spatstat/0000755000176000001440000000000012333117317012146 5ustar ripleyusersspatstat/inst/0000755000176000001440000000000012324667727013142 5ustar ripleyusersspatstat/inst/CITATION0000755000176000001440000000311412324667727014301 0ustar ripleyuserscitHeader("To cite spatstat in publications use:") citEntry(entry = "Article", title = "{spatstat}: An {R} Package for Analyzing Spatial Point Patterns", author = personList(as.person("Adrian Baddeley"), as.person("Rolf Turner")), journal = "Journal of Statistical Software", year = "2005", volume = "12", number = "6", pages = "1--42", url = "http://www.jstatsoft.org/v12/i06/", textVersion = paste("Adrian Baddeley, Rolf Turner (2005).", "spatstat: An R Package for Analyzing Spatial Point Patterns.", "Journal of Statistical Software 12(6), 1-42.", "URL http://www.jstatsoft.org/v12/i06/.") ) citEntry(entry = "Article", title = "Hybrids of Gibbs Point Process Models and Their Implementation", author = personList(as.person("Adrian Baddeley"), as.person("Rolf Turner"), as.person("Jorge Mateu"), as.person("Andrew Bevan")), journal = "Journal of Statistical Software", year = "2013", volume = "55", number = "11", pages = "1--43", url = "http://www.jstatsoft.org/v55/i11/", textVersion = paste("Adrian Baddeley, Rolf Turner, Jorge Mateu, Andrew Bevan (2013).", "Hybrids of Gibbs Point Process Models and Their Implementation.", "Journal of Statistical Software, 55(11), 1-43.", "URL http://www.jstatsoft.org/v55/i11/."), header = "If you use hybrid models, please also cite:" ) spatstat/inst/doc/0000755000176000001440000000000012333041747013673 5ustar ripleyusersspatstat/inst/doc/getstart.pdf0000644000176000001440000034556712333041752016243 0ustar ripleyusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 3046 /Filter /FlateDecode /N 79 /First 642 >> stream xZYs8~_'rk*U>N8V';ZmȢG=$%%V"$&L@2Ô,`Zf9V,d.,bR(?Lj혔LZR19)e,X:hLP3阑*`2dFQ*LL bA`$SXRHaA10b2k@S[L̺elMh:l9hŜVfNb s:D0g mQhڭC{By@Y(1΀ČfC1,"&@}Xd0 YdiJLa(@( "!@3>9$6gPElonW0עq/$kz)e0mMρ{ |琇P7m6Mt׭/G &61z1"/V=ULjcVэM=RBFe$ש[hl2g };nQ*R>j ࢞)Ze<{::xoGm)#p}­~rSW>R˚Di&AC}3[64bQqݏG@s#NE'xV[.<տ@w!a}- R"38i0eDž<"]D7AIܕ;hDtvoQX÷d"ɜRfej~VmRNl-aT MݬcU;[d66 +2:h擜)tqu8 aT)4[JdGs%pWiX=)EjZhk}ф]{(R?fHKl?qiuy%Dw DX4#&HS#;Iw{Vb=oyjCP-ZWB>=xI4K9ԯncpM XIw칐eQZǬncV7W1^KZNkv_ V6e_ՂF<&}|`pH˛%~pL0o7"Iu4xw[W,>&HޭO%U}d>L9vy:%D]"Q'EӁru6Ͼ茺nH!p2mDT%ne<u;Z~ؓۗ͡A̞=T{.y;|nzv`ݴݧ3ROq k7H褿ZR\*,oF/fzr> stream 2014-05-09T10:56:41+08:00 2014-05-09T10:56:41+08:00 David M. 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H^q < endstream endobj startxref 117205 %%EOF spatstat/inst/doc/shapefiles.Rnw0000755000176000001440000004265312333041747016523 0ustar ripleyusers\documentclass[twoside,11pt]{article} % \VignetteIndexEntry{Handling shapefiles in the spatstat package} \SweaveOpts{eps=TRUE} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \usepackage{graphicx} \usepackage[colorlinks=true,urlcolor=blue]{hyperref} \usepackage{color} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\R}{{\sf R}} \begin{document} %\bibliographystyle{plain} \thispagestyle{empty} <>= library(spatstat) options(useFancyQuotes=FALSE) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") @ \title{Handling shapefiles in the \texttt{spatstat} package} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{ \Sexpr{sdate} \\ \pkg{spatstat} version \texttt{\Sexpr{sversion}} } \maketitle This vignette explains how to read data into the \pkg{spatstat} package from files in the popular `shapefile' format. This vignette is part of the documentation included in \pkg{spatstat} version \texttt{\Sexpr{sversion}}. The information applies to \pkg{spatstat} versions \texttt{1.36-0} and above. \section{Shapefiles} A shapefile represents a list of spatial objects --- a list of points, a list of lines, or a list of polygonal regions --- and each object in the list may have additional variables attached to it. A dataset stored in shapefile format is actually stored in a collection of text files, for example \begin{verbatim} mydata.shp mydata.prj mydata.sbn mydata.dbf \end{verbatim} which all have the same base name \texttt{mydata} but different file extensions. To refer to this collection you will always use the filename with the extension \texttt{shp}, for example \texttt{mydata.shp}. \section{Helper packages} \label{S:helpers} We'll use two other packages% \footnote{In previous versions of \pkg{spatstat}, the package \pkg{gpclib} was also needed for some tasks. This is no longer required.} to handle shapefile data. The \pkg{maptools} package is designed specifically for handling file formats for spatial data. It contains facilities for reading and writing files in shapefile format. The \pkg{sp} package supports a standard set of spatial data types in \R. These standard data types can be handled by many other packages, so it is useful to convert your spatial data into one of the data types supported by \pkg{sp}. \section{How to read shapefiles into \pkg{spatstat}} To read shapefile data into \pkg{spatstat}, you follow two steps: \begin{enumerate} \item using the facilities of \pkg{maptools}, read the shapefiles and store the data in one of the standard formats supported by \pkg{sp}. \item convert the \pkg{sp} data type into one of the data types supported by \pkg{spatstat}. \end{enumerate} \subsection{Read shapefiles using \pkg{maptools}} Here's how to read shapefile data. \begin{enumerate} \item ensure that the package \pkg{maptools} is installed. You will need version \texttt{0.7-16} or later. \item start R and load the package: <>= library(maptools) @ \item read the shapefile into an object in the \pkg{sp} package using \texttt{readShapeSpatial}, for example <>= x <- readShapeSpatial("mydata.shp") @ \item To find out what kind of spatial objects are represented by the dataset, inspect its class: <>= class(x) @ The class may be either \texttt{SpatialPoints} indicating a point pattern, \texttt{SpatialLines} indicating a list of polygonal lines, or \texttt{SpatialPolygons} indicating a list of polygons. It may also be \texttt{SpatialPointsDataFrame}, \texttt{SpatialLinesDataFrame} or \texttt{SpatialPolygonsDataFrame} indicating that, in addition to the spatial objects, there is a data frame of additional variables. The classes \texttt{SpatialPixelsDataFrame} and \texttt{SpatialGridDataFrame} represent pixel image data. \end{enumerate} Here are some examples, using the example shapefiles supplied in the \pkg{maptools} package itself. % fake data because we don't want spatstat to depend on maptools <>= baltim <- columbus <- fylk <- list() class(baltim) <- "SpatialPointsDataFrame" class(columbus) <- "SpatialPolygonsDataFrame" class(fylk) <- "SpatialLinesDataFrame" @ <>= setwd(system.file("shapes", package="maptools")) baltim <- readShapeSpatial("baltim.shp") columbus <- readShapeSpatial("columbus.shp") fylk <- readShapeSpatial("fylk-val.shp") @ <<>>= class(baltim) class(columbus) class(fylk) @ \subsection{Convert data to \pkg{spatstat} format} To convert the dataset to an object in the \pkg{spatstat} package, the procedure depends on the type of data, as explained below. \subsubsection{Objects of class \texttt{SpatialPoints}} An object \texttt{x} of class \texttt{SpatialPoints} represents a spatial point pattern. Use \verb!as(x, "ppp")! or \texttt{as.ppp(x)} to convert it to a spatial point pattern in \pkg{spatstat}. The window for the point pattern will be taken from the bounding box of the points. You will probably wish to change this window, usually by taking another dataset to provide the window information. Use \verb![.ppp! to change the window: if \texttt{X} is a point pattern object of class \verb!"ppp"! and \texttt{W} is a window object of class \verb!"owin"!, type <>= X <- X[W] @ \subsubsection{Objects of class \texttt{SpatialPointsDataFrame }} An object \texttt{x} of class \texttt{SpatialPointsDataFrame} represents a pattern of points with additional variables (`marks') attached to each point. It includes an object of class \texttt{SpatialPoints} giving the point locations, and a data frame containing the additional variables attached to the points. Use \verb!as(x, "ppp")! or \texttt{as.ppp(x)} to convert an object \texttt{x} of class \texttt{SpatialPointsDataFrame} to a spatial point pattern in \pkg{spatstat}. In this conversion, the data frame of additional variables in \texttt{x} will become the \texttt{marks} of the point pattern \texttt{z}. <>= y <- as(x, "ppp") @ Before the conversion you can extract the data frame of auxiliary data by \verb!df <- x@data! or \verb!df <- slot(x, "data")!. After the conversion you can extract these data by \verb!df <- marks(y)!. For example: <>= balt <- as(baltim, "ppp") bdata <- slot(baltim, "data") @ \subsubsection{Objects of class \texttt{SpatialLines}} \label{spatiallines.2.psp} A ``line segment'' is the straight line between two points in the plane. In the \pkg{spatstat} package, an object of class \texttt{psp} (``planar segment pattern'') represents a pattern of line segments, which may or may not be connected to each other (like matches which have fallen at random on the ground). In the \pkg{sp} package, an object of class \texttt{SpatialLines} represents a \textbf{list of lists} of \textbf{connected curves}, each curve consisting of a sequence of straight line segments that are joined together (like several pieces of a broken bicycle chain.) So these two data types do not correspond exactly. The list-of-lists hierarchy in a \texttt{SpatialLines} object is useful when representing internal divisions in a country. For example, if \texttt{USA} is an object of class \texttt{SpatialLines} representing the borders of the United States of America, then \verb!USA@lines! might be a list of length 52, with \verb!USA@lines[[i]]! representing the borders of the \texttt{i}-th State. The borders of each State consist of several different curved lines. Thus \verb!USA@lines[[i]]@Lines[[j]]! would represent the \texttt{j}th piece of the boundary of the \texttt{i}-th State. If \texttt{x} is an object of class \texttt{SpatialLines}, there are several things that you might want to do: \begin{enumerate} \item collect together all the line segments (all the segments that make up all the connected curves) and store them as a single object of class \texttt{psp}. \begin{quote} To do this, use \verb!as(x, "psp")! or \texttt{as.psp(x)} to convert it to a spatial line segment pattern. \end{quote} \item convert each connected curve to an object of class \texttt{psp}, keeping different