct) ct <- ct / c(sqrt(st(as.vector(ct)) %*% as.vector(ct))) At <- a %*% st(ct) - ct %*% st(a) salph <- sqrt(1 - abt ** 2) calph <- abt Id <- diag(rep(1, times = d)) U2 <- Id + salph * At + (calph - 1) * (a %*% st(a) + ct %*% st(ct)) yst1 <- t(H) %*% U1 %*% preshape(zst1) yst2 <- t(H) %*% U2 %*% preshape(zst2) ybind <- cbind(yst1, yst2) zr1 <- array(0, c(k, 2, nsam1)) zr2 <- array(0, c(k, 2, nsam2)) zr3 <- array(0, c(k, 2, nsam1 + nsam2)) zr1[, 1, ] <- Re(zst1) zr1[, 2, ] <- Im(zst1) zr2[, 1, ] <- Re(zst2) zr2[, 2, ] <- Im(zst2) zr3[, 1, ] <- cbind(Re(zst1), Re(zst2)) zr3[, 2, ] <- cbind(Im(zst1), Im(zst2)) yr1 <- array(0, c(k, 2, nsam1)) yr2 <- array(0, c(k, 2, nsam2)) yr3 <- array(0, c(k, 2, nsam1 + nsam2)) yr1[, 1, ] <- Re(yst1) yr1[, 2, ] <- Im(yst1) yr2[, 1, ] <- Re(yst2) yr2[, 2, ] <- Im(yst2) yr3[, 1, ] <- cbind(Re(yst1), Re(yst2)) yr3[, 2, ] <- cbind(Im(yst1), Im(yst2)) Gtem <- Goodall2D(zr1, zr2) Htem <- Hotelling2D(zr1, zr2) Jtem <- Hotelling2Djames(zr1, zr2) Gumc <- Gtem$F Humc <- Htem$F Jumc <- Jtem$Tsq Gtabpval <- Gtem$pval Htabpval <- Htem$pval Jtabpval <- Jtem$pval if (B > 0) { Tu <- rep(0, times = B) Gu <- Tu Hu <- Tu Ju <- Tu cat("Resampling...") cat(c("No of resamples = ", B, "\n")) if (permutation) { cat("Permutations - sampling without replacement \n") } if (permutation == FALSE) { cat("Bootstrap - sampling with replacement \n") } for (i in 1:B) { cat(c(i, " ")) select1 <- sample(1:nsam1, replace = TRUE) select2 <- sample(1:nsam2, replace = TRUE) zb1 <- yst1[, select1] zb2 <- yst2[, select2] zbgh1 <- yr1[, , select1] zbgh2 <- yr2[, , select2] if (permutation) { select0 <- sample(c(1:(nsam1 + nsam2)), (nsam1 + nsam2), replace = FALSE) select1 <- select0[1:nsam1] select2 <- select0[(nsam1 + 1):(nsam1 + nsam2)] zb1 <- zr3[, 1, select1] + 1i * zr3[, 2, select1] zb2 <- zr3[, 1, select2] + 1i * zr3[, 2, select2] zbgh1 <- yr3[, , select1] zbgh2 <- yr3[, , select2] } LL <- (MGM(zb1) + MGM(zb2)) * (nsam1 + nsam2) LL1 <- cbind(Re(LL), Im(LL)) LL2 <- cbind(-Im(LL), Re(LL)) LL <- rbind(LL1, LL2) #lmin<-min(eigen(LL,symmetric=TRUE,only.values=TRUE,EISPACK=TRUE)$values) lmin <- min(eigen(LL, symmetric = TRUE, only.values = TRUE)$values) Tu[i] <- lmin Gu[i] <- Goodall2D(zbgh1, zbgh2)$F Hu[i] <- Hotelling2D(zbgh1, zbgh2)$F Ju[i] <- Hotelling2Djames(zbgh1, zbgh2)$Tsq } Tu <- sort(Tu) numbig <- length(Tu[Tumc < Tu]) pvalb <- (1 + numbig) / (B + 1) Gu <- sort(Gu) numbig <- length(Gu[Gumc < Gu]) pvalG <- (1 + numbig) / (B + 1) Hu <- sort(Hu) numbig <- length(Hu[Humc < Hu]) pvalH <- (1 + numbig) / (B + 1) Ju <- sort(Ju) numbig <- length(Ju[Jumc < Ju]) pvalJ <- (1 + numbig) / (B + 1) cat(" \n") out <- list( lambda = 0, lambda.pvalue = 0, lambda.table.pvalue = 0, H = 0, H.pvalue = 0, H.table.pvalue = 0, J = 0, J.pvalue = 0, J.table.pvalue = 0, G = 0, G.pvalue = 0, G.table.pvalue = 0 ) out$lambda <- Tumc out$lambda.pvalue <- pvalb out$lambda.table.pvalue <- 1 - pchisq(Tumc, 2 * k - 4) out$H <- Humc out$H.pvalue <- pvalH out$H.table.pvalue <- Htabpval out$J <- Jumc out$J.pvalue <- pvalJ out$J.table.pvalue <- Jtabpval out$G <- Gumc out$G.pvalue <- pvalG out$G.table.pvalue <- Gtabpval } if (resamples == 0) { out <- list( lambda = 0, lambda.table.pvalue = 0, H = 0, H.table.pvalue = 0, J = 0, J.table.pvalue = 0, G = 0, G.table.pvalue = 0 ) out$lambda <- Tumc out$lambda.table.pvalue <- 1 - pchisq(Tumc, 2 * k - 4) out$H <- Humc out$H.table.pvalue <- Htabpval out$J <- Jumc out$J.table.pvalue <- Jtabpval out$G <- Gumc out$G.table.pvalue <- Gtabpval } out } #================================================================================== prcomp1 <- function (x, retx = TRUE, center = TRUE, scale. = FALSE, tol = NULL, svd = TRUE) { x <- as.matrix(x) x <- scale(x, center = center, scale = scale.) if (svd == FALSE) { a <- eigen(cov(x)) r <- list(sdev = 0, rotation = 0, x = 0) r$sdev <- sqrt(abs(a$values)) r$rotation <- a$vectors r$x <- x %*% a$vectors } else { s <- svd(x, nu = 0) if (!is.null(tol)) { rank <- sum(s$d > (s$d[1] * tol)) if (rank < ncol(x)) s$v <- s$v[, 1:rank, drop = FALSE] } s$d <- s$d / sqrt(max(1, nrow(x) - 1)) dimnames(s$v) <- list(colnames(x), paste("PC", seq(len = ncol(s$v)), sep = "")) r <- list(sdev = s$d, rotation = s$v) if (retx) r$x <- x %*% s$v class(r) <- "prcomp1" } r } #================================================================================== defplotsize3 <- function(Y) { out <- list( xl = 0, yl = 0, zl = 0, xu = 0, yu = 0, zu = 0, width = 0 ) n <- dim(Y)[3] xm <- mean(Y[, 1,]) ym <- mean(Y[, 2,]) zm <- mean(Y[, 3,]) x <- Y x[, 1,] <- Y[, 1,] - xm x[, 2,] <- Y[, 2,] - ym x[, 3,] <- Y[, 3,] - zm mn1 <- min(x[, 1, ]) mn2 <- min(x[, 2, ]) mn3 <- min(x[, 3, ]) mx1 <- max(x[, 1, ]) mx2 <- max(x[, 2, ]) mx3 <- max(x[, 3, ]) xl <- -max(-mn1, mx1) yl <- -max(-mn2, mx2) zl <- -max(-mn3, mx3) width <- max(-2 * xl,-2 * yl,-2 * zl) out$xl <- -width / 2 * 1.2 + xm out$yl <- -width / 2 * 1.2 + ym out$zl <- -width / 2 * 1.2 + zm out$xu <- width / 2 * 1.2 + xm out$yu <- width / 2 * 1.2 + ym out$zu <- width / 2 * 1.2 + zm out$width <- width * 1.2 out } #================================================================================== procOPA <- function(A, B, scale = TRUE, reflect = FALSE) { out <- list( R = 0, s = 0, Ahat = 0, Bhat = 0, OSS = 0, rmsd = 0 ) if (is.complex(sum(A)) == TRUE) { k <- length(A) Areal <- matrix(0, k, 2) Areal[, 1] <- Re(A) Areal[, 2] <- Im(A) A <- Areal } if (is.complex(sum(B)) == TRUE) { k <- length(B) Breal <- matrix(0, k, 2) Breal[, 1] <- Re(B) Breal[, 2] <- Im(B) B <- Breal } k <- dim(A)[1] if (reflect == FALSE) { R <- fort.ROTATION(A, B) } else { R <- fort.ROTATEANDREFLECT(A, B) } s <- 1 if (scale == TRUE) { s <- fos(A, B) if (reflect == TRUE) { s <- fos.REFLECT(A, B) } } Ahat <- fcnt(A) Bhat <- fcnt(B) %*% R * s resid <- Ahat - Bhat OSS <- sum(diag(t(resid) %*% resid)) out$R <- R out$s <- s out$Ahat <- Ahat out$Bhat <- Bhat m <- dim(Ahat)[2] out$OSS <- OSS out$rmsd <- sqrt(OSS / (k)) out } #================================================================================== defplotsize2 <- function(Y, project = c(1, 2)) { out <- list( xl = 0, yl = 0, xu = 0, yu = 0, width = 0 ) n <- dim(Y)[3] xm <- mean(Y[, project[1],]) ym <- mean(Y[, project[2],]) x <- Y x[, project[1],] <- Y[, project[1],] - xm x[, project[2],] <- Y[, project[2],] - ym out <- list(xl = 0, yl = 0, width = 0) mn1 <- min(x[, project[1], ]) mn2 <- min(x[, project[2], ]) mx1 <- max(x[, project[1], ]) mx2 <- max(x[, project[2], ]) xl <- -max(-mn1, mx1) yl <- -max(-mn2, mx2) width <- max(-2 * xl,-2 * yl) out$xl <- -width / 2 * 1.2 + xm out$yl <- -width / 2 * 1.2 + ym out$xu <- width / 2 * 1.2 + xm out$yu <- width / 2 * 1.2 + ym out$width <- width * 1.2 out } #================================================================================== plotshapes <- function(A, B = 0, joinline = c(1, 1), orthproj = c(1, 2), color = 1, symbol = 1) { CHECKOK <- TRUE if (is.array(A) == FALSE) { if (is.matrix(A) == FALSE) { cat("Error !! argument should be an array or matrix \n") CHECKOK <- FALSE } } if (CHECKOK) { k <- dim(A)[1] m <- dim(A)[2] kk <- k if (k >= 15) { kk <- 1 } par(pty = "s") #if (length(c(B))==1){ #par(mfrow=c(1,1)) #} if (length(c(B)) != 1) { par(mfrow = c(1, 2)) } if (length(dim(A)) == 3) { A <- A[, orthproj, ] } if (is.matrix(A) == TRUE) { a <- array(0, c(k, 2, 1)) a[, , 1] <- A[, orthproj] A <- a } out <- defplotsize2(A) width <- out$width if (length(c(B)) != 1) { if (length(dim(B)) == 3) { B <- B[, orthproj, ] } if (is.matrix(B) == TRUE) { a <- array(0, c(k, 2, 1)) a[, , 1] <- B[, orthproj] B <- a } ans <- defplotsize2(B) width <- max(out$width, ans$width) } n <- dim(A)[3] lc <- length(color) lt <- k * m * n / lc color <- rep(color, times = lt) lc <- length(symbol) lt <- k * m * n / lc symbol <- rep(symbol, times = lt) plot( A[, , 1], xlim = c(out$xl, out$xl + width), ylim = c(out$yl, out$yl + width), type = "n", xlab = " ", ylab = " " ) for (i in 1:n) { select <- ((i - 1) * k * m + 1):(i * k * m) points(A[, , i], pch = symbol[select], col = color[select]) lines(A[joinline, , i]) } if (length(c(B)) != 1) { A <- B if (is.matrix(A) == TRUE) { a <- array(0, c(k, 2, 1)) a[, , 1] <- A A <- a } out <- defplotsize2(A) n <- dim(A)[3] plot( A[, , 1], xlim = c(ans$xl, ans$xl + width), ylim = c(ans$yl, ans$yl + width), type = "n", xlab = " ", ylab = " " ) for (i in 1:n) { points(A[, , i], pch = symbol[select], col = color[select]) lines(A[joinline, , i]) } } } } #================================================================================== BoxM <- function(A, B, npc) { #carries out Box's M test #(see Mardia, Kent, Bibby 1979, p140) #in: data arrays A, B #out: z$M M statistic # z$df degrees of freedom for approx distn of chi-squared statistic # z$pval p-value z <- list(M = 0, df = 0, pval = 0) n1 <- dim(A)[3] n2 <- dim(B)[3] k <- dim(A)[1] m <- dim(A)[2] if (m > 2) { print("Only works for 2D data at the moment!") } if (m == 2) { C <- array(0, c(k, m, n1 + n2)) C[, , 1:n1] <- A C[, , (n1 + 1):(n1 + n2)] <- B Cpr <- procrustes2d(C, 1, 2) p <- npc ng <- 2 n <- n1 + n2 S1 <- var(t(Cpr$tan[1:npc, 1:n1])) S2 <- var(t(Cpr$tan[1:npc, (n1 + 1):(n1 + n2)])) Su <- ((n1 - 1) * S1 + (n2 - 1) * S2) / (n1 + n2 - 2) S1inv <- eigen(S1)$vectors %*% diag(1 / eigen(S1)$values) %*% t(eigen(S1)$vectors) S2inv <- eigen(S2)$vectors %*% diag(1 / eigen(S2)$values) %*% t(eigen(S2)$vectors) logdet1 <- sum(log(eigen(S1inv %*% Su)$values)) logdet2 <- sum(log(eigen(S2inv %*% Su)$values)) gam <- 1 - ((2 * p ^ 2 + 3 * p - 1) / (6 * (p + 1) * (ng - 1))) * (1 / (n1 - 1) + 1 / (n2 - 1) - 1 / (n - ng)) M <- gam * ((n1 - 1) * logdet1 + (n2 - 1) * logdet2) df <- (p * (p + 1) * (ng - 1)) / 2 pval <- 1 - pchisq(M, df) z$M <- M z$df <- df z$pval <- pval } return(z) } #================================================================================== Goodall2D <- function(A, B) { #Calculates Goodall's two sample F test for 2d data only #in: data arrays A, B k x 2 x n data arrays #out: z$F F statistic # z$df1, z$df2 degrees of freedom # z$pval: p-value z <- list( F = 0, pval = 0, df1 = 0, df2 = 0 ) n1 <- dim(A)[3] n2 <- dim(B)[3] k <- dim(A)[1] m <- dim(A)[2] if (m != 2) { print("Data not two dimensional") return(z) } p <- 2 * k - 4 Apr <- procrustes2d(A, 1, 2) Bpr <- procrustes2d(B, 1, 2) top <- sin(riemdist(Apr$mshape, Bpr$mshape)) ^ 2 bot <- Apr$rmsd1 ^ 2 * n1 + Bpr$rmsd1 ^ 2 * n2 Fstat <- ((n1 + n2 - 2) / (1 / n1 + 1 / n2) * top) / bot pval <- 1 - pf(Fstat, p, (n1 + n2 - 2) * p) z$F <- Fstat z$pval <- pval z$df1 <- p z$df2 <- (n1 + n2 - 2) * p return(z) } #================================================================================== Goodalltest <- function(A, B, tol1 = 1e-07, tol2 = tol1) { #Calculates Goodall's two sample F test #in: data arrays A, B: #out: z$F F statistic # z$df1, z$df2 degrees of freedom # z$pval: p-value z <- list( F = 0, pval = 0, df1 = 0, df2 = 0 ) n1 <- dim(A)[3] n2 <- dim(B)[3] k <- dim(A)[1] m <- dim(A)[2] p <- min(k * m - (m * (m - 1)) / 2 - 1 - m, n1 + n2 - 2) Apr <- procrustesGPA(A, tol1, tol2) Bpr <- procrustesGPA(B, tol1, tol2) top <- sin(riemdist(Apr$mshape, Bpr$mshape)) ^ 2 bot <- Apr$rmsd1 ^ 2 * n1 + Bpr$rmsd1 ^ 2 * n2 Fstat <- ((n1 + n2 - 2) / (1 / n1 + 1 / n2) * top) / bot pval <- 1 - pf(Fstat, p, (n1 + n2 - 2) * p) z$F <- Fstat z$pval <- pval z$df1 <- p z$df2 <- (n1 + n2 - 2) * p return(z) } #================================================================================== Hotelling2D <- function (A, B) { z <- list( Tsq.partition = 0, Tsq = 0, F.partition = 0, F = 0, pval = 0, df1 = 0, df2 = 0, T.df1 = 0, T.df2 = 0 ) n1 <- dim(A)[3] n2 <- dim(B)[3] n <- n1 + n2 k <- dim(A)[1] m <- dim(B)[2] if (m != 2) { print("Data not two dimensional") return(z) } else { pool <- array(0, c(k, m, n)) pool[, , 1:n1] <- A pool[, , (n1 + 1):n] <- B poolpr <- procrustes2d(pool, 1, 2) S1 <- var(t(poolpr$tan[, 1:n1])) S2 <- var(t(poolpr$tan[, (n1 + 1):(n1 + n2)])) gamma <- realtocomplex(preshape(poolpr$mshape)) Sw <- ((n1 - 1) * S1 + (n2 - 1) * S2) / (n1 + n2 - 2) p <- 2 * k - 4 # pcar <- eigen(Sw,EISPACK=TRUE)$vectors[, 1:p] pcar <- eigen(Sw)$vectors[, 1:p] pcasd <- sqrt(abs(eigen(Sw)$values[1:p])) ####### add small offset if defecient in rank if (pcasd[p] < 0.000001) { offset <- 0.000001 cat("*") pcasd <- sqrt(pcasd ** 2 + offset ** 2) } ####################################### pcax <- t(poolpr$tan) %*% pcar h <- defh(k - 1) zero <- matrix(0, k - 1, k) H <- cbind(h, zero) H1 <- cbind(zero, h) H <- rbind(H, H1) meanxy <- t(H) %*% V(gamma) realrot <- t(H) %*% pcar one1 <- matrix(1 / n1, n1, 1) one2 <- matrix(1 / n2, n2, 1) oneone <- rbind(one1,-one2) vbar <- poolpr$tan %*% oneone scores1 <- matrix(vbar, 1, (2 * k - 2)) %*% pcar scores <- scores1 / pcasd F.partition <- ((scores[1:p] ^ 2) * (n1 * n2 * (n1 + n2 - p - 1))) / ((n1 + n2) * (n1 + n2 - 2) * p) FF <- sum(F.partition) pval <- 1 - pf(FF, p, (n1 + n2 - p - 1)) z$F.partition <- F.partition z$F <- FF z$pval <- pval z$df1 <- p z$T.df1 <- p z$df2 <- (n1 + n2 - p - 1) mm <- n - 2 z$T.df2 <- mm z$Tsq <- FF * (n1 + n2) * (n1 + n2 - 2) * p / (n1 * n2) / (n1 + n2 - p - 1) z$Tsq.partition <- F.partition * (n1 + n2) * (n1 + n2 - 2) * p / (n1 * n2) / (n1 + n2 - p - 1) return(z) } } #================================================================================== Hotellingtest <- function (A, B, tol1 = 1e-07, tol2 = 1e-07) { z <- list( Tsq.partition = 0, Tsq = 0, F.partition = 0, F = 0, pval = 0, df1 = 0, df2 = 0, T.df1 = 0, T.df2 = 0 ) n1 <- dim(A)[3] n2 <- dim(B)[3] n <- n1 + n2 k <- dim(A)[1] m <- dim(B)[2] pool <- array(0, c(k, m, n)) pool[, , 1:n1] <- A pool[, , (n1 + 1):n] <- B poolpr <- procrustesGPA(pool, tol1, tol2, approxtangent = FALSE) S1 <- var(t(poolpr$tan[, 1:n1])) S2 <- var(t(poolpr$tan[, (n1 + 1):(n1 + n2)])) Sw <- ((n1 - 1) * S1 + (n2 - 1) * S2) / (n1 + n2 - 2) p <- min(k * m - (m * (m - 1)) / 2 - 1 - m, n1 + n2 - 2) eva <- eigen(Sw, symmetric = TRUE) pcar <- eva$vectors[, 1:p] pcasd <- sqrt(abs(eva$values[1:p])) ####### add small offset if defecient in rank if (pcasd[p] < 0.000001) { offset <- 0.000001 cat("*") pcasd <- sqrt(pcasd ** 2 + offset) } ####################################### lam <- rep(0, times = (k * m - m)) lam[1:p] <- 1 / pcasd ** 2 Suinv <- eva$vectors %*% diag(lam) %*% t(eva$vectors) # check <- p # for (i in 1:p) { # if (pcasd[p + 1 - i] < 1e-04) { # check <- p + 1 - i - 1 # } # } # p <- check pcax <- t(poolpr$tan) %*% pcar one1 <- matrix(1 / n1, n1, 1) one2 <- matrix(1 / n2, n2, 1) oneone <- rbind(one1,-one2) vbar <- poolpr$tan %*% oneone scores1 <- matrix(vbar, 1, m * k - m) %*% pcar scores <- scores1 / pcasd # tem<-c(t(vbar)%*%Suinv%*%vbar) #(=Dsq)# F.partition <- ((scores[1:p] ^ 2) * (n1 * n2 * (n1 + n2 - p - 1))) / ((n1 + n2) * (n1 + n2 - 2) * p) FF <- sum(F.partition) pval <- 1 - pf(FF, p, (n1 + n2 - p - 1)) z$F.partition <- F.partition z$F <- FF z$pval <- pval z$df1 <- p z$T.df1 <- p z$df2 <- (n1 + n2 - p - 1) mm <- n - 2 z$T.df2 <- mm z$Tsq <- FF * (n1 + n2) * (n1 + n2 - 2) * p / (n1 * n2) / (n1 + n2 - p - 1) z$Tsq.partition <- F.partition * (n1 + n2) * (n1 + n2 - 2) * p / (n1 * n2) / (n1 + n2 - p - 1) return(z) } # Hotellingtest<-function(A, B, tol1=1e05,tol2=1e05) # OLD VERSION using $tan rather than $tanpartial #{ #Calculates two sample Hotelling Tsq test for testing whether #mean shapes are equal (m - Dimensions where m >= 2) #in: A, B the k x m x n arrays of data for each group #out: z$F : F-statistic # z$df1, z$df2 : dgrees of freedom # z$pval: pvalue # z <- list(Tsq.partition = 0, Tsq = 0, F.partition = 0, F = 0, pval = 0, # df1 = 0, df2 = 0, T.df1 = 0, T.df2 = 0) # n1 <- dim(A)[3] # n2 <- dim(B)[3] # n <- n1 + n2 # k <- dim(A)[1] # m <- dim(B)[2] # pool <- array(0, c(k, m, n)) # pool[, , 1:n1] <- A # pool[, , (n1 + 1):n] <- B # poolpr <- procrustesGPA(pool,tol1,tol2) # S1 <- var(t(poolpr$tan[, 1:n1])) # S2 <- var(t(poolpr$tan[, (n1 + 1):(n1 + n2)])) # Sw <- ((n1 - 1) * S1 + (n2 - 1) * S2)/(n1 + n2 - 2) # p <- min(k * m - (m * (m - 1))/2 - 1 - m, n1 + n2 - 2) # pcar <- eigen(Sw)$vectors[, 1:p] # pcasd <- sqrt(eigen(Sw)$values[1:p]) # check<-p ## checks to see if rank is reasonable # for (i in 1:p){ # if (pcasd[p+1-i] < 0.0001){ # check<-p+1-i-1 # } # } # p<-check # pcax <- t(poolpr$tan) %*% pcar # one1 <- matrix(1/n1, n1, 1) # one2 <- matrix(1/n2, n2, 1) # oneone <- rbind(one1, - one2) # vbar <- poolpr$tan %*% oneone # scores1 <- matrix(vbar, 1, m*k) %*% pcar # scores <- scores1/pcasd # F.partition <- ((scores[1:p]^2) * (n1 * n2 * (n1 + n2 - p - 1)))/((n1 + # n2) * (n1 + n2 - 2) * p) # FF <- sum(F.partition) # pval <- 1 - pf(FF, p, (n1 + n2 - p - 1)) # z$F.partition <- F.partition # z$F <- FF # z$pval <- pval # z$df1 <- p # z$T.df1 <- p # z$df2 <- (n1 + n2 - p - 1) # mm <- n - 2 # z$T.df2 <- mm # z$Tsq <- (FF * (mm * p))/(mm - p + 1) # z$Tsq.partition <- (F.partition * (mm * p))/(mm - p + 1) # return(z) #} #================================================================================== I2mat <- function(Be) { zero <- rep(0, times = dim(Be)[1] ^ 2) zero <- matrix(zero, dim(Be)[1], dim(Be)[2]) temp <- cbind(Be, zero) temp1 <- cbind(zero, Be) tem <- rbind(temp, temp1) tem } #================================================================================== tpsgrid.old <- function (TT, YY, xbegin = -999, ybegin = -999, xwidth = -999, opt = 2, ext = 0.1, ngrid = 22, cex = 1, pch = 20, col = 2) { k <- nrow(TT) if (xwidth == -999) { bb <- array(TT, c(dim(TT), 1)) aa <- defplotsize2(bb) xwidth <- aa$width } if (xbegin == -999) { bb <- array(TT, c(dim(TT), 1)) aa <- defplotsize2(bb) xbegin <- aa$xl } if (ybegin == -999) { bb <- array(TT, c(dim(TT), 1)) aa <- defplotsize2(bb) ybegin <- aa$yl } xstart <- xbegin ystart <- ybegin ngrid <- trunc(ngrid / 2) * 2 kx <- ngrid ky <- ngrid - 1 l <- kx * ky step <- xwidth / (kx - 1) r <- 0 X <- rep(0, times = kx) Y2 <- rep(0, times = ky) for (p in 1:kx) { ystart <- ybegin xstart <- xstart + step for (q in 1:ky) { ystart <- ystart + step r <- r + 1 X[r] <- xstart Y2[r] <- ystart } } refc <- matrix(c(X, Y2), kx * ky, 2) TPS <- bendingenergy(TT) gamma11 <- TPS$gamma11 gamma21 <- TPS$gamma21 gamma31 <- TPS$gamma31 W <- gamma11 %*% YY ta <- t(gamma21 %*% YY) B <- gamma31 %*% YY WtY <- t(W) %*% YY trace <- c(0) for (i in 1:2) { trace <- trace + WtY[i, i] } benergy <- 16 * pi * trace if (m == 3) { benergy <- 8 * pi * trace } l <- kx * ky phi <- matrix(0, l, 2) s <- matrix(0, k, 1) for (i in 1:l) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(refc[i,] - TT[m,]) } phi[i,] <- ta + t(B) %*% refc[i,] + t(W) %*% s } par(pty = "s") if (opt == 2) { par(mfrow = c(1, 2)) order <- linegrid(refc, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth * ext, xbegin + xwidth * (1 + ext)), ylim = c( ybegin - (xwidth * ky) / kx * ext, ybegin + (xwidth * ky) / kx * (1 + ext) ), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(TT, cex = cex, pch = pch, col = col) } order <- linegrid(phi, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth * ext, xbegin + xwidth * (1 + ext)), ylim = c(ybegin - (xwidth * ext * ky) / kx, ybegin + (xwidth * (1 + ext) * ky) / kx), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(YY, cex = cex, pch = pch, col = col) } # # #================================================================================== V <- function(z) { #input complex k -vector #ouput vectorized 2k vector of real stacked on imaginary components x <- c(Re(z), Im(z)) x } #================================================================================== Vinv <- function(x) { #input vectorized 2k vector of x1 stacked on x2 components #input complex k -vector of the form x1 + 1i*x2 nel <- length(x) / 2 zx <- x[1:nel] zy <- x[(nel + 1):(2 * nel)] z <- zx + (1i) * zy z } #================================================================================== Vmat <- function(z) { #as Vinv but input is a k x n complex matrix # output 2k x n matrix of stacked real then complex components x <- rbind(Re(z), Im(z)) x } #================================================================================== bendingenergy <- function (TT) { z <- list( gamma11 = 0, gamma21 = 0, gamma31 = 0, prinwarps = 0, prinwarpeval = 0, Un = 0 ) k <- nrow(TT) m <- dim(TT)[2] S <- matrix(0, k, k) for (i in 1:k) { for (j in 1:k) { S[i, j] <- sigmacov(TT[i,] - TT[j,]) } } one <- matrix(1, k, 1) zero <- matrix(0, m + 1, m + 1) # P <- cbind(S, one, TT) P <- rbind(S, t(one)) Q <- rbind(P, t(TT)) O <- cbind(one, TT) U <- rbind(O, zero) star <- cbind(Q, U) star <- matrix(star, k + m + 1, k + m + 1) A <- eigen(star, symmetric = TRUE) deltainv <- diag(1 / A$values) gamma <- A$vectors starinv <- gamma %*% deltainv %*% t(gamma) gamma11 <- matrix(0, k, k) for (i in 1:k) { for (j in 1:k) { gamma11[i, j] <- starinv[i, j] } } gamma21 <- matrix(0, 1, k) for (i in 1:1) { for (j in 1:k) { gamma21[i, j] <- starinv[k + 1, j] } } gamma31 <- matrix(0, m, k) for (i in 1:(m)) { for (j in 1:k) { gamma31[i, j] <- starinv[i + k + 1, j] } } prinwarp <- eigen(gamma11, symmetric = TRUE) prinwarps <- prinwarp$vectors prinwarpeval <- prinwarp$values ####need to rotate to compute affine components Rot <- prcomp(TT)$rotation TT <- TT %*% Rot if (m == 2) { meanxy <- c(TT[, 1], TT[, 2]) alpha <- sum(meanxy[1:k] ^ 2) beta <- sum(meanxy[(k + 1):(2 * k)] ^ 2) u1 <- c(alpha * meanxy[(k + 1):(2 * k)], beta * meanxy[1:k]) u2 <- c(-beta * meanxy[1:k], alpha * meanxy[(k + 1):(2 * k)]) u1 <- u1 / sqrt(alpha * beta) / sqrt(alpha + beta) u2 <- u2 / sqrt(alpha * beta) / sqrt(alpha + beta) Un <- matrix(0, 2 * k, 2) Un[, 1] <- u1 Un[, 2] <- u2 Vn <- Un Vn[, 1] <- cbind(Un[1:k, 1], Un[(k + 1):(2 * k), 1]) %*% t(Rot) Vn[, 2] <- cbind(Un[1:k, 2], Un[(k + 1):(2 * k), 2]) %*% t(Rot) Un <- Vn } if (m == 3) { meanxy <- c(TT[, 1], TT[, 2], TT[, 3]) alpha <- sum(meanxy[1:k] ^ 2) beta <- sum(meanxy[(k + 1):(2 * k)] ^ 2) gamma <- sum(meanxy[(2 * k + 1):(3 * k)] ^ 2) mu <- meanxy[1:k] nu <- meanxy[(k + 1):(2 * k)] omega <- meanxy[(2 * k + 1):(3 * k)] ze <- rep(0, times = k) u1 <- c(ze , alpha * beta * omega , alpha * gamma * nu) / sqrt(alpha ^ 2 * beta ^ 2 * gamma + alpha ^ 2 * gamma ^ 2 * beta) u2 <- c(alpha * beta * omega , ze, beta * gamma * mu) / sqrt(beta ^ 2 * alpha ^ 2 * gamma + beta ^ 2 * gamma ^ 2 * alpha) u3 <- c(alpha * gamma * nu , beta * gamma * mu, ze) / sqrt(alpha ^ 2 * gamma ^ 2 * beta + beta ^ 2 * gamma ^ 2 * alpha) u4 <- c(ze , ze , omega) / sqrt(gamma) u5 <- c(-beta * gamma * mu , alpha * gamma * nu, ze) / sqrt(alpha * gamma ^ 2 * beta ^ 2 + beta * gamma ^ 2 * alpha ^ 2) tem <- c(-gamma * beta * mu , ze, beta * alpha * omega) / sqrt(beta ^ 2 * alpha * gamma ^ 2 + beta ^ 2 * gamma * alpha ^ 2) tem2 <- tem - u5 * sum(u5 * tem) u4 <- tem2 / Enorm(tem2) Un <- matrix(0, 3 * k, 5) Un[, 1] <- u1 Un[, 2] <- u2 Un[, 3] <- u3 Un[, 4] <- u4 Un[, 5] <- u5 Vn <- Un Vn[, 1] <- cbind(Un[1:k, 1], Un[(k + 1):(2 * k), 1], Un[(2 * k + 1):(3 * k), 1]) %*% t(Rot) Vn[, 2] <- cbind(Un[1:k, 2], Un[(k + 1):(2 * k), 2], Un[(2 * k + 1):(3 * k), 2]) %*% t(Rot) Vn[, 3] <- cbind(Un[1:k, 3], Un[(k + 1):(2 * k), 3], Un[(2 * k + 1):(3 * k), 3]) %*% t(Rot) Vn[, 4] <- cbind(Un[1:k, 4], Un[(k + 1):(2 * k), 4], Un[(2 * k + 1):(3 * k), 4]) %*% t(Rot) Vn[, 5] <- cbind(Un[1:k, 5], Un[(k + 1):(2 * k), 5], Un[(2 * k + 1):(3 * k), 5]) %*% t(Rot) Un <- Vn } z$gamma11 <- gamma11 z$gamma21 <- gamma21 z$gamma31 <- gamma31 z$prinwarps <- prinwarps z$prinwarpeval <- prinwarpeval z$Un <- Un return(z) } #================================================================================== shaperw <- function(proc , alpha = 1, affine = FALSE) { rw <- proc if ((alpha != 0) || (affine == TRUE)) { k <- dim(proc$mshape)[1] m <- dim(proc$mshape)[2] n <- dim(proc$mshape)[3] if (dim(proc$tan)[1] == (k * m - m)) { if (m == 2) { He <- t(defh(k - 1)) Ze <- He * 0 HH <- rbind(cbind(He, Ze) , cbind(Ze, He)) proc$tan <- HH %*% proc$tan } if (m == 3) { He <- t(defh(k - 1)) Ze <- He * 0 HH <- rbind(cbind(He, Ze, Ze) , cbind(Ze, He , Ze) , cbind(Ze, Ze, He)) proc$tan <- HH %*% proc$tan } } nconstr <- m + m * (m - 1) / 2 + 1 M <- k * m - nconstr if (m == 2) { bb <- bendingenergy(proc$mshape) Gamma11 <- bb$gamma11 Be <- rbind(cbind(Gamma11, Gamma11 * 0) , cbind(Gamma11 * 0, Gamma11)) Un <- bb$Un Bedim <- 2 } if (m == 3) { bb <- bendingenergy(proc$mshape) Gamma11 <- bb$gamma11 Ze <- Gamma11 * 0 Be <- rbind(cbind(Gamma11, Ze, Ze) , cbind(Ze, Gamma11, Ze) , cbind(Ze, Ze, Gamma11)) Un <- bb$Un Bedim <- 5 } ev <- eigen(Be, symmetric = TRUE) evpw <- eigen(Gamma11, symmetric = TRUE) Beminusalpha <- ev$vectors %*% diag(c(ev$values[1:(M - Bedim)] ** (-alpha / 2), rep(0, times = nconstr + Bedim))) %*% t(ev$vectors) Bealpha <- ev$vectors %*% diag(c(ev$values[1:(M - Bedim)] ** (alpha / 2), rep(0, times = nconstr + Bedim))) %*% t(ev$vectors) evbe <- ev SS <- Beminusalpha %*% var(t(proc$tan)) %*% Beminusalpha ev <- eigen(SS) relw.vec <- ev$vectors relw.sd <- sqrt(abs(ev$values)) # ratio of eigenvalues of warps (quoted in book) rw$percent <- relw.sd ** 2 / sum(relw.sd ** 2) * 100 sgnchange <- sample(c(-1, 1), size = m * k , replace = TRUE) rw$pcar <- Bealpha %*% relw.vec %*% diag(sgnchange) rw$pcasd <- relw.sd rw$rawscores <- t(t(relw.vec) %*% Beminusalpha %*% proc$tan) sd <- sqrt(abs(diag(var((rw$rawscores) )))) rw$scores <- (rw$rawscores) %*% diag(1 / sd) rw$stdscores <- rw$scores rw$scores <- rw$rawscores ## partial warp scores n <- proc$n evbend <- eigen(Gamma11, symmetric = TRUE) partialwarpscores <- array(0 , c(n , m , k)) for (i in 1:m) { partialwarpscores[, i, ] <- t(t(evbend$vectors) %*% proc$rotated[, i, ]) } rw$principalwarps <- evpw$vectors[, (k - m - 1):1] rw$principalwarps.eigenvalues <- evpw$values[(k - m - 1):1] rw$partialwarpscores <- partialwarpscores[, , (k - m - 1):1] sumvar <- rep(0, times = (k - m - 1)) for (i in 1:(k - m - 1)) { sumvar[i] <- sum(diag(var(partialwarpscores[, , k - m - i]))) } rw$partialwarps.percent <- sumvar / sum(proc$pcasd ** 2) * 100 } if (affine == TRUE) { dimun <- dim(Un)[2] rw$pcar <- Un %*% diag(sgnchange[1:(dimun)]) pcno <- c(1:dimun) rw$rawscores <- t(Un) %*% proc$tan sd <- sqrt(abs(diag(var( t(rw$rawscores) )))) rw$pcasd <- sd rw$percent <- sd ** 2 / sum(proc$pcasd ** 2) * 100 rw$scores <- t(rw$rawscores) %*% diag(1 / sd) rw$rawscores <- t(rw$rawscores) ####### tem <- prcomp1((rw$rawscores)) npc <- 0 rw$stdscores <- tem$x for (i in 1:length(tem$sdev)) { if (tem$sdev[i] > 1e-07) { npc <- npc + 1 } } for (i in 1:npc) { rw$stdscores[, i] <- tem$x[, i] / tem$sdev[i] } rw$pcasd <- tem$sdev rw$percent <- tem$sdev ** 2 / sum(proc$pcasd ** 2) * 100 rw$pcar <- Un %*% tem$rotation rw$rawscores <- tem$x rw$scores <- rw$rawscores } rw } #================================================================================== bookstein2d <- function(A, l1 = 1, l2 = 2) { #input: A: k x 2 x n array of 2D data, or k x n complex matrix #l1,l2: baseline choice for sending to (-0.5,0),(0.5,0) #output: z$bshpv - Bookstein shape variables array (including baseline) # z$mshape - Bookstein mean shape (including baseline points) z <- list( k = 0, n = 0, mshape = 0, bshpv = 0 ) if (is.complex(sum(A)) == TRUE) { n <- dim(A)[2] k <- dim(A)[1] B <- array(0, c(k, 2, n)) B[, 1, ] <- Re(A) B[, 2, ] <- Im(A) A <- B } if (is.matrix(A) == TRUE) { bb <- array(A, c(dim(A), 1)) A <- bb } k <- dim(A)[1] m <- 2 n <- dim(A)[3] reorder <- c(l1, l2, c(1:k)[-c(l1, l2)]) A[, , ] <- A[reorder, , 1:n] bshpv <- array(0, c(k, m, n)) for (i in 1:n) { bshpv[, , i] <- bookstein.shpv(A[, , i]) } bookmean <- matrix(0, k, m) for (i in 1:n) { bookmean <- bookmean + bshpv[, , i] } bookmean <- bookmean / n bookmean[reorder, ] <- bookmean bshpv[reorder, ,] <- bshpv glim <- max(-min(bshpv), max(bshpv)) #par(pty="s") #par(mfrow=c(1,1)) #plot(bshpv[,,1],xlim=c(-glim,glim),ylim=c(-glim,glim),type="n",xlab="u",ylab="v") #for (i in 1:n) #{ #for (j in 1:k){ #text(bshpv[j,1,i],bshpv[j,2,i],as.character(j)) #} #} z$mshape <- bookmean z$bshpv <- bshpv z$k <- k z$n <- n return(z) } #================================================================================== bookstein.shpv <- function(x) { #input x: k x 2 matrix or complex k-vector #output u: k x 2 matrix of Bookstein shape variables # with baseline sent to (-0.5,0) (0.5,0) if (is.complex(x)) { x <- complextoreal(x) } nj <- dim(x)[1] j <- rep(1, times = nj) w <- (x[, 1] + (1i) * x[, 2] - (j * (x[1, 1] + (1i) * x[1, 2]))) / (x[2, 1] + (1i) * x[2, 2] - x[1, 1] - (1i) * x[1, 2]) - 0.5 w <- w[1:nj] y <- (Re(w)) z <- (Im(w)) u <- cbind(y, z) u <- matrix(u, nj, 2) u } #================================================================================== bookstein.shpv.complex <- function(z) { #input z: complex k vector #output u: k-2 complex vector of Bookstein shape variables # with baseline sent to (-0.5) (0.5) nj <- length(z) x <- matrix(cbind(Re(z), Im(z)), nj, 2) j <- rep(1, times = nj) w <- (x[, 1] + (1i) * x[, 2] - (j * (x[1, 1] + (1i) * x[1, 2]))) / (x[2, 1] + (1i) * x[2, 2] - x[1, 1] - (1i) * x[1, 2]) - 0.5 u <- w[3:nj] u } #================================================================================== cbevec <- function(z) { t1 <- reassqpr(z) # t2 <- eigen(t1,symmetric=TRUE,EISPACK=TRUE) t2 <- eigen(t1, symmetric = TRUE) reagamma <- t2$vectors[, 1] # print(t2$values/sum(t2$values)) gamma <- Vinv(reagamma) gamma } #================================================================================== cbevectors <- function(z, j) { t1 <- reassqpr(z) # t2 <- eigen(t1,symmetric=TRUE,EISPACK=TRUE) t2 <- eigen(t1, symmetric = TRUE) reagamma <- t2$vectors[, j] gamma <- Vinv(reagamma) gamma } #================================================================================== ild_centroid.size <- function(x) { #returns the centroid size of a configuration (or configurations) #input: k x m matrix/or a complex k-vector # or input a real k x m x n array to get a vector of sizes for a sample if ((is.vector(x) == FALSE) && is.complex(x)) { k <- nrow(x) n <- ncol(x) tem <- array(0, c(k, 2, n)) tem[, 1, ] <- Re(x) tem[, 2, ] <- Im(x) x <- tem } { if (length(dim(x)) == 3) { n <- dim(x)[3] sz <- rep(0, times = n) k <- dim(x)[1] h <- defh(k - 1) for (i in 1:n) { xh <- h %*% x[, , i] sz[i] <- sqrt(sum(diag(t(xh) %*% xh))) } sz } else { if (is.vector(x) && is.complex(x)) { x <- cbind(Re(x), Im(x)) } k <- nrow(x) h <- defh(k - 1) xh <- h %*% x size <- sqrt(sum(diag(t(xh) %*% xh))) size } } } #================================================================================== ild_centroid.size.complex <- function(zstar) { #returns the centroid size of a complex vector zstar h <- defh(nrow(as.matrix(zstar)) - 1) ztem <- h %*% zstar size <- sqrt(diag(Re(st(ztem) %*% ztem))) size } #================================================================================== ild_centroid.size.mD <- function(x) { #returns the centroid size of a k x m matrix if (is.complex(x)) { x <- cbind(Re(x), Im(x)) } k <- nrow(x) h <- defh(k - 1) xh <- h %*% x size <- sqrt(sum(diag(t(xh) %*% xh))) size } #================================================================================== complextoreal <- function(z) { #input complex k-vector - return k x 2 matrix nj <- length(z) x <- matrix(cbind(Re(z), Im(z)), nj, 2) x } #================================================================================== ild_defh <- function(nrow) { #Defines and returns an nrow x (nrow+1) Helmert sub-matrix k <- nrow h <- matrix(0, k, k + 1) j <- 1 while (j <= k) { jj <- 1 while (jj <= j) { h[j, jj] <- -1 / sqrt(j * (j + 1)) jj <- jj + 1 } h[j, j + 1] <- j / sqrt(j * (j + 1)) j <- j + 1 } h } #================================================================================== full.procdist <- function(x, y) { #input k x 2 matrices x, y #output full Procrustes distance rho between x,y sin(riemdist(x, y)) } #================================================================================== genpower <- function(Be, alpha) { k <- dim(Be)[1] if (alpha == 0) { gen <- diag(rep(1, times = k)) } else { l <- k - 3 # eb <- eigen(Be, symmetric = TRUE,EISPACK=TRUE) eb <- eigen(Be, symmetric = TRUE) ev <- c(eb$values[1:l] ^ (-alpha / 2), 0, 0, 0) gen <- eb$vectors %*% diag(ev) %*% t(eb$vectors) gen } } #================================================================================== isotropy.test <- function(sd, p, n) { #LR test for isotropy with Bartlett adjustment #in: sd - square roots of eigenvalues of covariance matrix # p - the number of larger eigenvalues to consider # n - sample size #out: z$bartlett - test statistic (e.g. see Mardia, Kent, Bibby, 1979, p235) # z$pval - p-value z <- list(bartlett = 0, pval = 0) tem <- sd ^ 2 bartlett <- (log(mean(tem[1:p])) - mean(log(tem[1:p]))) * p * (n - (2 * p + 11) / 6) pval <- 1 - pchisq(bartlett, ((p + 2) * (p - 1)) / 2) z$bartlett <- bartlett z$pval <- pval return(z) } #================================================================================== linegrid <- function(ref, kx, ky) { n <- ky m <- kx w <- n * m newgrid1 <- matrix(0, w, 2) v <- m * 0.5 k <- 0 for (l in 1:v) { k <- k + 1 a <- (n + m - 1) * (k - 1) + 1 b <- n * ((2 * k) - 1) d <- 2 * n * k for (j in a:b) { newgrid1[j,] <- ref[j,] } for (u in 1:n) { down <- d - u + 1 up <- b + u newgrid1[up,] <- ref[down,] } } newgrid2 <- matrix(0, w, 2) for (i in 1:v) { z <- (2 * i) - 1 for (x in 1:m) { r1 <- m * (z - 1) + x e <- n * (x - 1) + z newgrid2[r1,] <- ref[e,] } } y <- v - 1 for (p in 1:y) { f <- 2 * p for (q in 1:m) { r2 <- m * (f - 1) + q s <- n * (m - 1) + f - n * (q - 1) newgrid2[r2,] <- ref[s,] } } order <- rbind(newgrid1, newgrid2) order } #================================================================================== mahpreshapedist <- function(z, m, pcar, pcasdev) { if (is.double(z) == TRUE) z <- realtocomplex(z) if (is.double(m) == TRUE) m <- realtocomplex(m) w <- preshape(z) y <- preshape(m) zp <- project(w, y) k <- length(pcasdev) / 2 if (pcasdev[2 * k - 1] < 1e-07) pcasdev[2 * k - 1] <- 1e+22 if (pcasdev[2 * k] < 1e-07) pcasdev[2 * k] <- 1e+22 Sinv <- (pcar) %*% diag(1 / pcasdev ^ 2) %*% t(pcar) Z <- V(zp) d2 <- t(Z) %*% Sinv %*% (Z) dist <- sqrt(d2) dist } makearray <- function(x, k, m, n) { #makes a k x m x n array from a dataset read in as a table tem <- c(t(x)) tem <- array(tem, c(m, k, n)) tem <- aperm(tem, c(2, 1, 3)) tem } #================================================================================== movie <- function(mean, pc, sd, xl, xu, yl, yu, lineorder, movielength = 20) { k <- length(mean) / 2 for (i in 1:movielength) { plotPDMnoaxis(mean, pc * (-1) ^ i, sd, xl, xu, yl, yu, lineorder) } plot( mean[c(1:k)], mean[c((k + 1):(2 * k))], xlim = c(xl, xu), ylim = c(yl, yu), xlab = " ", ylab = " ", axes = FALSE ) } #================================================================================== ild_Enorm <- function(X) { #finds Euclidean/Frobenius norm of a matrix X if (is.complex(X)) { n <- sqrt(sum(diag(Re(st(X) %*% X)))) } else { n <- sqrt(sum(diag(t(X) %*% X))) } n } #================================================================================== partial.procdist <- function(x, y) { #input k x 2 matrices x, y #output partial Procrustes distance rho between x,y sqrt(2) * sqrt(1 - cos(riemdist(x, y))) } #================================================================================== partialwarpgrids <- function(TT, YY, xbegin, ybegin, xwidth, nr, nc, mag) { # #affine grid and partial warp grids for the TPS deformation of TT to YY #displayed as an nr x nc array of plots #mag = magnification effect k <- nrow(TT) YY <- TT + (YY - TT) * mag xstart <- xbegin ystart <- ybegin kx <- 22 ky <- 21 l <- kx * ky step <- xwidth / (kx - 1) r <- 0 X <- rep(0, times = 220) Y2 <- rep(0, times = 220) for (p in 1:kx) { ystart <- ybegin xstart <- xstart + step for (q in 1:ky) { ystart <- ystart + step r <- r + 1 X[r] <- xstart Y2[r] <- ystart } } refc <- matrix(c(X, Y2), kx * ky, 2) TPS <- bendingenergy(TT) gamma11 <- TPS$gamma11 gamma21 <- TPS$gamma21 gamma31 <- TPS$gamma31 W <- gamma11 %*% YY ta <- t(gamma21 %*% YY) B <- gamma31 %*% YY WtY <- t(W) %*% YY R <- matrix(0, k, 2) par(mfrow = c(nr, nc)) par(pty = "s") #AFFINEPART phi <- matrix(0, l, 2) s <- matrix(0, k, 1) for (i in 1:l) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(refc[i,] - TT[m,]) } phi[i,] <- ta + t(B) %*% refc[i,] } newpt <- matrix(0, k, 2) for (i in 1:k) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(TT[i,] - TT[m,]) } newpt[i,] <- ta + t(B) %*% TT[i,] } order <- linegrid(phi, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth / 10, xbegin + (xwidth * 11) / 10), ylim = c(ybegin - (xwidth / 10 * ky) / kx, ybegin + ((xwidth * 11) / 10 * ky) / kx), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(newpt, cex = 2) for (jnw in 1:(k - 3)) { nw <- k - 2 - jnw phi <- matrix(0, l, 2) s <- matrix(0, k, 1) for (i in 1:l) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(refc[i,] - TT[m,]) } phi[i,] <- refc[i,] + TPS$prinwarpeval[nw] * t(YY) %*% TPS$prinwarps[, nw] %*% t(TPS$prinwarps[, nw]) %*% s } newpt <- matrix(0, k, 2) for (i in 1:k) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(TT[i,] - TT[m,]) } newpt[i,] <- TT[i,] + TPS$prinwarpeval[nw] * t(YY) %*% TPS$prinwarps[, nw] %*% t(TPS$prinwarps[, nw]) %*% s } R <- newpt - TT + R order <- linegrid(phi, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth / 10, xbegin + (xwidth * 11) / 10), ylim = c(ybegin - (xwidth / 10 * ky) / kx, ybegin + ((xwidth * 11) / 10 * ky) / kx), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(newpt, cex = 2) } #percentage (need to normalize) d2 <- sin(riemdist(YY, TT)) ^ 2 d3 <- sin(riemdist(R + TT, TT)) ^ 2 percentaff <- (d2 - d3) / d2 * 100 print("percent affine") print(percentaff) } #================================================================================== partialwarps <- function(mshape, rotated) { #obtain the affine and partial warp scores for a dataset #where the reference configuration is mshape and the full procrustes #rotated figures are given in the array rotated #output: y$pwpwercent percentage of variability (squared Procrustes distance) # in the direction of each of the affine and principal warps # y$pwscores: the affine and partial warps scores # y <- list(pwpercent = 0, pwscores = 0, unpercent = 0) k <- nrow(mshape) n <- dim(rotated)[3] msh <- mshape rot <- rotated TPS <- bendingenergy(msh) FX <- rot[, 1,] FY <- rot[, 2,] U <- TPS$prinwarps[, 1:(k - 3)] partialX <- t(U) %*% FX partialY <- t(U) %*% FY Un <- TPS$Un UnXY <- t(Un) %*% rbind(FX, FY) scores <- matrix(0, 2 * (k - 3), n) for (i in 1:(k - 3)) { r <- 2 * i - 1 scores[r,] <- partialX[k - 2 - i,] scores[r + 1,] <- partialY[k - 2 - i,] } scores <- rbind(UnXY, scores) percwarp <- rep(0, times = (k - 2)) sumev <- sum(eigen(var(t(scores)))$values) for (i in 1:(k - 2)) { sum1 <- sum(eigen(var(t(scores[(2 * i - 1):(2 * i),])))$values) percwarp[i] <- sum1 / sumev } unpercent <- c(0, 0) unpercent[1] <- var(scores[1,]) / sumev unpercent[2] <- var(scores[2,]) / sumev y$unpercent <- unpercent y$pwpercent <- percwarp y$pwscores <- t(scores) return(y) } #================================================================================== plot2rwscores <- function(rwscores, rw1, rw2, ng1, ng2) { par(pch = "x") glim <- max(-min(rwscores), max(rwscores)) plot( rwscores[1:ng1, rw1], rwscores[1:ng1, rw2], xlim = c(-glim, glim), ylim = c(-glim, glim), xlab = " ", ylab = " " ) par(pch = "+") points(rwscores[(ng1 + 1):(ng1 + ng2), rw1], rwscores[(ng1 + 1):(ng1 + ng2), rw2]) } #================================================================================== plotPDM <- function(mean, pc, sd, xl, xu, yl, yu, lineorder) { for (i in c(-3, 0, 3)) { fig <- mean + i * pc * sd k <- length(mean) / 2 figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] plot( figx, figy, axes = TRUE, xlab = " ", ylab = " ", ylim = c(yl, yu), xlim = c(xl, xu) ) # par(lty = i + 1) lines(figx[lineorder], figy[lineorder]) if (i == -3) title(sub = "mean - c sd") if (i == 0) title(sub = "mean") if (i == 3) title(sub = "mean + c sd") par(lty = 1) } } #================================================================================== plotPDM2 <- function(mean, pc, sd, xl, xu, yl, yu, lineorder) { par(lty = 1) k <- length(mean) / 2 plot( mean[1:k], mean[(k + 1):(2 * k)], axes = TRUE, xlab = " ", ylab = " ", ylim = c(yl, yu), xlim = c(xl, xu) ) for (i in c(-3:3)) { fig <- mean + i * pc * sd figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] # if (i < 0) { par(lty = 1) par(pch = "*") } if (i == 0) { par(lty = 4) par(pch = 1) } if (i > 0) { par(lty = 2) par(pch = "+") } points(figx, figy) lines(figx[lineorder], figy[lineorder]) } } #================================================================================== plotPDM3 <- function(mean, pc, sd, xl, xu, yl, yu, lineorder) { par(lty = 1) k <- length(mean) / 2 figx <- matrix(0, 2 * k, 7) figy <- figx plot( mean[1:k], mean[(k + 1):(2 * k)], axes = TRUE, xlab = " ", ylab = " ", ylim = c(yl, yu), xlim = c(xl, xu) ) for (i in c(-3:3)) { fig <- mean + i * pc * sd figx[, i + 4] <- fig[1:k] figy[, i + 4] <- fig[(k + 1):(2 * k)] } for (i in 1:k) { # par(lty = 2) # lines(figx[i, 1:4], figy[i, 1:4]) par(lty = 1) lines(figx[i, 4:7], figy[i, 4:7]) } } #================================================================================== plotPDMbook <- function(mean, pc, sd, xl, xu, yl, yu, lineorder) { par(lty = 1) k <- length(mean) / 2 figx <- matrix(0, 2 * k, 7) figy <- figx plot( bookstein.shpv(cbind(mean[1:k], mean[(k + 1):(2 * k)])), axes = TRUE, xlab = " ", ylab = " ", ylim = c(yl, yu), xlim = c(xl, xu) ) for (i in c(-3:3)) { fig <- mean + i * pc * sd figx[, i + 4] <- fig[1:k] figy[, i + 4] <- fig[(k + 1):(2 * k)] u <- bookstein.shpv(cbind(figx[, i + 4], figy[, i + 4])) figx[, i + 4] <- u[, 1] figy[, i + 4] <- u[, 2] } for (i in 1:k) { # par(lty = 2) # lines(figx[i, 1:4], figy[i, 1:4]) par(lty = 1) lines(figx[i, 4:7], figy[i, 4:7]) } } #================================================================================== plotPDMnoaxis <- function(mean, pc, sd, xl, xu, yl, yu, lineorder) { for (i in c(-3:3)) { fig <- mean + i * pc * sd k <- length(mean) / 2 figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] plot( figx, figy, axes = FALSE, xlab = " ", ylab = " ", ylim = c(yl, yu), xlim = c(xl, xu) ) lines(figx[lineorder], figy[lineorder]) for (ii in 1:1000) { aa <- 1 } } } #================================================================================== pointsPDMnoaxis3 <- function(mean, pc, sd, xl, xu, yl, yu, lineorder, i) { fig <- mean + i * pc * sd k <- length(mean) / 2 figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] points(figx, figy) text(figx, figy, 1:k) lines(figx[lineorder], figy[lineorder]) } #================================================================================== plotpairscores <- function(scores, nr, nc, ng1, ng2, ch1, ch2) { #plots pairs of scores score 2 vs score 1, score 4 vs score 3 etc #in an nr x nc grid of plots par(pty = "s") par(cex = 2) par(mfrow = c(nr, nc)) k <- ncol(scores) / 2 + 2 glim <- max(-min(scores), max(scores)) for (i in 1:(k - 2)) { plot( scores[1:ng1, (2 * i - 1)], scores[1:ng1, (2 * i)], pch = ch1, xlim = c(-glim, glim), ylim = c(-glim, glim), xlab = " ", ylab = " " ) points(scores[(ng1 + 1):(ng1 + ng2), (2 * i - 1)], scores[(ng1 + 1):(ng1 + ng2), (2 * i)], pch = ch2) } } ################################# #================================================================================== plotpca <- function (proc, pcno, type, mag, xl, yl, width, joinline = c(1, 1), project = c(1, 2)) { k <- proc$k zero <- matrix(0, k - 1, k) h <- defh(k - 1) H <- cbind(h, zero) H1 <- cbind(zero, h) H <- rbind(H, H1) if (project[1] == 1) { select1 <- 1:k } if (project[1] == 2) { select1 <- (k + 1):(2 * k) } if (project[1] == 3) { select1 <- (2 * k + 1):(3 * k) } if (project[2] == 1) { select2 <- 1:k } if (project[2] == 2) { select2 <- (k + 1):(2 * k) } if (project[2] == 3) { select2 <- (2 * k + 1):(3 * k) } select <- c(select1, select2) meanxy <- c(proc$mshape[, project[1]], proc$mshape[, project[2]]) if (dim(proc$pcar)[1] == (2 * (k - 1))) { pcarot <- (t(H) %*% proc$pcar)[select, ] } if (dim(proc$pcar)[1] != (2 * (k - 1))) { pcarot <- proc$pcar[select, ] } par(pty = "s") par(lty = 1) np <- length(pcno) nr <- trunc((length(pcno) + 1) / 2) if (type == "g") { par(mfrow = c(nr, 2)) if (np == 1) { par(mfrow = c(1, 1)) } for (i in 1:np) { j <- pcno[i] fig <- meanxy + pcarot[, j] * 3 * mag * proc$pcasd[j] figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] YY <- cbind(figx, figy) tpsgrid(cbind(proc$mshape[, project[1]], proc$mshape[, project[2]]) , YY, xl, yl, width, 1, 0.1, 22) } } else { if (type == "r") { par(mfrow = c(np, 3)) for (i in 1:np) { j <- pcno[i] plotPDM(meanxy, pcarot[, j], mag * proc$pcasd[j], xl, xl + width, yl, yl + width, joinline) title(as.character( paste( "PC ", as.character(pcno[i]), ": ", as.character(round(proc$percent[i], 1)), "%" ) )) } } else { if (type == "v") { par(mfrow = c(nr, 2)) if (np == 1) { par(mfrow = c(1, 1)) } for (i in 1:np) { j <- pcno[i] plotPDM3(meanxy, pcarot[, j], mag * proc$pcasd[j], xl, xl + width, yl, yl + width, joinline) title(as.character( paste( "PC ", as.character(pcno[i]), ": ", as.character(round(proc$percent[i], 1)), "%" ) )) } } else { if (type == "b") { par(mfrow = c(nr, 2)) if (np == 1) { par(mfrow = c(1, 1)) } for (i in 1:np) { j <- pcno[i] plotPDMbook(meanxy, pcarot[, j], mag * proc$pcasd[j],-0.6, 0.6, -0.6, 0.6, joinline) title(as.character( paste( "PC ", as.character(pcno[i]), ": ", as.character(round(proc$percent[i], 1)), "%" ) )) } } else { if (type == "s") { par(mfrow = c(nr, 2)) if (np == 1) { par(mfrow = c(1, 1)) } for (i in 1:np) { j <- pcno[i] plotPDM2( meanxy, pcarot[, j], mag * proc$pcasd[j], xl, xl + width, yl, yl + width, joinline ) title(as.character( paste( "PC ", as.character(pcno[i]), ": ", as.character(round(proc$percent[i], 1)), "%" ) )) } } else { if (type == "m") { par(mfrow = c(1, 1)) for (i in 1:np) { j <- pcno[i] cat(paste("PC ", pcno[i], " \n")) movie( meanxy, pcarot[, j], mag * proc$pcasd[j], xl, xl + width, yl, yl + width, joinline, 20 ) } } } } } } } par(mfrow = c(1, 1)) } ############################################## #================================================================================== plotprinwarp <- function(TT, xbegin, ybegin, xwidth, nr, nc) { # #plots the principal warps of TT as perspective plots #the plots are displayed in an nr x nc array of plots kx <- 21 k <- nrow(TT) l <- kx ^ 2 xstart0 <- xbegin ystart0 <- ybegin xstart <- xstart0 ystart <- ystart0 step <- xwidth / kx r <- 0 X <- rep(0, times = l) Y2 <- rep(0, times = l) for (p in 1:kx) { ystart <- ystart0 xstart <- xstart + step for (q in 1:kx) { ystart <- ystart + step r <- r + 1 X[r] <- xstart Y2[r] <- ystart } } refperp <- matrix(c(X, Y2), l, 2) xstart <- xstart0 xgrid <- rep(0, times = kx) for (i in 1:kx) { xstart <- xstart + step xgrid[i] <- xstart } ystart <- ystart0 ygrid <- rep(0, times = kx) for (i in 1:kx) { ystart <- ystart + step ygrid[i] <- ystart } TPS <- bendingenergy(TT) prinwarp <- TPS$prinwarps phi <- matrix(0, l, k - 3) s <- matrix(0, k, 1) for (i in 1:l) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(refperp[i,] - TT[m,]) } phi[i,] <- diag(sqrt(TPS$prinwarpeval[1:(k - 3)])) %*% t(prinwarp[, 1:(k - 3)]) %*% s } phiTT <- matrix(0, k, k - 3) for (i in 1:k) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(TT[i,] - TT[m,]) } phiTT[i,] <- diag(sqrt(TPS$prinwarpeval[1:(k - 3)])) %*% t(prinwarp[, 1:(k - 3)]) %*% s } par(mfrow = c(nr, nc)) for (nw in 1:(k - 3)) { zgrid <- matrix(0, kx, kx) m <- 0 for (i in 1:kx) { for (j in 1:kx) { m <- m + 1 zgrid[i, j] <- phi[m, k - 2 - nw] } } zpersp <- persp(xgrid, ygrid, zgrid, axes = TRUE) # NB the following is an S-Plus function : use trans3d() in R # points(perspp(TT[, 1], TT[, 2], phiTT[, k - 2 - nw], zpersp), # cex = 2) } } #================================================================================== plotproc <- function(proc, xl, yl, width, joinline = c(1, 1)) { #provides plots of the full Procrustes rotated objects in proc #proc is an S object of the type output from the function procrustes2d #xl, yl lower xlimit and ylimit in plot #width = width (and height) of the square plotting region par(pty = "s") plot( proc$rotated[, , 1], xlim = c(xl, xl + width), ylim = c(yl, yl + width), type = "n", xlab = "", ylab = "" ) for (i in 1:proc$n) { points(proc$rotated[, , i]) lines(proc$rotated[joinline, , i]) } } #================================================================================== plotrelwarp <- function(mshape, rotsd, pcno, type, mag, xl, yl, width, joinline) { #provides PC plots: similar to plotpca but different argument #here rotsd is the rotation x s.d. , and can be from the usual # PCA or from using relative warps #pcno is a vector of the numbers (index) of PCs to be plotted #e.g. pcno<-c(1,2,4,7) will plot the four PCs no. 1,2,4,7 #type = type of display # "r" : rows along PCs evaluated at c = -3,-2,-1,0,1,2,3 sd's along PC # "v" : vectors drawn from mean to +/- 3 sd's along PC # "b" : vectors drawn as in `v' but using Bookstein shape variables # "s" : plots along c= -3, -2, -1, 0, 1, 2, 3 superimposed # "m" : movie backward and forwards from -3 to +3 sd's along PC # #mag = magnification of effect (1 = use s.d.'s from the data) #xl, yl lower xlimit and ylimit in plot #width = width (and height) of the square plotting region #joinline = vector of landmark numbers which are joined up in the plot by #straight lines: joinline = c(1,1) will give no lines # k <- nrow(mshape) pcarot <- rotsd par(pty = "s") par(lty = 1) meanxy <- c(mshape[, 1], mshape[, 2]) np <- length(pcno) if (type == "g") { par(mfrow = c(1, np)) for (i in 1:np) { j <- pcno[i] fig <- meanxy + pcarot[, j] * mag * 3 figx <- fig[1:k] figy <- fig[(k + 1):(2 * k)] YY <- cbind(figx, figy) tpsgrid(mshape, YY, xl, yl, width, 1, 0.1, 22) } } else { if (type == "r") { par(mfrow = c(np, 7)) for (i in 1:np) { j <- pcno[i] plotPDM(meanxy, pcarot[, j], mag, xl, xl + width, yl, yl + width, joinline) } } else { if (type == "v") { par(mfrow = c(1, np)) for (i in 1:np) { j <- pcno[i] plotPDM3(meanxy, pcarot[, j], mag, xl, xl + width, yl, yl + width, joinline) } } else { if (type == "b") { par(mfrow = c(1, np)) for (i in 1:np) { j <- pcno[i] plotPDMbook(meanxy, pcarot[, j], mag, xl, xl + width, yl, yl + width, joinline) } } else { if (type == "s") { par(mfrow = c(1, np)) for (i in 1:np) { j <- pcno[i] plotPDM2(meanxy, pcarot[, j], mag, xl, xl + width, yl, yl + width, joinline) } } else { if (type == "m") { par(mfrow = c(1, 1)) for (i in 1:np) { j <- pcno[i] movie(meanxy, pcarot[, j], mag, xl, xl + width, yl, yl + width, joinline, 20) } } } } } } } par(mfrow = c(1, 1)) } #================================================================================== ild_preshape <- function(x) { #input k x m matrix / complex k-vector #output k-1 x m matrix / k-1 x 1 complex matrix if (is.complex(x)) { k <- nrow(as.matrix(x)) h <- defh(k - 1) zstar <- x ztem <- h %*% zstar size <- sqrt(diag(Re(st(ztem) %*% ztem))) if (is.vector(zstar)) z <- ztem / size if (is.matrix(zstar)) z <- ztem %*% diag(1 / size) } else { if (length(dim(x)) == 3) { k <- dim(x)[1] h <- defh(k - 1) n <- dim(x)[3] m <- dim(x)[2] z <- array(0, c(k - 1, m, n)) for (i in 1:n) { z[, , i] <- h %*% x[, , i] size <- centroid.size(x[, , i]) z[, , i] <- z[, , i] / size } } else { k <- nrow(as.matrix(x)) h <- defh(k - 1) ztem <- h %*% x size <- centroid.size(x) z <- ztem / size } } z } #================================================================================== ild_preshape.mD <- function(x) { #input k x m matrix #output k-1 x 1 matrix h <- defh(nrow(x) - 1) ztem <- h %*% x size <- centroid.size.mD(x) z <- ztem / size z } #================================================================================== ild_preshape.mat <- function(zstar) { h <- defh(nrow(as.matrix(zstar)) - 1) ztem <- h %*% zstar size <- sqrt(diag(Re(st(ztem) %*% ztem))) if (is.vector(zstar)) z <- ztem / size if (is.matrix(zstar)) z <- ztem %*% diag(1 / size) z } #================================================================================== ild_preshapetoicon <- function(z) { #convert a preshape (real or complex) to an icon in configuration space h <- defh(nrow(z)) t(h) %*% z } # # # #prcomp1<-function(x, retx = TRUE) #{ # s <- svd(scale(x, scale = FALSE), nu = 0) # remove column means # rank <- sum(s$d > 0) # if(rank < ncol(x)) # s$v <- s$v[, 1:rank] # s$d <- s$d/sqrt(max(1, nrow(x) - 1)) # if(retx) # list(sdev = s$d, rotation = s$v, x = x %*% s$v) # else list(sdev = s$d, rotation = s$v) #} #================================================================================== prinwscoregrids <- function(TT, TPS, score, xbegin, ybegin, xwidth, nr, nc) { #grids displaying the effect of each principal warp at `score' #along each warp. Grids displayed in an nr x nc array par(pty = "s") par(mfrow = c(nr, nc)) k <- nrow(TT) xstart <- xbegin ystart <- ybegin kx <- 22 ky <- 21 l <- kx * ky step <- xwidth / (kx - 1) r <- 0 X <- rep(0, times = 220) Y2 <- rep(0, times = 220) for (p in 1:kx) { ystart <- ybegin xstart <- xstart + step for (q in 1:ky) { ystart <- ystart + step r <- r + 1 X[r] <- xstart Y2[r] <- ystart } } refc <- matrix(c(X, Y2), kx * ky, 2) # TPS <- bendingenergy(TT) for (jnw in 1:(k - 3)) { nw <- k - 2 - jnw phi <- matrix(0, l, 2) s <- matrix(0, k, 1) for (i in 1:l) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(refc[i,] - TT[m,]) } phi[i,] <- refc[i,] + sqrt(TPS$prinwarpeval[nw]) * score * t(TPS$prinwarps[, nw]) %*% s } newpt <- matrix(0, k, 2) for (i in 1:k) { s <- matrix(0, k, 1) for (m in 1:k) { s[m,] <- sigmacov(TT[i,] - TT[m,]) } newpt[i,] <- TT[i,] + sqrt(TPS$prinwarpeval[nw]) * score * t(TPS$prinwarps[, nw]) %*% s } order <- linegrid(phi, kx, ky) plot( order[1:l, 1], order[1:l, 2], type = "l", xlim = c(xbegin - xwidth / 10, xbegin + (xwidth * 11) / 10), ylim = c(ybegin - (xwidth / 10 * ky) / kx, ybegin + ((xwidth * 11) / 10 * ky) / kx), xlab = " ", ylab = " " ) lines(order[(l + 1):(2 * l), 1], order[(l + 1):(2 * l), 2], type = "l") points(newpt, cex = 2) } } #================================================================================== procdistreflect <- function(x, y) { #input k x m matrices x, y #output reflection shape distance (rho*) between them #if x, y are not too far apart then (rho*)=rho (Riemannian dist) if (sum((x - y) ^ 2) == 0) { riem <- 0 } if (sum((x - y) ^ 2) != 0) { m <- ncol(x) z <- preshape(x) w <- preshape(y) Q <- t(z) %*% w %*% t(w) %*% z ev <- sqrt(eigen(Q, symmetric = TRUE)$values) # riem <- acos(sum(ev)) riem <- acos(min(sum(ev), 1)) } riem } #================================================================================== procrustes2d <- function(x, l1 = 1, l2 = 2, approxtangent = FALSE, expomap = FALSE) { #input k x 2 x n real array, or k x n complex matrix #mean shape will have landmarks l1, l2 horizontal (l1 left, l2 right) # #output: # z$k : no of landmarks # z$m : no of dimensions (=2 here) # z$n : sample size # z$tan : the real 2k-2 x n matrix of partial Procrustes tangent coordinates # with pole given by the preshape of the full Procrustes mean # z$rotated : the k x m x n array of real full Procrustes rotated data # z$pcar : the columns are eigenvectors (PCs) of the sample covariance Sv of z$tan # z$pcasd : the square roots of eigenvalues of Sv (s.d.'s of PCs) # z$percent : the % of variability explained by the PCs # z$scores : PC scores normalised to have unit variance # z$rawscores : PC scores (unnormalised) # z$size : the centroid sizes of the configurations # z$rho : Kendall's Procrustean (Riemannian) distance rho to the mean shape # z$rmsrho : r.m.s. of rho # z$rmsd1 : r.m.s. of full Procrustes distances to the mean shape d1 # z <- list( k = 0, m = 0, n = 0, rotated = 0, tan = 0, pcar = 0, scores = 0, rawscores = 0, pcasd = 0, percent = 0, size = 0, rho = 0, rmsrho = 0, rmsd1 = 0, mshape = 0 ) if (is.complex(x) == FALSE) { x <- x[, 1,] + (1i) * x[, 2,] } # cat("Procrustes 2D eigenanalysis \n") k <- nrow(x) n <- ncol(x) h <- defh(k - 1) zp <- preshape(x) gamma <- cbevec(zp) cbmean <- t(h) %*% gamma theta <- Arg(cbmean[l2] - cbmean[l1]) cbmeanrot <- exp((-0 - 1i) * theta) * cbmean gamma <- h %*% cbmeanrot tan <- project(zp, gamma) icon <- array(0, c(k, 2, n)) tanapprox <- matrix(0, 2 * k, n) size <- rep(0, times = n) rho <- rep(0, times = n) mu <- complextoreal(cbmeanrot) sum <- 0 for (i in 1:n) { tem <- tanfigurefull(tan[, i], gamma) icon[, 1, i] <- Re(tem) icon[, 2, i] <- Im(tem) sum <- sum + icon[, , i] size[i] <- centroid.size(x[, i]) rho[i] <- riemdist(x[, i], c(cbmeanrot)) } xbar <- sum / n rv <- Vmat(tan) if (approxtangent == TRUE) { for (i in 1:n) { tanapprox[, i] <- as.vector(icon[, , i]) - as.vector(xbar) } tanapprox <- tanapprox / centroid.size(xbar) pca <- prcomp1(t(tanapprox)) z$tan <- tanapprox } if (expomap == TRUE) { temp <- rv for (i in 1:(n)) { temp[, i] <- rv[, i] / Enorm(rv[, i]) * rho[i] } rv <- temp } if (approxtangent == FALSE) { pca <- prcomp1(t(rv)) z$tan <- rv } z$pcar <- pca$rotation z$pcasd <- pca$sdev z$percent <- z$pcasd ^ 2 / sum(z$pcasd ^ 2) * 100 z$rotated <- icon npc <- 0 for (i in 1:length(pca$sdev)) { if (pca$sdev[i] > 1e-07) { npc <- npc + 1 } } z$scores <- pca$x z$rawscores <- pca$x for (i in 1:npc) { z$scores[, i] <- pca$x[, i] / pca$sdev[i] } z$rho <- rho z$size <- size z$mshape <- mu z$k <- k z$m <- 2 z$n <- n z$rmsrho <- sqrt(mean(rho ^ 2)) z$rmsd1 <- sqrt(mean(sin(rho) ^ 2)) return(z) } #================================================================================== testmeanshapes.old <- function(A, B, Hotelling = TRUE, tol1 = 1e05, tol2 = 1e05) { if (is.complex(A)) { tem <- array(0, c(nrow(A), 2, ncol(A))) tem[, 1,] <- Re(A) tem[, 2,] <- Im(A) A <- tem } if (is.complex(B)) { tem <- array(0, c(nrow(B), 2, ncol(B))) tem[, 1,] <- Re(B) tem[, 2,] <- Im(B) B <- tem } m <- dim(A)[2] if (Hotelling == TRUE) { if (m == 2) { test <- Hotelling2D(A, B) } if (m > 2) { test <- Hotellingtest(A, B, tol1 = tol1, tol2 = tol2) } cat( "Hotelling's T^2 test: ", c("Test statistic = ", round(test$F, 2)), c("\n p-value = ", round(test$pval, 4)), c("Degrees of freedom = ", test$df1, test$df2), "\n" ) } if (Hotelling == FALSE) { if (m == 2) { test <- Goodall2D(A, B) } if (m > 2) { test <- Goodalltest(A, B, tol1 = tol1, tol2 = tol2) } cat( "Goodall's F test: ", c("Test statistic = ", round(test$F, 2)), c("\n p-value = ", round(test$pval, 4)), c("Degrees of freedom = ", test$df1, test$df2), "\n" ) } test } #================================================================================== procGPA <- function(x, scale = TRUE, reflect = FALSE, eigen2d = FALSE, tol1 = 1e-05, tol2 = tol1, tangentcoords = "residual", proc.output = FALSE, distances = TRUE, pcaoutput = TRUE, alpha = 0, affine = FALSE) { # # n <- dim(x)[length(dim(x))] if ((n > 100) & (distances == TRUE)) { print("To speed up use option distances=FALSE") } if ((n > 100) & (pcaoutput == TRUE)) { print("To speed up use option pcaoutput=FALSE") } if (scale == TRUE) { if (tangentcoords == "residual") { tangentresiduals <- TRUE expomap <- FALSE } if (tangentcoords == "partial") { tangentresiduals <- FALSE expomap <- FALSE } if (tangentcoords == "expomap") { tangentresiduals <- FALSE expomap <- TRUE } } if (scale == FALSE) { #all three options are equivalent if (tangentcoords == "residual") { tangentresiduals <- TRUE expomap <- FALSE } if (tangentcoords == "partial") { tangentresiduals <- TRUE expomap <- FALSE } if (tangentcoords == "expomap") { tangentresiduals <- TRUE expomap <- FALSE } } approxtangent <- tangentresiduals if (is.complex(x)) { tem <- array(0, c(nrow(x), 2, ncol(x))) tem[, 1,] <- Re(x) tem[, 2,] <- Im(x) x <- tem } m <- dim(x)[2] n <- dim(x)[3] if (reflect == FALSE) { if ((m == 2) && (scale == TRUE)) { if (eigen2d == TRUE) { out <- procrustes2d(x, approxtangent = approxtangent, expomap = expomap) } else { out <- procrustesGPA( x, tol1, tol2, approxtangent = approxtangent, proc.output = proc.output, distances = distances, pcaoutput = pcaoutput, reflect = reflect, expomap = expomap ) } } if ((m > 2) && (scale == TRUE)) { out <- procrustesGPA( x, tol1, tol2, approxtangent = approxtangent, proc.output = proc.output , distances = distances, pcaoutput = pcaoutput, reflect = reflect, expomap = expomap ) } if (scale == FALSE) { out <- procrustesGPA.rot( x, tol1, tol2, approxtangent = approxtangent, proc.output = proc.output, distances = distances, pcaoutput = pcaoutput, reflect = reflect, expomap = expomap ) } } if (reflect == TRUE) { if (scale == TRUE) { out <- procrustesGPA( x, tol1, tol2, approxtangent = approxtangent, proc.output = proc.output, distances = distances, pcaoutput = pcaoutput, reflect = reflect, expomap = expomap ) } if (scale == FALSE) { out <- procrustesGPA.rot( x, tol1, tol2, approxtangent = approxtangent, proc.output = proc.output, distances = distances, pcaoutput = pcaoutput, reflect = reflect, expomap = expomap ) } } out$stdscores <- out$scores out$scores <- out$rawscores if (approxtangent == FALSE) { out$mshape <- out$mshape / centroid.size(out$mshape) for (i in 1:n) { out$rotated[, , i] <- out$rotated[, , i] / centroid.size(out$rotated[, , i]) } } rw <- out rw <- shaperw(out, alpha = alpha , affine = affine) rw$GSS <- sum((n - 1) * rw$pcasd ** 2) rw } #================================================================================== procrustesGPA <- function (x, tol1 = 1e-05, tol2 = 1e-05, distances = TRUE, pcaoutput = TRUE, approxtangent = TRUE, proc.output = FALSE, reflect = FALSE, expomap = FALSE) { z <- list( k = 0, m = 0, n = 0, rotated = 0, tan = 0, pcar = 0, scores = 0, rawscores = 0, pcasd = 0, percent = 0, size = 0, rho = 0, rmsrho = 0, rmsd1 = 0, mshape = 0 ) if (is.complex(x)) { tem <- array(0, c(nrow(x), 2, ncol(x))) tem[, 1,] <- Re(x) tem[, 2,] <- Im(x) x <- tem } k <- dim(x)[1] m <- dim(x)[2] n <- dim(x)[3] x <- cnt3(x) zgpa <- fgpa(x, tol1, tol2, proc.output = proc.output, reflect = reflect) if (distances == TRUE) { if (proc.output) { cat("Shape distances and sizes calculation ...