connected curves separate. To do this, type something like the following: <>= out <- lapply(x@lines, function(z) { lapply(z@Lines, as.psp) }) @ The result will be a \textbf{list of lists} of objects of class \texttt{psp}. Each one of these objects represents a connected curve, although the \pkg{spatstat} package does not know that. The list structure will reflect the list structure of the original \texttt{SpatialLines} object \texttt{x}. If that's not what you want, then use \verb!curvelist <- do.call("c", out)! or <>= curvegroup <- lapply(out, function(z) { do.call("superimposePSP", z)}) @ to collapse the list-of-lists-of-\texttt{psp}'s into a list-of-\texttt{psp}'s. In the first case, \texttt{curvelist[[i]]} is a \texttt{psp} object representing the \texttt{i}-th connected curve. In the second case, \texttt{curvegroup[[i]]} is a \texttt{psp} object containing all the line segments in the \texttt{i}-th group of connected curves (for example the \texttt{i}-th State in the \texttt{USA} example). \end{enumerate} The window for the spatial line segment pattern can be specified as an argument \texttt{window} to the function \texttt{as.psp}. \subsubsection{Objects of class \texttt{SpatialLinesDataFrame}} An object \texttt{x} of class \texttt{SpatialLinesDataFrame} is a \texttt{SpatialLines} object with additional data. The additional data is stored as a data frame \verb!x@data! with one row for each entry in \verb!x@lines!, that is, one row for each group of connected curves. In the \pkg{spatstat} package, an object of class \texttt{psp} (representing a collection of line segments) may have a data frame of marks. Note that each \emph{line segment} in a \texttt{psp} object may have different mark values. If \texttt{x} is an object of class \texttt{SpatialLinesDataFrame}, there are two things that you might want to do: \begin{enumerate} \item collect together all the line segments that make up all the connected lines, and store them as a single object of class \texttt{psp}. \begin{quote} To do this, use \verb!as(x, "psp")! or \texttt{as.psp(x)} to convert it to a marked spatial line segment pattern. \end{quote} \item keep each connected curve separate, and convert each connected curve to an object of class \texttt{psp}. To do this, type something like the following: <>= out <- lapply(x@lines, function(z) { lapply(z@Lines, as.psp) }) dat <- x@data for(i in seq(nrow(dat))) out[[i]] <- lapply(out[[i]], "marks<-", value=dat[i, , drop=FALSE]) @ The result is a list-of-lists-of-\texttt{psp}'s. See the previous subsection for explanation on how to change this using \texttt{c()} or \texttt{superimposePSP}. \end{enumerate} In either case, the mark variables attached to a particular \emph{group of connected lines} in the \texttt{SpatialLinesDataFrame} object, will be duplicated and attached to each \emph{line segment} in the resulting \texttt{psp} object. \subsubsection{Objects of class \texttt{SpatialPolygons}} First, so that we don't go completely crazy, let's introduce some terminology. A \emph{polygon} is a closed curve that is composed of straight line segments. You can draw a polygon without lifting your pen from the paper. \setkeys{Gin}{width=0.4\textwidth} \begin{center} <>= data(chorley) plot(as.owin(chorley), lwd=3, main="polygon") @ \end{center} A \emph{polygonal region} is a region in space whose boundary is composed of straight line segments. A polygonal region may consist of several unconnected pieces, and each piece may have holes. The boundary of a polygonal region consists of one or more polygons. To draw the boundary of a polygonal region, you may need to lift and drop the pen several times. \setkeys{Gin}{width=0.4\textwidth} \begin{center} <>= data(demopat) plot(as.owin(demopat), col="blue", main="polygonal region") @ \end{center} An object of class \texttt{owin} in \pkg{spatstat} represents a polygonal region. It is a region of space that is delimited by boundaries made of lines. An object \texttt{x} of class \texttt{SpatialPolygons} represents a \textbf{list of polygonal regions}. For example, a single object of class \texttt{SpatialPolygons} could store information about every State in the United States of America (or the United States of Malaysia). Each State would be a separate polygonal region (and it might contain holes such as lakes). There are two things that you might want to do with an object of class \texttt{SpatialPolygons}: \begin{enumerate} \item combine all the polygonal regions together into a single polygonal region, and convert this to a single object of class \texttt{owin}. \begin{quote} For example, you could combine all the States of the USA together and obtain a single object that represents the territory of the USA. To do this, use \verb!as(x, "owin")! or \texttt{as.owin(x)}. The result is a single window (object of class \texttt{"owin"}) in the \pkg{spatstat} package. \end{quote} \item keep the different polygonal regions separate; convert each one of the polygonal regions to an object of class \texttt{owin}. \begin{quote} For example, you could keep the States of the USA separate, and convert each State to an object of class \texttt{owin}. \end{quote} To do this, type the following: <>= regions <- slot(x, "polygons") regions <- lapply(regions, function(x) { SpatialPolygons(list(x)) }) windows <- lapply(regions, as.owin) @ The result is a list of objects of class \texttt{owin}. Often it would make sense to convert this to a tessellation object, by typing <>= te <- tess(tiles=windows) @ \end{enumerate} {\bf The following is different from what happened in previous versions of \pkg{spatstat}} (prior to version \texttt{1.36-0}.) During the conversion process, the geometry of the polygons will be automatically ``repaired'' if needed. Polygon data from shapefiles often contain geometrical inconsistencies such as self-intersecting boundaries and overlapping pieces. For example, these can arise from small errors in curve-tracing. Geometrical inconsistencies are tolerated in an object of class \texttt{SpatialPolygons} which is a list of lists of polygonal curves. However, they are not tolerated in an object of class \texttt{owin}, because an \texttt{owin} must specify a well-defined region of space. These data inconsistencies must be repaired to prevent technical problems. \pkg{Spatstat} uses polygon-clipping code to automatically convert polygonal lines into valid polygon boundaries. The repair process changes the number of vertices in each polygon, and the number of polygons (if you chose option 1). To disable the repair process, set \texttt{spatstat.options(fixpolygons=FALSE)}. \subsubsection{Objects of class \texttt{SpatialPolygonsDataFrame}} What a mouthful! An object \texttt{x} of class \texttt{SpatialPolygonsDataFrame} represents a list of polygonal regions, with additional variables attached to each region. It includes an object of class \texttt{SpatialPolygons} giving the spatial regions, and a data frame containing the additional variables attached to the regions. The regions are extracted by <>= y <- as(x, "SpatialPolygons") @ and you then proceed as above to convert the curves to \pkg{spatstat} format. The data frame of auxiliary data is extracted by \verb!df <- x@data! or \verb!df <- slot(x, "data")!. For example: <>= cp <- as(columbus, "SpatialPolygons") cregions <- slot(cp, "polygons") cregions <- lapply(cregions, function(x) { SpatialPolygons(list(x)) }) cwindows <- lapply(cregions, as.owin) @ There is currently no facility in \pkg{spatstat} for attaching marks to an \texttt{owin} object directly. However, \pkg{spatstat} supports objects called \textbf{hyperframes}, which are like data frames except that the entries can be any type of object. Thus we can represent the \texttt{columbus} data in \pkg{spatstat} as follows: <>= ch <- hyperframe(window=cwindows) ch <- cbind.hyperframe(ch, columbus@data) @ Then \texttt{ch} is a hyperframe containing a column of \texttt{owin} objects followed by the columns of auxiliary data. \subsubsection{Objects of class \texttt{SpatialGridDataFrame} and \texttt{SpatialPixelsDataFrame}} An object \texttt{x} of class \texttt{SpatialGridDataFrame} represents a pixel image on a rectangular grid. It includes a \texttt{SpatialGrid} object \texttt{slot(x, "grid")} defining the full rectangular grid of pixels, and a data frame \texttt{slot(x, "data")} containing the pixel values (which may include \texttt{NA} values). The command \texttt{as(x, "im")} converts \texttt{x} to a pixel image of class \texttt{"im"}, taking the pixel values from the \emph{first column} of the data frame. If the data frame has multiple columns, these have to be converted to separate pixel images in \pkg{spatstat}. For example <>= y <- as(x, "im") ylist <- lapply(slot(x, "data"), function(z, y) { y[,] <- z; y }, y=y) @ An object \texttt{x} of class \texttt{SpatialPixelsDataFrame} represents a \emph{subset} of a pixel image. To convert this to a \pkg{spatstat} object, it should first be converted to a \texttt{SpatialGridDataFrame} by \texttt{as(x, "SpatialGridDataFrame")}, then handled as described above. \end{document} spatstat/inst/doc/updates.R0000644000176000001440000000361612333041747015471 0ustar ripleyusers### R code from vignette source 'updates.Rnw' ################################################### ### code chunk number 1: updates.Rnw:20-24 ################################################### library(spatstat) sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) ################################################### ### code chunk number 2: updates.Rnw:41-47 ################################################### z <- read.table("packagesizes.txt", header=TRUE) z$date <- as.Date(z$date) Plot <- function(fmla, ..., dat=z) { yvals <- eval(as.expression(fmla[[2]]), envir=dat) plot(fmla, ..., data=dat, type="l", xlab="", lwd=2, ylim=c(0, max(yvals))) } ################################################### ### code chunk number 3: updates.Rnw:49-50 ################################################### options(SweaveHooks=list(fig=function() par(mar=0.2+c(2,4,2,0)))) ################################################### ### code chunk number 4: updates.Rnw:56-61 ################################################### getOption("SweaveHooks")[["fig"]]() Plot((Rlines + srclines)/1000 ~ date, ylab="Lines of code (x 1000)", main="Spatstat growth") lines(srclines/1000 ~ date, data=z) text(as.Date("2013-01-01"), 9.5, "C code") text(as.Date("2013-01-01"), 50, "R code") ################################################### ### code chunk number 5: updates.Rnw:2494-2498 ################################################### nbugs <- nrow(news(grepl("^BUG", Category), package="spatstat")) nbugssince <- nrow(news(Version > "1.21-2" & grepl("^BUG", Category), package="spatstat")) ################################################### ### code chunk number 6: updates.Rnw:2505-2506 (eval = FALSE) ################################################### ## news(grepl("^BUG", Category), package="spatstat") spatstat/inst/doc/updates.Rnw0000644000176000001440000027104412333041747016040 0ustar ripleyusers\documentclass[11pt]{article} \usepackage{graphicx} \usepackage{Sweave} \usepackage{bm} \usepackage{anysize} \marginsize{2cm}{2cm}{2cm}{2cm} % \VignetteIndexEntry{Summary of Recent Updates to Spatstat} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\code}[1]{\texttt{#1}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} <>= library(spatstat) sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) @ \title{Summary of recent updates to \spst} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle This is a summary of changes that have been made to the \spst\ package since 2010, when the last set of workshop notes \cite{badd10wshop} was published. Those notes covered \spst\ version \texttt{1.21-2}. The current version of \spst\ is \texttt{\Sexpr{sversion}}. \Spst\ has almost tripled in size since 2010, and now contains over 100,000 lines of code. <>= z <- read.table("packagesizes.txt", header=TRUE) z$date <- as.Date(z$date) Plot <- function(fmla, ..., dat=z) { yvals <- eval(as.expression(fmla[[2]]), envir=dat) plot(fmla, ..., data=dat, type="l", xlab="", lwd=2, ylim=c(0, max(yvals))) } @ <>= options(SweaveHooks=list(fig=function() par(mar=0.2+c(2,4,2,0)))) @ \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.45\textwidth} \centerline{ <>= Plot((Rlines + srclines)/1000 ~ date, ylab="Lines of code (x 1000)", main="Spatstat growth") lines(srclines/1000 ~ date, data=z) text(as.Date("2013-01-01"), 9.5, "C code") text(as.Date("2013-01-01"), 50, "R code") @ } \tableofcontents \newpage \section{Overview} Following is an overview of the most important changes. \begin{itemize} \item \spst\ and all its dependencies are now Free Open Source. \item polygon clipping is now performed by the Free Open Source library \pkg{polyclip}. The library \pkg{gpclib} is no longer used. \item \spst\ now `Imports' (rather than `Depends' on) the libraries \pkg{mgcv}, \pkg{deldir}, \pkg{abind}, \pkg{tensor}, \pkg{polyclip}. This means that these libraries are not accessible to the user unless the user explicitly loads them by typing \code{library(mgcv)} and so on. \item polygon calculations are now enabled, always. \item geometrical errors in polygon data are now repaired automatically. \item Improvements to Gibbs models (\texttt{ppm}): \begin{itemize} \item now accepts syntax \verb!ppm(X ~ Z)! similar to \texttt{lm} and \texttt{glm}, as well as old syntax \verb!ppm(X, ~Z)! \item Covariates no longer have to be passed in the argument \texttt{covariates} but can be objects in the {\sf R} session; \item new 'logistic likelihood' method; \item variance-covariance matrix; \item print method shows standard errors and CI for coefficients; \item new interactions (\texttt{Triplets}, \texttt{Concom}); \item hybrids of interactions; \item self-starting feature of interactions; \item better control over dummy points; \item improved quadrature schemes (removing a source of bias); \item spatial covariate functions can depend on additional parameters; \item extract interaction from a fitted model; \item check whether model is valid; \item project to a valid model; \item calculate \emph{intensity} of fitted model; \item calculate $K$-function and pair correlation function of fitted model; \item confidence intervals using \texttt{confint}; \item improvements/extensions to \texttt{rmh}; \item exact simulation for many models; \item predict conditional intensity for another point pattern \item compute confidence intervals and prediction intervals for intensity, and for total number of points in a region. \item covariates can be tessellations. \end{itemize} \item Diagnostics for Gibbs models: Leverage, influence, partial residuals, score residuals, residual summary functions (\texttt{Kcom}, \texttt{Kres}, \texttt{Gcom}, \texttt{Gres}, \texttt{psst}, \texttt{psstA}, \texttt{psstG}, \texttt{compareFit}) \item Improvements to cluster/Cox models (\texttt{kppm}): \begin{itemize} \item now accepts syntax \verb!kppm(X ~ Z)! similar to \texttt{lm} and \texttt{glm}, as well as old syntax \verb!kppm(X, ~Z)! \item Covariates no longer have to be passed in the argument \texttt{covariates} but can be objects in the {\sf R} session; \item Guan's second order composite likelihood; \item new cluster types \texttt{"Cauchy"}, \texttt{"VarGamma"}; \item log-Gaussian Cox processes; \item fitted intensity; \item variance-covariance of trend parameters; \item generic support e.g. \texttt{coef}, \texttt{model.matrix}; \item conversion e.g. \texttt{as.fv} \item extract K function (\texttt{Kmodel}, \texttt{pcfmodel}); \item more control over trend (more arguments passed to \texttt{ppm}); \item confidence intervals using \texttt{confint} \item covariates can be tessellations. \end{itemize} \item Support for point patterns on a linear network, including geometrically-corrected linear $K$ function, pair correlation function, point process models, envelopes, tests. \item Support for replicated point patterns: An object of class \texttt{mppm} represents a point process model fitted to several point patterns. The patterns may be treated as independent replicate observations of the same model, or as the responses from different units in a designed experiment. Methods for this class include \texttt{plot}, \texttt{print}, \texttt{summary}, \texttt{fitted}, \texttt{predict}, \texttt{simulate}, ... \item Nonparametric/summary statistics e.g. \begin{itemize} \item \texttt{rhohat} (nonparametric estimate of intensity as function of covariate) \item \texttt{idw} (inverse distance weighted smoothing) \item \texttt{convolve.im} (image convolution) \item $k$-th nearest neighbours (\texttt{nndist}, \texttt{nncross}, \texttt{nnwhich}) \item more methods for \code{intensity}. \end{itemize} \item Statistical tools e.g. \begin{itemize} \item several algorithms for bandwidth selection (\texttt{bw.*}) \item bootstrap confidence intervals for summary functions (\texttt{lohboot}) \item pooling operations (\texttt{pool.*}) \end{itemize} \item Changes to \texttt{ppx} format: An object of class \texttt{ppx} may now include 'local' coordinates as well as 'spatial' and 'temporal' coordinates, and marks. \item Improvements to plots : \begin{itemize} \item colour map objects \item graphics symbol map objects \item texture map objects \item automatic legend in \texttt{plot.ppp} \item collision avoidance in \texttt{plot.fv} \item \texttt{plot.im(log=TRUE)} \item \texttt{plot.listof(equal.ribbon=TRUE)} \item improved labels in \texttt{plot.fv} \item Switch all plots from colour to greyscale by setting \texttt{spatstat.options(monochrome=TRUE)} \end{itemize} \item simulation: \begin{itemize} \item \texttt{rmh} interface restructured \item \texttt{rmh} can save transition history \item visual debugger for \texttt{rmh} \item exact simulation for many models \item quasirandom point patterns and sequences \end{itemize} \item envelopes: Extended functionality; Envelopes can be pooled and transformed. \item Many, many, many bug fixes. \item Substantially increased performance. \item data in \spst\ are now lazy-loaded, so you don't have to type \texttt{data(amacrine)}, etc. \item user interrupt: Long calculations in \spst\ now respond to the Interrupt/Stop signal. \item vignettes: New vignettes 'Getting Started with the Spatstat Package', 'Analysing Replicated Point Patterns in Spatstat' and `Summary of Recent Changes to Spatstat' \item demonstrations: New demonstration \texttt{demo(sumfun)} shows the nonparametric summary functions in \spst. \end{itemize} \section{New classes} Important new classes of objects are listed here. \begin{itemize} \item \texttt{linnet}: An object of class '\texttt{linnet}' represents a linear network, i.e. a connected network of line segments, such as a road network. Methods for this class include plot, print, summary etc. \item \texttt{lpp}: An object of class '\texttt{lpp}' represents a point pattern on a linear network, such as a record of the locations of road accidents on a road network. Methods for this class include plot, print, summary etc. \item \texttt{lppm}: An object of class '\texttt{lppm}' represents a point process model fitted to a point pattern on a linear network. Methods for this class include plot, print, summary, fitted, predict, simulate, ... \item \texttt{linim}: Pixel image on a linear network. \item \texttt{linfun} Function defined on a linear network (e.g. a distance function) \item \texttt{mppm}: An object of class '\texttt{mppm}' represents a point process model fitted to several point pattern datasets. The patterns may be treated as independent replicate observations of the same model, or as the responses from different units in a designed experiment. Methods for this class include plot, print, summary, fitted, predict, simulate, ... \item \texttt{rhohat}: Nonparametric estimate of intensity as function of covariate (result of '\texttt{rhohat}'). Can be used as an R function. Methods include \texttt{predict} \item \texttt{layered} : A simple mechanism for controlling plots that consist of several successive layers of data. \item \texttt{timed}: A simple mechanism for recording the computation time taken. \item \texttt{rat}: 'Ratio objects' (suitable for pooling) \item \texttt{objsurf}: An object of class '\texttt{objsurf}' contains values of the likelihood or objective function in a neighbourhood of the maximum. \item \texttt{simplepanel}: An object of class '\texttt{simplepanel}' represents a spatial arrangement of buttons that respond to mouse clicks, supporting a simple, robust graphical interface. \item \texttt{symbolmap}: An object of class '\texttt{symbolmap}' represents a mapping from data values to graphical symbols. This makes it easy to ensure that the \emph{same} graphics map is used in two different plots. The graphics map can also be plotted in its own right as a legend to the plots. \item \texttt{texturemap}: An object of class '\texttt{texturemap}' represents a mapping from data values to graphical textures. \end{itemize} \section{New datasets} The following datasets have been added to the package. \begin{itemize} \item \texttt{pyramidal}: Diggle-Lange-Benes data on pyramidal neurons in cingulate cortex. 