\n") } size <- rep(0, times = n) rho <- rep(0, times = n) size <- apply(x, 3, centroid.size) rho <- apply(x, 3, y <- function(x) { riemdist(x, zgpa$mshape) }) } tanpartial <- matrix(0, k * m - m , n) ident <- diag(rep(1, times = (m * k - m))) gamma <- as.vector(preshape(zgpa$mshape)) for (i in 1:n) { tanpartial[, i] <- (ident - gamma %*% t(gamma)) %*% as.vector(preshape(zgpa$r.s.r[, , i])) } if (expomap == TRUE) { temp <- tanpartial for (i in 1:(n)) { temp[, i] <- tanpartial[, i] / Enorm(tanpartial[, i]) * rho[i] } tanpartial <- temp } tan <- zgpa$r.s.r[, 1,] - zgpa$mshape[, 1] for (i in 2:m) { tan <- rbind(tan, zgpa$r.s.r[, i,] - zgpa$mshape[, i]) } if (pcaoutput == TRUE) { if (proc.output) { cat("PCA calculation ...\n") } if (approxtangent == FALSE) { pca <- prcomp1(t(tanpartial)) } if (approxtangent == TRUE) { pca <- prcomp1(t(tan)) } npc <- 0 for (i in 1:length(pca$sdev)) { if (pca$sdev[i] > 1e-07) { npc <- npc + 1 } } z$scores <- pca$x z$rawscores <- pca$x for (i in 1:npc) { z$scores[, i] <- pca$x[, i] / pca$sdev[i] } z$pcar <- pca$rotation z$pcasd <- pca$sdev z$percent <- z$pcasd ^ 2 / sum(z$pcasd ^ 2) * 100 } if (approxtangent == FALSE) { z$tan <- tanpartial } if (approxtangent == TRUE) { z$tan <- tan } if (distances == TRUE) { z$rho <- rho z$size <- size z$rmsrho <- sqrt(mean(rho ^ 2)) z$rmsd1 <- sqrt(mean(sin(rho) ^ 2)) } z$rotated <- zgpa$r.s.r z$mshape <- zgpa$mshape z$k <- k z$m <- m z$n <- n if (proc.output) { cat("Finished.\n") } return(z) } #================================================================================== procrustesGPA.rot <- function (x, tol1 = 1e-05, tol2 = 1e-05, distances = TRUE, pcaoutput = TRUE, approxtangent = TRUE, proc.output = FALSE, reflect = FALSE, expomap = FALSE) { z <- list( k = 0, m = 0, n = 0, rotated = 0, tan = 0, pcar = 0, scores = 0, rawscores = 0, pcasd = 0, percent = 0, size = 0, rho = 0, rmsrho = 0, rmsd1 = 0, mshape = 0 ) if (is.complex(x)) { tem <- array(0, c(nrow(x), 2, ncol(x))) tem[, 1,] <- Re(x) tem[, 2,] <- Im(x) x <- tem } k <- dim(x)[1] m <- dim(x)[2] n <- dim(x)[3] # print("GPA (rotation only)") x <- cnt3(x) zgpa <- fgpa.rot(x, tol1, tol2, proc.output = proc.output, reflect = reflect) if (distances == TRUE) { if (proc.output) { cat("Shape distances and sizes calculation ...\n") } size <- rep(0, times = n) rho <- rep(0, times = n) size <- apply(x, 3, centroid.size) rho <- apply(x, 3, y <- function(x) { riemdist(x, zgpa$mshape) }) } tanpartial <- matrix(0, k * m - m, n) ident <- diag(rep(1, times = (m * k - m))) gamma <- as.vector(preshape(zgpa$mshape)) for (i in 1:n) { tanpartial[, i] <- (ident - gamma %*% t(gamma)) %*% as.vector(preshape(zgpa$r.s.r[, , i])) } if (expomap == TRUE) { temp <- tanpartial for (i in 1:(n)) { temp[, i] <- tanpartial[, i] / Enorm(tanpartial[, i]) * rho[i] } tanpartial <- temp } tan <- zgpa$r.s.r[, 1,] - zgpa$mshape[, 1] for (i in 2:m) { tan <- rbind(tan, zgpa$r.s.r[, i,] - zgpa$mshape[, i]) } if (approxtangent == FALSE) { z$tan <- tanpartial } if (approxtangent == TRUE) { z$tan <- tan } if (pcaoutput == TRUE) { if (proc.output) { cat("PCA calculation ...\n") } if (approxtangent == FALSE) { pca <- prcomp1(t(tanpartial)) } if (approxtangent == TRUE) { pca <- prcomp1(t(tan)) } npc <- 0 for (i in 1:length(pca$sdev)) { if (pca$sdev[i] > 1e-07) { npc <- npc + 1 } } z$scores <- pca$x z$rawscores <- pca$x for (i in 1:npc) { z$scores[, i] <- pca$x[, i] / pca$sdev[i] } z$pcar <- pca$rotation z$pcasd <- pca$sdev z$percent <- z$pcasd ^ 2 / sum(z$pcasd ^ 2) * 100 } if (distances == TRUE) { z$rho <- rho z$size <- size z$rmsrho <- sqrt(mean(rho ^ 2)) z$rmsd1 <- sqrt(mean(sin(rho) ^ 2)) } z$rotated <- zgpa$r.s.r z$mshape <- zgpa$mshape z$k <- k z$m <- m z$n <- n if (proc.output) { cat("Finished.\n") } return(z) } #================================================================================== project <- function(z, gamma) { #input z: preshape, gamma: preshape (k-1 x 1 matrices) #output Kent's tangent plane coordinates #of z at the pole gamma (k-1 complex vector) nr <- nrow(z) nc <- ncol(z) g <- matrix(gamma, nr, 1) ident <- diag(nr) theta <- diag(c(exp((-0 - 1i) * Arg(st( g ) %*% z))), nc, nc) v <- (ident - g %*% st(g)) %*% z %*% theta v } #================================================================================== read.array <- function(name, k, m, n) { #input name : filename, k: no of points, m: no of dimensions, n: sample size #output x: k x m x n array of data #e.g. for 2D data assume file format x1 y1 x2 y2 .. xn yn for each object tem <- scan(name) tem <- array(tem, c(m, k, n)) tem <- aperm(tem, c(2, 1, 3)) x <- tem x } #================================================================================== read.in <- function(name, k, m) { #input name : filename, k: no of points, m: no of dimensions #output x: k x m x n array of data ( n: sample size) #e.g. for m=2-D data assume file format x1 y1 x2 y2 ... xk yk for each object #for m=3-D data: x1 y1 z1 x2 y2 z2 ... xk yk zk tem <- scan(name) n <- length(tem) / (k * m) tem <- array(tem, c(m, k, n)) tem <- aperm(tem, c(2, 1, 3)) x <- tem x } #================================================================================== realtocomplex <- function(x) { #input k x 2 matrix - return complex k-vector k <- nrow(x) zstar <- x[, 1] + (1i) * x[, 2] zstar } #================================================================================== reassqpr <- function(z) { j <- 1 nc <- ncol(z) nr <- nrow(z) stemp <- matrix(0, 2 * nr, 2 * nr) repeat { t1 <- matrix(z[, j], nr, 1) vz <- rbind(Re(t1), Im(t1)) viz <- rbind(Re((1i) * t1), Im((1i) * t1)) stemp <- stemp + vz %*% t(vz) + viz %*% t(viz) if (j == nc) break j <- j + 1 } stemp } #================================================================================== relwarps <- function(mshape, rotated, alpha) { #find the relative warps for a dataset with mshape as the reference #and `rotated' as the array of Procrustes rotated figures #alpha is the power of the bending energy # alpha=+1 : emphasizes large scale # alpha=-1 : emphasizes small scale #output: # z$rwarps : the relative warps # z$rwscores : the relative warp scores # z$rwpercent : the percentage of total variability explained by each #relative warp z <- list( rwarps = 0, rwscores = 0, rwpercent = 0, ev = 0, unif = 0, unscores = 0, lengths = 0 ) k <- nrow(mshape) TPS <- bendingenergy(mshape) Be <- TPS$gamma11 stackxy <- rbind(rotated[, 1,], rotated[, 2,]) n <- dim(rotated)[3] msum <- rep(0, times = 2 * k) for (i in 1:n) { msum <- msum + stackxy[, i] } msum <- msum / n meanxy <- msum cstackxy <- matrix(0, 2 * k, n) for (i in 1:n) { cstackxy[, i] <- stackxy[, i] - meanxy } Bpow <- genpower(Be, alpha) Bpowinv <- genpower(Be,-alpha) IBpow <- I2mat(Bpow) IBpowinv <- I2mat(Bpowinv) if (alpha == 0) { IBpow <- diag(rep(1, times = (2 * k))) IBpowinv <- diag(rep(1, times = (2 * k))) } stacknew <- IBpow %*% cstackxy gamma <- matrix(0, 2 * k, 2 * k) pcarotation <- eigen(stacknew %*% t(stacknew) / n, symmetric = TRUE)$vectors pcaev <- eigen(stacknew %*% t(stacknew) / n, symmetric = TRUE)$values pcasdev <- rep(0, times = 2 * k) for (i in 1:(2 * k)) { pcasdev[i] <- sqrt(abs(pcaev[i])) } scores <- t(IBpow %*% pcarotation) %*% cstackxy percent <- rep(0, times = 2 * k) for (i in 1:(2 * k)) { percent[i] <- pcasdev[i] ^ 2 } Un <- TPS$Un UnXY <- t(Un) %*% cstackxy z$unif <- Un %*% t(matrix(c(sqrt(var( UnXY[1,] )), 0, 0, sqrt(var( UnXY[2,] ))), 2, 2)) z$unscores <- t(UnXY) z$lengths <- sqrt(abs(percent)) z$rwarps <- IBpowinv %*% pcarotation %*% diag(pcasdev) z$rwscores <- t(scores) z$ev <- pcaev percentrw <- percent / sum(percent) * 100 z$rwpercent <- percentrw return(z) } #================================================================================== ssriemdist <- function(x, y, reflect = FALSE) { sx <- centroid.size(x) sy <- centroid.size(y) sd <- sx ** 2 + sy ** 2 - 2 * sx * sy * cos(riemdist(x, y, reflect = reflect)) sqrt(abs(sd)) } #================================================================================== riemdist <- function(x, y, reflect = FALSE) { #input two k x m matrices x, y or complex k-vectors #output Riemannian distance rho between them if (sum((x - y) ** 2) == 0) { riem <- 0 } if (sum((x - y) ** 2) != 0) { if (reflect == FALSE) { if (ncol(as.matrix(x)) < 3) { if (is.complex(x) == FALSE) { x <- realtocomplex(x) } if (is.complex(y) == FALSE) { y <- realtocomplex(y) } #riem <- c(acos(Mod(st(preshape(x)) %*% preshape(y)))) riem <- c(acos(min(1, ( Mod(st(preshape(x)) %*% preshape(y)) )))) } else { m <- ncol(x) z <- preshape(x) w <- preshape(y) Q <- t(z) %*% w %*% t(w) %*% z ev <- eigen(t(z) %*% w)$values check <- 1 for (i in 1:m) { check <- check * ev[i] } ev <- sqrt(abs(eigen(Q, symmetric = TRUE)$values)) if (Re(check) < 0) ev[m] <- -ev[m] riem <- acos(min(sum(ev), 1)) } } if (reflect == TRUE) { m <- ncol(x) z <- preshape(x) w <- preshape(y) Q <- t(z) %*% w %*% t(w) %*% z ev <- sqrt(abs(eigen(Q, symmetric = TRUE)$values)) riem <- acos(min(sum(ev), 1)) } } riem } #================================================================================== riemdist.complex <- function(z, w) { #input complex k-vectors z, w #output Riemannian distance rho between them c(acos(min(Mod( st(preshape(z)) %*% preshape(w) ), 1))) } #================================================================================== riemdist.mD <- function(x, y) { #input k x m matrices x, y #output Riemannian distance rho between them m <- ncol(x) z <- preshape.mD(x) w <- preshape.mD(y) Q <- t(z) %*% w %*% t(w) %*% z ev <- eigen(t(z) %*% w)$values check <- 1 for (i in 1:m) { check <- check * ev[i] } ev <- sqrt(eigen(Q, symmetric = TRUE)$values) if (check < 0) ev[m] <- -ev[m] riem <- acos(min(sum(ev), 1)) riem } #================================================================================== rotateaxes <- function(mshapein, rotatedin) { #Rotates a mean shape and the Procrustes rotated data to have #horizontal and vertical principal axes #output: z$mshape rotated mean shape # z$rotated rotated procrustes registered data # z$R the rotation matrix # z <- list(mshape = 0, rotated = 0, R = 0) n <- dim(rotatedin)[3] S <- var(mshapein) R <- eigen(S)$vectors msh <- mshapein %*% R ico <- rotatedin for (i in 1:n) { ico[, , i] <- rotatedin[, , i] %*% R } z$mshape <- msh z$rotated <- ico z$R <- R return(z) } #sigma<-function(x) #{ # length <- sqrt(x[1]^2 + x[2]^2) # if(length == 0) # sig <- 0 # else sig <- length^2 * log(length^2) # sig #} #================================================================================== sigmacov <- function(x) { # other radial basis functions/covariance functions are possible of course hh <- Enorm(x) if (hh == 0) sig <- 0 else { if (length(x) == 2) { sig <- hh ^ 2 * log(hh ^ 2) # null space includes affine terms (2D data) } if (length(x) == 3) { sig <- -hh # null space includes affine terms (3D data) } } sig } #================================================================================== st <- function(zstar) { #input complex matrix #output transpose of the complex conjugate st <- t(Conj(zstar)) st } #================================================================================== ild_tanfigure <- function(vv, gamma) { #inverse projection from complex tangent plane coordinates vv, using pole gamma #output centred icon k <- nrow(gamma) + 1 h <- defh(k - 1) zvv <- tanpreshape(vv, gamma) zstvv <- t(h) %*% zvv zstvv } #================================================================================== ild_tanfigurefull <- function(vv, gamma) { #inverse projection from complex tangent plane coordinates vv, using pole gamma #using Procrustes to with scaling to the pole gamma #output centred icon k <- nrow(gamma) + 1 f1 <- tanfigure(vv, gamma) h <- defh(k - 1) f2 <- t(h) %*% gamma beta <- Mod(st(f1) %*% f2) f1 <- f1 * c(beta) f1 } #================================================================================== tanpreshape <- function(vv, gamma) { #inverse projection from tangent plane coordinates vv, using pole gamma #output preshape z <- c((1 - st(vv) %*% vv) ^ 0.5) * gamma + vv z } #================================================================================== plot3Ddata <- function(dna.data, land = 1:k, objects = 1:n, joinline = c(1, 1)) { dna <- procGPA(dna.data[, , 1:2]) w1 <- defplotsize2(dna.data[, 1:2, ]) w2 <- defplotsize2(dna.data[, c(1, 3), ]) w3 <- defplotsize2(dna.data[, c(2, 3), ]) width <- max(c(w1$width, w2$width, w3$width)) xl <- min(c(w1$xl, w2$xl, w3$xl)) xu <- xl + width yl <- min(c(w1$yl, w2$yl, w3$yl)) yu <- yl + width n <- dim(dna.data)[3] k <- dim(dna.data)[1] m <- dim(dna.data)[2] par(mfrow = c(1, 1)) par(pty = "s") view1 <- 1 view2 <- 2 view3 <- 3 lineorder <- joinline for (j in 1:1) { for (ii in objects) { par(mfrow = c(2, 2)) mag <- 0 pcno <- 1 plotPDMnoaxis3( c(dna.data[land, view2, ii], dna.data[land, view3, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) mag <- 0 pcno <- 1 plotPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view3, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) mag <- 0 pcno <- 1 plotPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) plot( c(0, 0), c(50, 50), xlim = c(0, 0), ylim = c(0, 0), type = "n", xlab = " ", ylab = " ", axes = FALSE ) title(as.character(ii)) } } } #================================================================================== plot3Ddata.static <- function(dna.data, land = 1:k, objects = 1:n, joinline = c(1, 1)) { dna <- procGPA(dna.data[, , 1:2]) w1 <- defplotsize2(dna.data[, 1:2, ]) w2 <- defplotsize2(dna.data[, c(1, 3), ]) w3 <- defplotsize2(dna.data[, c(2, 3), ]) width <- max(c(w1$width, w2$width, w3$width)) xl <- min(c(w1$xl, w2$xl, w3$xl)) xu <- xl + width yl <- min(c(w1$yl, w2$yl, w3$yl)) yu <- yl + width n <- dim(dna.data)[3] k <- dim(dna.data)[1] m <- dim(dna.data)[2] par(mfrow = c(1, 1)) par(pty = "s") lineorder <- joinline par(mfrow = c(2, 2)) mag <- 0 pcno <- 1 ii <- 1 view1 <- 1 view2 <- 2 view3 <- 3 plotPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) for (ii in objects) { pointsPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } view1 <- 1 view2 <- 3 view3 <- 2 plotPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) for (ii in objects) { pointsPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } view1 <- 2 view2 <- 3 view3 <- 1 plotPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) for (ii in objects) { pointsPDMnoaxis3( c(dna.data[land, view1, ii], dna.data[land, view2, ii]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } } #================================================================================== plot3Dmean <- function(dna) { land <- 1:dim(dna$mshape)[1] w1 <- defplotsize2(dna$rotated[, 1:2, ]) w2 <- defplotsize2(dna$rotated[, c(1, 3), ]) w3 <- defplotsize2(dna$rotated[, c(2, 3), ]) width <- max(c(w1$width, w2$width, w3$width)) xl <- min(c(w1$xl, w2$xl, w3$xl)) xu <- xl + width yl <- min(c(w1$yl, w2$yl, w3$yl)) yu <- yl + width par(mfrow = c(2, 2)) par(pty = "s") plot( dna$mshape[land, 1], dna$mshape[land, 2], xlim = c(xl, xu), ylim = c(yl, yu), xlab = " ", ylab = " " ) text(dna$mshape[land, 1], dna$mshape[land, 2], land) lines(dna$mshape[land, 1], dna$mshape[land, 2]) plot( dna$mshape[land, 1], dna$mshape[land, 3], xlim = c(xl, xu), ylim = c(yl, yu), xlab = " ", ylab = " " ) text(dna$mshape[land, 1], dna$mshape[land, 3], land) lines(dna$mshape[land, 1], dna$mshape[land, 3]) plot( dna$mshape[land, 2], dna$mshape[land, 3], xlim = c(xl, xu), ylim = c(yl, yu), xlab = " ", ylab = " " ) text(dna$mshape[land, 2], dna$mshape[land, 3], land) lines(dna$mshape[land, 2], dna$mshape[land, 3]) title("Procrustes mean shape estimate") } #================================================================================== plot3Dpca <- function(dna, pcno, joinline = c(1, 1)) { #choose subset w1 <- defplotsize2(dna$rotated[, 1:2, ]) w2 <- defplotsize2(dna$rotated[, c(1, 3), ]) w3 <- defplotsize2(dna$rotated[, c(2, 3), ]) width <- max(c(w1$width, w2$width, w3$width)) xl <- min(c(w1$xl, w2$xl, w3$xl)) - width / 4 xu <- xl + width * 1.5 yl <- min(c(w1$yl, w2$yl, w3$yl)) - width / 4 yu <- yl + width * 1.5 k <- dim(dna$mshape)[1] lineorder <- joinline par(mfrow = c(1, 1)) cat("X-Y view \n") view1 <- 1 view2 <- 2 view3 <- 3 land <- c(1:k) for (j in 1:10) { for (ii in-12:12) { mag <- ii / 4 plotPDMnoaxis3( c(dna$mshape[land, view1], dna$mshape[land, view2]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } for (ii in-11:11) { mag <- -ii / 4 plotPDMnoaxis3( c(dna$mshape[land, view1], dna$mshape[land, view2]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } } #choose subset par(mfrow = c(1, 1)) cat("X-Z view \n") view1 <- 1 view2 <- 3 view3 <- 2 land <- c(1:k) for (j in 1:10) { for (ii in-12:12) { mag <- ii / 4 plotPDMnoaxis3( c(dna$mshape[land, view1], dna$mshape[land, view2]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } for (ii in-11:11) { mag <- -ii / 4 plotPDMnoaxis3( c(dna$mshape[land, view1], dna$mshape[land, view2]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } } #choose subset par(mfrow = c(1, 1)) cat("Y-Z view \n") view1 <- 2 view2 <- 3 view3 <- 1 land <- c(1:k) for (j in 1:10) { for (ii in-12:12) { mag <- ii / 4 plotPDMnoaxis3( c(dna$mshape[land, view1], dna$mshape[land, view2]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } for (ii in-11:11) { mag <- -ii / 4 plotPDMnoaxis3( c(dna$mshape[land, view1], dna$mshape[land, view2]), c(dna$pcar[((view1 - 1) * k + (land)), pcno], dna$pcar[((view2 - 1) * k + (land)), pcno]), mag * dna$pcasd[pcno], xl, xu, yl, yu, lineorder, 1 ) } } } #================================================================================== banner1 <- function(char) { par(mfrow = c(1, 1)) plot( c(0, 0), c(1, 1), axes = FALSE, type = "n", xlab = " ", ylab = " " ) a1 <- char if (length(a1) == 2) a1 <- paste(a1[1], a1[2]) if (length(a1) == 3) a1 <- paste(a1[1], a1[2], a1[3]) if (is.character(a1) == FALSE) char <- as.character(a1) title(a1) } #================================================================================== banner4 <- function(a1, a2, a3, a4) { par(mfrow = c(2, 2)) plot( c(0, 0), c(1, 1), axes = FALSE, type = "n", xlab = " ", ylab = " " ) if (length(a1) == 2) a1 <- paste(a1[1], a1[2]) if (length(a1) == 3) a1 <- paste(a1[1], a1[2], a1[3]) if (is.character(a1) == FALSE) a1 <- as.character(a1) title(a1) plot( c(0, 0), c(1, 1), axes = FALSE, type = "n", xlab = " ", ylab = " " ) if (length(a2) == 2) a2 <- paste(a2[1], a2[2]) if (length(a2) == 3) a2 <- paste(a2[1], a2[2], a2[3]) if (is.character(a2) == FALSE) a2 <- as.character(a2) title(a2) plot( c(0, 0), c(1, 1), axes = FALSE, type = "n", xlab = " ", ylab = " " ) if (length(a3) == 2) a3 <- paste(a3[1], a3[2]) if (length(a3) == 3) a3 <- paste(a3[1], a3[2], a3[3]) if (is.character(a3) == FALSE) a3 <- as.character(a3) title(a3) plot( c(0, 0), c(1, 1), axes = FALSE, type = "n", xlab = " ", ylab = " " ) if (length(a4) == 2) a4 <- paste(a4[1], a4[2]) if (length(a4) == 3) a4 <- paste(a4[1], a4[2], a4[3]) if (is.character(a4) == FALSE) a4 <- as.character(a4) title(a4) } ####### #exact Gaussian MLE - isotropic distribution #######not fully tested yet #================================================================================== isomle <- function(x) { if (is.complex(x)) { tem <- array(0, c(nrow(x), 2, ncol(x))) tem[, 1,] <- Re(x) tem[, 2,] <- Im(x) x <- tem } k <- dim(x)[1] m <- dim(x)[2] n <- dim(x)[3] if (m > 2) { print("Only valid for 2D data") } if (m == 2) { pm <- rep(0, times = 2 * k - 3) tem <- procrustes2d(x) tem1 <- bookstein.shpv(tem$mshape) sigm <- sum(diag(var(tem$tan))) / (n - 1) / 2 #cat("Isotropic shape MLE \n") pm[1:(k - 2)] <- tem1[3:k, 1] pm[(k - 1):(2 * k - 4)] <- tem1[3:k, 2] pm[2 * k - 3] <- 10 ans <- nlm(objfuniso, hessian = TRUE, pm, uu = x) #while (ans$code!=1){ #print("code not equal 1") #print(pm) #pm<-pm+rnorm(2*k-3,0,0.1) #pm[2*k-3]<-abs(pm[2*k-3]) #ans<-nlm(objfuniso,hessian=TRUE,pm,uu=x) #print(ans) #} out <- list( code = 0, mshape = 0, tau = 0, kappa = 0, varcov = 0, gradient = 0 ) mn <- matrix(0, k, 2) mn[1, 1] <- -0.5 mn[2, 1] <- 0.5 mn[3:k, 1] <- ans$estimate[1:(k - 2)] mn[3:k, 2] <- ans$estimate[(k - 1):(2 * k - 4)] out$mshape <- mn out$code <- ans$code out$loglike <- -ans$minimum out$gradient <- ans$gradient out$tau <- sqrt(1 / ans$estimate[2 * k - 3] ** 2) out$kappa <- centroid.size(mn) ** 2 / (4 * out$tau ** 2) out$varcov <- solve(ans$hessian) out$se <- c(sqrt(diag(out$varcov))) out$se[2 * k - 3] <- out$se[2 * k - 3] * out$tau ** 2 out } } #================================================================================== objfuniso <- function(pm, uu) { k <- dim(uu)[1] h <- defh(k - 1) zero <- matrix(0, k - 1, k) L1 <- cbind(h, zero) L2 <- cbind(zero, h) L <- rbind(L1, L2) mustar <- c(-1 / 2, 1 / 2, pm[1:(k - 2)], 0, 0, pm[(k - 1):(2 * k - 4)]) mu <- L %*% mustar obj <- -loglikeiso2(uu, mu, 1 / pm[2 * k - 3]) obj } #================================================================================== loglikeiso <- function(uu, mu, s) { nsam <- dim(uu)[3] sum <- 0 for (i in 1:nsam) { sum <- sum + log(isodens(uu[, , i], mu, s)) } sum } #================================================================================== loglikeiso2 <- function(uu, mu, s) { nsam <- dim(uu)[3] sum <- 0 for (i in 1:nsam) { sum <- sum + isologdens(uu[, , i], mu, s) } sum } #================================================================================== isodens <- function(usam, mu, s) { k <- dim(usam)[1] u <- kendall.shpv(usam) uuu <- u[, 1] vvv <- u[, 2] up <- c(1, uuu, 0, vvv) vp <- c(0, -vvv, 1, uuu) usu <- t(up) %*% up beta <- c(t(mu) %*% up, t(mu) %*% vp) sin2rho <- 1 - t(beta) %*% beta / (usu * c(t(mu) %*% mu)) kappa <- c(t(mu) %*% mu) / (4 * s ** 2) #finf<-gamma(k-1)*pi/(pi*usu)**(k-1) dens <- oneFone(k - 2, 2 * kappa * (1 - sin2rho)) %*% exp(-2 * kappa * sin2rho) dens } #================================================================================== isologdens <- function(usam, mu, s) { k <- dim(usam)[1] u <- kendall.shpv(usam) uuu <- u[, 1] vvv <- u[, 2] up <- c(1, uuu, 0, vvv) vp <- c(0, -vvv, 1, uuu) usu <- t(up) %*% up beta <- c(t(mu) %*% up, t(mu) %*% vp) sin2rho <- 1 - t(beta) %*% beta / (usu * c(t(mu) %*% mu)) kappa <- c(t(mu) %*% mu) / (4 * s ** 2) #finf<-lgamma(k-1)+log(pi)-(k-1)*log(pi*usu) dens <- loneFone(k - 2, 2 * kappa * (1 - sin2rho)) - 2 * kappa * sin2rho c(dens) } #================================================================================== loneFone <- function(r, x) { #note this is log 1F1(-r,1,-x) if (x > 1) { sum1 <- r * log(x) sum <- 0 for (j in 0:r) { sum <- sum + choose(r, j) * x ** (j - r) / gamma(j + 1) } out <- sum1 + log(sum) } if (x <= 1) { sum <- 0 for (j in 0:r) { sum <- sum + choose(r, j) * x ** (j) / gamma(j + 1) } out <- log(sum) } out } #================================================================================== ild_kendall.shpv <- function(x) { k <- dim(x)[1] h <- defh(k - 1) zz <- h %*% x kendall <- (zz[2:(k - 1), 1] + 1i * zz[2:(k - 1), 2]) / (zz[1, 1] + 1i * zz[1, 2]) kendall <- cbind(Re(kendall), Im(kendall)) kendall } #================================================================================== oneFone <- function(r, x) { #note this is 1F1(-r,1,-x) sum <- 0 for (j in 0:r) { sum <- sum + choose(r, j) * x ** j / gamma(j + 1) } sum } #================================================================================== permutationtest <- function(A, B, nperms = 200) { A1 <- A A2 <- B B <- nperms nsam1 <- dim(A1)[3] nsam2 <- dim(A2)[3] Gtem <- Goodalltest(A1, A2) Htem <- Hotellingtest(A1, A2) Gumc <- Gtem$F Humc <- Htem$F Gtabpval <- Gtem$pval Htabpval <- Htem$pval if (B > 0) { Apool <- array(0, c(dim(A1)[1], dim(A1)[2], dim(A1)[3] + dim(A2)[3])) Apool[, , 1:nsam1] <- A1 Apool[, , (nsam1 + 1):(nsam1 + nsam2)] <- A2 out <- list( H = 0, H.pvalue = 0, H.table.pvalue = 0, G = 0, G.pvalue = 0, G.table.pvalue = 0 ) Gu <- rep(0, times = B) Hu <- rep(0, times = B) cat("Permutations - sampling without replacement: ") cat(c("No of permutations = ", B, "\n")) for (i in 1:B) { cat(c(i, " ")) select <- sample(1:(nsam1 + nsam2)) Gu[i] <- Goodalltest(Apool[, , select[1:nsam1]] , Apool[, , select[(nsam1 + 1):(nsam2 + nsam1)]])$F Hu[i] <- Hotellingtest(Apool[, , select[1:nsam1]], Apool[, , select[(nsam1 + 1):(nsam1 + nsam2)]])$F } Gu <- sort(Gu) numbig <- length(Gu[Gumc < Gu]) pvalG <- (1 + numbig) / (B + 1) Hu <- sort(Hu) numbig <- length(Hu[Humc < Hu]) pvalH <- (1 + numbig) / (B + 1) cat(" \n") out$H <- Humc out$H.pvalue <- pvalH out$H.table.pvalue <- Htabpval out$G <- Gumc out$G.pvalue <- pvalG out$G.table.pvalue <- Gtabpval } if (B == 0) { out <- list( H = 0, H.table.pvalue = 0, G = 0, G.table.pvalue = 0 ) out$H <- Humc out$H.table.pvalue <- Htabpval out$G <- Gumc out$G.table.pvalue <- Gtabpval } out } #================================================================================== permutationtest <- permutationtest2 #================================================================================== frechet <- function(x, mean = "intrinsic") { if (mean == "intrinsic") { option <- 1 } if (mean == "partial.procrustes") { option <- 2 } if (mean == "full.procrustes") { option <- 3 } if (mean == "mle") { option <- 4 } if (is.double(mean)) { if (mean > 0) { option <- -mean } } n <- dim(x)[3] for (i in 1:n) { x[, , i] <- x[, , i] / centroid.size(x[, , i]) } if (option < 4) { pm <- procGPA(x, scale = FALSE, tol1 = 10 ^ (-8))$mshape m <- dim(x)[2] k <- dim(x)[1] ans <- list( mshape = 0, var = 0, code = 0, gradient = 0 ) out <- nlm( objfun, hessian = TRUE, c(pm), uu = x, option = option, iterlim = 1000 ) B <- matrix(out$estimate, k, m) ans$mshape <- procOPA(pm, B)$Bhat ans$var <- out$minimum ans$code <- out$code ans$gradient <- out$gradient } if (option == 4) { pm <- procGPA(x, scale = FALSE, tol1 = 10 ^ (-8))$mshape m <- dim(x)[2] k <- dim(x)[1] if (m == 2) { theta <- c(log(centroid.size(pm) ** 2 / (4 * 0.1 ** 2)), pm) ans <- list( mshape = 0, kappa = 0, code = 0, gradient = 0 ) out <- nlm( objfun4, hessian = TRUE, theta, uu = x, iterlim = 1000 ) B <- matrix(out$estimate[-1], k, m) ans$mshape <- procOPA(pm, B)$Bhat ans$kappa <- exp(out$estimate[1]) ans$loglike <- -out$minimum ans$code <- out$code ans$gradient <- out$gradient } if (m != 2) { print("MLE is only appropriate for planar shapes") } } ans } #================================================================================== objfun <- function(pm, uu, option) { m <- dim(uu)[2] k <- dim(uu)[1] pm <- matrix(pm, k, m) sum <- 0 for (i in 1:dim(uu)[3]) { if (option == 1) { sum <- sum + (riemdist(pm, uu[, , i])) ** 2 } if (option == 2) { sum <- sum + 4 * sin(riemdist(pm, uu[, , i]) / 2) ** 2 } if (option == 3) { sum <- sum + sin(riemdist(pm, uu[, , i])) ** 2 } if (option < 0) { h <- -option sum <- sum + ((1 - cos(riemdist(pm, uu[, , i])) ** (2 * h)) / h) } } sum } #================================================================================== objfun4 <- function(pm, uu) { m <- dim(uu)[2] k <- dim(uu)[1] n <- dim(uu)[3] kappa <- exp(pm[1]) pm <- matrix(pm[-1], k, m) sum <- 0 for (i in 1:n) { sin2rho <- sin(riemdist(pm, uu[, , i])) ** 2 sum <- sum + loneFone(k - 2, 2 * kappa * (1 - sin2rho)) - 2 * kappa * sin2rho } - sum } #================================================================================== MDSshape <- function(x, alpha = 1, projalpha = 1 / 2) { mu <- procGPA(x)$mshape k <- dim(x)[1] n <- dim(x)[3] m <- dim(x)[2] H <- defh(k - 1) sum <- matrix(0, k - 1, k - 1) for (i in 1:n) { Z <- preshape(x[, , i]) if (alpha == 1) { sum <- sum + (Z) %*% t((Z)) } if (alpha == 1 / 2) { ee <- eigen((Z) %*% t((Z)), symmetric = TRUE) sum <- sum + ee$vectors %*% diag(sqrt(abs(ee$values))) %*% t(ee$vectors) } } eig <- eigen(sum / n, symmetric = TRUE) lam <- eig$values if (m == 2) { if (projalpha == 1 / 2) { meanshape <- cbind( t(H) %*% (sqrt(lam[1]) * eig$vectors[, 1]) / sqrt(lam[1] + lam[2]) , -t(H) %*% (sqrt(lam[2]) * eig$vectors[, 2]) / sqrt(lam[1] + lam[2]) ) } if (projalpha == 1) { lambar <- (lam[1] + lam[2]) / 2 meanshape <- cbind(t(H) %*% (sqrt(lam[1] - lambar + 1 / m) * eig$vectors[, 1]) , -t(H) %*% (sqrt(lam[2] - lambar + 1 / m) * eig$vectors[, 2])) } } if (m == 3) { if (projalpha == 1 / 2) { meanshape <- cbind( t(H) %*% (sqrt(lam[1]) * eig$vectors[, 1]) / sqrt(lam[1] + lam[2] + lam[3]) , t(H) %*% (sqrt(lam[2]) * eig$vectors[, 2]) / sqrt(lam[1] + lam[2] + lam[3]), t(H) %*% (sqrt(lam[3]) * eig$vectors[, 3]) / sqrt(lam[1] + lam[2] + lam[3]) ) } if (projalpha == 1) { lambar <- (lam[1] + lam[2] + lam[3]) / 3 meanshape <- cbind( t(H) %*% (sqrt(abs( lam[1] - lambar + 1 / m )) * eig$vectors[, 1]) , t(H) %*% (sqrt(abs( lam[2] - lambar + 1 / m )) * eig$vectors[, 2]) , t(H) %*% (sqrt(abs( lam[3] - lambar + 1 / m )) * eig$vectors[, 3]) ) } } if (riemdist(meanshape, mu) > riemdist(meanshape, mu, reflect = TRUE)) { meanshape[, m] <- -meanshape[, m] } meanshape } ################################################################################################ #The Procrustes routines in the next part were initially # written by Mohammad Faghihi (University of Leeds) 1993, although many improvements, corrections, # and speed-ups have been done since then. # add(a3) compute the summation of a3[,,i]'s # bgpa(a3) compute the scaling coefficients (bi's) # close1(a) adds one additional row to matrix a that is the same as the first row # cnt3(a3) replace each a3[ , , i] by fcnt(a3[ , , i]) # del(po, w1) plots point of po and joins them by contiguity matrix w1. # dif(a3) compute sum( tr (xi-xj)'(xi-xj) )/n^2 for i kk) { # qq<-diag(cov(vec1(zd))) qq <- rep(0, times = nn) for (i in 1:n) { qq[i] <- var(omat[i, ]) * (n - 1) / n omat[i, ] <- omat[i, ] - mean(omat[i, ]) } omat <- diag(sqrt(1 / qq)) %*% omat n <- kk Lmat <- t(omat) %*% omat / n eig <- eigen(Lmat, symmetric = TRUE) U <- eig$vectors lambda <- eig$values V <- omat %*% U vv <- rep(0, times = n) for (i in 1:n) { vv[i] <- sqrt(t(V[, i]) %*% V[, i]) V[, i] <- V[, i] / vv[i] } delta <- sqrt(abs(lambda / n)) * vv od <- order(delta, decreasing = TRUE) delta <- delta[od] V <- V[, od] h <- sqrt(s / aa) * V[, 1] } if (kk >= nn) { zz <- cor(vec1(zd)) h <- sqrt(s / aa) * eigen(zz)$vectors[, 1] } h <- abs(h) return(h) } #================================================================================== close1 <- function(a) { a1 <- matrix(0:0, nrow = dim(a)[1] + 1, ncol = dim(a)[2]) for (i in 1:dim(a)[1]) { a1[i,] <- a[i,] } a1[dim(a)[1] + 1,] <- a[1,] a1 } #================================================================================== cnt3 <- function(a3) { #zz <- array(c(0:0), dim = c(dim(a3)[1], dim(a3)[2], dim(a3)[3])) #for(i in 1:dim(a3)[3]) { #zz[, , i] <- fcnt(a3[, , i]) #} zz <- apply(a3, 3, fcnt) zz <- array(zz, dim(a3)) return(zz) } #================================================================================== del <- function(po, w1) { plot(po, type = "n", xlab = "x", ylab = "y") text(po) n <- dim(po)[1] for (i in 1:n) { for (j in i:n) { if (w1[i, j] > 0) { a1 <- c(po[i, 1], po[j, 1]) b1 <- c(po[i, 2], po[j, 2]) lines(a1, b1) } } } } #================================================================================== dis <- function(a, b, c) { d <- 0 d[1] <- sqrt((a[1] - b[1]) ^ 2 + (a[2] - b[2]) ^ 2) d[2] <- sqrt((a[1] - c[1]) ^ 2 + (a[2] - c[2]) ^ 2) d[3] <- sqrt((c[1] - b[1]) ^ 2 + (c[2] - b[2]) ^ 2) d } #================================================================================== dif.old <- function(a3) { s <- 0 for (i in 1:(dim(a3)[3] - 1)) { for (j in (i + 1):dim(a3)[3]) { s <- s + ((Enorm(a3[, , i] - a3[, , j])) ^ 2) } } return(s) } #dif<-function(a3) #original (slow) version #{ # s <- 0 #n<-dim(a3)[3] #mshape<-add(a3)/n #psum<-0 #for (i in 1:n){ #x<-a3[,,i]-mshape #psum<-psum+sum(diag(t(x)%*%x)) #} #psum*n #} #dif<-function(a3) ##faster version #{ #x<-sweep(a3,c(1,2),apply(a3,c(1,2),mean)) #z<-Enorm(as.vector(x))^2/dim(a3)[3] #z #} #================================================================================== dif <- function (a3) { #version that does not depend on scale of original measurements # assumes already centred cc <- centroid.size(add(a3) / dim(a3)[3]) x <- sweep(a3, c(1, 2), apply(a3, c(1, 2), mean)) z <- Enorm(as.vector(x) / cc) ^ 2 / dim(a3)[3] z } #================================================================================== fJ <- function(n) { zz <- matrix(1:1, n, n) H <- diag(n) - (1 / n) * zz H } #================================================================================== fcel <- function(n, d) { v <- ceiling(sqrt(n)) p <- matrix(c(0:0), n, 2) for (i in 1:v) { for (j in 1:v) { if ((v * (i - 1) + j) < (n + 1)) { p[(v * (i - 1) + j), 1] <- (d / 4) * (-1) ^ i + (d * j) p[(v * (i - 1) + j), 2] <- i * ((d * sqrt(3)) / 2) } } } p } #================================================================================== fcnt <- function(a) { aa <- fJ(dim(a)[1]) %*% a aa } #================================================================================== fgpa.singleiteration <- function(a3, p) { # Note this is an approximation to GPA - # It carries out an initial match by optimally rotating all the data, # the rescaling the observations, then rotating the observations # NB it does not repeat this until convergence, but in practice # for many real datasets this gives an excellent registration # zd <- list( rot. = 0, r.s.r. = 0, Gpa = 0, I.no. = 0, mshape = 0 ) zd$rot. <- rgpa(a3, p) zz <- rgpa(sgpa(zd$rot.