31 point patterns divided into 3 groups. \item \texttt{waterstriders}: Nummelin-Penttinen waterstriders data. Three independent replicates of a point pattern formed by insects. \item \texttt{simba}: Simulated data \item \texttt{demohyper}: Simulated data example for \texttt{mppm}. Point patterns and pixel image covariates, in two groups with different regression coefficients. \item \texttt{clmfires}: Forest fires in Castilla-La Mancha \item \texttt{gordon}: People sitting on the grass in Gordon Square, London \item \texttt{hyytiala}: Mixed forest in Hyytiala, Finland (marked by species) \item \texttt{paracou}: Kimboto trees in Paracou, French Guiana (marked as adult/juvenile) \item \texttt{waka}: Trees in Waka national park (marked with diameters) \item \texttt{mucosa}: Cells in gastric mucosa \item \texttt{gorillas}: Gorilla nest sites in a National Park in Cameroon. \item \texttt{chicago}: Street crimes in the University district of Chicago. A point pattern on a linear network. \item \texttt{flu}: Spatial point patterns giving the locations of influenza virus proteins on cell membranes. Kindly released by Dr George Leser and Dr Robert Lamb. \item \texttt{simplenet}: Simple example of a linear network \end{itemize} \section{New Functions} Following is a list of all the functions that have been added. \begin{itemize} \item \texttt{split.hyperframe}: method for \texttt{split} for hyperframes. \item \texttt{complementarycolour}: Compute the complementary colour value of a given colour value, or the complementary colour map of a given colour map. \item \texttt{dmixpois}, \texttt{pmixpois}, \texttt{qmixpois}, \texttt{rmixpois}: (log-)normal mixture of Poisson distributions. \item \texttt{gauss.hermite}: Gauss-Hermite quadrature approximation to the expectation of any function of a normally-distributed random variable. \item \texttt{minnndist}, \texttt{maxnndist}: faster ways to compute \texttt{min(nndist(X))} and \texttt{max(nndist(X))} \item \texttt{boundingbox}: Generic function, replaces \texttt{bounding.box}. \item \texttt{Smoothfun}: create a \texttt{function(x,y)} equivalent to the result of \texttt{Smooth.ppp} \item \code{vdCorput}, \code{Halton}, \code{Hammersley}, \code{rQuasi}: quasirandom point patterns and sequences. \item \code{ellipse}: create an elliptical window. \item \code{clickbox}: interactively specify a rectangle by point-and-click on a graphics device. \item \code{symbolmap}: Create a graphics symbol map (a mapping from data values to graphical symbols) \item \code{update.symbolmap}: Modify a graphics symbol map \item \code{invoke.symbolmap}: Apply graphics symbol map to data values, and plot the symbols \item \code{plot.symbolmap}: Plot the graphics symbol map in the style of a legend \item \code{add.texture}: Draw a simple texture inside a specified region. \item \code{textureplot}: Display a factor-valued pixel image using texture fill. \item \code{texturemap}: Create a graphics texture map (a mapping from data values to graphical textures) \item \code{plot.texturemap}: Plot the graphics texture map in the style of a legend \item \code{reload.or.compute}: Utility for use in {\sf R} scripts. Either reload results from file, or compute them. \item \code{im.apply}: Apply a function to corresponding pixel values in several images. \item \code{hexgrid}, \code{hextess}: Create a hexagonal grid of points, or a tessellation of hexagonal tiles \item \code{shift.tess}, \code{rotate.tess}, \code{reflect.tess}, \code{scalardilate.tess}, \code{affine.tess}: Apply a geometrical transformation to a tessellation. \item \code{quantile.ewcdf}: Extract quantiles from a weighted cumulative distribution function. \item \code{scanLRTS}: Evaluate the spatially-varying test statistic for the scan test. \item \code{intensity.splitppp}: Estimate intensity in each component of a split point pattern. \item \code{intensity.quadratcount}: Use quadrat counts to estimate intensity in each quadrat. \item \code{as.owin.quadratcount}, \code{as.owin.quadrattest}: Extract the spatial window in which quadrat counts were performed. \item \texttt{mppm}: Fit a Gibbs model to several point patterns. The point pattern data may be organised as a designed experiment and the model may depend on covariates associated with the design. \item \texttt{anova.mppm}: Analysis of Deviance for models of class \texttt{mppm} \item \texttt{coef.mppm}: Extract fitted coefficients from a model of class \texttt{mppm} \item \texttt{fitted.mppm}: Fitted intensity or conditional intensity for a model of class \texttt{mppm} \item \texttt{kstest.mppm}: Kolmogorov-Smirnov test of goodness-of-fit for a model of class \texttt{mppm} \item \texttt{logLik.mppm}: log likelihood or log pseudolikelihood for a model of class \texttt{mppm} \item \texttt{plot.mppm}: Plot the fitted intensity or conditional intensity of a model of class \texttt{mppm} \item \texttt{predict.mppm}: Compute the fitted intensity or conditional intensity of a model of class \texttt{mppm} \item \texttt{quadrat.test.mppm}: Quadrat counting test of goodness-of-fit for a model of class \texttt{mppm} \item \texttt{residuals.mppm}: Point process residuals for a model of class \texttt{mppm} \item \texttt{subfits}: Extract point process models for each individual point pattern dataset, from a model of class \texttt{mppm}. \item \texttt{vcov.mppm}: Variance-covariance matrix for a model of class \texttt{mppm} \item \texttt{integral.msr}: Integral of a measure. \item \texttt{objsurf}: For a model fitted by optimising an objective function, this command computes the objective function in a neighbourhood of the optimal value. \item \texttt{contour.objsurf}, \texttt{image.objsurf}, \texttt{persp.objsurf}, \texttt{plot.objsurf}: Plot an 'objsurf' object. \item \texttt{fvnames}: Define groups of columns in a function value table, for use in \texttt{plot.fv}, etc \item \texttt{multiplicity}: New generic function for which \texttt{multiplicity.ppp} is a method. \item \texttt{unique.ppx}, \texttt{duplicated.ppx}, \texttt{multiplicity.ppx}: Methods for \texttt{unique}(), \texttt{duplicated}() and \texttt{multiplicity}() for '\texttt{ppx}' objects. These also work for '\texttt{pp3}' and \texttt{lpp} objects. \item \texttt{closepairs}, \texttt{crosspairs}, \texttt{closepaircounts}, \texttt{crosspaircounts}: Low-level functions for finding all close pairs of points \item \texttt{nnfun.ppp}, \texttt{distfun.ppp}: New argument \texttt{k} allows these functions to compute $k$-th nearest neighbours. \item \texttt{nndensity}: Estimate point process intensity using $k$-th nearest neighbour distances \item \texttt{simplepanel}, \texttt{run.simplepanel}: Support for a simple point-and-click interface for general use. \item \texttt{as.lpp}: Convert data to a point pattern on a linear network. \item \texttt{distfun.lpp}: Distance function for point pattern on a linear network. \item \texttt{eval.linim}: Evaluate expression involving pixel images on a linear network. \item \texttt{linearKcross}, \texttt{linearKdot}, \texttt{linearKcross.inhom}, \texttt{linearKdot.inhom}: Multitype $K$ functions for point patterns on a linear network \item \texttt{linearmarkconnect}, \texttt{linearmarkequal}: Mark connection function and mark equality function for multitype point patterns on a linear network \item \texttt{linearpcfcross}, \texttt{linearpcfdot}, \texttt{linearpcfcross.inhom}, \texttt{linearpcfdot.inhom}: Multitype pair correlation functions for point patterns on a linear network \item \texttt{linfun}: New class of functions defined on a linear network \item \texttt{nndist.lpp}, \texttt{nnwhich.lpp}, \texttt{nncross.lpp}: Methods for \texttt{nndist}, \texttt{nnwhich}, \texttt{nncross} for point patterns on a linear network \item \texttt{nnfun.lpp}: Method for \texttt{nnfun} for point patterns on a linear network \item \texttt{vcov.lppm}: Variance-covariance matrix for parameter estimates of a fitted point process model on a linear network. \item \texttt{bilinearform} Computes a bilinear form \item \texttt{tilenames}, \verb!tilenames<-!: Extract or change the names of tiles in a tessellation. \item \texttt{bw.ppl}: Likelihood cross-validation technique for bandwidth selection in kernel smoothing. \item \texttt{is.lppm}, \texttt{is.kppm}, \texttt{is.slrm}: Tests whether an object is of class 'lppm', 'kppm' or 'slrm' \item \texttt{Smooth}: New generic function for spatial smoothing. \item \texttt{Smooth.ppp}, \texttt{Smooth.fv}, \texttt{Smooth.msr}: Methods for \texttt{Smooth} (identical to \texttt{smooth.ppp}, \texttt{smooth.fv}, \texttt{smooth.msr} respectively) \item \texttt{fitted.kppm}: Method for \texttt{fitted} for cluster/Cox models \item \texttt{nncross.pp3}: Method for \texttt{nncross} for point patterns in 3D \item \texttt{nnmark}: Mark of nearest neighbour - can be used for interpolation \item \texttt{dclf.progress}, \texttt{mad.progress}: Progress plots (envelope representations) for the DCLF and MAD tests. \item \texttt{deriv.fv}: Numerical differentiation for \texttt{fv} objects. \item \texttt{interp.colourmap}: Smooth interpolation of colour map objects - makes it easy to build colour maps with gradual changes in colour \item \texttt{tweak.colourmap} Change individual colour values in a colour map object \item \texttt{beachcolourmap}: Colour scheme appropriate for `altitudes' (signed numerical values) \item \texttt{as.fv}: Convert various kinds of data to an 'fv' object \item \texttt{quadscheme.logi}: Generates quadrature schemes for the logistic method of ppm. \item \texttt{beginner} Introduction for beginners. \item \texttt{nnmap}: Given a point pattern, finds the k-th nearest point in the pattern from each pixel in a raster. \item \texttt{coef.fii}, \texttt{coef.summary.fii}: Extract the interaction coefficients of a fitted interpoint interaction \item \texttt{edges2vees}: Low-level function for finding triples in a graph. \item \texttt{affine.lpp}, \texttt{shift.lpp}, \texttt{rotate.lpp}, \texttt{rescale.lpp}, \texttt{scalardilate.lpp}: Geometrical transformations for point patterns on a linear network \item \texttt{affine.linnet}, \texttt{shift.linnet}, \texttt{rotate.linnet}, \texttt{rescale.linnet}, \texttt{scalardilate.linnet}: Geometrical transformations for linear networks \item \verb![.linnet! Subset operator for linear networks \item \texttt{timed}: Records the computation time taken \item \texttt{connected.ppp}: Find clumps in a point pattern. \item \texttt{kstest.lpp}, \texttt{kstest.lppm}: The spatial Kolmogorov-Smirnov test can now be applied to point patterns on a linear network (class \texttt{lpp}) and point processes on a linear network (class \texttt{lppm}). \item \texttt{bermantest.lpp}, \texttt{bermantest.lppm}: Berman's Z1 and Z2 tests can now be applied to point patterns on a linear network (class \texttt{lpp}) and point processes on a linear network (class \texttt{lppm}). \item \texttt{rhohat.lpp}, \texttt{rhohat.lppm}: Nonparametric estimation of the dependence of a point pattern on a spatial covariate: \texttt{rhohat} now applies to objects of class \texttt{lpp} and \texttt{lppm}. \item \texttt{intensity.lpp}: Empirical intensity of a point pattern on a linear network. \item \texttt{as.function.rhohat}: Converts a \texttt{rhohat} object to a function, with extrapolation beyond the endpoints. \item \verb![.layered!: Subset operator for layered objects. \item \texttt{shift}, \texttt{rotate}, \texttt{affine}, \texttt{rescale}, \texttt{reflect}, \texttt{flipxy}, \texttt{scalardilate}: These geometrical transformations now work for \texttt{layered} objects. \item \texttt{iplot.layered}: Interactive plotting for \texttt{layered} objects. \item \texttt{as.owin.layered}: Method for as.owin for layered objects. \item \verb![.owin!: Subset operator for windows, equivalent to \texttt{intersect.owin}. \item \texttt{rcellnumber}: Generates random integers for the Baddeley-Silverman counterexample. \item \texttt{is.lpp}: Tests whether an object is a point pattern on a linear network. \item \texttt{is.stationary.lppm}, \texttt{is.poisson.lppm}: New methods for \texttt{is.stationary} and \texttt{is.poisson} for class \texttt{lppm} \item \texttt{sessionLibs}: Print library names and version numbers (for use in \texttt{Sweave} scripts) \item \texttt{round.ppp}: Round the spatial coordinates of a point pattern to a specified number of decimal places. \item \texttt{rounding}: Determine whether a dataset has been rounded. \item \texttt{Hybrid}: The hybrid of several point process interactions [Joint research with Jorge Mateu and Andrew Bevan] \item \texttt{is.hybrid}: Recognise a hybrid interaction or hybrid point process model. \item \texttt{Finhom}, \texttt{Ginhom}, \texttt{Jinhom} Inhomogeneous versions of the F, G and J functions \item \texttt{delaunay.distance}: Graph distance in the Delaunay triangulation. \item \texttt{distcdf}: Cumulative distribution function of the distance between two independent random points in a given window. \item \texttt{bw.frac}: Bandwidth selection based on window geometry \item \texttt{shortside.owin}, \texttt{sidelengths.owin}: Side lengths of (enclosing rectangle of) a window \item \texttt{clusterset}: Allard-Fraley estimator of high-density features in a point pattern \item \texttt{pool.quadrattest}: Pool several quadrat tests \item \texttt{nnfun}: Nearest-neighbour map of a point pattern or a line segment pattern \item \texttt{as.ppm}: Converts various kinds of objects to \texttt{ppm}. \item \texttt{crossdist.lpp}: Shortest-path distances between pairs of points in a linear network \item \texttt{nobs.lppm}: Method for \texttt{nobs} for \texttt{lppm} objects. \item \texttt{as.linim}: Converts various kinds of objects to \texttt{linim}. \item \texttt{model.images.slrm}: Method for \texttt{model.images} for \texttt{slrm} objects \item \texttt{rotate.im}: Rotate a pixel image \item \texttt{parres}: Partial residual plots for spatial point process models. A diagnostic for the form of a covariate effect. \item \texttt{addvar}: Added variable plots for spatial point process models. A diagnostic for the existence of a covariate effect. \item \texttt{intensity}, \texttt{intensity.ppp}, \texttt{intensity.ppm} Calculate the intensity of a dataset or fitted model. Includes new approximation to the intensity of a fitted Gibbs model \item \texttt{LambertW}: Lambert's W-function \item \texttt{exactMPLEstrauss}: Fits the stationary Strauss point process model using an exact maximum pseudolikelihood technique. This is mainly intended for technical investigation of algorithms. \item \texttt{split.ppx}: Method for \texttt{split} for multidimensional point patterns (class \texttt{ppx}). This also works for point patterns on a linear network (class \texttt{lpp}). \item \texttt{model.images}: This function is now generic, with methods for classes \texttt{ppm}, \texttt{kppm}, \texttt{lppm} \item \texttt{model.frame}, \texttt{model.matrix}: These generic functions now have methods for classes \texttt{kppm}, \texttt{lppm} \item \texttt{as.owin.kppm}, \texttt{as.owin.lppm}: New methods for 'as.owin' for objects of class \texttt{kppm}, \texttt{lppm} \item \texttt{as.linnet.lppm}: Extracts the linear network in which a point process model was fitted. \item \texttt{dclf.test}: Perform the Diggle (1986)/ Cressie (1991)/ Loosmore and Ford (2006) test of CSR (or another model) \item \texttt{mad.test}: Perform the Maximum Absolute Deviation test of CSR (or another model). \item \texttt{convolve.im}: Compute convolution of pixel images. \item \texttt{Kmulti.inhom}: Counterpart of \texttt{Kmulti} for spatially-varying intensity. \item \texttt{rmhexpand}: Specify a simulation window, or a rule for expanding the simulation window, in Metropolis-Hastings simulation (\texttt{rmh}) \item \texttt{transect.im}: Extract pixel values along a line transect. \item \texttt{affine.im}: Apply an affine transformation to a pixel image. \item \texttt{scalardilate}: Perform scalar dilation of a geometrical object relative to a specified origin. \item \texttt{reflect}: Reflect a geometrical object through the origin. \item \verb![.lpp!, \verb![.ppx!: Subset operators for the classes \texttt{lpp} (point pattern on linear network) and \texttt{ppx} (multidimensional space-time point pattern). \item \texttt{is.rectangle}, \texttt{is.polygonal}, \texttt{is.mask}: Determine whether a window w is a rectangle, a domain with polygonal boundaries, or a binary pixel mask. \item \texttt{has.offset}: Determines whether a fitted model object (of any kind) has an offset. \item \texttt{lohboot}: Computes bootstrap confidence bands for pair correlation function and $K$ function using Loh's (2008) mark bootstrap. \item \texttt{rho2hat}: Bivariate extension of \texttt{rhohat} for estimating spatial residual risk, or intensity as a function of two covariates. \item \texttt{rStraussHard}: Perfect simulation for Strauss-hardcore process (with $\gamma \le 1$) \item \texttt{simulate.slrm}: Method for \texttt{simulate} for spatial logistic regression models. \item \texttt{labels.ppm}, \texttt{labels.kppm}, \texttt{labels.slrm}: Methods for \texttt{labels} for fitted point process models. \item \texttt{commonGrid}: Determine a common spatial domain and pixel resolution for several pixel images and/or binary masks \item \texttt{as.matrix.owin}: Converts a window to a logical matrix. \item \texttt{rDiggleGratton}, \texttt{rDGS}, \texttt{rHardcore}: Perfect simulation for the Diggle-Gratton process, Diggle-Gates-Stibbard process, and Hardcore process. \item \texttt{bw.scott}: Scott's rule of thumb for bandwidth selection in multidimensional smoothing \item \texttt{valid.ppm}: Checks whether a fitted point process model is a valid point process \item \texttt{project.ppm}: Forces a fitted point process model to be a valid point process \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Leverage and influence for point process models \item \texttt{ippm}: Experimental extension to 'ppm' which fits irregular parameters in trend by Fisher scoring algorithm. \item \texttt{Tstat}: Third order summary statistic for point patterns based on counting triangles. \item \texttt{rCauchy}, \texttt{rVarGamma}: simulation of a Neyman-Scott process with Cauchy clusters or Variance Gamma (Bessel) clusters. Contributed by Abdollah Jalilian. \item \texttt{rPoissonCluster}: simulation of a general Poisson cluster process \item \texttt{model.covariates}: Identify the covariates involved in a model (\texttt{lm}, \texttt{glm}, \texttt{ppm} etc) \item \texttt{as.im.distfun}: Converts a 'distfun' to a pixel image. \item \texttt{cauchy.estK}, \texttt{cauchy.estpcf}, \texttt{vargamma.estK}, \texttt{vargamma.estpcf}: Low-level model-fitting functions for the Neyman-Scott process with Cauchy or Variance-Gamma cluster kernel. Contributed by Abdollah Jalilian. \item \texttt{Triplets}: Geyer's triplet interaction, for point process models \item \texttt{coef.summary.ppm}: You can now type \texttt{coef(summary(fit))} to extract a table of the fitted coefficients of the point process model \texttt{fit} \item \texttt{scan.test}: Spatial scan test of clustering \item \texttt{rat}: New class of 'ratio objects' \item \texttt{pool.rat}: New method for \texttt{pool}. Combines K function estimates for replicated point patterns (etc) by computing ratio-of-sums \item \texttt{unnormdensity}: Weighted kernel density with weights that do not sum to 1 and may be negative. \item \texttt{compatible}: New generic function with methods for \texttt{fv}, \texttt{im}, \texttt{fasp} and \texttt{units}. \item \texttt{imcov}: Spatial covariance function of pixel image or spatial cross-covariance function of two pixel images \item \texttt{harmonise.im}: Make several pixel images compatible by converting them to the same pixel grid \item \texttt{contour.listof}, \texttt{image.listof}: Methods for \texttt{contour}() and \texttt{image}() for lists of objects \item \texttt{dummify}: Convert data to numeric values by constructing dummy variables. \item \texttt{pool}: Pool data from several objects of the same class \item \texttt{pool.envelope} Pool simulated data from several envelope objects and create a new envelope \item \texttt{pool.fasp}: Pool simulated data from several function arrays and create a new array \item \texttt{envelope.envelope}: Recalculate an envelope from simulated data using different parameters \item \texttt{bw.diggle}: Bandwidth selection for density.ppp by mean square error cross-validation. \item \texttt{bw.smoothppp}: Bandwidth selection for smooth.ppp by least-squares cross-validation. \item \texttt{layered}, \texttt{plot.layered}: A simple mechanism for controlling plots that consist of several successive layers of data. \item \texttt{model.depends}: Given a fitted model (of any kind), identify which of the covariates is involved in each term of the model. \item \texttt{model.is.additive}: Determine whether a fitted model (of any kind) is additive, in the sense that each term in the model involves at most one covariate. \item \texttt{sumouter}, \texttt{quadform}: Evaluate certain quadratic forms. \item \texttt{flipxy}: Exchange x and y coordinates. \item \texttt{idw}: Inverse-distance weighted smoothing. \item \texttt{localKinhom}, \texttt{localLinhom}, \texttt{localpcfinhom}: Inhomogeneous versions of \texttt{localK}, \texttt{localL}, \texttt{localpcf}: \item \texttt{envelope.lpp}: Simulation envelopes for point patterns on a linear network \item \texttt{lineardisc}: Compute the 'disc' of radius $r$ in a linear network \item \texttt{linearpcf}: Pair correlation for point pattern on a linear network \item \texttt{linearKinhom}, \texttt{linearpcfinhom}: Inhomogeneous versions of the K function and pair correlation function for point patterns on a linear network \item \texttt{lppm}: Fit point process models on a linear network. \item \texttt{anova.lppm}: Analysis of deviance for point process models on a linear network. \item \texttt{predict.lppm}: Prediction for point process models on a linear network. \item \texttt{envelope.lppm}: Simulation envelopes for point process models on a linear network. \item \texttt{linim}: Pixel image on a linear network \item \texttt{plot.linim}: Plot a pixel image on a linear network \item \texttt{Gcom}, \texttt{Gres}, \texttt{Kcom}, \texttt{Kres}: New diagnostics for fitted Gibbs or Poisson point process models based on score residuals. \texttt{Gcom} is the compensator of the $G$ function; \texttt{Gres} is the residual of the $G$ function; \texttt{Kcom} is the compensator of the $K$ function; \texttt{Kres} is the residual of the $K$ function. \item \texttt{psst}, \texttt{psstA}, \texttt{psstG}: New diagnostics for fitted Gibbs or Poisson point process models based on pseudoscore residuals. \texttt{psst} is the pseudoscore diagnostic for a general alternative; \texttt{psstA} is the pseudoscore diagnostic for an Area-interaction alternative; \texttt{psstG} is the pseudoscore diagnostic for a Geyer saturation alternative. \item \texttt{compareFit}: Computes and compares several point process models fitted to the same dataset, using a chosen diagnostic. \item \texttt{as.interact}: Extracts the interpoint interaction structure (without parameters) from a fitted point process model or similar object. \item \texttt{is.stationary}, \texttt{is.poisson}: New generic functions for testing whether a point process model is stationary and/or Poisson. Methods for \texttt{ppm}, \texttt{kppm}, \texttt{slrm} etc. \item \texttt{raster.xy}: raster coordinates of a pixel mask \item \texttt{zapsmall.im}: 'zapsmall' for pixel images \item \texttt{linearK}: Computes the Okabe-Yamada network K-function for a point pattern on a linear network. \item \texttt{pairdist.lpp}: Shortest-path distances between each pair of points on a linear network. \item \texttt{vcov.kppm}: Asymptotic variance-covariance matrix for regression parameters in kppm object. [Contributed by Abdollah Jalilian and Rasmus Waagepetersen] \item \texttt{rLGCP}: Simulation of log-Gaussian Cox processes [Contributed by Abdollah Jalilian and Rasmus Waagepetersen] \item \texttt{predict.rhohat}: Method for \texttt{predict} for objects of class \texttt{rhohat}. Computes a pixel image of the predicted intensity. \item \texttt{Kmodel}, \texttt{pcfmodel}: Generic functions that compute the K-function or pair correlation function of a point process \emph{model}. There are methods for \texttt{kppm} and \texttt{ppm}. \item \texttt{as.function.fv} Converts a function value table (class \texttt{fv}) to a function in R \item \texttt{coef.kppm}: Method for \texttt{coef} for objects of class \texttt{kppm} \item \texttt{unitname}, \verb!unitname<-!: These generic functions now have methods for fitted model objects (classes \texttt{ppm}, \texttt{slrm}, \texttt{kppm}, \texttt{minconfit}) and quadrature schemes (\texttt{quad}). \item \texttt{nobs.ppm}: Method for \texttt{nobs} for class \texttt{ppm}. Returns the number of points in the original data. \item \texttt{rgbim}, \texttt{hsvim}: Specify three colour channels. These functions convert three pixel images with numeric values into a single image whose pixel values are strings representing colours. \item \texttt{scaletointerval}: Generic utility function to rescale data (including spatial data) to a specified interval \item \texttt{runiflpp}: Uniformly distributed random points on a linear network \item \texttt{rpoislpp}: Poisson point process on a linear network \item \texttt{clickjoin}: Interactive graphics to create a linear network \item \texttt{superimpose}: The function 'superimpose' is now generic, with methods for ppp, psp and a default method. \item \texttt{as.ppp.psp}: New method for \texttt{as.ppp} extracts the endpoints and marks from a line segment pattern \item \texttt{runiflpp}: Uniformly distributed random points on a linear network \item \texttt{rpoislpp}: Poisson point process on a linear network \item \texttt{clickjoin}: Interactive graphics to create a linear network \item \texttt{superimpose}: The function \texttt{superimpose} is now generic, with methods for \texttt{ppp}, \texttt{psp} and a default method. \item \texttt{nsegments}: Number of segments in a line segment pattern \item \texttt{clarkevans.test} Classical Clark-Evans test of randomness \item \texttt{msr} New class \texttt{msr} of signed measures and vector-valued measures supporting residual analysis. \item \texttt{quadrat.test.quadratcount}: Method for \texttt{quadrat.test} for objects of class \texttt{quadratcount} (allows a $\chi^2$ test to be performed on quadrat counts rather than recomputing from the original data) \item \texttt{tile.areas}: Computes areas of tiles in a tessellation (efficiently) \end{itemize} \section{Precis of all changes} Here is the text from the `overview' sections of the News and Release Notes for each update. \begin{itemize} \item Likelihood cross-validation for smoothing bandwidth. \item Faster computations for pairwise interaction models. \item More flexible models of intensity in cluster/Cox processes. \item New generic function for smoothing. \item Substantial acceleration of many functions \item New 'logistic likelihood' method for fitting Gibbs models. \item Nearest neighbours for point patterns in 3D \item Nearest-neighbour interpolation in 2D \item New 'progress plots' \item Hard core thresholds can be estimated automatically. \item More support for colour maps \item More support for 'fv' objects \item More support for pooling of envelopes. \item More functionality for nearest neighbours \item More support for 'layered' objects. \item Find clumps in a point pattern. \item Connected component interaction model. \item Improvements to interactive plots. \item Visual debugger for Metropolis-Hastings algorithm. \item Rounding of spatial coordinates \item The spatstat manual now exceeds 1000 pages. \item Hybrids of Gibbs point process models. \item Second order composite likelihood method for kppm. \item Inhomogeneous F, G and J functions. \item Delaunay graph distance \item Pixel images can now be displayed with a logarithmic colour map. \item random sequential packing \item Allard-Fraley estimator \item method for pooling several quadrat tests \item better control over dummy points in ppm \item nearest neighbour map \item changes to subsetting of images \item New code for Partial Residual Plots and Added Variable Plots. \item maximum profile pseudolikelihood computations vastly accelerated. \item now possible to capture every k-th state of Metropolis-Hastings algorithm. \item scope of 'intensity.ppm' extended. \item quadrat.test can now perform Monte Carlo tests and one/two-sided tests \item improvements to 'plot.fv' \item improvement to 'rescale' \item some datasets reorganised/corrected. \item New approximation to the intensity of a fitted Gibbs model. \item Improvements to 3D summary functions. \item A multidimensional point pattern (ppx) can now have 'local' coordinates as well as spatial and temporal coordinates and marks. \item More support for fitted cluster models (kppm). \item split method for multidimensional point patterns (ppx) and point patterns on a linear network (lpp). \item Variance estimates are now available for all Gibbs point process models. \item Cressie-Loosmore-Ford test implemented \item plot.fv now avoids collisions between the legend and the graphics. \item Extension to predict.ppm \item Improvements to envelopes and multitype summary functions. \item Line transects of a pixel image. \item Changes to defaults in Metropolis-Hastings simulations. \item More geometrical operations \item Variance-covariance matrix for Gibbs point process models. \item Bootstrap confidence bands for pair correlation function and K function. \item Nearest neighbour distance computations accelerated. \item Improved support for fitted point process models. \item Improved mechanism for handling 'invalid' point processes \item New functions for perfect simulation \item Code for ensuring a fitted point process model is a valid point process \item Leverage and influence for point process models \item New cluster models (support for model-fitting and simulation). \item Fit irregular parameters in trend of point process model \item Third order summary statistic. \item Geyer's triplet interaction \item more functionality for replicated point patterns \item changed default for simulation window in point process simulation \item changed default for edge correction in Kcom, Gcom \item data in spatstat is now lazy-loaded \item Spatial Scan Test \item Functionality for replicated point patterns \item Spatial covariance functions of windows and pixel images. \item Area-interaction models can now be fitted in non-rectangular windows \item New vignette on 'Getting Started with Spatstat' \item Functions to pool data from several objects of the same class. \item Bandwidth selection for density.ppp and smooth.ppp \item Layered plots. \item Model-handling facilities. \item Inverse-distance weighted smoothing. \item Inhomogeneous versions of neighbourhood density functions. \item changes to renormalisation of estimates in Kinhom and pcfinhom \item new diagnostics based on score residuals \item Fitting and simulation of