$rotated), p) zd$r.s.r. <- zz$rotated zd$Gpa <- zz$dif zd$I.no. <- zz$r.no. zd$mshape <- msh(zd$r.s.r.) return(zd) } #================================================================================== fgpa <- function(a3, tol1, tol2, proc.output = FALSE, reflect = FALSE) { # # Fully iterative fgpa (now assumes a3 is already centred) # # zd <- list( rot. = 0, r.s.r. = 0, Gpa = 0, I.no. = 0, mshape = 0 ) p <- tol1 if (proc.output) { cat(" Step | Objective function | change \n") } if (proc.output) { cat("---------------------------------------------------\n") } x1 <- dif(a3) if (proc.output) { cat("Initial objective fn", x1, " - \n") } if (proc.output) { cat("-----------------------------------------\n") } zz <- rgpa(a3, p, proc.output = proc.output, reflect = reflect) x2 <- dif(zz$rotated) if (proc.output) { cat("Rotation step 0", x2, x1 - x2, " \n") } if (proc.output) { cat("-----------------------------------------\n") } ii <- 1 zz <- rgpa( sgpa(zz$rotated, proc.output = proc.output), p, proc.output = proc.output, reflect = reflect ) x1 <- x2 x2 <- dif(zz$rotated) rho <- x1 - x2 if (proc.output) { cat("Scale/rotate step ", ii, x2, rho, " \n") } if (proc.output) { cat("-----------------------------------------\n") } if (rho > tol2) { while (rho > tol2) { x1 <- x2 ii <- ii + 1 zz <- rgpa( sgpa(zz$rotated, proc.output = proc.output), p, proc.output = proc.output, reflect = reflect ) x2 <- dif(zz$rotated) rho <- x1 - x2 if (proc.output) { cat("Scale/rotate step ", ii, x2, rho, " \n") } if (proc.output) { cat("-----------------------------------------\n") } } } zd$r.s.r. <- zz$rotated zd$Gpa <- zz$dif zd$I.no. <- ii zd$mshape <- msh(zd$r.s.r.) return(zd) } #================================================================================== fgpa.rot <- function(a3, tol1, tol2, proc.output = FALSE, reflect = FALSE) { # Assumes that a3 has been centred already zd <- list( rot. = 0, r.s.r. = 0, Gpa = 0, I.no. = 0, mshape = 0 ) p <- tol1 zz <- rgpa(a3, p, proc.output = proc.output, reflect = reflect) x1 <- msh(zz$rotated) ii <- zz$r.no. # zz <- rgpa(zz$rotated, p,proc.output=proc.output,reflect=reflect) #x2<-msh(zz$rotated) #rho<-riemdist(x1,x2) # while (rho > tol2){ #print(rho) #x1<-x2 #ii<-ii+1 # zz <- rgpa(zz$rotated, p,proc.output=proc.output) # x2<-msh(zz$rotated) #rho<-riemdist(x1,x2) # } zd$r.s.r. <- zz$rotated zd$Gpa <- zz$dif zd$I.no. <- ii zd$mshape <- msh(zd$r.s.r.) return(zd) } #================================================================================== fopa <- function(a, b) { abar <- fcnt(a) bbar <- fcnt(b) q1 <- sum(diag(abar %*% t(abar))) q2 <- fos(a, b) ^ 2 * sum(diag(bbar %*% t(bbar))) q3 <- 2 * fos(a, b) * sum(diag(fort(a, b) %*% t(abar) %*% bbar)) gs <- q1 + q2 - q3 gs } #================================================================================== fort.ROTATEANDREFLECT <- function(a, b) { x <- t(fcnt(a)) %*% fcnt(b) xsvd <- svd(x) t <- xsvd$v %*% t(xsvd$u) return(t) } #================================================================================== fos.REFLECT <- function(a, b) { abar <- fcnt(a) bbar <- fcnt(b) z <- ftrsq(abar, bbar) / sum(diag(t(bbar) %*% bbar)) z } #================================================================================== fos <- function (a, b) { z <- cos(riemdist(a, b)) * centroid.size(a) / centroid.size(b) z } #================================================================================== ftrsq <- function(a, b) { z <- sum(sqrt(abs(eigen( t(b) %*% a %*% t(a) %*% b )$values))) z } #================================================================================== graf <- function(a3) { l <- 0 xmin <- 0 xmax <- 0 ymin <- 0 ymax <- 0 for (i in 1:dim(a3)[3]) { xmin[i] <- min(a3[, 1, i]) xmax[i] <- max(a3[, 1, i]) ymin[i] <- min(a3[, 2, i]) ymax[i] <- max(a3[, 2, i]) } l <- c(min(xmin), min(ymin), max(xmax), max(ymax)) plot((min(l) - 1):(max(l) + 1), (min(l) - 1):(max(l) + 1), type = "n") for (i in 1:dim(a3)[3]) { lines(close1(a3[, , i])) } } #================================================================================== msh <- function(a3) { s <- 0 # print("finding mean shape") m <- apply(a3, c(1, 2), mean) # print("found mean shape") # for(i in 1:dim(a3)[3]) { # s <- s + a3[, , i] # } # m <- (1/dim(a3)[3]) * s return(m) } #Enorm<-function(a) #{ # return(sqrt(sum(diag(t(a) %*% a)))) #} #================================================================================== rgpa <- function(a3, p, reflect = FALSE, proc.output = FALSE) { # assumes a3 already centred now if (reflect == TRUE) { fort <- fort.ROTATEANDREFLECT } zd <- list( rotated = 0, dif = 0, r.no. = 0, inc = 0 ) l <- dim(a3)[3] a <- 0 d <- 0 n <- 0 # zz <- cnt3(a3) zz <- a3 # print("Rotations ...") # print("Iteration,meanSS before,meanSS after,difference,tolerance") d[1] <- 10 ^ 12 d[2] <- dif(zz) a[1] <- d[2] s <- add(zz) # print(c(d[1],d[2])) if (dif(zz) > p) { while (d[1] - d[2] > p) { n <- n + 1 d[1] <- d[2] for (i in 1:l) { old <- zz[, , i] zz[, , i] <- old %*% fort(((1 / (l - 1)) * (s - old)), old) s <- s - old + zz[, , i] } d[2] <- dif(zz) a[n + 1] <- d[2] # print(c(n,d[1],d[2],d[1]-d[2],p)) if (proc.output) { cat(" Rotation iteration ", n, d[2], d[1] - d[2], " \n") } } } zd$rotated <- zz zd$dif <- a zd$r.no. <- n zd$inc <- d[1] - d[2] if (proc.output) { cat("-----------------------------------------\n") } fort <- fort.ROTATION return(zd) } # sgpa<-function(a3) #{ # zz <- a3 # a <- bgpa(zz) # for(i in 1:dim(a3)[3]) { # zz[, , i] <- a[i] * a3[, , i] # } # return(zz) #} #================================================================================== sgpa <- function(a3, proc.output = FALSE) { #assumes a3 is centred zz <- a3 di <- dim(a3) a <- bgpa(zz, proc.output = proc.output) i <- rep(dim(a3)[1] * dim(a3)[2], dim(a3)[3]) sequen <- rep(a, i) zz <- array(as.vector(a3) * sequen, di) if (proc.output) { cat(" Scaling updated \n") } return(zz) } #================================================================================== sh <- function(a) { u1 <- (a[2, 1] - a[1, 1]) / sqrt(2) u2 <- (a[2, 2] - a[1, 2]) / sqrt(2) v1 <- (2 * a[3, 1] - a[2, 1] - a[1, 1]) / sqrt(6) v2 <- (2 * a[3, 2] - a[2, 2] - a[1, 2]) / sqrt(6) d <- c(0, 0) d[1] <- (u1 * v1 + u2 * v2) / (u1 ^ 2 + u2 ^ 2) d[2] <- (u1 * v2 - u2 * v1) / (u1 ^ 2 + u2 ^ 2) d } #================================================================================== sim1 <- function(n, d, s) { a <- fcel(n, d) sig <- matrix(c(1:1), n, 1)[, 1] sig <- sig * s b <- a b[, 1] <- rnorm(n, mean = a[, 1], sd = sig) b[, 2] <- rnorm(n, mean = a[, 2], sd = sig) b } #================================================================================== vec1 <- function(a3) { #zz <- array(c(0:0), dim = c((dim(a3)[1] * dim(a3)[2]), dim(a3)[3])) #for(i in 1:dim(a3)[3]) { #for(j in 1:dim(a3)[2]) { #for(k in 1:dim(a3)[1]) { #zz[((j - 1) * dim(a3)[1] + k), i] <- a3[k, j, i #] #} #} #} zz <- matrix(a3, dim(a3)[1] * dim(a3)[2], dim(a3)[3]) return(zz) } #================================================================================== fort.ROTATION <- function(a, b) { x <- t(fcnt(a)) %*% fcnt(b) xsvd <- svd(x) v <- xsvd$v u <- xsvd$u tt <- v %*% t(u) chk1 <- Re(prod(eigen(v)$values)) chk2 <- Re(prod(eigen(u)$values)) if ((chk1 < 0) && (chk2 > 0)) { v[, dim(v)[2]] <- v[, dim(v)[2]] * (-1) tt <- v %*% t(u) } if ((chk2 < 0) && (chk1 > 0)) { u[, dim(u)[2]] <- u[, dim(u)[2]] * (-1) tt <- v %*% t(u) } return(tt) } ############end of Mohammad Faghihi's (adapted) routines #alias functions (all lower-case) hotelling2d <- Hotelling2D hotellingtest <- Hotellingtest procrustesgpa <- procrustesGPA goodall2d <- Goodall2D goodalltest <- Goodalltest # alias TPSgrid <- tpsgrid #if you wish the default to *not* include reflection #invariance (as is normal in shape analysis) then you need the line below. fort <- fort.ROTATION ################################################################################ # # Datasets # ################################################################################ #================================================================================== # Gorillas #================================================================================== gorf.dat<-array(c(5,193,53,-27,0,0,0,33,-2,105,18,176,72,114,92,38 ,51,191,55,-31,0,0,0,33,25,106,56,171,98,105,99,15 ,36,187,59,-31,0,0,0,36,12,102,38,171,91,103,100,19 ,23,202,48,-30,0,0,0,39,3,103,33,180,84,112,94,28 ,30,185,62,-25,0,0,0,32,11,101,37,168,85,106,96,21 ,4,195,65,-21,0,0,0,34,-4,100,15,180,69,120,102,34 ,37,195,62,-32,0,0,0,35,20,101,50,173,102,105,105,22 ,41,191,58,-34,0,0,0,34,15,100,47,175,93,105,99,18 ,40,190,52,-33,0,0,0,38,13,107,44,176,88,113,102,31 ,-4,179,62,-21,0,0,0,29,1,89,9,164,70,111,100,36 ,41,206,53,-25,0,0,0,39,11,104,47,177,95,111,95,26 ,33,197,55,-30,0,0,0,35,7,106,39,175,89,111,95,24 ,-12,205,52,-15,0,0,0,38,-10,111,4,187,66,129,80,44 ,13,186,56,-32,0,0,0,34,8,101,25,166,80,105,97,26 ,20,186,45,-31,0,0,0,34,10,96,31,165,84,104,90,19 ,29,183,55,-31,0,0,0,32,10,98,39,163,82,106,95,17 ,11,203,57,-28,0,0,0,39,-2,106,23,182,77,122,100,36 ,37,187,54,-27,0,0,0,34,11,100,43,171,84,106,93,28 ,49,191,53,-31,0,0,0,35,21,102,54,172,94,98,99,18 ,-8,191,57,-34,0,0,0,32,-7,93,6,173,71,119,101,30 ,43,184,49,-32,0,0,0,33,14,100,49,165,91,99,98,20 ,57,185,62,-37,0,0,0,35,22,103,61,169,96,100,104,24 ,-10,196,55,-20,0,0,0,38,-10,107,5,181,73,123,88,46 ,20,195,60,-28,0,0,0,32,6,101,33,173,84,114,100,30 ,35,202,59,-27,0,0,0,34,6,108,41,182,83,117,99,31 ,1,188,60,-19,0,0,0,35,-2,99,12,170,70,119,93,45 ,24,194,52,-24,0,0,0,39,8,105,34,174,80,115,95,32 ,25,204,55,-27,0,0,0,34,7,108,35,185,83,118,92,32 ,36,198,47,-30,0,0,0,39,14,110,43,177,92,105,98,25 ,8,198,53,-35,0,0,0,34,4,101,22,175,82,111,100,24),c(2,8,30)) gorf.dat<-aperm(gorf.dat,c(2,1,3)) gorm.dat<-array(c(53,220,46,-35,0,0,0,37,12,122,58,204,93,117,103,28 ,57,219,50,-43,0,0,0,37,13,119,61,198,102,110,104,20 ,89,227,52,-47,0,0,0,32,35,120,93,201,131,92,104,4 ,46,222,51,-45,0,0,0,30,11,113,54,196,101,117,101,16 ,85,220,48,-38,0,0,0,39,28,125,87,203,121,106,103,7 ,64,208,43,-39,0,0,0,36,22,111,67,191,104,102,101,18 ,67,216,51,-37,0,0,0,35,17,119,68,191,108,109,94,15 ,35,236,61,-42,0,0,0,33,2,119,43,211,90,126,104,30 ,116,218,40,-38,0,0,0,41,41,124,116,201,133,94,103,12 ,56,234,60,-34,0,0,0,34,12,121,58,215,109,119,112,28 ,40,223,58,-36,0,0,0,34,9,113,46,202,97,120,112,24 ,94,223,49,-57,0,0,0,33,31,122,94,206,136,99,113,-1 ,68,222,59,-41,0,0,0,30,18,119,68,204,104,114,98,11 ,65,224,56,-33,0,0,0,35,15,130,67,205,108,115,95,20 ,67,214,52,-47,0,0,0,36,26,115,74,192,114,105,104,11 ,110,213,52,-46,0,0,0,37,42,121,109,190,133,97,108,-8 ,46,219,50,-42,0,0,0,36,11,121,56,199,104,108,102,21 ,79,209,66,-43,0,0,0,35,24,114,84,193,108,115,109,14 ,58,244,74,-22,0,0,0,37,7,131,64,219,98,128,100,29 ,43,236,64,-43,0,0,0,33,12,124,52,215,110,121,105,7 ,70,226,54,-37,0,0,0,39,28,121,74,204,122,105,107,7 ,68,224,55,-37,0,0,0,35,18,121,71,207,109,108,98,13 ,34,247,63,-35,0,0,0,35,4,124,45,225,104,135,110,29 ,49,236,59,-40,0,0,0,38,19,127,59,219,105,121,109,25 ,98,195,44,-44,0,0,0,36,30,116,98,177,121,89,105,10 ,109,208,49,-40,0,0,0,36,29,125,105,189,120,102,102,12 ,61,224,51,-35,0,0,0,41,15,122,67,206,107,121,103,25 ,43,213,49,-57,0,0,0,28,20,111,58,194,112,106,108,6 ,26,249,67,-14,0,0,0,38,-11,130,33,225,87,148,97,53),c(2,8,29)) gorm.dat<-aperm(gorm.dat,c(2,1,3)) #================================================================================== # mice #================================================================================== qset2.dat<-array(c(117.98,219.62,114.52,41.93,166.15,113.59,206.54,121.79,165.11,142.92,62.07,136.58 ,105.52,235.08,109.96,57.31,165.44,126.42,223.05,140.88,169.58,156.83,59.5,138.02 ,142.83,222,132.08,40.2,188.8,115.39,236.9,125.08,193.18,142.22,83.37,141.47 ,126.35,204.27,113.2,36.04,171.77,102.43,228.93,111.75,173.55,131.8,66.26,126.65 ,99.11,231.52,119.58,44.52,169.28,129.51,219.93,141.98,167.65,154.41,56.83,141.64 ,134.14,228.48,129.09,56.98,175.85,125.39,217.57,138.01,179.29,152.67,73.84,153.08 ,119.8,219.48,122.36,48.52,171.44,115.58,216.57,131.13,170.86,148.36,65.8,138.8 ,105.62,222.74,96.27,51.3,152.13,119.07,205.02,131.95,157.36,141.61,52.35,142.73 ,122.15,202.41,128.75,32.09,176.88,102.84,220.81,119.41,177.78,131.04,76.65,120.61 ,127.78,223.48,117.65,53.94,183.45,123.87,236.96,133.84,184.24,149.04,77.96,149.95 ,123.4,200.6,116.57,28.43,172.67,97.33,221.34,106.24,175.29,122.05,64.69,115.53 ,132.4,227.96,127.46,50.4,171.57,118.67,203.72,131.91,175.25,152.38,71.98,138.5 ,123.39,216.23,113.96,29.9,167.68,105.58,202.15,114.18,172.01,133.8,65.78,125.86 ,136.83,207.84,117.37,33.67,178.1,100.65,233.17,111.44,184.05,124.85,69.8,129.69 ,143.31,219.71,122.1,48.08,177.23,113.52,221.38,122.32,180.86,140.87,80.11,151.72 ,105.96,218.06,100.46,48.73,155.23,119.51,217.76,130.01,159.11,145.49,51.29,139.66 ,115.73,234.14,115.74,56.11,168.42,128.73,220.32,135.6,169.4,154.65,66.02,152.32 ,101.73,215.74,102.67,35.73,151.69,110.19,199.12,120.24,151.6,136.36,48.82,132.42 ,124.93,222.42,130.09,46.89,163.86,118.81,197.76,137.59,165.11,148.92,67.45,139.67 ,104.68,231.95,88.17,53.4,150.87,128.76,203.38,143.96,151.59,152.11,41.85,151.15 ,123.93,242.1,121.99,64.08,175.47,143.77,211.56,155.4,173.07,164.91,68.98,158.26 ,137.21,207.27,130.76,34.56,188.92,105.07,242.85,109.74,187.67,127.43,80.29,127.14 ,107.35,212.21,89.5,38.88,151.63,105.73,196.56,113.82,152.69,133.73,46.53,135.99),c(2,6,23)) qset2.dat<-aperm(qset2.dat,c(2,1,3)) qcet2.dat<-array(c(168.59,35.66,159.99,215.17,104.93,141.07,49.01,115.13,101.69,110.87,227.11,120.51 ,165,38.35,163.89,214.32,108.26,144.79,50.17,124.02,103.5,113.13,220.43,122.65 ,166.97,39.44,163.63,221.62,108.18,147.07,54.11,137.43,109.15,118.11,227.89,128.32 ,164.02,44.22,171.76,237.29,95.93,161.44,32,131.85,95.91,126.54,222.54,133.51 ,153.46,40.67,156.62,225.12,103.81,152.44,43.65,139.01,99.95,120.7,216.62,132.9 ,148.3,52.66,147.61,239.16,90.37,164.39,32.39,145.13,88.1,130.85,209.22,145.88 ,141.33,32.7,128.49,215.09,71.39,135.31,18.32,115.62,77.48,102.92,186.28,125.49 ,130.21,18.71,136.38,201.17,68.85,125.19,11.43,94.64,64.22,90.83,184.64,107 ,130,26.8,134.24,217.41,77.07,141.93,14.66,122.68,74.56,105.14,189.9,109.31 ,134.99,22.44,116.08,205.87,74.69,130.05,22.18,96.09,68.08,95.91,185.97,115.99 ,146.87,22.6,111.5,201.74,77.67,124.1,23.04,97.03,80.91,85.93,192.2,118.4 ,146.26,23.38,119.94,209.46,75.94,125.31,19.72,100.92,82.97,87.47,192.22,109.63 ,138.32,25.44,119.25,208.88,71.18,133.17,16.68,103.45,69.21,98.08,178.43,123.71 ,142.57,19.25,99.1,197.59,62.7,111.06,3.94,86.78,69.17,79.06,180.48,117.38 ,144.57,21.44,129,204.76,80.93,121.11,18.96,103.58,82.35,90.59,191.23,111.98 ,137.6,19.71,120.16,214.71,77.7,128.06,16.08,102.22,73.54,90.19,193.21,118.14 ,123.81,22.67,118.93,207.44,73.63,129.11,4.94,104.21,71.72,100.35,191.62,119.61 ,131.1,28.64,94.82,211.86,59.44,129.47,3.89,102.71,70.28,95.52,178.71,131.69 ,155.84,22.41,112.53,207.45,68.64,118.65,8.37,97.65,78.78,85.28,184.6,114.92 ,174.04,27.44,108.13,202.49,80.74,111.51,20.1,79.49,96.21,80.84,205.25,127.23 ,127.85,20.44,119.59,208.01,61.98,125.83,4.2,112.66,80.06,90.68,182.45,108.83 ,146.48,28.36,113.82,214.92,75.14,127.12,14.06,98.64,81.57,96.68,198.33,125.63 ,139.46,33.99,99.48,220.24,73.24,135.58,8.99,105,75.01,103.38,191.51,135.63 ,152.27,30.86,138.67,226.92,91.78,137.46,24.93,121.51,92.28,107.03,216.37,131.07 ,139.15,28.78,133.63,210.56,88.84,131.16,26.42,112.92,87.97,99.71,201.01,119.24 ,153.32,20.49,109.35,201.5,76.58,109.87,10.91,91.13,74.45,85.03,183.14,119.64 ,166.02,23.04,140.31,215.68,91.82,128.43,26.01,109.78,94.61,99.14,212.09,119.48 ,127.79,22.26,136.79,202.76,89.87,131.73,26.63,113.72,88.85,90.81,198.88,105.03 ,169.22,26.47,158.71,210.32,102.37,134.39,30.64,108.87,100.56,97.55,220.44,125.16 ,146.88,23.83,116.78,218.55,84.03,132.22,25.08,111.84,89.31,94.31,194.7,129.03), c(2,6,30)) qcet2.dat<-aperm(qcet2.dat,c(2,1,3)) qlet2.dat<-array(c(134.26,224.28,122.34,36.86,174.35,111.24,235.79,127.9,174.49,142.43,51.5,141.89 ,139.29,231.38,80.82,47.82,159.37,105.97,236.93,120.91,162.59,139.69,39.92,163.16 ,151.57,219.95,104.42,49.26,162.95,105.68,241.12,123.23,181.45,133.49,60.47,151.83 ,146.16,231.16,95.78,46.87,170.85,111.77,234.72,109.48,178.8,140.05,56.79,157.8 ,150.16,222.81,81.59,53.08,161.66,102.76,238.7,94,173.74,131.99,42.42,166.01 ,134.04,218.32,84.43,40.37,155.91,104.39,227.07,112.75,163.69,135.45,45.04,154 ,141,221.6,98.86,46.69,160.43,112.77,218.15,114.61,162.83,136.17,37.06,153.86 ,123.63,231.23,66.42,46.17,140.02,108.31,217.82,118,147.98,137.78,25.35,159.24 ,137.62,226.64,96.5,39.64,164.59,105.39,235.04,102.87,169.37,134.5,46.83,150.8 ,173.79,206.17,90.39,27.9,161.84,84.83,234.85,91.6,180.59,110.28,55.57,160.27 ,117.39,233.75,114.47,42.63,167.1,124.53,229.45,135.47,167.66,150.42,40.92,142.56 ,131.55,225.48,102.05,26.81,161.73,103.06,244.48,115.44,170.49,133.86,38.65,144.13 ,134.78,226.28,110.56,41.28,170.17,112.5,231.45,114.1,176.5,139.26,51.28,145.88 ,115.95,227.77,85.99,46.33,156.48,111.17,220.59,117.43,161.6,140.85,43.06,154.56 ,133.09,226.95,99.38,40.42,160.22,111.1,236.36,117.74,168.23,138.63,44.79,149.37 ,125.36,216.11,93.92,36.95,161.42,103.33,220.04,104.98,165.19,129.44,43.48,133.74 ,123.1,202.86,99.27,42.82,157.22,98.93,206.46,111.18,162.05,129.3,64.39,123.39 ,121.37,217.11,108.09,34.09,155.47,105.03,214.66,121.61,160.24,135.04,48.64,133.8 ,120.54,232.1,85.37,53.23,157.81,109.98,213.66,117.86,169.01,146.01,38.88,151.63 ,126.42,222.64,82.96,46.57,157.34,100.52,218.67,101.9,165.27,131.51,42.51,147.56 ,126.91,220.11,105.38,35.76,158.15,109.6,207.12,117.23,160.11,138.72,40.17,142.75 ,117.87,227.17,113.96,49.9,163.19,119.44,228.16,124.54,163.3,152.93,42.17,141.99 ,125.23,224.48,93.4,39.47,166.22,109.39,234.83,108.47,165.65,139.89,42.06,144.54), c(2,6,23)) qlet2.dat<-aperm(qlet2.dat,c(2,1,3)) #================================================================================== digit3.dat<-c(9,27,12,31,17,36,26,39,34,37,36,33,38,27,35,19,30,15,21,14,21,8,16,6,8,5 ,17,40,21,38,26,36,27,32,25,28,22,27,19,29,24,25,26,20,28,16,26,13,18,14,15,17 ,19,38,24,38,29,33,30,29,27,24,21,25,17,26,27,24,30,22,31,19,31,16,27,15,24,15 ,9,40,15,43,24,41,29,36,24,30,20,26,12,22,20,22,24,20,21,16,18,14,13,12,9,10 ,14,41,21,42,29,42,35,37,32,33,26,30,16,26,25,26,29,24,33,20,30,16,23,11,16,12 ,24,39,28,40,35,38,38,35,34,30,29,27,22,24,27,24,29,22,31,19,28,15,20,11,13,12 ,9,39,15,39,21,40,25,36,23,31,21,27,19,25,21,25,23,24,25,22,22,19,15,17,8,17 ,8,38,14,41,25,43,29,38,25,33,18,29,8,28,12,27,16,25,18,23,13,21,7,21,1,22 ,4,34,12,39,22,42,31,36,27,30,23,28,11,25,20,25,22,24,22,22,19,19,13,18,8,18 ,21,36,25,37,31,36,33,32,32,28,29,25,27,22,29,21,31,20,31,18,28,16,24,16,20,16 ,14,40,20,39,25,37,27,31,26,28,20,29,16,31,21,28,25,23,28,16,25,13,17,15,13,18 ,12,40,20,42,30,42,36,33,31,24,23,22,16,23,25,22,31,18,33,13,31,9,24,8,17,8 ,9,35,17,36,26,34,30,31,26,27,20,25,13,27,19,25,23,21,26,15,22,12,12,12,7,13 ,17,38,24,39,30,37,34,34,31,28,22,25,16,28,21,26,27,24,30,20,26,15,18,14,10,17 ,21,35,27,36,36,35,39,28,38,22,34,18,28,19,31,18,33,17,31,15,26,15,20,17,14,20 ,16,40,20,43,25,39,27,31,24,24,19,21,17,23,19,22,21,21,23,21,22,18,19,16,15,16 ,15,41,21,45,34,44,40,39,36,35,26,30,16,29,24,25,28,20,31,16,28,14,21,14,12,12 ,11,42,22,42,32,39,35,34,32,29,25,26,20,27,25,26,31,23,35,19,31,14,21,12,16,15 ,5,44,15,43,24,41,29,36,22,28,13,28,5,29,14,28,24,26,29,22,26,19,17,17,10,20 ,14,37,19,39,25,38,28,32,25,26,20,22,14,23,17,23,21,20,23,17,21,15,16,15,11,15 ,16,35,22,38,30,36,32,29,29,23,23,20,17,20,20,19,24,17,26,14,21,11,16,12,12,15 ,14,38,17,40,25,42,28,38,27,32,24,28,20,25,23,25,26,24,28,21,24,18,18,17,10,18 ,7,40,13,43,22,45,31,42,27,38,21,34,13,32,18,31,24,30,27,27,23,23,15,22,6,22 ,14,35,21,36,26,34,31,30,28,26,25,22,21,18,21,17,22,16,23,15,20,12,13,10,5,10 ,10,46,17,47,27,43,29,36,26,30,22,29,16,28,20,27,21,25,23,21,21,19,15,20,9,20 ,18,39,24,42,33,41,38,35,37,30,32,28,28,27,33,22,37,18,41,15,37,13,29,11,21,12 ,18,38,22,42,30,42,34,36,33,32,29,30,22,28,25,26,28,24,28,20,27,19,22,18,18,18 ,9,41,17,43,30,40,34,31,30,23,23,19,11,19,15,17,18,13,21,10,17,8,12,7,5,7 ,8,36,12,42,20,43,25,38,24,35,23,33,21,32,20,31,20,30,20,27,16,25,9,24,2,25 ,19,41,24,45,33,45,38,38,36,31,28,27,21,23,24,22,26,20,28,17,26,14,20,13,14,11) digit3.dat<-array(digit3.dat,c(2,13,30)) digit3.dat<-aperm(digit3.dat,c(2,1,3)) digit3.dat[,2,]<- -digit3.dat[,2,] #================================================================================== dna.dat<-array(c(23.825,12.021,13.002,25.742,18.923,13.225,22.784,25.153,15.445,17.418 ,28.811,17.309,12.468,29.084,21.876,8.439,26.152,26.442,5.606,21.087 ,29.894,6.200,14.944,33.112,8.585,12.034,38.280,14.445,8.903,39.890 ,20.638,10.881,42.477,21.716,27.837,44.502,24.662,23.765,40.767,25.795 ,17.985,37.076,24.130,12.686,33.111,20.240,10.380,28.222,14.758,7.779 ,24.003,9.126,10.368,21.474,7.514,14.143,16.620,5.636,20.061,13.796 ,9.181,24.591,9.975,15.219,27.372,7.792 ,24.426,12.320,12.815,25.129,18.383,14.600,22.555,25.151,16.215,17.277 ,28.975,17.620,12.170,28.852,22.133,7.779,25.623,26.239,5.167,20.176 ,30.154,6.302,14.201,32.961,8.863,11.842,38.282,15.019,9.028,40.102 ,21.051,10.202,42.586,22.535,27.544,45.013,24.570,23.693,40.375,25.299 ,18.777,35.736,23.716,12.495,33.049,19.815,9.500,29.441,13.961,7.614 ,25.021,9.187,10.748,21.303,7.266,14.440,16.882,5.328,20.518,13.417 ,9.450,24.724,9.887,16.128,27.037,7.757 ,24.824,12.355,12.721,25.459,18.787,14.482,22.545,24.678,16.606,17.068 ,28.216,18.062,11.696,28.643,22.449,7.256,25.495,26.539,4.443,20.008 ,29.999,5.302,14.541,33.584,8.475,12.111,38.538,14.696,9.392,40.333 ,21.118,10.517,42.136,23.203,27.877,44.420,25.594,23.483,39.833,25.459 ,18.596,35.894,23.239,12.499,33.283,19.753,10.123,28.758,14.685,7.357 ,24.571,9.730,10.141,20.369,7.686,14.172,15.883,5.535,20.148,13.033 ,9.722,25.009,10.287,16.126,27.525,8.623 ,24.562,12.271,12.907,25.641,18.820,14.322,22.811,24.763,17.048,17.248 ,28.619,18.489,11.519,27.989,22.598,8.070,25.157,27.032,3.894,19.924 ,29.213,5.220,15.143,33.490,8.348,12.542,38.506,14.119,9.535,40.897 ,20.276,10.987,42.136,23.192,28.071,44.539,25.274,23.775,39.509,25.630 ,18.350,35.879,23.652,12.505,32.860,19.781,9.775,28.642,14.454,6.712 ,24.583,9.440,9.756,20.462,7.865,14.225,16.493,5.837,20.340,13.680 ,9.425,25.287,10.594,15.637,27.792,8.320 ,24.498,12.655,13.535,26.012,19.043,14.582,22.839,24.695,17.030,17.673 ,28.305,18.898,12.275,28.466,22.641,7.960,25.669,26.958,4.000,20.256 ,29.476,5.319,15.623,33.821,8.755,12.476,38.043,14.771,9.263,39.758 ,20.522,10.340,41.694,22.720,27.780,45.260,25.492,23.558,40.577,25.904 ,18.294,35.791,23.664,13.116,32.898,19.581,9.743,29.357,14.351,7.149 ,25.092,10.135,9.427,20.128,7.067,13.562,15.964,5.322,20.148,13.918 ,9.684,24.717,10.987,15.626,27.045,8.706 ,24.253,12.608,13.053,25.931,19.065,13.470,23.190,24.682,16.697,17.355 ,28.266,18.178,11.941,28.445,21.624,8.302,26.165,26.289,4.645,20.902 ,29.472,5.834,15.445,33.346,9.014,13.039,38.182,14.821,8.706,39.192 ,20.416,10.356,42.138,21.676,27.355,45.520,25.249,22.900,41.575,26.118 ,18.221,36.773,23.771,12.865,32.905,19.117,9.404,29.920,14.366,7.244 ,25.218,9.684,9.810,21.100,7.052,13.603,16.361,5.871,20.211,14.759 ,9.332,24.692,10.714,14.984,27.381,8.114 ,23.794,12.783,13.221,26.116,19.069,14.260,22.944,24.986,16.310,17.113 ,28.409,17.881,11.562,28.432,21.504,7.479,25.690,25.928,4.418,20.599 ,30.049,5.580,15.584,34.067,9.173,12.544,38.631,15.060,9.018,38.984 ,20.102,10.329,42.574,22.345,27.701,44.920,25.689,22.778,41.294,26.502 ,17.802,36.716,23.573,12.346,33.446,19.038,9.376,29.982,14.359,7.379 ,25.213,9.768,10.069,20.982,7.294,13.751,16.439,5.559,20.199,13.972 ,9.213,24.830,10.359,15.581,27.031,7.719 ,23.617,12.350,13.813,25.639,19.130,14.870,22.756,25.155,16.183,16.854 ,28.676,17.591,11.233,28.507,21.359,7.934,26.050,26.376,5.116,20.641 ,29.944,5.718,15.029,32.991,8.692,12.395,37.840,14.732,8.728,39.201 ,19.511,9.906,43.019,22.376,27.740,44.419,25.920,23.168,41.030,26.208 ,18.416,36.239,23.729,12.432,33.529,19.435,9.740,29.434,14.798,7.666 ,24.759,9.937,10.295,20.754,7.938,13.711,15.998,5.671,20.047,14.131 ,9.114,24.716,10.494,14.954,26.951,7.747 ,24.012,12.503,13.953,25.912,19.342,14.449,23.029,25.100,16.549,17.484 ,28.586,18.903,11.667,28.481,21.397,7.785,26.260,26.607,4.911,20.648 ,29.762,5.753,15.350,33.300,8.908,12.434,37.994,14.578,8.736,39.552 ,19.979,10.470,43.119,22.662,27.821,43.727,25.443,22.420,41.221,26.247 ,17.848,36.494,24.167,12.379,32.929,19.582,9.488,29.216,14.702,7.711 ,24.595,9.384,9.829,20.604,7.131,13.683,16.123,5.060,20.085,13.915 ,8.908,25.200,11.222,14.847,27.059,8.911 ,23.851,12.509,13.804,25.990,19.002,14.469,22.931,24.771,16.634,17.508 ,28.373,18.286,11.915,28.212,21.270,7.770,25.930,26.098,5.343,20.386 ,29.828,5.846,15.077,33.966,9.031,12.428,38.933,15.099,8.924,39.810 ,20.518,10.602,43.163,21.689,28.245,42.375,25.115,22.774,40.248,25.516 ,17.567,36.126,23.642,11.533,33.571,19.658,9.291,29.426,14.590,7.776 ,24.159,9.043,9.992,20.933,7.292,14.245,16.582,5.694,20.509,13.985 ,8.699,25.401,10.715,15.055,27.404,8.281 ,23.695,12.829,13.504,25.764,19.437,13.969,22.597,25.450,15.948,17.092 ,28.423,18.022,11.790,28.676,21.803,7.059,25.934,26.430,5.091,20.793 ,30.234,5.589,15.473,34.066,8.997,11.769,38.160,14.696,8.522,40.353 ,19.819,10.878,44.196,22.554,28.318,43.074,25.597,23.197,40.337,25.806 ,17.780,36.375,23.787,11.741,33.154,19.557,8.962,29.841,14.286,7.574 ,25.083,9.196,9.944,20.748,7.313,14.236,16.045,5.646,20.541,13.471 ,9.889,25.682,11.896,15.361,27.634,8.380 ,24.570,13.140,12.786,26.102,19.946,13.576,22.686,25.498,15.318,17.702 ,28.165,18.723,12.523,28.503,22.326,7.233,26.220,26.025,4.907,21.266 ,30.023,5.678,15.793,33.796,9.233,12.043,37.348,14.858,8.771,40.203 ,19.898,10.903,44.171,21.444,28.378,42.556,25.142,23.227,39.449,25.636 ,17.412,36.446,23.332,11.752,33.264,18.848,9.020,29.643,13.468,7.620 ,24.944,9.220,9.848,21.160,7.691,13.422,15.760,6.713,19.640,13.470 ,9.683,25.976,12.855,14.939,27.417,8.986 ,24.627,13.046,12.380,25.895,19.776,12.833,23.067,25.155,15.595,18.103 ,27.715,18.877,13.546,27.639,23.414,7.305,26.190,26.006,4.724,20.975 ,29.586,5.140,15.664,33.846,9.195,11.925,37.119,15.034,8.789,39.473 ,20.075,10.553,43.273,21.398,28.628,42.349,25.145,23.406,40.183,25.822 ,18.112,36.110,23.461,12.655,32.655,18.873,9.201,29.785,13.440,7.458 ,25.414,8.322,9.906,21.597,7.603,13.366,16.335,6.232,19.783,14.282 ,9.705,25.919,12.853,14.983,27.636,9.035 ,24.787,13.151,13.411,26.120,19.950,13.492,22.909,25.361,15.449,17.724 ,28.036,18.727,13.426,27.879,23.848,7.001,25.988,26.517,5.155,20.872 ,30.584,5.420,15.382,34.616,9.694,11.348,37.645,15.821,8.699,39.613 ,20.186,10.669,43.251,21.809,28.473,42.674,25.263,23.078,39.879,25.499 ,17.890,35.848,23.637,12.841,32.064,18.802,8.835,28.782,13.400,7.644 ,24.509,8.059,10.687,21.628,7.038,13.879,16.268,6.444,19.486,12.936 ,9.772,25.582,11.871,15.490,27.474,8.755 ,24.879,12.862,13.363,25.855,19.829,14.067,22.454,25.488,15.988,16.932 ,28.288,18.371,13.232,28.098,23.747,7.640,26.351,27.493,5.631,20.657 ,30.620,5.432,14.891,34.055,10.077,11.331,37.364,15.912,8.673,39.810 ,20.365,10.935,43.227,22.006,28.874,42.468,25.377,23.089,40.458,25.434 ,18.097,36.265,23.154,12.725,32.439,18.303,8.809,29.316,12.224,7.154 ,25.247,8.256,10.648,21.434,6.867,13.846,16.261,6.495,19.229,12.785 ,9.444,25.660,11.445,16.015,27.106,9.083 ,24.569,13.132,13.356,26.118,20.059,13.598,22.943,25.493,16.332,17.156 ,28.220,18.641,12.640,27.993,23.201,6.940,26.026,26.789,5.822,20.299 ,30.624,5.577,14.426,34.013,9.260,11.671,38.441,15.405,9.037,40.045 ,20.732,11.190,43.389,22.255,28.629,41.898,25.166,23.030,39.331,25.211 ,17.886,34.989,22.927,11.813,32.321,18.034,7.986,30.112,12.507,7.039 ,25.429,7.760,10.850,22.086,6.699,14.098,16.608,6.578,19.622,12.735 ,9.516,25.603,10.904,15.494,27.452,8.388 ,24.374,13.368,13.547,25.968,20.247,13.246,23.008,25.503,15.953,17.295 ,28.819,18.144,12.941,28.253,23.238,7.486,25.753,26.647,5.568,20.694 ,30.577,5.546,14.753,34.301,9.336,11.829,38.770,14.971,8.889,41.320 ,20.733,10.856,43.703,22.427,28.706,42.112,25.228,23.369,39.109,25.342 ,17.710,35.477,23.173,11.708,32.903,18.173,7.920,30.467,12.415,7.036 ,25.282,8.608,10.579,22.073,6.742,14.416,16.828,6.456,19.579,12.318 ,9.521,25.792,11.092,15.795,27.217,8.544 ,24.523,13.575,13.730,26.283,20.256,14.113,22.742,25.391,16.837,16.910 ,28.361,18.931,12.169,28.452,22.750,7.157,25.331,26.758,5.413,20.606 ,30.489,5.381,15.019,34.458,9.238,12.654,38.882,15.055,9.116,40.835 ,20.612,11.073,44.104,22.835,28.368,41.865,25.681,23.191,38.900,25.820 ,17.319,35.189,23.205,11.268,33.056,18.536,8.055,29.949,12.768,7.279 ,25.385,7.923,10.343,21.699,7.260,13.681,16.484,6.793,20.112,13.511 ,9.567,26.174,11.279,15.334,27.686,8.372 ,24.006,13.405,12.592,26.309,20.061,13.619,23.040,25.554,16.139,17.822 ,28.432,19.405,12.417,28.345,23.022,7.234,25.262,26.351,5.410,20.248 ,30.270,5.563,14.632,34.272,9.276,12.834,39.126,14.824,9.049,40.659 ,19.558,11.043,44.656,22.847,28.845,41.482,25.470,23.095,38.692,25.220 ,16.932,36.035,22.990,11.096,33.874,18.029,7.900,30.415,12.609,6.544 ,25.900,8.550,9.951,21.714,7.448,13.457,16.513,6.870,19.847,13.544 ,8.872,25.764,11.318,14.641,27.537,7.839 ,23.936,13.085,12.376,25.865,19.427,13.664,23.213,25.058,16.283,18.112 ,28.607,19.261,12.743,28.401,22.584,7.267,25.067,26.234,6.259,20.058 ,30.295,5.223,14.693,34.091,8.846,12.606,38.941,14.581,8.891,40.367 ,19.148,10.977,44.551,22.080,28.672,41.808,25.625,23.181,39.061,25.517 ,17.658,35.650,23.555,11.141,33.784,18.422,7.879,30.872,13.176,6.986 ,25.093,9.595,10.402,21.787,7.283,14.052,17.155,6.272,19.831,13.550 ,8.912,25.658,10.834,15.066,27.919,8.719 ,24.228,13.221,11.812,25.933,19.620,13.289,23.382,25.319,15.563,18.068 ,28.246,18.967,13.613,28.286,22.944,8.360,25.533,27.070,6.114,20.845 ,30.299,5.328,14.990,34.186,8.986,11.480,38.107,14.578,9.012,40.742 ,18.999,11.541,45.355,21.695,28.682,41.301,25.220,23.429,38.858,25.281 ,17.872,35.522,23.677,11.484,33.448,19.107,8.155,29.984,12.684,7.155 ,25.575,10.248,10.120,21.324,7.475,14.113,16.755,5.670,20.340,13.921 ,9.459,25.472,10.983,15.771,28.089,9.318 ,24.316,12.458,11.843,25.508,18.965,13.747,23.278,24.916,16.394,17.774 ,28.402,19.187,12.378,28.542,22.605,7.252,25.365,26.306,6.069,20.368 ,29.967,5.496,14.233,33.573,8.681,11.885,38.394,14.356,9.525,41.153 ,19.296,11.200,44.990,21.956,28.698,41.790,25.477,23.507,38.898,25.134 ,17.916,35.541,23.580,11.891,33.423,18.965,7.818,30.491,13.199,7.030 ,25.775,9.665,9.849,21.557,7.725,13.950,16.492,6.011,20.248,14.478 ,9.515,25.732,11.493,16.071,28.073,8.995 ,24.069,12.514,11.576,26.193,19.281,13.560,23.049,25.025,15.906,17.225 ,28.166,19.122,12.145,27.688,22.386,7.635,25.248,26.821,5.438,20.677 ,30.447,5.051,15.003,34.500,9.197,12.486,38.468,14.699,9.325,40.616 ,19.694,10.810,44.394,21.784,28.486,41.204,25.422,23.303,39.170,25.769 ,17.606,36.130,23.125,11.743,33.599,18.675,8.250,30.057,12.399,6.446 ,25.964,9.671,9.713,21.051,8.200,13.572,16.156,6.096,20.125,14.059 ,9.421,25.580,11.507,15.633,28.168,9.245 ,24.382,13.415,12.005,26.227,19.807,13.778,23.018,25.170,16.477,17.422 ,28.183,18.648,12.599,27.936,22.634,7.930,25.547,26.937,5.015,20.995 ,30.642,5.322,15.062,34.391,9.387,12.104,38.000,14.870,9.037,40.766 ,20.116,11.114,44.270,22.390,28.993,40.814,25.331,22.929,39.238,25.429 ,17.057,36.390,22.793,10.993,34.070,18.588,7.965,30.069,11.850,6.919 ,26.147,9.683,9.913,20.736,7.799,13.653,15.823,5.725,20.032,14.331 ,9.347,25.125,11.310,15.548,27.697,8.797 ,25.071,13.359,12.219,25.922,20.228,13.700,22.969,25.646,16.479,17.218 ,28.753,18.370,12.285,28.254,22.454,8.012,26.006,26.558,5.684,20.677 ,30.618,5.143,14.471,34.316,8.841,12.183,39.055,14.522,8.799,41.346 ,19.438,10.811,44.824,22.870,28.797,40.740,25.663,23.231,38.089,25.721 ,17.236,35.440,22.782,11.278,33.869,18.262,8.303,30.135,11.771,6.803 ,25.902,9.355,9.961,21.065,8.367,13.499,15.573,5.697,19.805,14.183 ,8.787,25.293,11.196,14.928,27.345,8.810 ,24.555,13.167,12.945,26.062,19.730,14.230,23.077,25.395,16.617,17.485 ,28.522,18.766,12.427,28.226,22.678,7.346,25.153,26.424,5.141,20.778 ,31.057,5.181,14.573,34.397,9.210,12.140,38.451,14.821,8.758,40.470 ,20.120,10.521,44.182,22.452,28.993,40.984,25.474,23.382,38.705,25.796 ,17.536,35.590,23.012,12.041,32.760,18.130,9.197,29.415,11.158,6.757 ,26.335,9.712,9.912,20.939,7.884,13.871,15.872,5.667,20.538,14.465 ,9.330,25.550,11.486,15.120,27.100,8.018 ,25.137,12.870,12.482,26.450,19.304,14.192,22.839,25.036,16.498,17.098 ,27.988,18.477,12.516,27.594,23.342,6.652,25.339,26.480,5.038,20.611 ,30.998,5.596,14.238,33.760,9.367,12.383,38.383,14.898,8.935,40.587 ,20.080,11.064,43.809,22.269,28.515,41.425,25.532,22.994,39.101,25.689 ,17.577,35.290,23.238,12.103,32.263,18.131,8.696,29.280,11.572,6.737 ,25.943,9.747,10.059,20.420,7.506,14.235,15.951,5.520,20.764,14.883 ,9.477,25.594,12.102,14.816,27.279,8.341 ,24.811,13.177,12.248,26.359,19.647,13.791,22.819,25.076,15.935,17.259 ,28.137,18.816,12.031,27.817,22.524,7.139,25.285,26.476,5.233,20.844 ,30.918,5.023,14.625,34.304,8.951,12.113,38.641,14.903,9.183,40.263 ,19.489,10.837,44.214,22.943,28.849,41.934,25.785,23.491,38.818,25.458 ,17.501,35.809,23.084,12.499,32.507,18.711,8.415,29.596,11.995,6.648 ,25.674,9.782,9.834,20.682,7.330,14.037,16.282,5.679,20.535,14.148 ,9.529,25.973,12.347,14.857,27.412,8.891 ,25.461,13.475,12.459,26.240,19.803,14.240,22.439,25.184,16.246,17.018 ,28.482,18.729,12.138,27.810,22.628,6.935,25.500,26.392,5.181,21.303 ,30.774,5.269,15.405,34.596,9.057,12.253,38.675,14.733,9.098,40.950 ,18.928,10.930,44.884,22.702,28.490,41.516,25.756,22.649,39.048,26.150 ,16.724,36.124,23.328,11.649,32.925,18.644,8.006,30.218,12.586,6.889 ,25.838,9.876,10.168,20.938,7.544,13.875,16.115,5.352,20.441,14.098 ,9.147,25.602,11.691,14.869,27.495,8.845 ,24.646,13.201,12.498,26.020,19.910,14.081,22.700,25.203,16.348,16.921 ,28.147,18.532,12.219,27.992,22.139,7.297,25.197,26.330,5.477,21.167 ,31.099,5.289,15.822,35.342,8.801,12.200,39.234,14.381,9.267,41.504 ,19.133,11.253,44.763,22.661,28.604,41.983,25.373,23.002,39.097,25.724 ,16.979,36.597,23.274,11.205,33.696,18.583,7.895,30.512,12.581,6.908 ,26.393,10.461,9.886,20.595,7.139,14.317,16.469,5.308,21.069,14.978 ,8.870,25.450,10.652,14.988,27.488,7.901) ,c(3,22,30)) dna.dat<-aperm(dna.dat,c(2,1,3)) # # # #================================================================================== macf.dat<-c(54.33203,24.10905,69.5 ,141.80250,21.59643,69.5 ,132.23880,62.78124,69.5 ,88.22106,52.28123,69.5 ,147.10890,26.61518,90.0 ,107.42800,27.77578,101.0 ,99.74427,46.85715,97.0 ,58.35540,29.57223,67.0 ,134.87930,26.38988,67.0 ,120.57150,66.43083,67.0 ,79.14922,52.19386,67.0 ,138.17850,37.67144,86.0 ,101.76780,34.47324,97.5 ,90.83484,54.19082,92.0 ,50.04349,15.22191,71.5 ,139.76850,21.33820,71.5 ,124.26710,63.79000,71.5 ,80.94720,51.47673,71.5 ,147.78980,32.26446,94.0 ,102.32870,25.23133,105.5 ,90.64163,47.04449,98.5 ,41.93115,24.83244,70.5 ,138.72930,22.35828,70.5 ,122.68840,65.04043,70.5 ,74.09913,53.69709,70.5 ,142.63240,35.36625,92.0 ,97.04822,33.37946,111.5 ,89.51760,56.40900,96.5 ,48.44877,35.75250,68.0 ,134.68970,33.55962,68.0 ,120.88830,73.77755,68.0 ,77.94841,65.53058,68.0 ,135.42950,42.75692,91.0 ,101.92880,40.64505,99.5 ,87.04530,59.04715,92.5 ,44.05272,36.38397,70.0 ,133.47320,45.07240,70.0 ,114.88450,83.74655,70.0 ,70.13158,71.71946,70.0 ,139.94810,54.24338,87.0 ,97.87708,47.19924,105.5 ,80.04594,65.13947,96.5 ,53.94042,32.50219,69.0 ,136.19530,33.46588,69.0 ,120.75410,74.12678,69.0 ,78.24210,60.88469,69.0 ,142.38210,44.35960,89.5 ,100.75480,38.09180,101.0 ,87.92361,55.80280,94.5 ,45.11740,11.62884,68.5 ,132.08950,17.75877,68.5 ,112.66980,57.22899,68.5 ,71.76939,47.64127,68.5 ,136.93780,26.69818,88.5 ,96.43125,21.78416,101.0 ,84.39475,41.89694,95.0 ,42.09966,16.11359,69.0 ,131.92440,19.70687,69.0 ,121.73400,57.71608,69.0 ,72.94730,49.99466,69.0 ,136.93340,26.92302,89.0 ,96.84825,26.58288,103.0 ,84.30907,48.96717,94.5) macf.dat<-array(macf.dat,c(3,7,9)) macf.dat<-aperm(macf.dat,c(2,1,3)) macm.dat<-c(34.82811,16.50834,77.5 ,138.91980,15.13858,77.5 ,125.15760,58.60464,77.5 ,72.28854,49.79207,77.5 ,146.19080,22.68885,100.0 ,99.30268,24.86908,117.0 ,91.79910,46.49960,107.0 ,40.40179,3.73932,73.0 ,132.23560,7.56574,73.0 ,114.63210,53.28955,73.0 ,70.66502,33.57051,73.0 ,139.58480,21.61227,90.5 ,97.93692,8.49867,108.5 ,79.90506,28.91153,100.5 ,40.54510,9.51130,75.0 ,136.61260,15.82863,75.0 ,106.90960,63.82611,75.0 ,76.19816,46.63517,75.0 ,145.02210,30.40421,94.5 ,101.72660,19.45746,113.5 ,86.97967,43.98130,105.5 ,21.11454,16.57673,75.0 ,131.52700,23.12809,75.0 ,109.44810,63.03707,75.0 ,61.73774,53.69610,75.0 ,135.91480,34.78890,101.5 ,90.65395,25.30813,117.5 ,75.66082,49.38123,105.5 ,30.79976,19.21503,73.5 ,134.92160,32.11148,73.5 ,115.81510,69.88405,73.5 ,67.15240,57.06633,73.5 ,139.56950,44.82271,97.5 ,95.38217,25.95223,112.0 ,78.97741,47.89584,107.0 ,18.88770,10.47136,74.5 ,130.35790,12.40497,74.5 ,114.85390,57.63774,74.5 ,63.47649,48.13175,74.5 ,138.25830,25.62929,97.5 ,89.01810,18.95535,117.0 ,75.67622,43.31009,104.5 ,40.28789,14.90687,69.0 ,134.29020,14.66977,69.0 ,125.54870,56.83236,69.0 ,75.68020,53.65364,69.0 ,142.36350,24.22211,92.5 ,99.44497,25.41932,106.0 ,87.63929,45.45810,99.5 ,25.38359,10.64805,72.5 ,130.99770,9.63434,72.5 ,118.65580,54.78021,72.5 ,68.79280,48.67834,72.5 ,139.77820,20.83856,97.0 ,91.36346,16.29169,111.5 ,75.55544,44.28398,101.0 ,27.93545,5.21197,71 ,130.98990,4.76235,71 ,103.16230,51.99304,71 ,70.59641,43.71388,71 ,136.03820,13.90246,92 ,91.75840,14.31955,109 ,79.95213,35.70748,95) macm.dat<-array(macm.dat,c(3,7,9)) macm.dat<-aperm(macm.dat,c(2,1,3)) #================================================================================== sooty.dat<- c(-1426,-310.4167 ,-1424,-160.4167 ,-1117,320.5833 ,-755,854.5833 ,1238,1363.5833 ,2330,471.5833 ,1435,-748.4167 ,771,-557.4167 ,433,-395.4167 ,-176,-299.4167 ,-376,-290.4167 ,-933,-248.4167 ,-1000.20254,-1601.5969 ,-1076.57007,-1266.4282 ,-1124.65334,-635.6890 ,-1193.94980,147.7853 ,-61.16474,1895.7533 ,1484.57069,2113.5422 ,1649.32657,746.7048 ,1212.33458,388.9087 ,730.79486,166.1701 ,156.62415,-383.9590 ,-88.03479,-533.8656 ,-689.07556,-1037.3256) sooty.dat<-array(sooty.dat,c(2,12,2)) sooty.dat<-aperm(sooty.dat,c(2,1,3)) #================================================================================== panf.dat<-array(c(47,-23,0,0,0,32,12,87,21,156,31,133,66,92,83,20, 63,-22,0,0,0,29,6,89,14,157,23,136,62,101,95,24, 56,-11,0,0,0,31,2,89,4,159,17,141,54,107,86,34, 51,-27,0,0,0,29,12,89,30,156,39,135,71,94,86,18, 51,-19,0,0,0,35,8,97,23,169,36,143,67,101,85,25, 54,-23,0,0,0,34,14,84,36,155,44,133,76,87,89,19, 53,-21,0,0,0,30,5,90,24,162,31,139,63,99,86,21, 56,-23,0,0,0,33,16,92,30,156,40,131,75,95,95,15, 55,-20,0,0,0,33,12,89,13,157,31,136,63,97,91,22, 35,-23,0,0,0,26,9,81,11,153,26,131,64,92,84,18, 48,-25,0,0,0,30,23,89,44,160,49,139,81,95,95,18, 35,-34,0,0,0,30,10,84,23,153,36,128,68,95,94,13, 46,-23,0,0,0,28,4,92,14,163,27,137,60,101,86,27, 42,-23,0,0,0,30,19,88,33,163,45,130,77,95,93,20, 50,-19,0,0,0,32,7,90,14,157,25,139,68,101,87,23, 54,-19,0,0,0,29,6,92,9,163,20,140,64,104,92,26, 46,-31,0,0,0,25,3,89,2,167,24,136,63,95,85,19, 47,-19,0,0,0,29,7,84,6,148,22,126,58,89,80,24, 46,-27,0,0,0,31,12,86,23,156,35,130,72,93,89,18, 49,-23,0,0,0,29,14,88,25,159,36,133,76,90,90,16, 50,-23,0,0,0,32,9,92,14,167,31,141,71,97,87,28, 40,-23,0,0,0,30,13,92,30,167,40,140,74,99,91,21, 32,-25,0,0,0,36,7,96,12,170,22,147,61,108,81,35, 41,-30,0,0,0,29,18,87,31,160,43,135,74,87,89,10, 46,-25,0,0,0,29,15,88,23,163,37,134,70,91,85,22, 43,-22,0,0,0,33,-1,96,10,178,24,153,66,105,87,32),c(2,8,26)) panf.dat<-aperm(panf.dat,c(2,1,3)) select<-c(5,1,2,3,4,6,7,8) panf.dat<-panf.dat[select,,] panm.dat<-array(c(43,-21,0,0,0,34,14,101,25,179,40,150,75,104,90,31, 48,-23,0,0,0,31,5,92,11,166,24,144,63,99,82,24, 43,-23,0,0,0,29,13,92,21,161,33,138,68,100,84,21, 45,-32,0,0,0,30,8,100,14,163,28,143,74,102,97,21, 40,-27,0,0,0,29,7,93,12,166,23,147,69,102,90,25, 49,-19,0,0,0,33,3,94,11,165,23,144,64,108,89,30, 55,-17,0,0,0,31,6,97,17,168,29,144,69,101,85,23, 49,-27,0,0,0,26,11,92,16,178,30,152,78,100,89,23, 48,-23,0,0,0,29,10,96,32,166,42,139,69,96,87,21, 49,-19,0,0,0,29,3,100,14,172,25,152,63,109,87,32, 49,-26,0,0,0,34,8,91,22,168,34,140,74,93,93,17, 52,-26,0,0,0,32,7,92,10,172,28,143,66,100,93,23, 36,-26,0,0,0,28,11,92,25,165,34,140,71,98,90,18, 46,-26,0,0,0,33,12,94,20,174,35,145,71,106,93,24, 47,-22,0,0,0,31,10,88,29,156,34,138,68,93,91,20, 47,-25,0,0,0,30,0,97,2,172,19,147,62,102,82,26, 53,-19,0,0,0,29,6,80,2,142,13,123,54,100,91,31, 49,-24,0,0,0,29,5,90,14,168,31,144,77,96,89,22, 52,-25,0,0,0,30,6,93,15,167,25,147,74,99,92,24, 42,-27,0,0,0,32,11,87,26,159,37,137,76,95,87,21, 37,-25,0,0,0,29,-2,87,-2,175,16,152,63,107,91,33, 41,-27,0,0,0,25,6,87,13,167,29,141,70,99,89,28, 46,-24,0,0,0,35,12,98,15,175,31,152,76,106,92,27, 45,-22,0,0,0,29,8,90,12,176,24,151,70,104,88,24, 46,-27,0,0,0,29,2,88,6,166,23,138,67,100,86,24, 43,-20,0,0,0,33,7,94,17,165,30,143,70,93,81,24, 44,-20,0,0,0,29,1,87,0,154,19,133,64,98,80,16, 52,-28,0,0,0,28,2,84,18,155,25,135,63,97,93,21),c(2,8,28)) panm.dat<-aperm(panm.dat,c(2,1,3)) select<-c(5,1,2,3,4,6,7,8) panm.dat<-panm.dat[select,,] pongof.dat<-array(c(43,-31,0,0,0,29,13,90,45,150,48,126,74,80,91,19, 49,-31,0,0,0,33,28,93,70,152,72,130,86,78,85,-5, 51,-36,0,0,0,32,34,100,74,152,74,131,87,73,92,-2, 48,-26,0,0,0,32,10,89,35,154,40,136,65,88,86,14, 56,-29,0,0,0,24,11,87,44,155,48,133,72,82,91,12, 49,-30,0,0,0,29,25,96,72,159,69,137,85,78,93,9, 51,-28,0,0,0,26,11,94,50,162,49,132,75,87,89,13, 57,-24,0,0,0,35,10,98,55,164,53,138,77,88,93,21, 37,-29,0,0,0,38,30,93,63,161,68,129,86,68,87,6, 53,-30,0,0,0,29,5,88,39,147,38,131,64,84,90,14, 58,-24,0,0,0,30,0,88,14,151,24,133,59,91,88,21, 54,-26,0,0,0,36,11,106,41,173,48,146,78,94,99,24, 59,-25,0,0,0,29,4,102,28,177,34,156,63,100,90,25, 32,-36,0,0,0,25,26,90,71,144,69,124,82,72,91,3, 52,-30,0,0,0,35,21,99,55,164,59,143,79,90,95,22, 51,-27,0,0,0,35,11,92,37,152,42,132,69,88,95,26, 47,-24,0,0,0,35,7,98,36,163,38,138,66,89,87,21, 60,-23,0,0,0,23,-2,82,28,158,32,135,56,90,89,25, 46,-31,0,0,0,25,4,87,34,145,37,120,66,80,89,20, 46,-28,0,0,0,36,29,94,73,148,71,123,82,74,92,11, 32,-37,0,0,0,36,32,88,81,140,81,117,91,63,90,-5, 43,-27,0,0,0,25,2,90,32,159,37,131,62,91,87,22, 38,-27,0,0,0,30,4,93,30,160,36,136,60,92,86,27, 38,-27,0,0,0,34,14,92,53,155,48,129,71,82,84,24),c(2,8,24)) pongof.dat<-aperm(pongof.dat,c(2,1,3)) select<-c(5,1,2,3,4,6,7,8) pongof.dat<-pongof.dat[select,,] pongom.dat<-array(c(49,-45,0,0,0,26,25,106,68,190,68,151,84,80,100,14, 64,-28,0,0,0,31,10,106,46,185,53,156,77,95,99,14, 55,-31,0,0,0,33,23,113,72,186,72,160,92,95,102,21, 64,-25,0,0,0,36,5,109,35,188,42,165,69,102,101,33, 46,-39,0,0,0,31,36,106,97,155,89,133,95,74,92,1, 53,-28,0,0,0,33,17,111,54,185,59,159,87,88,98,16, 47,-36,0,0,0,36,35,114,72,183,74,150,94,81,105,15, 44,-35,0,0,0,37,15,110,69,183,74,153,84,89,94,11, 55,-43,0,0,0,26,24,105,71,172,75,146,91,81,104,1, 49,-33,0,0,0,35,11,113,58,188,56,159,79,92,93,24, 45,-32,0,0,0,29,20,107,67,184,67,155,85,82,93,11, 48,-34,0,0,0,32,33,116,89,192,91,160,100,81,99,5, 41,-51,0,0,0,29,50,100,127,139,115,119,105,53,96,-19, 60,-45,0,0,0,32,36,102,108,163,97,129,98,75,98,-6, 65,-35,0,0,0,30,24,112,65,188,72,158,88,87,103,18, 54,-28,0,0,0,33,7,114,33,206,42,172,74,98,94,28, 41,-39,0,0,0,34,24,115,79,188,79,154,93,79,99,2, 42,-40,0,0,0,39,41,114,112,187,102,147,112,70,97,-14, 65,-27,0,0,0,30,17,109,62,187,66,151,83,82,96,18, 54,-36,0,0,0,30,29,122,65,204,70,176,93,87,96,9, 55,-37,0,0,0,32,25,116,75,190,71,155,88,84,98,8, 50,-35,0,0,0,26,8,101,39,172,49,148,75,87,98,18, 78,-31,0,0,0,28,15,119,56,204,60,179,91,94,103,8, 42,-32,0,0,0,34,37,117,102,181,99,148,102,83,97,5, 52,-39,0,0,0,38,15,111,58,201,63,158,89,91,95,-8, 47,-37,0,0,0,23,37,98,85,160,83,136,89,75,94,-5, 49,-37,0,0,0,34,37,115,105,179,98,151,96,80,97,5, 48,-32,0,0,0,36,10,113,53,189,57,166,79,100,99,21, 41,-24,0,0,0,39,4,128,42,209,47,178,73,102,93,21, 50,-32,0,0,0,39,27,121,73,198,75,166,95,89,101,15),c(2,8,30)) pongom.dat<-aperm(pongom.dat,c(2,1,3)) select<-c(5,1,2,3,4,6,7,8) pongom.dat<-pongom.dat[select,,] #================================================================================== schizophrenia.dat<-c( 0.345632 , -0.0360314 , -0.356301 , 0.0234333 , 0.0119311 , 0.17692 , 0.37789 , 0.480402 , 0.719631 , -0.41189 , 0.397921 , -0.140558 , 0.351751 , -0.385748 , 0.333756 , -0.655051 , 0.032181 , -0.275235 , 0.112563 , -0.506533 , -0.233126 , -0.28334 , -0.667337 , 0.0522613 , 0.188945 , -0.142714 , 0.237198 , 0.048306 , -0.340236 , 0.0997385 , -0.0161814 , 0.201017 , 0.301584 , 0.516546 , 0.510795 , -0.323537 , 0.269407 , -0.0562209 , 0.239301 , -0.253218 , 0.229339 , -0.48236 , 0.0201328 , -0.122625 , 0.048306 , -0.341874 , -0.200997 , -0.122698 , -0.62316 , 0.120534 , 0.124688 , -0.0262477 , 0.341616 , 0.048306 , -0.408509 , 0.119819 , -0.00814926 , 0.277322 , 0.39797 , 0.62498 , 0.591116 , -0.299441 , 0.35776 , -0.0361405 , 0.27143 , -0.249202 , 0.273516 , -0.530553 , 0.032181 , -0.110577 , 0.0724024 , -0.34589 , -0.188949 , -0.138762 , -0.707498 , 0.140615 , 0.124688 , -0.0262477 , 0.329567 , -0.10832 , -0.359859 , 0.0341931 , -0.056342 , 0.152824 , 0.377668 , 0.474464 , 0.673363 , -0.462009 , 0.373825 , -0.228912 , 0.323639 , -0.450005 , 0.34179 , -0.735372 , 0.0924219 , -0.295315 , 0.100555 , -0.540522 , -0.181195 , -0.260518 , -0.651361 , 0.0963372 , 0.174798 , -0.178741 , 0.193021 , 0.0683864 , -0.539516 , 0.210918 , -0.16074 , 0.333555 , 0.293246 , 0.639288 , 0.520753 , -0.301366 , 0.245311 , -0.0281084 , 0.142916 , -0.28133 , 0.128938 , -0.510473 , -0.152558 , -0.118609 , -0.0761516 , -0.379879 , -0.414127 , -0.127988 , -0.848201 , 0.246974 , -0.00592517 , -0.00203414 , 0.337599 , -0.076192 , -0.356431 , 0.146356 , 0.068156 , 0.201017 , 0.520571 , 0.464342 , 0.601074 , -0.518234 , 0.321616 , -0.208831 , 0.251349 , -0.409844 , 0.197211 , -0.695211 , -0.00797966 , -0.251139 , -0.00787855 , -0.500361 , -0.26726 , -0.201009 , -0.719602 , 0.25526 , 0.110557 , -0.140611 , 0.223261 , 0.228767 , -0.431293 , 0.230133 , -0.0439952 , 0.389752 , 0.23941 , 0.819652 , 0.641234 , -0.088515 , 0.377841 , 0.140566 , 0.339703 , -0.10864 , 0.393998 , -0.345813 , 0.0201328 , -0.0583678 , 0.172844 , -0.283494 , -0.268058 , -0.0890055 , -0.777652 , 0.251188 , 0.154806 , 0.12832 , 0.243341 , -0.052358 , -0.451374 , 0.145795 , -0.0480112 , 0.297382 , 0.408185 , 0.574616 , 0.56291 , -0.538339 , 0.31561 , -0.190749 , 0.251349 , -0.437957 , 0.169098 , -0.715291 , 0.00005 , -0.194914 , 0.000153631 , -0.492329 , -0.278085 , -0.117118 , -0.797732 , 0.255204 , 0.122674 , -0.0905492 , 0.287518 , 0.0600918 , -0.503583 , 0.121699 , -0.0861578 , 0.307424 , 0.355977 , 0.614776 , 0.603071 , -0.277295 , 0.303562 , -0.0100258 , 0.259382 , -0.293379 , 0.241387 , -0.538584 , -0.0521564 , -0.114593 , 0.0443303 , -0.343735 , -0.274068 , -0.0930217 , -0.805764 , 0.138738 , 0.12669 , 0.00182023 , 0.319646 , 0.20467 , -0.431294 , 0.153827 , -0.0660775 , 0.391762 , 0.29172 , 0.793493 , 0.771746 , -0.00420256 , 0.407979 , 0.166681 , 0.387896 , -0.12872 , 0.393998 , -0.394006 , 0.0864079 , -0.0382915 , 0.168828 , -0.279477 , -0.225876 , -0.109086 , -0.779633 , 0.126677 , 0.190947 , 0.126318 , 0.303582 , 0.208686 , -0.395149 , 0.302422 , -0.00985258 , 0.387746 , 0.388105 , 0.72522 , 0.611103 , -0.289343 , 0.327658 , 0.0743116 , 0.283478 , -0.229121 , 0.201226 , -0.494408 , 0.00608663 , -0.0141951 , 0.0362983 , -0.279477 , -0.24194 , -0.00466831 , -0.699312 , 0.339529 , 0.12669 , 0.0901736 , 0.287518 , -0.0362937 , -0.428786 , 0.133507 , -0.0801519 , 0.233129 , 0.374063 , 0.614792 , 0.671344 , -0.478098 , 0.383883 , -0.134524 , 0.315606 , -0.417876 , 0.265483 , -0.675131 , 0.0342193 , -0.19491 , 0.0443223 , -0.466226 , -0.266036 , -0.201455 , -0.735453 , 0.202987 , 0.176577 , -0.0906634 , 0.251373 , 0.212702 , -0.42477 , 0.149571 , -0.124329 , 0.345579 , 0.145148 , 0.767403 , 0.671344 , -0.00821852 , 0.424044 , 0.138569 , 0.407976 , -0.116671 , 0.44219 , -0.377942 , 0.122573 , -0.0583636 , 0.202939 , -0.283647 , -0.14957 , -0.0970378 , -0.755533 , 0.114633 , 0.180593 , 0.0940755 , 0.29555 , -0.0362937 , -0.348465 , 0.000976592 , -0.0480233 , 0.148792 , 0.273662 , 0.458166 , 0.65528 , -0.341552 , 0.371835 , -0.106411 , 0.327655 , -0.377715 , 0.333757 , -0.65505 , 0.0984763 , -0.247119 , 0.118601 , -0.500514 , -0.181699 , -0.253664 , -0.675212 , 0.0182479 , 0.192642 , -0.122792 , 0.309487 , 0.265173 , -0.3438 , 0.289185 , 0.0199632 , 0.482141 , 0.355444 , 0.735605 , 0.613122 , -0.156788 , 0.357761 , 0.128518 , 0.315607 , -0.132736 , 0.305644 , -0.36991 , 0.0121007 , 0.0380178 , 0.100555 , -0.223253 , -0.182924 , 0.0134128 , -0.623245 , 0.301185 , 0.162814 , 0.142714 , 0.193021 , 0.305334 , -0.472766 , 0.163996 , -0.172808 , 0.38174 , 0.0847168 , 0.829799 , 0.538908 , 0.114214 , 0.249327 , 0.273096 , 0.199141 , 0.0319229 , 0.273516 , -0.165091 , -0.0360921 , 0.0340017 , 0.0563381 , -0.141071 , -0.257222 , -0.0263121 , -0.791835 , 0.0884059 , -0.00382644 , 0.178572 , 0.39784 , -0.011935 , -0.440185 , 0.132558 , -0.00814926 , 0.309451 , 0.470037 , 0.542738 , 0.580994 , -0.385704 , 0.389889 , -0.212847 , 0.283478 , -0.401812 , 0.237372 , -0.675131 , 0.0201328 , -0.267203 , 0.0644106 , -0.508393 , -0.23742 , -0.196261 , 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0.203157 , -0.365667 , 0.124922 , -0.675131 , -0.0119958 , -0.206962 , -0.0279589 , -0.476265 , -0.285613 , -0.192245 , -0.651357 , 0.176687 , 0.0944764 , -0.126532 , 0.317519 , -0.0761919 , -0.219297 , -0.0300639 , 0.072172 , 0.140776 , 0.40578 , 0.430287 , 0.580994 , -0.457993 , 0.345712 , -0.216863 , 0.287494 , -0.417876 , 0.2695 , -0.663083 , 0.096438 , -0.259171 , 0.0764588 , -0.496345 , -0.165131 , -0.24847 , -0.518831 , -0.00808046 , 0.162749 , -0.178741 , 0.363819 , -0.132663 , -0.295482 , 0.140679 , 0.178874 , 0.174894 , 0.568759 , 0.29153 , 0.593042 , -0.550362 , 0.345712 , -0.297185 , 0.267414 , -0.50623 , 0.136969 , -0.74742 , -0.0039636 , -0.275235 , 0.0724427 , -0.520442 , -0.239946 , -0.153262 , -0.524623 , 0.229146 , 0.194967 , -0.196981 , 0.255389 , 0.160493 , -0.435309 , 0.214068 , -0.124316 , 0.357623 , 0.267522 , 0.65901 , 0.597058 , -0.136708 , 0.305552 , 0.0763094 , 0.255365 , -0.140768 , 0.257451 , -0.402038 , 0.032181 , -0.0382875 , 0.084491 , -0.279478 , -0.243962 , -0.0167163 , -0.761587 , 0.231107 , 0.0945651 , 0.0881595 , 0.279486 , 0.00788297 , -0.330892 , 0.141779 , 0.0222759 , 0.267264 , 0.386119 , 0.484248 , 0.574959 , -0.365648 , 0.33569 , -0.102395 , 0.263398 , -0.345587 , 0.233355 , -0.566697 , -0.0180098 , -0.178854 , 0.0965392 , -0.387911 , -0.233908 , -0.12515 , -0.627023 , 0.206999 , 0.0681437 , -0.0866473) schizo.dat<-schizophrenia.dat schizophrenia.dat<-array(schizophrenia.dat,c(2,13,28)) schizophrenia.dat<-aperm(schizophrenia.dat,c(2,1,3)) schizo.dat<-array(schizo.dat,c(2,13,28)) schizo.dat<-aperm(schizo.dat,c(2,1,3)) braincon.dat<-schizo.dat[,,1:14] brainscz.dat<-schizo.dat[,,15:28] ###################### Additional functions by other authors ################## # # ============================================================================= # Authors # ============================================================================= # Gregorio Quintana-Orti # Depto. de Ingenieria y Ciencia de Computadores, # Universitat Jaume I, # 12.071 Castellon, Spain # Amelia Simo # Depto. de Matematicas, # Universitat Jaume I, # 12.071 Castellon, Spain # # ============================================================================= # Copyright # ============================================================================= # Copyright (C) 2018, # Universitat Jaume I. # # ============================================================================= # Disclaimer # ============================================================================= # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # # This module contains several modifications of some functions provided by # the noteworthy "shapes" package by Ian L. Dryden. These new implementations # have been accelerated to be much faster for medium and large datasets # than the original codes. All the other functions in the library that employ # the accelerated ones will also take advantage of this performance # improvement. # # The new code includes the original code in commented lines with four "#" # chars as a reference. # # ============================================================================= # ============================================================================= uji_preshape = function( x ) { # # It computes the preshape in a faster way on medium and large datasets # on real (non-complex) data. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if ( is.complex( x ) ) { # # Complex case. # k <- nrow( as.matrix( x ) ) h <- uji_defh( k - 1 ) zstar <- x ztem <- h %*% zstar size <- sqrt( diag( Re( st( ztem ) %*% ztem ) ) ) if ( is.vector( zstar ) ) z <- ztem / size if ( is.matrix(zstar ) ) z <- ztem %*% diag( 1.0 / size ) } else { # # Real case. # if (length(dim(x)) == 3) { # # Argument X is a 3D array. # k <- dim( x )[ 1 ] #### h <- uji_defh( k - 1 ) n <- dim( x )[ 3 ] m <- dim( x )[ 2 ] z <- array( 0, c( k - 1, m, n ) ) for ( i in 1 : n ) { #### z[, , i] <- h %*% x[, , i] # Accelerated code. z[ , , i ] <- multiply_by_helmert( x[ , , i ] ) size <- uji_centroid.size( x[ , , i ] ) z[ , , i ] <- z[ , , i ] / size } } else { # # Argument X is not a 3D array. # k <- nrow( as.matrix( x ) ) #### h <- defh(k - 1) #### ztem <- h %*% x # Accelerated code. ztem <- multiply_by_helmert( x ) size <- uji_centroid.size( x ) z <- ztem / size } } return( z ) } # ============================================================================= uji_centroid.size = function( x ) { # # It returns the centroid size of a configuration (or configurations). # Input: # k x m matrix, or # a complex k-vector, or # a real k x m x n array to get a vector of sizes for a sample # # It computes the centroid size in a faster way on medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if ((is.vector(x) == FALSE) && is.complex(x)) { k <- nrow(x) n <- ncol(x) tem <- array(0, c(k, 2, n)) tem[, 1, ] <- Re(x) tem[, 2, ] <- Im(x) x <- tem } { if (length( dim( x ) ) == 3 ) { # # Argument x is a 3D array. # n <- dim( x )[ 3 ] sz <- rep( 0, times = n ) k <- dim( x )[ 1 ] #### h <- defh( k - 1 ) for ( i in 1 : n ) { #### xh <- h %*% x[, , i] #### sz[ i ] <- sqrt( sum( diag( t( xh ) %*% xh ) ) ) # Accelerated code. xh <- multiply_by_helmert( x[ , , i ] ) sz[ i ] <- uji_Enorm( xh ) } sz } else { if ( is.vector( x ) && is.complex( x ) ) { x <- cbind( Re( x ), Im( x ) ) } k <- nrow( x ) #### h <- defh(k - 1) #### xh <- h %*% x #### size <- sqrt( sum( diag( t( xh ) %*% xh ) ) ) #cat( "pepe\n" ) # Accelerated code. xh <- multiply_by_helmert( x ) size <- uji_Enorm( xh ) size } } } # ============================================================================= uji_defh = function( nrow ) { # # It generates a Helmert matrix in a faster way on medium and large datasets. # The use of this function should be avoided when the Helmert matrix is # just built to multiply another matrix or vector. # In this case, the "multiply_by_helmert_implicitly" and # "multiply_by_transpose_of_helmert_implicitly" should be employed since # this approach is much faster. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # k <- nrow h <- matrix( 0, k, k + 1 ) if( nrow > 0 ) { for( j in seq( 1, k ) ) { val = -1 / sqrt( j * ( j + 1 ) ) h[ j, seq( 1, j ) ] = val h[ j, j+1 ] = - j * val } } h } # ============================================================================= uji_Enorm = function( X ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if( is.complex( X ) ) { #### n <- sqrt( sum( diag( Re( st(X) %*% X ) ) ) ) n <- sqrt( sum( Re( X )^2 + Im( X )^2 ) ) } else { #### n <- sqrt(sum(diag(t(X) %*% X))) n <- sqrt( sum( X^2 ) ) } return( n ) } # ============================================================================= uji_distProcrustesFull = function( P1, P2 ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### H <- defh( dim( P1 )[ 1 ] ) #### Q1 <- t( H ) %*% rootmat( P1 ) #### Q2 <- t( H ) %*% rootmat( P2 ) # Accelerated code. Q1 <- multiply_by_transpose_of_helmert( rootmat( P1 ) ) Q2 <- multiply_by_transpose_of_helmert( rootmat( P2 ) ) ans <- riemdist( Q1, Q2, reflect = TRUE ) ans } # ============================================================================= uji_distProcrustesSizeShape = function( P1, P2 ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### H <- defh( dim( P1 )[ 1 ] ) #### Q1 <- t( H ) %*% rootmat( P1 ) #### Q2 <- t( H ) %*% rootmat( P2 ) # Accelerated code. Q1 <- multiply_by_transpose_of_helmert( rootmat( P1 ) ) Q2 <- multiply_by_transpose_of_helmert( rootmat( P2 ) ) ans <- sqrt(centroid.size(Q1)^2 + centroid.size(Q2)^2 - 2 * centroid.size(Q1) * centroid.size(Q2) * cos(riemdist(Q1, Q2, reflect = TRUE))) ans } # ============================================================================= uji_distCholesky = function( P1, P2 ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### H <- defh( dim( P1 )[ 1 ] ) #### Q1 <- t( H ) %*% t( chol( P1 ) ) #### Q2 <- t( H ) %*% t( chol( P2 ) ) # Accelerated code. Q1 <- multiply_by_transpose_of_helmert( t( chol( P1 ) ) ) Q2 <- multiply_by_transpose_of_helmert( t( chol( P2 ) ) ) ans <- Enorm( Q1 - Q2 ) ans } # ============================================================================= uji_estSS = function( S, weights = 1 ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # M <- dim( S )[ 3 ] k <- dim( S )[ 1 ] #### H <- defh( k ) if ( length( weights ) == 1 ) { weights <- rep( 1, times = M ) } Q <- array( 0, c( k+1, k, M ) ) for ( j in 1 : M ){ #### Q[,,j]<-t(H)%*%(rootmat(S[,,j])) # Accelerated code. Q[ , , j ] <- multiply_by_transpose_of_helmert( rootmat( S[ , , j ] ) ) } ans <- procWGPA( Q, fixcovmatrix = diag( k + 1 ), scale = FALSE, reflect = TRUE, sampleweights = weights ) #### H%*%ans$mshape%*%t(H%*%ans$mshape) # Accelerated code. auxMat = multiply_by_helmert( ans$mshape ) return( auxMat %*% t( auxMat ) ) } # ============================================================================= uji_estShape = function( S, weights = 1 ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # M <- dim( S )[ 3 ] k <- dim( S )[ 1 ] H <- defh( k ) if ( length( weights ) == 1 ) { weights <- rep( 1, times = M ) } Q <- array( 0, c( k+1, k, M ) ) for ( j in 1 : M ) { #### Q[,,j]<-t(H)%*%(rootmat(S[,,j])) # Accelerated code. Q[ , , j ] <- multiply_by_transpose_of_helmert( rootmat( S[ , , j ] ) ) } ans <- procWGPA( Q, fixcovmatrix = diag( k + 1 ), scale = TRUE, reflect = TRUE, sampleweights = weights) #### H%*%ans$mshape%*%t(H%*%ans$mshape) # Accelerated code. auxMat = multiply_by_helmert( ans$mshape ) return( auxMat %*% t( auxMat ) ) } # ============================================================================= uji_centroid.size.complex = function( zstar ) { # # It returns the centroid size of a complex vector zstar. # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### h <- defh( nrow( as.matrix( zstar ) ) - 1 ) #### ztem <- h %*% zstar # Accelerated code. ztem <- multiply_by_helmert( zstar ) size <- sqrt( diag( Re( st( ztem ) %*% ztem ) ) ) size } # ============================================================================= uji_centroid.size.mD = function( x ) { # # It returns the centroid size of a k x m matrix. # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if( is.complex( x ) ) { x <- cbind( Re( x ), Im( x ) ) } #### k <- nrow( x ) #### h <- defh( k - 1 ) #### xh <- h %*% x #### size <- sqrt( sum( diag( t( xh ) %*% xh ) ) ) # Accelerated code. xh <- multiply_by_helmert( x ) size <- uji_Enorm( xh ) return( size ) } # ============================================================================= uji_preshape.mD = function( x ) { # # Input: k x m matrix # Output: k-1 x 1 matrix # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### h <- defh( nrow( x ) - 1 ) #### ztem <- h %*% x #### size <- centroid.size.mD( x ) # Accelerated code. ztem <- multiply_by_helmert( x ) size <- uji_centroid.size.mD( x ) z <- ztem / size return( z ) } # ============================================================================= uji_preshape.mat = function( zstar ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### h <- defh( nrow( as.matrix( zstar ) ) - 1 ) #### ztem <- h %*% zstar # Accelerated code. ztem <- multiply_by_helmert( zstar ) size <- sqrt( diag( Re( st( ztem ) %*% ztem ) ) ) if( is.vector( zstar ) ) z <- ztem / size if( is.matrix( zstar ) ) z <- ztem %*% diag( 1.0 / size ) return( z ) } # ============================================================================= uji_tanfigure = function( vv, gamma ) { # # Inverse projection from complex tangent plane coordinates vv, using pole # gamma. # Output: centred icon # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # zvv <- tanpreshape(vv, gamma) #### k <- nrow( gamma ) + 1 #### h <- defh( k - 1 ) #### zstvv <- t( h ) %*% zvv # Accelerated code. zstvv <- multiply_by_transpose_of_helmert( zvv ) return( zstvv ) } # ============================================================================= uji_tanfigurefull = function( vv, gamma ) { # # Inverse projection from complex tangent plane coordinates vv, using pole # gamma # Using Procrustes to with scaling to the pole gamma. # Output: centred icon # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # f1 <- uji_tanfigure( vv, gamma ) #### k <- nrow( gamma ) + 1 #### h <- defh( k - 1 ) #### f2 <- t(h) %*% gamma # Accelerated code. f2 <- multiply_by_transpose_of_helmert( gamma ) beta <- Mod( st( f1 ) %*% f2 ) f1 <- f1 * c( beta ) f1 } # ============================================================================= uji_kendall.shpv = function( x ) { # # Accelerated version of the original function for medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # k <- dim( x )[ 1 ] #### h <- defh( k - 1 ) #### zz <- h %*% x # Accelerated code. zz <- multiply_by_helmert( x ) kendall <- ( zz[2:(k-1),1] + 1i*zz[2:(k-1),2] ) / ( zz[1,1] + 1i*zz[1,2] ) kendall <- cbind( Re( kendall ), Im( kendall ) ) kendall } # ============================================================================= multiply_by_helmert = function( x ) { # # This code multiplies the "x" argument by the transpose of the Helmert matrix # of the corresponding size. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # # # threshold chosen as 30 # if( nrow( x ) < 30 ) { xh = multiply_by_helmert_explicitly( x ) } else { xh = multiply_by_helmert_implicitly( x ) } xh } # ============================================================================= multiply_by_helmert_explicitly = function( x ) { # # This code multiplies the "x" argument by the transpose of the Helmert matrix # of the corresponding size by explicitly building the matrix. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # k <- nrow( x ) h <- defh( k - 1 ) xh <- h %*% x xh } # ============================================================================= multiply_by_helmert_implicitly = function( x ) { # # This code multiplies the "x" argument by the Helmert matrix of the # corresponding size without explicitly building the matrix in order to # increase performances. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # m = dim( x )[ 1 ] - 1 n = dim( x )[ 2 ] result <- matrix( 0, m, n ) vsum <- rep( 0, n ) if( m > 0 ) { for( i in seq( 1, m ) ) { val = -1 / sqrt( i * ( i + 1 ) ) hi = val hip1 = - i * val vsum = vsum + x[ i, ] result[ i, ] = vsum * hi + x[ i + 1, ] * hip1 } } return( result ) } # ============================================================================= multiply_by_transpose_of_helmert = function( x ) { # # This code multiplies the "x" argument by the transpose of the Helmert matrix # of the corresponding size. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # k = nrow( x ) if( k < 30 ) { result = multiply_by_transpose_of_helmert_explicitly( x ) } else { result = multiply_by_transpose_of_helmert_implicitly( x ) } return( result ) } # ============================================================================== multiply_by_transpose_of_helmert_explicitly = function( x ) { # # This code multiplies the "x" argument by the transpose of the Helmert matrix # of the corresponding size by explicitly building the matrix. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### m = dim( x )[ 1 ] m = nrow( x ) h = defh( m ) result = t( h ) %*% x return( result ) } # ============================================================================= multiply_by_transpose_of_helmert_implicitly = function( x ) { # # This code multiplies the "x" argument by the transpose of the Helmert matrix # of the corresponding size without explicitly building the matrix in order to # increase performances. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # #### m = dim( x )[ 1 ] + 1 #### n = dim( x )[ 2 ] m = nrow( x ) + 1 n = ncol( x ) result <- matrix( 0, m, n ) rowAccum <- rep( 0, n ) if( m > 0 ) { for( i in seq( m, 1, by = -1 ) ) { val = - 1 / sqrt( ( i - 1 ) * i ) hi = val hip1 = - ( i - 1 ) * val if( i == 1 ) { result[ i, ] = rowAccum } else { result[ i, ] = hip1 * x[ i - 1, ] + rowAccum rowAccum = rowAccum + hi * x[ i - 1, ] } } } return( result ) } # ============================================================================= # ============================================================================= uji2_centroid.size = function( x ) { # # It returns the centroid size of a configuration (or configurations). # Input: # k x m matrix, or # a complex k-vector, or # a real k x m x n array to get a vector of sizes for a sample # # It computes the centroid size in a faster way on medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if ((is.vector(x) == FALSE) && is.complex(x)) { k <- nrow(x) n <- ncol(x) tem <- array(0, c(k, 2, n)) tem[, 1, ] <- Re(x) tem[, 2, ] <- Im(x) x <- tem } { if (length( dim( x ) ) == 3 ) { # # Argument x is a 3D array. # n <- dim( x )[ 3 ] k <- dim( x )[ 1 ] sz <- rep( 0, times = n ) for ( i in 1 : n ) { xh <- multiply_by_helmert( x[ , , i ] ) sz[ i ] <- Enorm( xh ) } sz } else { if ( is.vector( x ) && is.complex( x ) ) { x <- cbind( Re( x ), Im( x ) ) } #### k <- nrow( x ) #### h <- defh(k - 1) #### xh <- h %*% x #### size <- sqrt( sum( diag( t( xh ) %*% xh ) ) ) # Accelerated code. xh <- multiply_by_helmert( x ) size <- Enorm( xh ) size } } } uji3_centroid.size = function( x ) { # # It returns the centroid size of a configuration (or configurations). # Input: # k x m matrix, or # a complex k-vector, or # a real k x m x n array to get a vector of sizes for a sample # # It computes the centroid size in a faster way on medium and large datasets. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if ((is.vector(x) == FALSE) && is.complex(x)) { k <- nrow(x) n <- ncol(x) tem <- array(0, c(k, 2, n)) tem[, 1, ] <- Re(x) tem[, 2, ] <- Im(x) x <- tem } { if (length( dim( x ) ) == 3 ) { # # Argument x is a 3D array. # n <- dim( x )[ 3 ] k <- dim( x )[ 1 ] z <- multiply_by_helmert_implicitly_3d( x ) sz <- rep( 0, times = n ) for ( i in 1 : n ) { sz[ i ] <- Enorm( z[ , , i ] ) } sz } else { if ( is.vector( x ) && is.complex( x ) ) { x <- cbind( Re( x ), Im( x ) ) } #### k <- nrow( x ) #### h <- defh(k - 1) #### xh <- h %*% x #### size <- sqrt( sum( diag( t( xh ) %*% xh ) ) ) # Accelerated code. xh <- multiply_by_helmert( x ) size <- Enorm( xh ) size } } } # ============================================================================= multiply_by_helmert_implicitly_3d = function( x ) { # # This code multiplies the "x" argument by the Helmert matrix of the # corresponding size without explicitly building the matrix in order to # increase performances. # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # # Initialize result and vsum. k <- dim( x )[ 1 ] - 1 m <- dim( x )[ 2 ] n <- dim( x )[ 3 ] result <- array( 0, c( k, m, n ) ) vsum <- matrix( 0, m, n ) if( m > 0 ) { for( i in seq( 1, k ) ) { val = -1 / sqrt( i * ( i + 1 ) ) hi = val hip1 = - i * val vsum = vsum + x[ i, , ] result[ i, , ] = vsum * hi + x[ i + 1, , ] * hip1 } } return( result ) } # =========================== # Replace original functions # =========================== # ============================================================================= defh = function( nrow ) { # # Written by G. Quintana-Orti and Amelia Simo, University Jaume I, Spain. # This code is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED. # if ( nrow < 100 ) { return( ild_defh( nrow ) ) } else { return( uji_defh( nrow ) ) } } centroid.size <- function(x){ if (is.vector(x)==FALSE){ k<-dim(x)[1] m<-dim(x)[2] if ( ( m == 2 ) | ( m == 3 ) ) { # Matrices with 2D or 3D landmarks. if ( k < 40 ) { return( ild_centroid.size(x) ) } else { return( uji3_centroid.size(x) ) } } else { # Often square or nearly-square matrices where m is larger if ( k < 85 ) { return( ild_centroid.size(x) ) } else { return( uji2_centroid.size(x) ) } } } else{ return(ild_centroid.size(x)) } } Enorm<-uji_Enorm preshapetoicon<-ild_preshapetoicon preshape<-ild_preshape distProcrustesFull <- uji_distProcrustesFull distProcrustesSizeShape <- uji_distProcrustesSizeShape distCholesky <- uji_distCholesky estSS <- ild_estSS estShape <- ild_estShape centroid.size.complex <- uji_centroid.size.complex centroid.size.mD <- uji_centroid.size.mD preshape.mD <- uji_preshape.mD preshape.mat <- uji_preshape.mat tanfigure <- uji_tanfigure tanfigurefull <- uji_tanfigurefull kendall.shpv <- uji_kendall.shpv ########################################################################## r-cran-shapes-1.2.6/data/000077500000000000000000000000001412660331700150775ustar00rootroot00000000000000r-cran-shapes-1.2.6/data/apes.rda000066400000000000000000000052711412660331700165240ustar00rootroot00000000000000BZh91AY&SY(=¸•Ýÿs|tUUÿÿÿÿÿÿÿß@@@€À@@` ˜>‚âÀS€éãR…^÷DU?ÐLš4i  šOC¦„ÈG´‘¦™š§¤Ñ£ jz#B)êièÑ©©èM¡Šla¨Ó'¨茀%=!*`˜2`ÓÀ&M`žª• 4hÈÀÐhh4ihd 4dd@ˆDÑ4Ó h Ô44 €hƒ@È4~Ì`‰ ZŠ ‰SQ¨,”Â"ÊVid‘dFÛZ‘ŒU¤k³¹tÊDiTR#!¨Å\>Ë,1HpI’¹Íç9À8!-"ÇdxÑ— ¹i8\¸Ð±Ý&&<Ì-¡æo44W…ËBé$’o6ôÂc{™e­»Ì $ Bê]é Y]œÌmæíæ4œ/6³!y„›–+Zç5vŽ/ n·g ¼¹ÇK9µ ›tÞæí.Ìœr… Jµ]‹HÖTä»Z‘"Ä‘XÁn²D„„X0i„ ÈÆ#$j5%$)VbÔjE‘’ ŒÔ¬ F¢2¡!BFB0R2 Ù 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%‘+‘Å¥%;ã'ù¥Mÿ/±}ð[Ž`Tg¼OõŠÝg®0 ‹YZr-cran-shapes-1.2.6/demo/000077500000000000000000000000001412660331700151125ustar00rootroot00000000000000r-cran-shapes-1.2.6/demo/00Index000077500000000000000000000000741412660331700162500ustar00rootroot00000000000000shapes Examples of statistical shape analysis r-cran-shapes-1.2.6/demo/shapes.R000077500000000000000000000033511412660331700165250ustar00rootroot00000000000000#2D example : female and male Gorillas (cf. Dryden and Mardia, 1998) data(gorf.dat) data(gorm.dat) n1<-dim(gorf.dat)[3] n2<-dim(gorm.dat)[3] k<-dim(gorf.dat)[1] m<-dim(gorf.dat)[2] gor.dat<-array(0,c(k,2,n1+n2)) gor.dat[,,1:n1]<-gorf.dat gor.dat[,,(n1+1):(n1+n2)]<-gorm.dat plotshapes(gorf.dat,gorm.dat) gorf<-procGPA(gorf.dat) gorm<-procGPA(gorm.dat) plotshapes(gorf$rotated,gorm$rotated) gor<-procGPA(gor.dat) shapepca(gor,type="r",mag=3) cat("First 3 PCs for pooled gorilla data") for (ii in 1:1000000){a<-1} shapepca(gor,type="v",mag=3) cat("First 3 PCs for pooled gorilla data") for (ii in 1:1000000){a<-1} gor.gp<-c(rep("f",times=30),rep("m",times=29)) x<-cbind(gor$size,gor$rho,gor$scores[,1:3]) pairs(x,panel=function(x,y) text(x,y,gor.gp), label=c("s","rho","score 1","score 2","score 3")) #Some tests test1<-testmeanshapes(gorf.dat,gorm.dat) print(test1) cat("Highly significant difference in mean shape") gorf<-procGPA(gorf.dat) gorm<-procGPA(gorm.dat) mag<-2 TT<-gorf$mshape YY<-gorm$mshape par(mfrow=c(1,2)) YY<-TT+(YY-TT)*mag tpsgrid(TT,YY,-150,-150,300,2,0.1,22) title("TPS grid: Female mean (left) to Male mean (right)") cat("##########################################################\n") cat("#3D example\n#") cat("##########################################################\n") # Male macaques data (Dryden/Mardia 1998) data(macm.dat) out<-procGPA(macm.dat) par(mfrow=c(2,2)) plot(out$rawscores[,1],out$rawscores[,2],xlab="PC1",ylab="PC2") title("PC scores") plot(out$rawscores[,2],out$rawscores[,3],xlab="PC2",ylab="PC3") plot(out$rawscores[,1],out$rawscores[,3],xlab="PC1",ylab="PC3") plot(out$size,out$rho,xlab="size",ylab="rho") title("Size versus shape distance") r-cran-shapes-1.2.6/man/000077500000000000000000000000001412660331700147415ustar00rootroot00000000000000r-cran-shapes-1.2.6/man/apes.Rd000077500000000000000000000020631412660331700161640ustar00rootroot00000000000000\name{apes} \alias{apes} \title{Great ape data} \description{Great ape skull landmark data. 8 landmarks in 2 dimensions, 167 individuals } \usage{data(apes)} \format{ apes$x : An array of dimension 8 x 2 x 167 apes$group : Species and sex of each specimen: "gorf" 30 female gorillas, "gorm" 29 male gorillas, "panf" 26 female chimpanzees, "pamm" 28 male chimpanzees, "pongof" 24 female orang utans, "pongom" 30 male orang utans. } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(apes) par(mfrow=c(1,2)) plotshapes(apes$x[,,apes$group=="gorf"],symbol="f") plotshapes(apes$x[,,apes$group=="gorm"],symbol="m") } \keyword{datasets} r-cran-shapes-1.2.6/man/bookstein2d.Rd000077500000000000000000000023461412660331700174630ustar00rootroot00000000000000\name{bookstein2d} \alias{bookstein2d} \title{Bookstein's baseline registration for 2D data} \description{Carries out Bookstein's baseline registration and calculates a mean shape} \usage{bookstein2d(A,l1=1,l2=2)} \arguments{ \item{A}{a k x 2 x n real array, or k x n complex matrix, where k is the number of landmarks, n is the number of observations} \item{l1}{l1: an integer : l1 is sent to (-1/2,0) in the registration} \item{l2}{l2: an integer : l2 is sent to (1/2,0) in the registration} } \value{A list with components: \item{k}{number of landmarks} \item{n}{sample size} \item{mshape}{Bookstein mean shape with baseline l1, l2} \item{bshpv}{the k x n x 2 array of Bookstein shape variables, including the baseline} } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Chapter 2. Bookstein, F. L. (1986) Size and shape spaces for landmark data in two dimensions (with discussion). Statistical Science, 1:181-242. } \author{Ian Dryden} \examples{ data(gorf.dat) data(gorm.dat) bookf<-bookstein2d(gorf.dat) bookm<-bookstein2d(gorm.dat) plotshapes(bookf$mshape,bookm$mshape,joinline=c(1,6,7,8,2,3,4,5,1)) } \keyword{multivariate} r-cran-shapes-1.2.6/man/brains.Rd000077500000000000000000000014721412660331700165150ustar00rootroot00000000000000\name{brains} \alias{brains} \title{Brain landmark data} \description{24 landmarks located in 58 adult healthy brains} \usage{data(brains)} \format{A list with components: brains$x : An array of dimension 24 x 3 x 58 containing the landmarks in 3D brains$sex : Sex of each volunteer (m or f) brains$age : Age of each volunteer brains$handed : Handedness of each volunteer (r or l) brains$grp : group label: 1= right-handed males, 2=left-handed males, 3=right-handed females, 4=left-handed females } \references{ Free, S.L., O'Higgins, P., Maudgil, D.D., Dryden, I.L., Lemieux, L., Fish, D.R. and Shorvon, S.D. (2001). Landmark-based morphometrics of the normal adult brain using MRI. Neuroimage , 13 , 801--813. } \examples{ data(brains) # plot first three brains shapes3d(brains$x[,,1:3]) } \keyword{datasets} r-cran-shapes-1.2.6/man/centroid.size.Rd000077500000000000000000000011421412660331700200110ustar00rootroot00000000000000\name{centroid.size} \alias{centroid.size} \title{Centroid size} \description{Calculate cetroid size from a configuration or a sample of configurations. } \usage{centroid.size(x)} \arguments{ \item{x}{For a single configuration k x m matrix or complex k-vector For a sample of configurations k x m x n array or k x n complex matrix } } \value{ Centroid size(s) } \references{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. } \examples{ data(mice) centroid.size(mice$x[,,1]) } \author{Ian Dryden} \keyword{multivariate} r-cran-shapes-1.2.6/man/cortical.Rd000077500000000000000000000022371412660331700170370ustar00rootroot00000000000000\name{cortical} \alias{cortical} \title{Cortical surface data} \description{Cortical surface data, from MR scans. Axial slice outlines with 500 points on each outline. 68 individuals. } \usage{data(cortical)} \format{ cortical$age ( age) cortical$group ( Control, Schizophrenia) cortical$sex ( 1 = male, 2 = female) cortical$symm ( a symmetry measure from the original 3D cortical surface ) cortical$x (500 x , y coordinates of an axial slice through the cortical surface intersecting the anterior and posterior commissures) cortical$r (500 radii from equal angular polar coordinates ) } \source{ Brignell, C.J., Dryden, I.L., Gattone, S.A., Park, B., Leask, S., Browne, W.J. and Flynn, S. (2010). Surface shape analysis, with an application to brain surface asymmetry in schizophrenia. Biostatistics, 11, 609-630. Dryden, I.L. (2005). Statistical analysis on high-dimensional spheres and shape spaces. Annals of Statistics, 33, 1643-1665 } \references{ Original MR data from Sean Flynn (UBC) in collaboration with Bert Park (Nottingham). } \examples{ data(cortical) plotshapes(cortical$x) } \keyword{datasets} r-cran-shapes-1.2.6/man/digit3.dat.Rd000077500000000000000000000011311412660331700171610ustar00rootroot00000000000000\name{digit3.dat} \alias{digit3.dat} \title{Digit 3 data} \description{Handwritten digit `3' data. 13 landmarks in 2 dimensions, 30 individuals } \usage{data(digit3.dat)} \format{ An array of dimension 13 x 2 x 30 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ http://www.maths.nott.ac.uk/personal/ild/bookdata/digit3.dat Data from Cath Anderson } \examples{ data(digit3.dat) k<-dim(digit3.dat)[1] n<-dim(digit3.dat)[3] plotshapes(digit3.dat,joinline=c(1:13)) } \keyword{datasets} r-cran-shapes-1.2.6/man/distcov.Rd000077500000000000000000000026461412660331700167160ustar00rootroot00000000000000\name{distcov} \alias{distcov} %- Also NEED an `\alias' for EACH other topic documented here. \title{Compute a distance between two covariance matrices} \description{Compute a distance between two covariance matrices, with non-Euclidean options. } \usage{ distcov(S1, S2, method="Riemannian",alpha=1/2) } %- maybe also `usage' for other objects documented here. \arguments{ \item{S1}{Input a covariance matrix (square, symmetric, positive definite)} \item{S2}{Input another covariance matrix of the same size } \item{method}{The type of distance to be used: "Procrustes": Procrustes size-and-shape metric, "ProcrustesShape": Procrustes metric with scaling, "Riemannian": Riemannian metric, "Cholesky": Cholesky based distance, "Power: Power Euclidean, with power alpha, "Euclidean": Euclidean metric, "LogEuclidean": Log-Euclidean metric, "RiemannianLe": Another Riemannian metric.} \item{alpha}{The power to be used in the power Euclidean metric } } \value{The distance } \references{Dryden, I.L., Koloydenko, A. and Zhou, D. (2009). Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. Annals of Applied Statistics, 3, 1102-1123.} \author{Ian Dryden} \seealso{estcov} \examples{ A <- diag(5) B <- A + .1*matrix(rnorm(25),5,5) S1<-A S2<- B%*%t(B) distcov( S1, S2, method="Procrustes") } \keyword{multivariate} r-cran-shapes-1.2.6/man/dna.dat.Rd000077500000000000000000000004211412660331700165410ustar00rootroot00000000000000\name{dna.dat} \alias{dna.dat} \title{DNA data} \description{Part of a 3D DNA molecule moving in time, k = 22 atoms, 30 time points} \usage{data(dna.dat)} \format{ An array of dimension 22 x 3 x 30 } \examples{ data(dna.dat) plotshapestime3d(dna.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/estcov.Rd000077500000000000000000000040441412660331700165400ustar00rootroot00000000000000\name{estcov} \alias{estcov} %- Also NEED an `\alias' for EACH other topic documented here. \title{Weighted Frechet mean of covariance matrices} \description{Computes the weighted Frechet means of an array of covariance matrices, with different options for the covariance metric. Also carries out principal co-ordinate analysis of the covariance matrices} \usage{ estcov(S , method="Riemannian",weights=1,alpha=1/2,MDSk=2) } %- maybe also `usage' for other objects documented here. \arguments{ \item{S}{Input an array of covariance matrices of size k x k x n where each matrix is square, symmetric and positive definite} \item{method}{The type of distance to be used: "Procrustes": Procrustes size-and-shape metric, "ProcrustesShape": Procrustes metric with scaling, "Riemannian": Riemannian metric, "Cholesky": Cholesky based distance, "Power: Power Euclidean, with power alpha, "Euclidean": Euclidean metric, "LogEuclidean": Log-Euclidean metric, "RiemannianLe": Another Riemannian metric. } \item{weights}{The weights to be used for calculating the mean. If weights=1 then equal weights are used, otherwise the vector must be of length n.} \item{alpha}{The power to be used in the power Euclidean metric} \item{MDSk}{The number of MDS components in the principal co-ordinate analysis} } \value{A list with values \item{mean}{The weighted mean covariance matrix} \item{sd}{The weighted standard deviation} \item{pco}{Principal co-ordinates (from multidimensional scaling with the metric)} \item{eig}{The eigenvalues from the principal co-ordinate analysis} } \references{Dryden, I.L., Koloydenko, A. and Zhou, D. (2009). Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. Annals of Applied Statistics, 3, 1102-1123.} \author{Ian Dryden} \seealso{distcov} \examples{ S <- array(0,c(5,5,10) ) for (i in 1:10){ tem <- diag(5)+.1*matrix(rnorm(25),5,5) S[,,i]<- tem%*%t(tem) } estcov( S , method="Procrustes") } \keyword{multivariate} r-cran-shapes-1.2.6/man/frechet.Rd000077500000000000000000000040661412660331700166610ustar00rootroot00000000000000\name{frechet} \alias{frechet} \title{Mean shape estimators} \description{Calculation of different types of Frechet mean shapes, or the isotropic offset Gaussian MLE mean shape } \usage{ frechet(x, mean="intrinsic") } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{Input k x m x n real array, where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{mean}{Type of mean shape. The Frechet mean shape is obtained by minimizing sum d(x_i,mu)^2 with respect to mu. Different estimators are obtained with different choices of distance d. "intrinsic" intrinsic mean shape (d = rho = Riemannian distance); "partial.procrustes" partial Procrustes (d = 2*sin(rho/2)); "full.procrustes" full Procrustes (d = sin(rho)); h (positive real number) M-estimator (d^2 = (1 - cos^(2h)(rho))/h) Kent (1992); "mle" - isotropic offset Gaussian MLE of Mardia and Dryden (1989) } } \value{A list with components \item{mshape}{Mean shape estimate} \item{var}{Minimized Frechet variance (not available for MLE)} \item{kappa}{(if available) The estimated kappa for the MLE} \item{code}{Code from optimization, as given by function nlm - should be 1 or 2} \item{gradient}{Gradient from the optimization, as given by function nlm - should be close to zero} } \references{ Dryden, I. L. (1991). Discussion to `Procrustes methods in the statistical analysis of shape' by C.R. Goodall. Journal of the Royal Statistical Society, Series B, 53:327-328. Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Kent, J. T. (1992). New directions in shape analysis. In Mardia, K. V., editor, The Art of Statistical Science, pages 115-127. Wiley, Chichester. Mardia, K. V. and Dryden, I. L. (1989b). The statistical analysis of shape data. Biometrika, 76:271-282. } \author{Ian Dryden} \seealso{procGPA} \examples{ #2D example : female and male Gorillas (cf. Dryden and Mardia, 2016) data(gorf.dat) frechet(gorf.dat[,,1:4],mean="intrinsic") } \keyword{multivariate} r-cran-shapes-1.2.6/man/gels.Rd000077500000000000000000000007331412660331700161700ustar00rootroot00000000000000\name{gels} \alias{gels} \title{Electrophoresis gel data} \description{Electrophoresis gel data. 10 invariant spots have been picked out by an expert on two electrophoretic gels. } \usage{data(gels)} \format{ An array of dimension 10 x 2 x 2 } \source{ Dryden, I. L. and Walker, G. (1999). Highly resistant regression and object matching. Biometrics, 55, 820-825. } \references{ Data from Chris Glasbey (BioSS) } \examples{ data(gels) plotshapes(gels) } \keyword{datasets} r-cran-shapes-1.2.6/man/gorf.dat.Rd000077500000000000000000000013541412660331700167420ustar00rootroot00000000000000\name{gorf.dat} \alias{gorf.dat} \title{Female gorilla data} \description{Female gorilla skull data. 8 landmarks in 2 dimensions, 30 individuals } \usage{data(gorf.dat)} \format{ An array of dimension 8 x 2 x 30 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ http://www.maths.nott.ac.uk/personal/ild/bookdata/gorf.dat Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(gorf.dat) plotshapes(gorf.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/gorm.dat.Rd000077500000000000000000000013471412660331700167530ustar00rootroot00000000000000\name{gorm.dat} \alias{gorm.dat} \title{Male gorilla data} \description{Male gorilla skull data. 8 landmarks in 2 dimensions, 29 individuals } \usage{data(gorm.dat)} \format{ An array of dimension 8 x 2 x 29 } \source{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ http://www.maths.nott.ac.uk/personal/ild/bookdata/gorm.dat Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(gorm.dat) plotshapes(gorm.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/groupstack.Rd000077500000000000000000000027051412660331700174210ustar00rootroot00000000000000\name{groupstack} \alias{groupstack} %- Also NEED an `\alias' for EACH other topic documented here. \title{Combine two or more groups of configurations} \description{Combine two or more groups of configurations and create a group label vector. (Maximum 8 groups). } \usage{ groupstack(A1, A2, A3=0, A4=0, A5=0, A6=0, A7=0, A8=0) } %- maybe also `usage' for other objects documented here. \arguments{ \item{A1}{Input k x m x n real array of the Procrustes transformed configurations, where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{A2}{Input k x m x n real array of the Procrustes original configurations, where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{A3}{ Optional array} \item{A4}{ Optional array} \item{A5}{ Optional array} \item{A6}{ Optional array} \item{A7}{ Optional array} \item{A8}{ Optional array} } \value{A list with components \item{x}{The combined array of all configurations} \item{groups}{The group labels (integers)} } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester.} \author{Ian Dryden} \seealso{procGPA} \examples{ #2D example : female and male Gorillas (cf. Dryden and Mardia, 2016) data(gorf.dat) data(gorm.dat) groupstack(gorf.dat,gorm.dat) } \keyword{multivariate} r-cran-shapes-1.2.6/man/humanmove.Rd000077500000000000000000000012501412660331700172300ustar00rootroot00000000000000\name{humanmove} \alias{humanmove} \title{Human movement data} \description{Human movement data. 4 landmarks in 2 dimensions, 5 individuals observed at 10 times. } \usage{data(humanmove)} \format{ humanmove: An array of landmark configurations 4 x 2 x 10 x 5 } \source{ Alshabani, A. K. S. and Dryden, I. L. and Litton, C. D. and Richardson, J. (2007). Bayesian analysis of human movement curves, J. Roy. Statist. Soc. Ser. C, 56, 415--428. } \references{ Data from James Richardson. } \examples{ data(humanmove) #plotshapes(humanmove[,,,1]) #for (i in 2:5){ #for (j in 1:4){ #for (k in 1:10){ #points(humanmove[j,,k,i],col=i) #} #} #} } \keyword{datasets} r-cran-shapes-1.2.6/man/macaques.Rd000077500000000000000000000013421412660331700170320ustar00rootroot00000000000000\name{macaques} \alias{macaques} \title{Male and Female macaque data} \description{Male and female macaque skull data. 7 landmarks in 3 dimensions, 18 individuals (9 males, 9 females) } \usage{data(macaques)} \format{ macaques$x : An array of dimension 7 x 3 x 18 macaques$group : A factor indicating the sex (`m' for male and `f' for female) } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ Dryden, I. L. and Mardia, K. V. (1993). Multivariate shape analysis. Sankhya Series A, 55, 460-480. Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(macaques) shapes3d(macaques$x[,,1]) } \keyword{datasets} r-cran-shapes-1.2.6/man/macf.dat.Rd000077500000000000000000000007621412660331700167150ustar00rootroot00000000000000\name{macf.dat} \alias{macf.dat} \title{Female macaque data} \description{Female macaque skull data. 7 landmarks in 3 dimensions, 9 individuals } \usage{data(macf.dat)} \format{ An array of dimension 7 x 3 x 9 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(macf.dat) plotshapes(macf.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/macm.dat.Rd000077500000000000000000000007561412660331700167270ustar00rootroot00000000000000\name{macm.dat} \alias{macm.dat} \title{Male macaque data} \description{Male macaque skull data. 7 landmarks in 3 dimensions, 9 individuals } \usage{data(macm.dat)} \format{ An array of dimension 7 x 3 x 9 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(macm.dat) plotshapes(macm.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/mice.Rd000077500000000000000000000023041412660331700161470ustar00rootroot00000000000000\name{mice} \alias{mice} \title{T2 mouse vertabrae data} \description{T2 mouse vertebrae data - 6 landmarks in 2 dimensions, in 3 groups (30 Control, 23 Large, 23 Small mice). The 6 landmarks are obtained using a semi-automatic method at points of high curvature. This particular strain of mice is the `QE' strain. In addition pseudo-landmarks are given around each outlines. } \usage{data(mice)} \format{ mice$x : An array of dimension 6 x 2 x 76 of the two dimensional co-ordinates of 6 landmarks for each of the 76 mice. mice$group : Group labels. "c" Control, "l" Large, "s" Small mice mice$outlines : An array of dimension 60 x 2 x 76 containing the 6 landmarks and 54 pseudo-landmarks, with 9 pseudo-landmarks approximately equally spaced between each pair of landmarks. } \source{ Dryden, I.L. and Mardia, K.V. (1998). Statistical Shape Analysis, Wiley, Chichester. p313 } \references{ Mardia, K. V. and Dryden, I. L. (1989). The statistical analysis of shape data. Biometrika, 76, 271-281. Data from Paul O'Higgins (Hull-York Medical School) and David Johnson (Leeds) } \examples{ data(mice) plotshapes(mice$x,symbol=as.character(mice$group),joinline=c(1,6,2:5,1)) } \keyword{datasets} r-cran-shapes-1.2.6/man/panf.dat.Rd000077500000000000000000000010521412660331700167240ustar00rootroot00000000000000\name{panf.dat} \alias{panf.dat} \title{Female chimpanzee data} \description{Female chimpanzee skull data. 8 landmarks in 2 dimensions, 26 individuals } \usage{data(panf.dat)} \format{ An array of dimension 8 x 2 x 26 } \source{ O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(panf.dat) plotshapes(panf.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/panm.dat.Rd000077500000000000000000000010461412660331700167360ustar00rootroot00000000000000\name{panm.dat} \alias{panm.dat} \title{Male chimpanzee data} \description{Male chimpanzee skull data. 8 landmarks in 2 dimensions, 28 individuals } \usage{data(panm.dat)} \format{ An array of dimension 8 x 2 x 28 } \source{ O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(panm.dat) plotshapes(panm.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/plot3darcs.Rd000077500000000000000000000035451412660331700173200ustar00rootroot00000000000000\name{plot3darcs} \alias{plot3darcs} \title{Modes of variation plots for PCA and PNSS} \description{Modes of variation plots for PCA and PNSS based on 3D views and arcs along a mode. c * sd : the extent along lower and upper principal arcs. The lower principal arc -> 0 -> upper principal arc has a total of 2*nn+1 configurations with: nn configurations along the negative principal arc to 0; one configuration at the PNS mean; nn configurations along the positive principal arc. } \usage{ plot3darcs(x,pcno=1,c=1,nn=100,boundary.data=TRUE,view.theta=0,view.phi=0,type="pnss") } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{Output from pnss3d} \item{pcno}{The number of the PC/PNSS component. The default is 1, the first PC/PNSS} \item{c}{Number of standard deviations along each arc} \item{nn}{In total 2 * nn + 1 configurations: n configurations on arc from negative to 0; 1 configuration at 0; nn configurations from 0 to positive} \item{boundary.data}{Logical for whether to use boundary data or not. } \item{view.theta}{Viewing angle theta} \item{view.phi}{Viewing angle phi} \item{type}{"pnss" principal nested sphere mean and arc, or "pca" Procrustes mean and linear PC.} } \value{A list with components \item{PNSmean}{the PNSS mean} \item{lu.arc}{the configurations along the arc} } \references{ Dryden, I.L., Kim, K., Laughton, C.A. and Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13, 2213-2234. Jung, S., Dryden, I.L. and Marron, J.S. (2012). Analysis of principal nested spheres. Biometrika, 99, 551-568. } \author{Kwang-Rae Kim, Ian Dryden} \seealso{pns, pns4pc, pnss3d} \examples{ ans <- pnss3d(digit3.dat, sphere.type="BIC", n.pc=5) #aa <- plot3darcs(ans,c=2,pcno=1) #bb <- plot3darcs(ans,c=2,pcno=1,type="pca") } \keyword{multivariate} r-cran-shapes-1.2.6/man/plotshapes.Rd000077500000000000000000000027351412660331700174240ustar00rootroot00000000000000\name{plotshapes} \alias{plotshapes} %- Also NEED an `\alias' for EACH other topic documented here. \title{Plot configurations} \description{ Plots configurations. Either one or two groups of observations can be plotted on the same scale. } \usage{ plotshapes(A, B = 0, joinline = c(1, 1),orthproj=c(1,2),color=1,symbol=1) } %- maybe also `usage' for other objects documented here. \arguments{ \item{A}{k x m x n array, or k x m matrix for first group} \item{B}{k x m x n array, or k x m matrix for 2nd group (can be missing)} \item{joinline}{A vector stating which landmarks are joined up by lines, e.g. joinline=c(1:n,1) will start at landmark 1, join to 2, ..., join to n, then re-join to landmark 1.} \item{orthproj}{A vector stating which two orthogonal projections will be used. For example, for m=3 dimensional data: X-Y projection given by c(1,2) (default), X-Z projection given by c(1,3), Y-Z projection given by c(2,3).} \item{color}{Colours for points. Can be a vector, e.g. 1:k gives each landmark a different colour for the specimens} \item{symbol}{Plotting symbols. Can be a vector, e.g. 1:k gives each landmark a different symbol for the specimens} } \value{ Just graphical output } \author{Ian Dryden} \seealso{shapepca,tpsgrid} \examples{ data(gorf.dat) data(gorm.dat) plotshapes(gorf.dat,gorm.dat,joinline=c(1,6,7,8,2,3,4,5,1)) data(macm.dat) data(macf.dat) plotshapes(macm.dat,macf.dat) } \keyword{hplot} \keyword{multivariate} r-cran-shapes-1.2.6/man/pns.Rd000077500000000000000000000044471412660331700160440ustar00rootroot00000000000000\name{pns} \alias{pns} \title{Principal Nested Spheres} \description{Calculation of Principal Nested Spheres } \usage{ pns(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{a (d + 1) x n data matrix where each column is a unit vector in S^d and n is the sample size. } \item{sphere.type}{ a character string specifying the type of sphere fitting method. "seq.test" specifies sequential tests to decide either "small" or "great"; "small" specifies Principal Nested SMALL Sphere; "great" specifies Principal Nested GREAT Sphere (radius pi/2); "BIC" specifies BIC statistic to decide either "small" or "great"; and "bi.sphere" specifies Principal Nested GREAT Sphere for the first part and Principal Nested SMALL Sphere for The default is "seq.test". } \item{alpha}{significance level (0 < alpha < 1) used when sphere.type = "seq.test". The default is 0.1. } \item{R}{the number of bootstrap samples to be evaluated for the sequential test. The default is 100.} \item{nlast.small.sphere}{the number of small spheres in the finishing part used when sphere.type = "bi.sphere".} } \value{A list with components \item{resmat}{the residual matrix (X_PNS). Each entry in row k works like the kth principal component $PNS = the list with the following components.} \item{radii}{the size (radius) of PNS.} \item{orthaxis}{the orthogonal axis v_i of subspheres.} \item{dist}{the distance r_i of subspheres} \item{pvalues}{the p-values of LRT and parametric boostrap tests (if any).} \item{ratio}{the estimated ratios. Now unavailable.} \item{mean}{the location of the PNS mean.} \item{sphere.type}{the type of method for fitting subspheres.} \item{percent}{proportion of variances explained.} } \references{ Dryden, I.L., Kim, K., Laughton, C.A. and Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13, 2213-2234. Jung, S., Dryden, I.L. and Marron, J.S. (2012). Analysis of principal nested spheres. Biometrika, 99, 551-568. } \author{Kwang-Rae Kim: R translation of Sungkyu Jung's matlab code} \seealso{pns4pc, pnss3d} \examples{ # out <- pc2sphere(x = gorf.dat, n.pc = 2) # spheredata <- t(out$spheredata) # pns.out <- pns(x = spheredata) } \keyword{multivariate} r-cran-shapes-1.2.6/man/pns4pc.Rd000077500000000000000000000035431412660331700164470ustar00rootroot00000000000000\name{pns4pc} \alias{pns4pc} \title{Principal Nested Shape Spaces from PCA} \description{Approximation of Principal Nested Shapes Spaces using PCA } \usage{ pns4pc(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0,n.pc=2) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{k x m x n array of landmark data.} \item{sphere.type}{ a character string specifying the type of sphere fitting method. "seq.test" specifies sequential tests to decide either "small" or "great"; "small" specifies Principal Nested SMALL Sphere; "great" specifies Principal Nested GREAT Sphere (radius pi/2); "BIC" specifies BIC statistic to decide either "small" or "great"; and "bi.sphere" specifies Principal Nested GREAT Sphere for the first part and Principal Nested SMALL Sphere for The default is "seq.test". } \item{alpha}{significance level (0 < alpha < 1) used when sphere.type = "seq.test". The default is 0.1. } \item{R}{the number of bootstrap samples to be evaluated for the sequential test. The default is 100.} \item{nlast.small.sphere}{the number of small spheres in the finishing part used when sphere.type = "bi.sphere".} \item{n.pc}{the number of PC scores to be used (n.pc >= 2)} } \value{A list with components \item{PNS}{the output of the function pns} \item{GPAout}{the result of GPA} \item{spheredata}{transformed spherical data from the PC scores} \item{percent}{proportion of variances explained.} } \references{ Dryden, I.L., Kim, K., Laughton, C.A. and Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13, 2213-2234. Jung, S., Dryden, I.L. and Marron, J.S. (2012). Analysis of principal nested spheres. Biometrika, 99, 551-568. } \author{Kwang-Rae Kim} \seealso{pns, pns4pc, pnss3d, plot3darcs} \examples{ pns4pc(digit3.dat,n.pc=2) } \keyword{multivariate} r-cran-shapes-1.2.6/man/pnss3d.Rd000077500000000000000000000037651412660331700164600ustar00rootroot00000000000000\name{pnss3d} \alias{pnss3d} \title{Principal Nested Shape Space Analysis} \description{Approximation of Principal Nested Shapes Spaces using PCA: 2D or 3D data, small or large samples } \usage{ pnss3d(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0,n.pc=3) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{k x m x n array of landmark data.} \item{sphere.type}{ a character string specifying the type of sphere fitting method. "seq.test" specifies sequential tests to decide either "small" or "great"; "small" specifies Principal Nested SMALL Sphere; "great" specifies Principal Nested GREAT Sphere (radius pi/2); "BIC" specifies BIC statistic to decide either "small" or "great"; and "bi.sphere" specifies Principal Nested GREAT Sphere for the first part and Principal Nested SMALL Sphere for The default is "seq.test". } \item{alpha}{significance level (0 < alpha < 1) used when sphere.type = "seq.test". The default is 0.1. } \item{R}{the number of bootstrap samples to be evaluated for the sequential test. The default is 100.} \item{nlast.small.sphere}{the number of small spheres in the finishing part used when sphere.type = "bi.sphere".} \item{n.pc}{the number of PC scores to be used (n.pc >= 2)} } \value{A list with components \item{PNS}{the output of the function pns} \item{GPAout}{the result of GPA} \item{spheredata}{transformed spherical data from the PC scores} \item{percent}{proportion of variances explained.} } \references{ Dryden, I.L., Kim, K., Laughton, C.A. and Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13, 2213-2234. Jung, S., Dryden, I.L. and Marron, J.S. (2012). Analysis of principal nested spheres. Biometrika, 99, 551-568. } \author{Kwang-Rae Kim, Ian Dryden} \seealso{pns, pns4pc, plot3darcs} \examples{ ans <- pnss3d(digit3.dat, sphere.type="BIC", n.pc=5) #aa <- plot3darcs(ans,c=2,pcno=1) #bb <- plot3darcs(ans,c=2,pcno=1,type="pca") } \keyword{multivariate} r-cran-shapes-1.2.6/man/pongof.dat.Rd000077500000000000000000000010641412660331700172730ustar00rootroot00000000000000\name{pongof.dat} \alias{pongof.dat} \title{Female orang utan data} \description{Female orang utan skull data. 8 landmarks in 2 dimensions, 30 individuals } \usage{data(pongof.dat)} \format{ An array of dimension 8 x 2 x 30 } \source{ O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(pongof.dat) plotshapes(pongof.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/pongom.dat.Rd000077500000000000000000000010601412660331700172760ustar00rootroot00000000000000\name{pongom.dat} \alias{pongom.dat} \title{Male orang utan data} \description{Male orang utan skull data. 8 landmarks in 2 dimensions, 30 individuals } \usage{data(pongom.dat)} \format{ An array of dimension 8 x 2 x 30 } \source{ O'Higgins, P. and Dryden, I. L. (1993). Sexual dimorphism in hominoids: further studies of craniofacial shape differences in Pan, Gorilla, Pongo, Journal of Human Evolution, 24, 183-205. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(pongom.dat) plotshapes(pongom.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/procGPA.Rd000077500000000000000000000136031412660331700165310ustar00rootroot00000000000000\name{procGPA} \alias{procGPA} %- Also NEED an `\alias' for EACH other topic documented here. \title{Generalised Procrustes analysis} \description{Generalised Procrustes analysis to register landmark configurations into optimal registration using translation, rotation and scaling. Reflection invariance can also be chosen, and registration without scaling is also an option. Also, obtains principal components, and some summary statistics. } \usage{ procGPA(x, scale = TRUE, reflect = FALSE, eigen2d = FALSE, tol1 = 1e-05, tol2 = tol1, tangentcoords = "residual", proc.output=FALSE, distances=TRUE, pcaoutput=TRUE, alpha=0, affine=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{Input k x m x n real array, (or k x n complex matrix for m=2 is OK), where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{scale}{Logical quantity indicating if scaling is required} \item{reflect}{Logical quantity indicating if reflection is required} \item{eigen2d}{Logical quantity indicating if complex eigenanalysis should be used to calculate Procrustes mean for the particular 2D case when scale=TRUE, reflect=FALSE} \item{tol1}{Tolerance for optimal rotation for the iterative algorithm: tolerance on the mean sum of squares (divided by size of mean squared) between successive iterations} \item{tol2}{tolerance for rescale/rotation step for the iterative algorithm: tolerance on the mean sum of squares (divided by size of mean squared) between successive iterations} \item{tangentcoords}{Type of tangent coordinates. If (SCALE=TRUE) the options are "residual" (Procrustes residuals, which are approximate tangent coordinates to shape space), "partial" (Kent's partial tangent co-ordinates), "expomap" (tangent coordinates from the inverse of the exponential map, which are the similar to "partial" but scaled by (rho/sin(rho)) where rho is the Riemannian distance to the pole of the projection. If (SCALE=FALSE) then all three options give the same tangent co-ordinates to size-and-shape space, which is simply the Procrustes residual X^P - mu. } \item{proc.output}{Logical quantity indicating if printed output during the iterations of the Procrustes GPA algorithm should be given} \item{distances}{Logical quantity indicating if shape distances and sizes should be calculated} \item{pcaoutput}{Logical quantity indicating if PCA should be carried out} \item{alpha}{The parameter alpha used for relative warps analysis, where alpha is the power of the bending energy matrix. If alpha = 0 then standard Procrustes PCA is carried out. If alpha = 1 then large scale variations are emphasized, if alpha = -1 then small scale variations are emphasised. Requires m=2 and m=3 dimensional data if alpha $!=$ 0.} \item{affine}{Logical. If TRUE then only the affine subspace of shape variability is considered.} } \value{A list with components \item{k}{no of landmarks} \item{m}{no of dimensions (m-D dimension configurations)} \item{n}{sample size} \item{mshape}{Procrustes mean shape. Note this is unit size if complex eigenanalysis used, but on the scale of the data if iterative GPA is used.} \item{tan}{The tangent shape (or size-and-shape) coordinates} \item{rotated}{the k x m x n array of full Procrustes rotated data} \item{pcar}{the columns are eigenvectors (PCs) of the sample covariance Sv of tan} \item{pcasd}{the square roots of eigenvalues of Sv using tan (s.d.'s of PCs)} \item{percent}{the percentage of variability explained by the PCs using tan. If alpha $!=0$ then it is the percent of non-affine variation of the relative warp scores. If affine is TRUE it is the percentage of total shape variability of each affine component.} \item{size}{the centroid sizes of the configurations} \item{stdscores}{standardised PC scores (each with unit variance) using tan} \item{rawscores}{raw PC scores using tan} \item{rho}{Kendall's Riemannian shape distance rho to the mean shape} \item{rmsrho}{root mean square (r.m.s.) of rho} \item{rmsd1}{r.m.s. of full Procrustes distances to the mean shape $d_F$} \item{GSS}{Minimized Procrustes sum of squares} } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Chapter 7. Goodall, C.R. (1991). Procrustes methods in the statistical analysis of shape (with discussion). Journal of the Royal Statistical Society, Series B, 53: 285-339. Gower, J.C. (1975). Generalized Procrustes analysis, Psychometrika, 40, 33--50. Kent, J.T. (1994). The complex Bingham distribution and shape analysis, Journal of the Royal Statistical Society, Series B, 56, 285-299. Ten Berge, J.M.F. (1977). Orthogonal Procrustes rotation for two or more matrices. Psychometrika, 42, 267-276. } \author{Ian Dryden, with input from Mohammad Faghihi and Alfred Kume} \seealso{procOPA,riemdist,shapepca,testmeanshapes} \examples{ #2D example : female and male Gorillas (cf. Dryden and Mardia, 2016) data(gorf.dat) data(gorm.dat) plotshapes(gorf.dat,gorm.dat) n1<-dim(gorf.dat)[3] n2<-dim(gorm.dat)[3] k<-dim(gorf.dat)[1] m<-dim(gorf.dat)[2] gor.dat<-array(0,c(k,2,n1+n2)) gor.dat[,,1:n1]<-gorf.dat gor.dat[,,(n1+1):(n1+n2)]<-gorm.dat gor<-procGPA(gor.dat) shapepca(gor,type="r",mag=3) shapepca(gor,type="v",mag=3) gor.gp<-c(rep("f",times=30),rep("m",times=29)) x<-cbind(gor$size,gor$rho,gor$scores[,1:3]) pairs(x,panel=function(x,y) text(x,y,gor.gp), label=c("s","rho","score 1","score 2","score 3")) ########################################################## #3D example data(macm.dat) out<-procGPA(macm.dat,scale=FALSE) par(mfrow=c(2,2)) plot(out$rawscores[,1],out$rawscores[,2],xlab="PC1",ylab="PC2") title("PC scores") plot(out$rawscores[,2],out$rawscores[,3],xlab="PC2",ylab="PC3") plot(out$rawscores[,1],out$rawscores[,3],xlab="PC1",ylab="PC3") plot(out$size,out$rho,xlab="size",ylab="rho") title("Size versus shape distance") } \keyword{multivariate} r-cran-shapes-1.2.6/man/procOPA.Rd000077500000000000000000000047721412660331700165500ustar00rootroot00000000000000\name{procOPA} \alias{procOPA} %- Also NEED an `\alias' for EACH other topic documented here. \title{Ordinary Procrustes analysis} \description{ Ordinary Procustes analysis : the matching of one configuration to another using translation, rotation and (possibly) scale. Reflections can also be included if desired. The function matches configuration B onto A by least squares.} \usage{ procOPA(A, B, scale = TRUE, reflect = FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{A}{k x m matrix (or complex k-vector for 2D data), of k landmarks in m dimensions. This is the reference figure.} \item{B}{k x m matrix (or complex k-vector for 2D data). This is the figure which is to be transformed.} \item{scale}{logical indicating if scaling is required} \item{reflect}{logical indicating if reflection is allowed} } \value{ A list with components: \item{R}{The estimated rotation matrix (may be an orthogonal matrix if reflection is allowed)} \item{s}{The estimated scale matrix} \item{Ahat}{The centred configuration A} \item{Bhat}{The Procrustes registered configuration B} \item{OSS}{The ordinary Procrustes sum of squares, which is $\|Ahat-Bhat\|^2$} \item{rmsd}{rmsd = sqrt(OSS/(km))} } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Chapter 7.} \author{Ian Dryden} \seealso{procGPA,riemdist,tpsgrid} \examples{ data(digit3.dat) A<-digit3.dat[,,1] B<-digit3.dat[,,2] ans<-procOPA(A,B) plotshapes(A,B,joinline=1:13) plotshapes(ans$Ahat,ans$Bhat,joinline=1:13) #Sooty Mangabey data data(sooty.dat) A<-sooty.dat[,,1] #juvenile B<-sooty.dat[,,2] #adult par(mfrow=c(1,3)) par(pty="s") plot(A,xlim=c(-2000,3000),ylim=c(-2000,3000),xlab=" ",ylab=" ") lines(A[c(1:12,1),]) points(B) lines(B[c(1:12,1),],lty=2) title("Juvenile (-------) Adult (- - - -)") #match B onto A out<-procOPA(A,B) #rotation angle print(atan2(out$R[1,2],out$R[1,1])*180/pi) #scale print(out$s) plot(A,xlim=c(-2000,3000),ylim=c(-2000,3000),xlab=" ",ylab=" ") lines(A[c(1:12,1),]) points(out$Bhat) lines(out$Bhat[c(1:12,1),],lty=2) title("Match adult onto juvenile") #match A onto B out<-procOPA(B,A) #rotation angle print(atan2(out$R[1,2],out$R[1,1])*180/pi) #scale print(out$s) plot(B,xlim=c(-2000,3000),ylim=c(-2000,3000),xlab=" ",ylab=" ") lines(B[c(1:12,1),],lty=2) points(out$Bhat) lines(out$Bhat[c(1:12,1),]) title("Match juvenile onto adult") } \keyword{multivariate} r-cran-shapes-1.2.6/man/procWGPA.Rd000077500000000000000000000073231412660331700166620ustar00rootroot00000000000000\name{procWGPA} \alias{procWGPA} %- Also NEED an `\alias' for EACH other topic documented here. \title{Weighted Procrustes analysis} \description{Weighted Procrustes analysis to register landmark configurations into optimal registration using translation, rotation and scaling. Registration without scaling is also an option. Also, obtains principal components, and some summary statistics. } \usage{ procWGPA(x, fixcovmatrix=FALSE, initial="Identity", maxiterations=10, scale=TRUE, reflect=FALSE, prior="Exponential",diagonal=TRUE,sampleweights="Equal") } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{Input k x m x n real array, where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{fixcovmatrix}{If FALSE then the landmark covariance matrix is estimated. If a fixed covariance matrix is desired then the value should be given here, e.g. fixcovmatrix=diag(8) for the identity matrix with 8 landmarks.} \item{initial}{The initial value of the estimated covariance matrix. "Identity" - identity matrix, "Rawdata" - based on sample variance of the raw landmarks. Also, could be a k x k symmetric positive definite matrix.} \item{maxiterations}{The maximum number of iterations for estimating the covariance matrix}, \item{scale}{Logical quantity indicating if scaling is required}, \item{reflect}{Logical quantity indicating if reflection invariance is required}, \item{prior}{Indicates the type of prior. "Exponential" is exponential for the inverse eigenvalues. "Identity" is an inverse Wishart with the identity matrix as parameters.} \item{diagonal}{Logical. Indicates if the diagonal of the landmark covariance matrix (only) should be used. Diagonal matrices can lead to some landmarks having very small variability, which may or may not be desirable.} \item{sampleweights}{Gives the weights of the observations in the sample, rather than the landmarks. This is a fixed quatity. "Equal" indicates that all observations in the sample have equal weight. The weights do not need to sum to 1. } } \value{A list with components \item{k}{no of landmarks} \item{m}{no of dimensions (m-D dimension configurations)} \item{n}{sample size} \item{mshape}{Weighted Procrustes mean shape.} \item{tan}{This is the mk x n matrix of Procrustes residuals $X_i^P$ - Xbar.} \item{rotated}{the k x m x n array of weighted Procrustes rotated data} \item{pcar}{the columns are eigenvectors (PCs) of the sample covariance Sv of tan} \item{pcasd}{the square roots of eigenvalues of Sv using tan (s.d.'s of PCs)} \item{percent}{the percentage of variability explained by the PCs using tan. } \item{size}{the centroid sizes of the configurations} \item{scores}{standardised PC scores (each with unit variance) using tan} \item{rawscores}{raw PC scores using tan} \item{rho}{Kendall's Riemannian distance rho to the mean shape} \item{rmsrho}{r.m.s. of rho} \item{rmsd1}{r.m.s. of full Procrustes distances to the mean shape $d_F$} \item{Sigmak}{Estimate of the sample covariance matrix of the landmarks} } \details{The factored covariance model is assumed: $Sigma_k x I_m$ with $Sigma_k$ being the covariance matrix of the landmarks, and the cov matrix at each landmark is the identity matrix.} \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Goodall, C.R. (1991). Procrustes methods in the statistical analysis of shape (with discussion). Journal of the Royal Statistical Society, Series B, 53: 285-339. } \author{Ian Dryden} \seealso{procGPA} \examples{ #2D example : female Gorillas (cf. Dryden and Mardia, 2016) data(gorf.dat) gor<-procWGPA(gorf.dat,maxiterations=3) } \keyword{multivariate} r-cran-shapes-1.2.6/man/procdist.Rd000077500000000000000000000026051412660331700170650ustar00rootroot00000000000000\name{procdist} \alias{procdist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Procrustes distance} \description{Calculates different types of Procrustes shape or size-and-shape distance between two configurations} \usage{ procdist(x, y,type="full",reflect=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{k x m matrix (or complex k-vector for 2D data) where k = number of landmarks and m = no of dimensions} \item{y}{k x m matrix (or complex k-vector for 2D data)} \item{type}{string indicating the type of distance; "full" full Procrustes distance, "partial" partial Procrustes distance, "Riemannian" Riemannian shape distance, "sizeandshape" size-and-shape Riemannian/Procrustes distance} \item{reflect}{ Logical. If reflect = TRUE then reflection invariance is included.} } \value{ The distance between the two configurations.} \references{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. } \seealso{procOPA,procGPA} \examples{ data(gorf.dat) data(gorm.dat) gorf<-procGPA(gorf.dat) gorm<-procGPA(gorm.dat) distfull<-procdist(gorf$mshape,gorm$mshape) cat("Full Procustes distance between mean shapes is ",distfull," \n") } \author{Ian Dryden} \keyword{multivariate}% __ONLY ONE__ keyword per line r-cran-shapes-1.2.6/man/qcet2.dat.Rd000077500000000000000000000011521412660331700170170ustar00rootroot00000000000000\name{qcet2.dat} \alias{qcet2.dat} \title{Control T2 mouse vertabrae data} \description{T2 mouse vertebrae data - control group. 6 landmarks in 2 dimensions, 30 individuals } \usage{data(qcet2.dat)} \format{ An array of dimension 6 x 2 x 30 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ http://www.maths.nott.ac.uk/personal/ild/bookdata/qcet2.dat Data from Paul O'Higgins (Hull-York Medical School) and David Johnson (Leeds) } \examples{ data(qcet2.dat) plotshapes(qcet2.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/qlet2.dat.Rd000077500000000000000000000011461412660331700170330ustar00rootroot00000000000000\name{qlet2.dat} \alias{qlet2.dat} \title{Large T2 mouse vertabrae data} \description{T2 mouse vertebrae data - large group. 6 landmarks in 2 dimensions, 23 individuals } \usage{data(qlet2.dat)} \format{ An array of dimension 6 x 2 x 23 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ http://www.maths.nott.ac.uk/personal/ild/bookdata/qlet2.dat Data from Paul O'Higgins (Hull-York Medical School) and David Johnson (Leeds) } \examples{ data(qlet2.dat) plotshapes(qlet2.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/qset2.dat.Rd000077500000000000000000000011461412660331700170420ustar00rootroot00000000000000\name{qset2.dat} \alias{qset2.dat} \title{Small T2 mouse vertabrae data} \description{T2 mouse vertebrae data - small group. 6 landmarks in 2 dimensions, 23 individuals } \usage{data(qset2.dat)} \format{ An array of dimension 6 x 2 x 23 } \source{ Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ http://www.maths.nott.ac.uk/personal/ild/bookdata/qset2.dat Data from Paul O'Higgins (Hull-York Medical School) and David Johnson (Leeds) } \examples{ data(qset2.dat) plotshapes(qset2.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/rats.Rd000077500000000000000000000013221412660331700162020ustar00rootroot00000000000000\name{rats} \alias{rats} \title{Rat skulls data} \description{Rat skulls data, from X rays. 8 landmarks in 2 dimensions, 18 individuals observed at 7, 14, 21, 30, 40, 60, 90, 150 days. } \usage{data(rats)} \format{ rats$x: An array of landmark configurations 144 x 2 x 2 rats$no: Individual rat number (note rats 3, 13, 20 missing due to incomplete data) rats$time observed time in days } \source{ Vilmann's rat data set (Bookstein, 1991, Morphometric Tools for Landmark Data: Geometry and Biology, pp. 408-414) } \references{ Bookstein, F.L. (1991). Morphometric tools for landmark data: geometry and biology, Cambridge University Press. } \examples{ data(rats) plotshapes(rats$x,col=1:8) } \keyword{datasets} r-cran-shapes-1.2.6/man/resampletest.Rd000077500000000000000000000104661412660331700177520ustar00rootroot00000000000000\name{resampletest} \alias{resampletest} %- Also NEED an `\alias' for EACH other topic documented here. \title{Tests for mean shape difference using complex arithmetic, including bootstrap and permutation tests. } \description{ Carries out tests to examine differences in mean shape between two independent populations. For 2D data the methods use complex arithmetic and exploit the geometry of the shape space (which is the main use of this function). An alternative faster, approximate procedure using Procrustes residuals is given by the function `testmeanshapes'. For 3D data tests are carried out on the Procrustes residuals, which is an approximation suitable for small variations in shape. Up to four test statistics are calculated: lambda : the asymptotically pivotal statistic $lambda_min$ from Amaral et al. (2007), equ.(14),(16) (m=2 only) H : Hotelling $T^2$ statistic (see Amaral et al., 2007, equ.(23), Dryden and Mardia, 2016, equ.(9.4)) J : James' statistic (see Amaral et al., 2007, equ.(24) ) (m=2 only) G : Goodall's F statistic (see Amaral et al., 2007, equ.(25), Dryden and Mardia, 2016, equ.(9.9)) p-values are given based on resampling as well as the usual table based p-values. Note when the sample sizes are low (compared to the number of landmarks) some regularization is carried out. In particular if Sw is a singular within group covariance matrix, it is replaced by Sw + 0.000001 (Identity matrix) and a `*' is printed in the output. } \usage{ resampletest(A, B, resamples = 200, replace = TRUE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{A}{The random sample for group 1: k x m x n1 array of data, where k is the number of landmarks and n1 is the sample size. (Alternatively a k x n1 complex matrix for 2D)} \item{B}{The random sample for group 3: k x m x n2 array of data, where k is the number of landmarks and n2 is the sample size. (Alternatively a k x n2 complex matrix for 2D)} \item{resamples}{Integer. The number of resampling iterations. If resamples = 0 then no resampling procedures are carried out, and the tabular p-values are given only.} \item{replace}{Logical. If replace = TRUE then for 2D data bootstrap resampling is carried out with replacement *within* each group. If replace = FALSE then permutation resampling is carried out (sampling without replacement in *pooled* samples).} } \value{ A list with components (or a subset of these) \item{lambda}{$lambda_min$ statistic} \item{lambda.pvalue}{p-value for $lambda_min$ test based on resampling} \item{lambda.table.pvalue}{p-value for $lambda_min$ test based on the asymptotic chi-squared distribution (large n1,n2)} \item{H}{The Hotelling $T^2$ statistic} \item{H.pvalue}{p-value for the Hotelling $T^2$ test based on resampling} \item{H.table.pvalue}{p-value for the Hotelling $T^2$ test based on the null F distribution, assuming normality and equal covariance matrices} \item{J}{The Hotelling $T^2$ statistic} \item{J.pvalue}{p-value for the Hotelling $T^2$ test based on resampling} \item{J.table.pvalue}{p-value for the Hotelling $T^2$ test based on the null F distribution, assuming normality and unequal covariance matrices} \item{G}{The Goodall $F$ statistic} \item{G.pvalue}{p-value for the Goodall test based on resampling} \item{G.table.pvalue}{p-value for the Goodall test based on the null F distribution, assuming normality and equal isotropic covariance matrices)} } \references{Amaral, G.J.A., Dryden, I.L. and Wood, A.T.A. (2007) Pivotal bootstrap methods for $k$-sample problems in directional statistics and shape analysis. Journal of the American Statistical Association. 102, 695-707. Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 9. Goodall, C. R. (1991). Procrustes methods in the statistical analysis of shape (with discussion). Journal of the Royal Statistical Society, Series B, 53: 285-339. } \author{Ian Dryden} \seealso{testmeanshapes} \examples{ #2D example : female and male Gorillas data(gorf.dat) data(gorm.dat) #just select 3 landmarks and the first 10 observations in each group select<-c(1,2,3) A<-gorf.dat[select,,1:10] B<-gorm.dat[select,,1:10] resampletest(A,B,resamples=100) } \keyword{multivariate}% at least one, from doc/KEYWORDS r-cran-shapes-1.2.6/man/riemdist.Rd000077500000000000000000000023761412660331700170630ustar00rootroot00000000000000\name{riemdist} \alias{riemdist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Riemannian shape distance} \description{Calculates the Riemannian shape distance rho between two configurations} \usage{ riemdist(x, y, reflect=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{k x m matrix (or complex k-vector for 2D data) where k = number of landmarks and m = no of dimensions} \item{y}{k x m matrix (or complex k-vector for 2D data)} \item{reflect}{ Logical. If reflect = TRUE then reflection invariance is included.} } \value{ The Riemannian shape distance rho between the two configurations. Note 0 <= rho <= pi/2 if no reflection invariance. (for the Riemannian size-and-shape distance use ssriemdist)} \references{ Kendall, D. G. (1984). Shape manifolds, Procrustean metrics and complex projective spaces, Bulletin of the London Mathematical Society, 16, 81-121. } \seealso{procOPA,procGPA} \examples{ data(gorf.dat) data(gorm.dat) gorf<-procGPA(gorf.dat) gorm<-procGPA(gorm.dat) rho<-riemdist(gorf$mshape,gorm$mshape) cat("Riemannian distance between mean shapes is ",rho," \n") } \author{Ian Dryden} \keyword{multivariate}% __ONLY ONE__ keyword per line r-cran-shapes-1.2.6/man/rigidbody.Rd000077500000000000000000000020001412660331700171770ustar00rootroot00000000000000\name{rigidbody} \alias{rigidbody} %- Also NEED an `\alias' for EACH other topic documented here. \title{Rigid body transformations} \description{Applies a rigid body transformations to a landmark configuration or array} \usage{ rigidbody(X,transx=0,transy=0,transz=0,thetax=0,thetay=0,thetaz=0) } %- maybe also `usage' for other objects documented here. \arguments{ \item{X}{k x m matrix, or k x m x n array where k = number of landmarks and m = no of dimensions and n is no of specimens} \item{transx}{negative shift in x-coordinates} \item{transy}{negative shift in y-coordinates} \item{transz}{negative shift in z-coordinates} \item{thetax}{Rotation about x-axis in degrees} \item{thetay}{Rotation about y-axis in degrees} \item{thetaz}{Rotation about z-axis in degrees} } \value{ The transformed coordinates (X - trans) Rx Ry Rz } \examples{ data(gorf.dat) plotshapes ( rigidbody(gorf.dat , 0, 0, 0, 0, 0, -90 ) ) } \author{Ian Dryden} \keyword{multivariate}% __ONLY ONE__ keyword per line r-cran-shapes-1.2.6/man/sand.Rd000077500000000000000000000024041412660331700161600ustar00rootroot00000000000000\name{sand} \alias{sand} \title{Sand particle outline data} \description{50 points on 24 sea sand and 25 river sand grain profiles in 2D. The original data were kindly provided by Professor Dietrich Stoyan (Stoyan and Stoyan, 1994; Stoyan, 1997). The 50 points on each outline were extracted at approximately equal arc-lengths by the method described in Kent et al. (2000, section 8.1)} \usage{data(sand)} \format{A list with components: sea$x : An array of dimension 50 x 2 x 49 containing the 50 point co-ordinates in 2D for each grain sea$group : The types of the sand grains: "sea", 24 particles from the Baltic Sea "river", 25 particles from the Caucasian River Selenchuk } \references{ Kent, J. T., Dryden, I. L. and Anderson, C. R. (2000). Using circulant symmetry to model featureless objects. Biometrika, 87, 527--544. Stoyan, D. (1997). Geometrical means, medians and variances for samples of particles. Particle Particle Syst. Charact. 14, 30--34. Stoyan, D. and Stoyan, H. (1994). Fractals, Random Shapes and Point Fields: Methods of Geometric Statistics, John Wiley, Chichester. } \examples{ data(sand) plotshapes(sand$x[,,sand$group=="sea"],sand$x[,,sand$group=="river"],joinline=c(1:50)) } \keyword{datasets} r-cran-shapes-1.2.6/man/schizophrenia.Rd000077500000000000000000000031511412660331700201010ustar00rootroot00000000000000\name{schizophrenia} \alias{schizophrenia} \title{Bookstein's schizophrenia data} \description{Bookstein's schizophrenia data. 13 landmarks in 2 dimensions, 28 individuals. The first 14 individuals are controls. The last fourteen cases were diagnosed with schizophrenia. The landmarks were taken in the near midline from MR images of the brain: (1) splenium, posteriormost point on corpus callosum; (2) genu, anteriormost point on corpus callosum; (3) top of corpus callosum, uppermost point on arch of callosum (all three to an approximate registration on the diameter of the callosum); (4) top of head, a point relaxed from a standard landmark along the apparent margin of the dura; (5) tentorium of cerebellum at dura; (6) top of cerebellum; (7) tip of fourth ventricle; (8) bottom of cerebellum; (9) top of pons, anterior margin; (10) bottom of pons, anterior margin; (11) optic chiasm; (12) frontal pole, extension of a line from landmark 1 through landmark 2 until it intersects the dura; (13) superior colliculus. } \usage{data(schizophrenia.dat)} \format{ schizophrenia$x : An array of dimension 13 x 2 x 28 schizophrenia$group : A factor of group labels `con' for Controls and `scz' for the schizophrenia patients. } \source{ Bookstein, F. L. (1996). Biometrics, biomathematics and the morphometric synthesis, Bulletin of Mathematical Biology, 58, 313--365. } \references{ Data kindly provided by Fred Bookstein (University of Washington and University of Vienna) } \examples{ data(schizophrenia) plotshapes(schizophrenia$x,symbol=as.integer(schizophrenia$group)) } \keyword{datasets} r-cran-shapes-1.2.6/man/schizophrenia.dat.Rd000077500000000000000000000027631412660331700206600ustar00rootroot00000000000000\name{schizophrenia.dat} \alias{schizophrenia.dat} \title{Bookstein's schizophrenia data} \description{Bookstein's schizophrenia data. 13 landmarks in 2 dimensions, 28 individuals. The first 14 individuals are controls. The last fourteen cases were diagnosed with schizophrenia. The landmarks were taken in the near midline from MR images of the brain: (1) splenium, posteriormost point on corpus callosum; (2) genu, anteriormost point on corpus callosum; (3) top of corpus callosum, uppermost point on arch of callosum (all three to an approximate registration on the diameter of the callosum); (4) top of head, a point relaxed from a standard landmark along the apparent margin of the dura; (5) tentorium of cerebellum at dura; (6) top of cerebellum; (7) tip of fourth ventricle; (8) bottom of cerebellum; (9) top of pons, anterior margin; (10) bottom of pons, anterior margin; (11) optic chiasm; (12) frontal pole, extension of a line from landmark 1 through landmark 2 until it intersects the dura; (13) superior colliculus. } \usage{data(schizophrenia.dat)} \format{ An array of dimension 13 x 2 x 28 } \source{ Bookstein, F. L. (1996). Biometrics, biomathematics and the morphometric synthesis, Bulletin of Mathematical Biology, 58, 313--365. } \references{ Data kindly provided by Fred Bookstein (University of Washington and University of Vienna) } \examples{ data(schizophrenia.dat) k<-dim(schizophrenia.dat)[1] n<-dim(schizophrenia.dat)[3] plotshapes(schizophrenia.dat) } \keyword{datasets} r-cran-shapes-1.2.6/man/shapepca.Rd000077500000000000000000000046461412660331700170310ustar00rootroot00000000000000\name{shapepca} \alias{shapepca} %- Also NEED an `\alias' for EACH other topic documented here. \title{Principal components analysis for shape} \description{ Provides graphical summaries of principal components for shape. } \usage{ shapepca(proc, pcno = c(1, 2, 3), type = "r", mag = 1, joinline = c(1, 1), project=c(1,2),scores3d=FALSE,color=2,axes3=FALSE,rglopen=TRUE,zslice=0) } %- maybe also `usage' for other objects documented here. \arguments{ \item{proc}{List given by the output from \code{procGPA()} } \item{pcno}{A vector of the PCs to be plotted} \item{type}{Options for the types of plot for the $m=2$ planar case: "r" : rows along PCs evaluated at c = -3,0,3 sd's along PC, "v" : vectors drawn from mean to +3 sd's along PC, "s" : plots along c= -3, -2, -1, 0, 1, 2, 3 superimposed, "m" : movie backward and forwards from -3 to +3 sd's along PC, "g" : TPS grid from mean to +3 sd's along PC. } \item{mag}{Magnification of the effect of the PC (scalar multiple of sd's)} \item{joinline}{A vector stating which landmarks are joined up by lines, e.g. joinline=c(1:n,1) will start at landmark 1, join to 2, ..., join to n, then re-join to landmark 1.} \item{project}{The default orthogonal projections if in higher than 2 dimensions} \item{scores3d}{Logical. If TRUE then a 3D scatterplot of the first 3 raw PC scores with labels in `pcno' is given, instead of the default plot of the mean and PC vectors.} \item{color}{Color of the spheres used in plotting. Default color = 2 (red). If a vector is given then the points are colored in that order.} \item{axes3}{Logical. If TRUE then the axes are plotted in a 3D plot.} \item{rglopen}{Logical. If TRUE open a new RGL window, otherwise plot in current window.} \item{zslice}{For 3D case, type = "g": the z co-ordinate(s) for the grid slice(s)} } \details{The mean and PCs are plotted. } \value{ No value is returned } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 7.} \author{Ian Dryden} \seealso{procGPA} \examples{ #2d example data(gorf.dat) data(gorm.dat) gorf<-procGPA(gorf.dat) gorm<-procGPA(gorm.dat) shapepca(gorf,type="r",mag=3) shapepca(gorf,type="v",mag=3) shapepca(gorm,type="r",mag=3) shapepca(gorm,type="v",mag=3) #3D example #data(macm.dat) #out<-procGPA(macm.dat) #movie #shapepca(out,pcno=1) } \keyword{hplot} \keyword{multivariate} r-cran-shapes-1.2.6/man/shapes-internal.Rd000077500000000000000000000124431412660331700203340ustar00rootroot00000000000000\name{shapes-internal} \alias{pc2sphere2} \alias{preshape2shape} \alias{tangentcoords.partial.inv} \alias{multiply_by_transpose_of_helmert_implicitly} \alias{multiply_by_helmert_implicitly_3d} \alias{uji3_centroid.size} \alias{uji2_centroid.size} \alias{multiply_by_transpose_of_helmert_explicitly} \alias{multiply_by_transpose_of_helmert} \alias{multiply_by_helmert_implicitly} \alias{multiply_by_helmert_explicitly} \alias{multiply_by_helmert} \alias{uji_preshape} \alias{uji_centroid.size} \alias{uji_defh} \alias{uji_Enorm} \alias{uji_distProcrustesFull} \alias{uji_distProcrustesSizeShape} \alias{uji_distCholesky} \alias{uji_estSS} \alias{uji_estShape} \alias{uji_centroid.size.complex} \alias{uji_centroid.size.mD} \alias{uji_preshape.mD} \alias{uji_preshape.mat} \alias{uji_tanfigure} \alias{uji_tanfigurefull} \alias{uji_kendall.shpv} \alias{ild_preshape} \alias{ild_centroid.size} \alias{ild_defh} \alias{ild_Enorm} \alias{ild_distProcrustesFull} \alias{ild_distProcrustesSizeShape} \alias{ild_distCholesky} \alias{ild_estSS} \alias{ild_estShape} \alias{ild_centroid.size.complex} \alias{ild_centroid.size.mD} \alias{ild_preshape.mD} \alias{ild_preshape.mat} \alias{ild_tanfigure} \alias{ild_tanfigurefull} \alias{ild_kendall.shpv} \alias{ild_preshapetoicon} \alias{Enormalize} \alias{ExpNPd} \alias{LRTpval} \alias{LogNPd} \alias{PNSe2s} \alias{PNSs2e} \alias{Plot3D} \alias{Procrustes.dist.full} \alias{UNIFORMdirections} \alias{col2RGB} \alias{flipud0} \alias{geodmeanS1} \alias{get.data.subsphere} \alias{get.prinarc} \alias{get.prinarc.subsphere} \alias{get.prinarc.value} \alias{getSubSphere} \alias{mod} \alias{objfn} \alias{pc2sphere} \alias{pcscore2sphere} \alias{plotshapes3d.pns} \alias{pns.pc} \alias{project.subsphere} \alias{randvonMisesFisherm} \alias{repmat} \alias{rot.mat} \alias{rotMat} \alias{shape.pcscores} \alias{shape.pcscores.partial} \alias{sphere.jac} \alias{sphere.obj} \alias{sphere.res} \alias{sphere2pcscore} \alias{sphereFit} \alias{tangent.coords.partial} \alias{tr} \alias{trans.subsphere} \alias{vMFtest} \alias{ped} \alias{pedreg} \alias{sooty.dat} \alias{MDSshape} \alias{distCholesky} \alias{distEuclidean} \alias{distLogEuclidean} \alias{distPowerEuclidean} \alias{distProcrustesFull} \alias{distProcrustesSizeShape} \alias{rootmat} \alias{distRiemPennec} \alias{Enorm} \alias{estChol} \alias{estShapes} \alias{estEuclid} \alias{estLogEuclid} \alias{estPowerEuclid} \alias{estLogRiem2} \alias{distRiemannianLe} \alias{estCholesky} \alias{estRiemLe} \alias{estShape} \alias{estSS} \alias{Hessian2} \alias{iglogl} \alias{Lambdamin} \alias{nsa} \alias{protein} \alias{James} \alias{select} \alias{procWGPA1} \alias{rotatexyz} \alias{objfun} \alias{objfun4} \alias{bootstraptest} \alias{testmeanshapes.old} \alias{permutationtest2} \alias{Goodall} \alias{Hotelling} \alias{abind} \alias{tpsgrid.old} \alias{shaperw} \alias{BoxM} \alias{Goodall2D} \alias{Goodalltest} \alias{Hotelling2D} \alias{Hotelling2Djames} \alias{Hotellingtest} \alias{MGM} \alias{I2mat} \alias{TPSgrid} \alias{V} \alias{Vinv} \alias{Vmat} \alias{add} \alias{as.3d} \alias{banner1} \alias{banner4} \alias{bendingenergy} \alias{bgpa} \alias{bookstein.shpv} \alias{bookstein.shpv.complex} \alias{braincon.dat} \alias{brainscz.dat} \alias{cbevec} \alias{cbevectors} \alias{centroid.size.complex} \alias{centroid.size.mD} \alias{close1} \alias{cnt3} \alias{complextoreal} \alias{defh} \alias{defplotsize2} \alias{defplotsize3} \alias{del} \alias{dif} \alias{dif.old} \alias{dis} \alias{fJ} \alias{fcel} \alias{fcnt} \alias{fgpa} \alias{fgpa.rot} \alias{fgpa.singleiteration} \alias{fopa} \alias{fort.ROTATEANDREFLECT} \alias{fort.ROTATION} \alias{fort} \alias{fos} \alias{fos.REFLECT} \alias{ftrsq} \alias{full.procdist} \alias{genpower} \alias{goodall2d} \alias{goodalltest} \alias{graf} \alias{hotelling2d} \alias{hotellingtest} \alias{isodens} \alias{isologdens} \alias{isomle} \alias{isotropy.test} \alias{kendall.shpv} \alias{linegrid} \alias{loglikeiso} \alias{loglikeiso2} \alias{loneFone} \alias{mahpreshapedist} \alias{makearray} \alias{movie} \alias{msh} \alias{norm} \alias{objfuniso} \alias{oneFone} \alias{partial.procdist} \alias{partialwarpgrids} \alias{partialwarps} \alias{permutationtest} \alias{plot2rwscores} \alias{plot3Ddata} \alias{plot3Ddata.static} \alias{plot3Dmean} \alias{plot3Dpca} \alias{plotPDM} \alias{plotPDM2} \alias{plotPDM3} \alias{plotPDMbook} \alias{plotPDMnoaxis} \alias{plotPDMnoaxis3} \alias{plotpairscores} \alias{plotpca} \alias{plotpca3d} \alias{plotprinwarp} \alias{plotproc} \alias{plotrelwarp} \alias{plotshapes3d} \alias{plotshapestime3d} \alias{pointsPDMnoaxis3} \alias{prcomp1} \alias{preshape} \alias{preshape.mD} \alias{preshape.mat} \alias{preshapetoicon} \alias{prinwscoregrids} \alias{procdistreflect} \alias{procrustes2d} \alias{procrustesGPA} \alias{procrustesGPA.rot} \alias{procrustesgpa} \alias{project} \alias{read.array} \alias{read.in} \alias{realtocomplex} \alias{reassqpr} \alias{relwarps} \alias{rgpa} \alias{riemdist.complex} \alias{riemdist.mD} \alias{rotateaxes} \alias{schizo.dat} \alias{sgpa} \alias{sh} \alias{sigmacov} \alias{sim1} \alias{st} \alias{tanfigure} \alias{tanfigurefull} \alias{tanpreshape} \alias{testshapes} \alias{vec1} \title{Internal function(s)} \description{Internal function(s)} \keyword{internal} r-cran-shapes-1.2.6/man/shapes.cva.Rd000077500000000000000000000023611412660331700172700ustar00rootroot00000000000000\name{shapes.cva} \alias{shapes.cva} %- Also NEED an `\alias' for EACH other topic documented here. \title{Canonical variate analysis for shapes} \description{Carry out canonical variate analysis for shapes (in two or more groups) } \usage{ shapes.cva(X,groups,scale=TRUE,ncv=2) } %- maybe also `usage' for other objects documented here. \arguments{ \item{X}{Input k x m x n real array of the configurations, where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{groups}{The group labels} \item{scale}{Logical, indicating if Procrustes scaling should be carried out} \item{ncv}{Number of canonical variates to display} } \value{A plot if ncv=2 or 3 and the Canonical Variate Scores} \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester.} \author{Ian Dryden} \seealso{procGPA} \examples{ #2D example : female and male apes (cf. Dryden and Mardia, 2016) data(pongof.dat) data(pongom.dat) data(panm.dat) data(panf.dat) apes <- groupstack( pongof.dat , pongom.dat , panm.dat, panf.dat ) shapes.cva( apes$x, apes$groups) } \keyword{multivariate} r-cran-shapes-1.2.6/man/shapes3d.Rd000077500000000000000000000024221412660331700167450ustar00rootroot00000000000000\name{shapes3d} \alias{shapes3d} \title{Plot 3D data} \description{Plot the landmark configurations from a 3D dataset} \usage{shapes3d(x,loop=0,type="p", color = 2, joinline=c(1:1), axes3=FALSE, rglopen=TRUE)} \arguments{ \item{x}{An array of size k x 3 x n, where k is the number of landmarks and n is the number of observations} \item{loop}{gives the number of times an animated loop through the observations is displayed (in order 1 to n). loop > 0 is suitable when a time-series of shapes is available. loop = 0 gives a plot of all the observations on the same figure. } \item{type}{Type of plot: "p" points, "dots" dots (quicker for large plots), "l" dots and lines though landmarks 1:k if `joinline' not stated} \item{color}{Colour of points (default color = 2 (red)). If a vector is given then the points are coloured in that order.} \item{joinline}{Join the numbered landmarks by lines} \item{axes3}{Logical. If TRUE then plot the axes.} \item{rglopen}{Logical. If TRUE then open a new RGL window, if FALSE then plot in current window.} } \value{ None } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. } \author{Ian Dryden} \examples{ data(dna.dat) shapes3d(dna.dat) } \keyword{multivariate} r-cran-shapes-1.2.6/man/shells.Rd000077500000000000000000000016351412660331700165320ustar00rootroot00000000000000\name{shells} \alias{shells} \title{Microfossil shell data} \description{Microfossil shell data. Triangles from 21 individuals. Lohmann (1983) published 21 mean outlines of the microfossil which were based on random samples of organisms taken at different latitudes in the South Indian Ocean. } \usage{data(shells)} \format{ shells$uv Scaled shape coordinates (Bookstein shape co-ordinates with base (0,0) and (1,0). shells$size Centroid size } \source{ Bookstein, F. L. (1986). Size and shape spaces for landmark data in two dimensions (with discussion). Statistical Science, 1:181-242. Lohmann, G. P. (1983). Eigenshape analysis of microfossils: a general morphometric procedure for describing changes in shape. Mathematical Geology, 15:659-672. } \references{ The data have been extracted from Fig. 7 of Bookstein (1986). } \examples{ data(shells) plotshapes(shells$uv) } \keyword{datasets} r-cran-shapes-1.2.6/man/sooty.Rd000077500000000000000000000012371412660331700164130ustar00rootroot00000000000000\name{sooty} \alias{sooty} \title{Sooty mangabey data} \description{Sooty mangabey data skull data. 12 landmarks in 2 dimensions, 2 individuals (juvenile and male adult) followed by three individuals, female adult, male adult. The first entries are rotated, translated versions of the 3rd and 7th figure. } \usage{data(sooty)} \format{ An array of dimension 12 x 2 x 7 } \source{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 1. } \references{ Data from Paul O'Higgins (Hull-York Medical School) } \examples{ data(sooty) plotshapes(sooty,joinline=c(1:12,1)) } \keyword{datasets} r-cran-shapes-1.2.6/man/ssriemdist.Rd000077500000000000000000000023461412660331700174260ustar00rootroot00000000000000\name{ssriemdist} \alias{ssriemdist} %- Also NEED an `\alias' for EACH other topic documented here. \title{Riemannian size-and-shape distance} \description{Calculates the Riemannian size-and-shape distance d_S between two configurations} \usage{ ssriemdist(x, y, reflect=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{k x m matrix (or complex k-vector for 2D data) where k = number of landmarks and m = no of dimensions} \item{y}{k x m matrix (or complex k-vector for 2D data)} \item{reflect}{ Logical. If reflect = TRUE then reflection invariance is included.} } \value{ The Riemannian size-and-shape distance d_S between the two configurations. (for the Riemannian shape distance use riemdist) } \references{ Le, H.-L. (1995). Mean size-and-shapes and mean shapes: a geometric point of view. Advances in Applied Probability, 27:44-55. } \seealso{procOPA,procGPA,riemdist} \examples{ data(gorf.dat) data(gorm.dat) gorf<-procGPA(gorf.dat,scale=FALSE) gorm<-procGPA(gorm.dat,scale=FALSE) ds<-ssriemdist(gorf$mshape,gorm$mshape) cat("Riemannian size-and-shape distance between mean size-and-shapes is ",ds," \n") } \author{Ian Dryden} \keyword{multivariate}% __ONLY ONE__ keyword per line r-cran-shapes-1.2.6/man/steroids.Rd000077500000000000000000000031101412660331700170620ustar00rootroot00000000000000\name{steroids} \alias{steroids} \title{Steroid data} \description{Steroid data. Between 42 and 61 atoms for each of 31 steroid molecules. } \usage{data(steroids)} \format{ steroids$x : An array of dimension 61 x 3 x 31 of 3D co-ordinates of the 31 steroids. If a molecules has less than 61 atoms then the remaining co-ordinates are all zero. steroids$activity : Activity class (`1' = high, `2' = intermediate, and `3' = low binding affinities to the corticosteroid binding globulin (CBG) receptor) steroids$radius : van der Waals radius (0 = missing value) steoirds$atom : atom type (0 = missing value) steroids$charge : partial charge (0 = missing value) steroids$names : steroid names } \source{ This particular version of the steroids data set of (x, y, z) atom co-ordinates and partial charges was constructed by Jonathan Hirst and James Melville (School of Chemistry, University of Nottingham). Also see Wagener, M., Sadowski, J., Gasteiger, J. (1995). J. Am. Chem. Soc., 117, 7769-7775. http://www2.ccc.uni-erlangen.de/services/steroids/ } \references{ Dryden, I.L., Hirst, J.D. and Melville, J.L. (2007). Statistical analysis of unlabelled point sets: comparing molecules in chemoinformatics. Biometrics, 63, 237-251. Czogiel I., Dryden, I.L. and Brignell, C.J. (2011). Bayesian matching of unlabeled point sets using random fields, with an application to molecular alignment. Annals of Applied Statistics, 5, 2603-2629. } \examples{ data(steroids) shapes3d(steroids$x[,,1]) } \keyword{datasets} r-cran-shapes-1.2.6/man/testmeanshapes.Rd000077500000000000000000000071741412660331700202700ustar00rootroot00000000000000\name{testmeanshapes} \alias{testmeanshapes} %- Also NEED an `\alias' for EACH other topic documented here. \title{Tests for mean shape difference, including permutation and bootstrap tests} \description{ Carries out tests to examine differences in mean shape between two independent populations, for $m=2$ or $m=3$ dimensional data. Tests are carried out using tangent co-ordinates. H : Hotelling $T^2$ statistic (see Dryden and Mardia, 2016, equ.(9.4)) G : Goodall's F statistic (see Dryden and Mardia, 2016, equ.(9.9)) J : James $T^2$ statistic (see Amaral et al., 2007) p-values are given based on resampling (either a bootstrap test or a permutation test) as well as the usual table based p-values. Bootstrap tests involve sampling with replacement under H0 (as in Amaral et al., 2007). Note when the sample sizes are low (compared to the number of landmarks) some minor regularization is carried out. In particular if Sw is a singular within group covariance matrix, it is replaced by Sw + 0.000001 (Identity matrix) and a `*' is printed in the output. } \usage{ testmeanshapes(A, B, resamples = 1000, replace = FALSE, scale= TRUE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{A}{The random sample for group 1: k x m x n1 array of data, where k is the number of landmarks and n1 is the sample size. (Alternatively a k x n1 complex matrix for 2D)} \item{B}{The random sample for group 2: k x m x n2 array of data, where k is the number of landmarks and n2 is the sample size. (Alternatively a k x n2 complex matrix for 2D)} \item{resamples}{Integer. The number of resampling iterations. If resamples = 0 then no resampling procedures are carried out, and the tabular p-values are given only.} \item{replace}{Logical. If replace = TRUE then bootstrap resampling is carried out with replacement *within* each group. If replace = FALSE then permutation resampling is carried out (sampling without replacement in *pooled* samples).} \item{scale}{Logical. Whether or not to carry out Procrustes with scaling in the procedure.} } \value{ A list with components \item{H}{The Hotelling statistic (F statistic)} \item{H.pvalue}{p-value for the Hotelling test based on resampling} \item{H.table.pvalue}{p-value for the Hotelling test based on the null F distribution, assuming normality and equal covariance matrices} \item{J}{The James $T^2$ statistic} \item{J.pvalue}{p-value for the James $T^2$ test based on resampling} \item{J.table.pvalue}{p-value for the James $T^2$ test based on the null F distribution, assuming normality but unequal covariance matrices} \item{G}{The Goodall $F$ statistic} \item{G.pvalue}{p-value for the Goodall test based on resampling} \item{G.table.pvalue}{p-value for the Goodall test based on the null F distribution, assuming normality and equal isotropic covariance matrices)} } \references{Amaral, G.J.A., Dryden, I.L. and Wood, A.T.A. (2007) Pivotal bootstrap methods for $k$-sample problems in directional statistics and shape analysis. Journal of the American Statistical Association. 102, 695-707. Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester. Chapter 9. Goodall, C. R. (1991). Procrustes methods in the statistical analysis of shape (with discussion). Journal of the Royal Statistical Society, Series B, 53: 285-339. } \author{Ian Dryden} \seealso{resampletest} \examples{ #2D example : female and male Gorillas data(gorf.dat) data(gorm.dat) A<-gorf.dat B<-gorm.dat testmeanshapes(A,B,resamples=100) } \keyword{multivariate}% at least one, from doc/KEYWORDS r-cran-shapes-1.2.6/man/tpsgrid.Rd000077500000000000000000000051561412660331700167160ustar00rootroot00000000000000\name{tpsgrid} \alias{tpsgrid} %- Also NEED an `\alias' for EACH other topic documented here. \title{Thin-plate spline transformation grids} \description{Thin-plate spline transformation grids from one set of landmarks to another. } \usage{ tpsgrid(TT, YY, xbegin=-999, ybegin=-999, xwidth=-999, opt=1, ext=0.1, ngrid=22, cex=1, pch=20, col=2,zslice=0, mag=1, axes3=FALSE) } %- maybe also `usage' for other objects documented here. \arguments{ \item{TT}{First object (source): (k x m matrix)} \item{YY}{Second object (target): (k x m matrix)} \item{xbegin}{lowest x value for plot: if -999 then a value is determined} \item{ybegin}{lowest y value for plot: if -999 then a value is determined } \item{xwidth}{width of plot: if -999 then a value is determined} \item{opt}{Option 1: (just deformed grid on YY is displayed), option 2: both grids are displayed} \item{ext}{Amount of border on plot in 2D case.} \item{ngrid}{Number of grid points: size is ngrid * (ngrid -1)} \item{cex}{Point size} \item{pch}{Point symbol} \item{col}{Point colour} \item{zslice}{For 3D case the scaled z co-ordinate(s) for the grid slice(s). The values are on a standardized scale as a proportion of height from the middle of the z-axis to the top and bottom. Values in the range -1 to 1 would be sensible.} \item{mag}{Exaggerate effect (mag > 1). Standard effect has mag=1.} \item{axes3}{Logical. If TRUE then the axes are plotted in a 3D plot.} } \details{ A square grid on the first configuration is deformed smoothly using a pair of thin-plate splines in 2D, or a triple of splines in 3D, to a curved grid on the second object. For 3D data the grid is placed at a constant z-value on the first figuure, indicated by the value of zslice. For 2D data the covariance function in the thin-plate spline is $sigma(h) = |h|^2 log |h|^2$ and in 3D it is given by $sigma(h) = -| h |$. } \value{ No returned value } \references{ Bookstein, F.L. (1989). Principal warps: thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 567--585. Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester. Chapter 12. } \author{Ian Dryden} \seealso{procGPA} \examples{ data(gorf.dat) data(gorm.dat) #TPS grid with shape change exaggerated (2x) gorf<-procGPA(gorf.dat) gorm<-procGPA(gorm.dat) TT<-gorf$mshape YY<-gorm$mshape tpsgrid(TT,YY,mag=2) title("TPS grid: Female mean (left) to Male mean (right)") } \keyword{multivariate}% at least one, from doc/KEYWORDS \keyword{hplot} r-cran-shapes-1.2.6/man/transformations.Rd000077500000000000000000000030231412660331700204620ustar00rootroot00000000000000\name{transformations} \alias{transformations} %- Also NEED an `\alias' for EACH other topic documented here. \title{Calculate similarity transformations} \description{Calculate similarity transformations between configurations in two arrays. } \usage{ transformations(Xrotated,Xoriginal) } %- maybe also `usage' for other objects documented here. \arguments{ \item{Xrotated}{Input k x m x n real array of the Procrustes transformed configurations, where k is the number of points, m is the number of dimensions, and n is the sample size. } \item{Xoriginal}{Input k x m x n real array of the Procrustes original configurations, where k is the number of points, m is the number of dimensions, and n is the sample size. } } \value{A list with components \item{translation}{The translation parameters. These are the relative translations of the centroids of the individuals.} \item{scale}{The scale parameters} \item{rotation}{The rotation parameters. These are the rotations between the individuals after they have both been centred.} } \references{Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with Applications in R (Second Edition). Wiley, Chichester.} \author{Ian Dryden} \seealso{procGPA} \examples{ #2D example : female and male Gorillas (cf. Dryden and Mardia, 2016) data(gorf.dat) Xorig <- gorf.dat Xrotated <- procGPA(gorf.dat)$rotated transformations(Xrotated,Xorig) } \keyword{multivariate}