log-Gaussian Cox processes with any covariance function \item More support for 'kppm' and 'rhohat' objects \item Metropolis-Hastings algorithm now saves its transition history \item Easier control of dummy points in ppm \item Convert an 'fv' object to an R function \item marked line segment patterns can now be plotted \item multitype point process models are now 'self-starting' \item new functions to manipulate colour images \item \texttt{superimpose} is now generic \item improved mathematical labels when plotting functions \item A line segment pattern can now have a data frame of marks. \item Increased numerical stability. \item New 'self-starting' feature of interpoint interactions. \item Point process model covariates may now depend on additional parameters. \item New class of signed measures, for residual analysis. \item Spatstat now has version nicknames. \item Improved quadrature schemes in \code{ppm} \item More methods for \code{intensity} \item Counterpart of \code{apply} for lists of images. \item Hexagonal grids and tessellations. \item Extensions to scan test and Allard-Fraley cluster set estimator \item Extensions and improvements to plotting functions. \item Improvements to labelling of \code{fv} objects. \item Geometrical errors in polygons are now repaired automatically. \item Switch all plots from colour to greyscale using a single command. \item Change parameters of fitted model before simulating it. \item Improvements to layout of printed output. \item Plots using texture fill. \item Changed default behaviour of perfect simulation algorithms. \item New syntax for point process models (\texttt{ppm}, \texttt{kppm}, \texttt{lppm}) equivalent to syntax of \texttt{lm}, \texttt{glm}, \ldots \item Covariates in \texttt{ppm}, \texttt{kppm} can be tessellations. \end{itemize} \section{Alphabetical list of changes} Here is a list of all changes made to existing functions, listed alphabetically. \begin{itemize} \item \code{adaptive.density}: Now has argument 'verbose' \item \texttt{affine}: Argument \texttt{vec} can be in various formats. \item \texttt{as.function.fv}: Now allows multiple columns to be interpolated. \item \texttt{alltypes} Now works for lpp objects \item \texttt{ants}: The function \verb!ants.extra$plot()! has been renamed plotit() for conformity with other datasets. The auxiliary data \texttt{ants.extra} now includes a function called \texttt{side} determining whether a given location is in the scrub or field region. Can be used as a covariate in \texttt{ppm}, \texttt{kppm}, \texttt{slrm}. \item \texttt{applynbd} The arguments \texttt{N}, \texttt{R} and \texttt{criterion} may now be specified together. \item \texttt{areaGain}, \texttt{areaLoss} These now handle arbitrary windows \texttt{W}. They are now more accurate when \texttt{r} is very small. \item \texttt{AreaInter} Point process models with the \texttt{AreaInter} interaction can now be fitted to point pattern data in any window. \item \texttt{as.box3}: Now accepts objects of class \texttt{ppx} or \texttt{boxx}. \item \texttt{as.fv}: Now handles objects of class \texttt{kppm} or \texttt{minconfit}. \item \texttt{as.mask}: Argument \texttt{eps} can now be a 2-vector, specifying x and y resolutions. \item \texttt{as.owin.ppm}, \texttt{as.owin.kppm}: New argument \texttt{from} allows the user to extract the spatial window of the point data (\texttt{from="points"}) or the covariate images (\texttt{from="covariates"}) \item \texttt{betacells}: This dataset has been restructured. The vector of cell profile areas, formerly given by \verb!betacells.extra$area!, has now been included as a column of marks in the point pattern \texttt{betacells}. \item \texttt{bw.relrisk}: Computation in large datasets accelerated. New arguments \texttt{hmin}, \texttt{hmax} control the range of trial values of bandwidth. \item \texttt{clusterset}: The argument \code{result} has been renamed \code{what}. It is now possible to give multiple values to \code{what} so that both types of result can be computed together. \item \texttt{colourmap}: The argument \texttt{col} can now be any kind of colour data \item \texttt{connected}: \texttt{connected} is now generic, with methods for \texttt{im}, \texttt{owin} and \texttt{ppp}. \item \texttt{crossdist.ppp}, \texttt{crossdist.pp3}, \texttt{crossdist.default}: New argument \texttt{squared} allows the squared distances to be computed (saving computation time in some applications) \item \texttt{crossing.psp}: New argument \texttt{fatal} allows the user to handle empty intersections \item \texttt{dclf.test}, \texttt{mad.test}: The argument X can now be an object produced by a previous call to \texttt{dclf.test} or \texttt{mad.test}. \item \texttt{default.dummy}: Can now generate dummy points as a quasirandom sequence. \item \texttt{default.rmhcontrol}, \texttt{default.expand}: These functions now work with models of class \texttt{rmhmodel} as well as \texttt{ppm}. \item \texttt{density.ppp}: \begin{itemize} \item The argument \texttt{weights} can now be a matrix or an \code{expression}. \item Numerical underflow no longer occurs when \texttt{sigma} is very small and \texttt{at="points"}. A warning is no longer issued. \item New argument \texttt{diggle} allows choice of edge correction. \item New argument \texttt{adjust} makes it easy to adjust the smoothing bandwidth. \end{itemize} \item \texttt{disc}: Argument \texttt{centre} can be in various formats. \item \texttt{distcdf}: Arguments W and V can now be point patterns. \item \texttt{distfun}: The internal format of objects of class \texttt{distfun} has been changed. \item \texttt{duplicated.ppp}, \texttt{unique.ppp} New argument \texttt{rule} allows behaviour to be consistent with package \texttt{deldir}. \item \texttt{effectfun}: Now has argument \texttt{se.fit} allowing calculation of standard errors and confidence intervals. \item \texttt{envelope}: All methods for \texttt{envelope} now handle \texttt{fun=NULL}. New argument \texttt{envir.simul} determines the environment in which to evaluate the expression \texttt{simulate}. \item \texttt{envelope.lpp}: Now handles multitype point patterns. \item \texttt{envelope.envelope}: New argument \texttt{transform} allows the user to apply a transformation to previously-computed summary functions. \item \texttt{envelope.envelope}: In \texttt{envelope(E, fun=NULL)} if \texttt{E} does not contain simulated summary functions, but does contain simulated point patterns, then \texttt{fun} now defaults to \texttt{Kest}, instead of flagging an error. \item \texttt{envelope}: Can now calculate an estimate of the true significance level of the "wrong" test (which declares the observed summary function to be significant if it lies outside the pointwise critical boundary anywhere). Controlled by new argument \texttt{do.pwrong}. \item \texttt{envelope}: Improved plot labels for envelopes that were generated using the \texttt{transform} argument. \item \texttt{erosion}, \texttt{dilation}, \texttt{opening}, \texttt{closing}: If the original set is a polygon, the result is a polygon. \item \texttt{eval.fv}, \texttt{eval.fasp}: Improved behaviour when plotted. Evaluation is now applied only to columns that contain values of the function itself (rather than values of the derivative, hazard rate, etc). This is controlled by the new argument \texttt{dotonly}. \item \texttt{eval.im}: Images with incompatible dimensions are now resampled to make them compatible (if \texttt{harmonize=TRUE}). When the argument \texttt{envir} is used, \texttt{eval.im} now recognises functions as well as variables in \texttt{envir} \item \texttt{exactMPLEstrauss}: New argument \texttt{project} determines whether the parameter gamma is constrained to lie in $[0,1]$. \item \texttt{expand.owin}: Functionality extended to handle all types of expansion rule. \item \texttt{F3est}: Calculation of theoretical Poisson curve (\texttt{theo}) has changed, and is now controlled by the argument \texttt{sphere}. \item \texttt{fitted.ppm}, \texttt{predict.ppm}: Argument \texttt{new.coef} specifies a vector of parameter values to replace the fitted coefficients of the model. \item \texttt{fryplot}: Now has arguments \texttt{to} and \texttt{from}, allowing selection of a subset of points. \item \texttt{fryplot}, \texttt{frypoints}: These functions now handle marked point patterns properly. \item \texttt{Hardcore}, \texttt{StraussHard}: The hard core distance \texttt{hc} can now be omitted; it will be estimated from data. \item \texttt{identify.ppp}: now handles multivariate marks. \item \texttt{im}, \texttt{as.im}: The pixel coordinates in an image object are now generated more accurately. This avoids a numerical error in \texttt{plot.im}. \item \texttt{image.listof}: New argument \texttt{equal.ribbon} allows several images to be plotted with the same colour map. If \texttt{equal.ribbon=TRUE}, the colour ribbon will no longer be displayed repeatedly for each panel, but will now be plotted only once, at the right hand side of the plot array. \item \texttt{inside.owin}: Now accepts the form \texttt{list(x,y)} for the first argument. \item \code{intensity.ppp}: Now has argument \code{weights}. \item \texttt{intensity.ppm}: Now works for stationary point process models with the interactions \texttt{DiggleGratton}, \texttt{DiggleGatesStibbard}, \texttt{Fiksel}, \texttt{PairPiece} and \texttt{Softcore}. \item \texttt{intersect.owin}, \texttt{union.owin}, \texttt{setminus.owin}: If A and B are polygons, the result is a polygon. \item \texttt{intersect.tess}, \texttt{dirichlet}: The tiles of the resulting tessellation are polygons if the input was polygonal. \item \texttt{iplot}: \texttt{iplot} is now generic, with methods for \texttt{ppp}, \texttt{layered} and \texttt{default}. \texttt{iplot} methods now support zoom and pan navigation. \item \texttt{Kest}: \begin{itemize} \item Now includes fast algorithm for \texttt{correction="none"} which will handle patterns containing millions of points. \item New option: \texttt{correction = "good"} selects the best edge correction that can be computed in reasonable time. \item Options \texttt{correction='border'} and \texttt{correction='none'} now run about 4 times faster, thanks to Julian Gilbey. \item New argument \texttt{ratio} determines whether the numerator and denominator of the estimate of the K-function will be stored. This enables analysis of replicated point patterns, using \texttt{pool.rat} to pool the K function estimates. \end{itemize} \item \texttt{Kcross}, \texttt{Kdot}, \texttt{Kmulti}: New argument \texttt{ratio} determines whether the numerator and denominator of the estimate of the multitype K-function will be stored. This enables analysis of replicated point patterns, using \texttt{pool.rat} to pool the K function estimates. \item \texttt{Kinhom} \begin{itemize} \item New argument \texttt{normpower} allows different types of renormalisation. \item Now accepts \texttt{correction="good"}. \item New argument \texttt{update}. If \texttt{lambda} is a fitted model (class \texttt{"ppm"} or \texttt{"kppm"}) and \texttt{update=TRUE}, the model is re-fitted to the data pattern, before the intensities are computed. \end{itemize} \item \texttt{Kmulti}, \texttt{Gmulti}, \texttt{Jmulti}: The arguments \texttt{I}, \texttt{J} can now be any kind of subset index or can be functions that yield a subset index. \item \texttt{Kcom}, \texttt{Gcom}: New argument \texttt{conditional} gives more explicit control over choice of edge correction in compensator. Simplified defaults for edge correction. \item \texttt{Kscaled}, \texttt{Lscaled}: Argument \texttt{lambda} can now be a fitted model object (class \texttt{"ppm"}). \item \texttt{kppm} \begin{itemize} \item now accepts syntax \verb!kppm(X ~ Z)! similar to \texttt{lm} and \texttt{glm}, as well as old syntax \verb!kppm(X, ~Z)! \item Covariates no longer have to be passed in the argument \texttt{covariates} but can be objects in the {\sf R} session; \item Now has the option of fitting models using Guan's (2006) second order composite likelihood. \item More flexible models of the intensity, and greater control over the intensity fitting procedure, are now possible using the arguments \texttt{covfunargs}, \texttt{use.gam}, \texttt{nd}, \texttt{eps} passed to \texttt{ppm}. \item Also the argument \texttt{X} may now be a quadrature scheme. \item Error messages from the optimising function \texttt{optim} are now trapped and handled. \item Confidence intervals for the fitted trend parameters can now be obtained using \texttt{confint} \item Now accepts \texttt{clusters="Cauchy"} or \texttt{clusters="VarGamma"} for the Neyman-Scott process with Cauchy or Variance-Gamma cluster kernel. \item Can now fit a log-Gaussian Cox process with any covariance function implemented by the \texttt{RandomFields} package. \item Covariates can now be tessellations. \end{itemize} \item \texttt{latest.news}: The text is now displayed one page at a time. Can now be executed by typing the name of the function without parentheses. \item \texttt{layered}, \code{layerplotargs}, \code{plot.layered}: The plotting argument can now be a list of length 1, which will be replicated to the correct length. \item \verb!layerplotargs<-!: Now handles any spatial object, converting it to a \texttt{layered} object. \item \texttt{Lest}: Now handles theoretical variance estimates (using delta method) if \texttt{var.approx=TRUE} \item \texttt{LennardJones} Increased numerical stability. New (optional) scaling argument \texttt{sigma0}. Interpoint distances are automatically rescaled using 'self-starting' feature. \item \texttt{lgcp.estK}, \texttt{Kmodel}: Computation can be greatly accelerated by setting \texttt{spatstat.options(fastK.lgcp=TRUE)}. \item \texttt{logLik.ppm}: New argument \texttt{new.coef} allows the user to evaluate the likelihood for a different value of the parameter. \item \texttt{lohboot}: When the result is plotted, the confidence limits are now shaded. New argument \texttt{global} allows global (simultaneous) confidence bands instead of pointwise confidence intervals. \item \texttt{lppm}: \begin{itemize} \item The model-fitting function \texttt{lppm} now accepts a syntax similar to \texttt{lm} or \texttt{glm}, for example \verb!lppm(X ~ Z)!, but still accepts the older syntax \verb!lppm(X, ~Z)!. To support both kinds of syntax, the function \texttt{lppm} is now generic, with methods for the classes \texttt{formula} and \texttt{lppp}. The \texttt{formula} method handles a syntax like \verb!lppm(X ~ Z)! while the \texttt{lpp} method handles the old syntax \verb!lppm(X, ~Z)!. \item Covariates no longer have to be passed in the argument \texttt{covariates}: variable names appearing in the model formula can simply be the names of objects in the {\sf R} session. \item New arguments \texttt{eps} and \texttt{nd} control the quadrature scheme. \item Stepwise model selection using \texttt{step} now works for \texttt{lppm} objects. \end{itemize} \item \texttt{markstat}: The arguments \texttt{N} and \texttt{R} may now be specified together. \item \texttt{marks.ppp}, \verb!marks<-.ppp!: New argument \texttt{drop} determines whether a data frame of marks with only one column will be converted to a vector. \item \texttt{model.depends}: Now also recognises 'offset' terms. \item \texttt{MultiStrauss}, \texttt{MultiHard}, \texttt{MultiStraussHard}: \begin{itemize} \item The argument \texttt{types} can now be omitted or NULL; it will be inferred from the point pattern data. \item The ordering of the arguments has changed. (The old syntax will still work.) \end{itemize} \item \texttt{nbfires}: For conformity with other datasets, there is now an object \texttt{nbfires.extra}. \item \texttt{nnclean}: Extra arguments are now passed to hist.default. The result now has attributes which give the fitted parameter values, information about the fitting procedure, and the histogram bar heights. \item \texttt{nncross}: This function is now generic, with methods for \texttt{ppp} and \texttt{default}. \texttt{nncross.ppp} can now find the $k$-th nearest neighbours, for any $k$. New arguments added to control the return value and the sorting of data. \item \texttt{nndist}, \texttt{nnwhich}: New argument \texttt{by} makes it possible to find nearest neighbours belonging to specified subsets in a point pattern, for example, the nearest neighbour of each type in a multitype point pattern. \item \texttt{nncorr}, \texttt{nnmean}, \texttt{nnvario} These functions now handle data frames of marks. \item pairwise interaction Gibbs models: Many calculations for these models have been accelerated. \item \texttt{owin}: Polygon data are no longer subjected to strict checks on their geometrical validity (self-intersections, overlaps, etc). Instead, geometrical inconsistencies in polygon data are automatically repaired. \item \texttt{pcf.ppp}: New argument \texttt{domain} allows the computation to be restricted to a subset of the window. \item \texttt{pcf.ppp}, \texttt{pcfinhom}: New argument \texttt{divisor} enables better performance of the estimator of pair correlation function for distances close to zero. \item \texttt{pcfcross, pcfdot}: Algorithms have been reimplemented using a single-pass kernel smoother and now run much faster. Bandwidth selection rule improved. \item \texttt{pcfinhom}: Now performs renormalisation of estimate. Default behaviour changed - estimates are now renormalised by default. \item \texttt{persp.im}: The \texttt{colmap} argument can now be a \texttt{colourmap} object \item pixel images and grids: The default size of a pixel grid, given by \texttt{spatstat.options("npixel")}, has been changed from 100 to 128. A power of 2 gives faster and more accurate results in many cases. \item \texttt{plot.fasp}, \texttt{plot.fv}, \texttt{plot.im}, \texttt{plot.ppp}, \texttt{plot.owin}, \texttt{plot.im}: Can be switched from colour to greyscale by setting \texttt{spatstat.options(monochrome=TRUE)}. \item \texttt{plot.colourmap}: Now passes arguments to \texttt{axis} to control the plot. Appearance of plot improved. \item \texttt{plot.envelope}: If the upper envelope is NA but the lower envelope is finite, the upper limit is now treated as +Infinity \item \texttt{plot.hyperframe}: The argument \texttt{e} now has a different format. Instead of \texttt{plot(h, plot(XYZ))} one must now type \texttt{plot(h, quote(plot(XYZ)))} This is necessary in order to avoid problems with 'S4 method dispatch' \item \texttt{plot.fasp}: New argument \texttt{samey} controls whether all panels have the same $y$ limits. Changed default value of \texttt{samex}. \item \texttt{plot.fii}: Increased resolution of the plot obtained from \texttt{plot(fitin(ppm(...)))} \item \texttt{plot.fv}: \begin{itemize} \item Improved collision-avoidance algorithm (for avoiding overlaps between curves and legend). \item Labelling of plots has been improved in some cases. \item New argument \texttt{log} allows plotting on logarithmic axes. \item The formula can now include the symbols \texttt{.x} and \texttt{.y} as abbreviation for the function argument and the recommended function value, respectively. \item New argument \texttt{add} \item Argument \texttt{legendmath} now defaults to TRUE. \item New argument \texttt{legendargs} gives more control over appearance of legend. \item Increased default spacing between lines in legend. \item Improved handling of argument \code{shade}. \end{itemize} \item \texttt{plot.fv}, \texttt{plot.envelope}: New argument \texttt{limitsonly} allows calculation of a common x,y scale for several plots. \item \texttt{plot.im}: \begin{itemize} \item Now handles many arguments recognised by plot.default such as \texttt{cex.main}. Also handles argument \texttt{box}. \item New argument \texttt{ribargs} contains parameters controlling the ribbon plot only. \item Now returns the colour map used. \item Argument \texttt{col} can now be a function. \item New argument \texttt{log} allows colour map to be equally spaced on a log scale. \item The colour ribbon can now be placed left, right, top or bottom using new argument \texttt{ribside}. \item Can now plot images whose pixel values are strings representing colours. New argument \texttt{valuesAreColours} \item New arguments \code{add}, \code{do.plot}, \texttt{clipwin}. \item More arguments recognised by \code{ribargs} \end{itemize} \item \texttt{plot.kstest}: Can now perform P-P and Q-Q plots as well. \item \texttt{plot.layered}: New arguments \code{add}, \code{main}. Better argument handling. \item \texttt{plot.listof},\texttt{ plot.splitppp}, \texttt{contour.listof}, \texttt{image.listof}: \begin{itemize} \item Default behaviour has changed: panels are now plotted on different scales. \item When \code{equal.scales=TRUE} the panels are plotted on exactly equal scales and are exactly aligned (under certain conditions). \item The arguments \texttt{panel.begin} and \texttt{panel.end} can now be objects such as windows. \item New arguments \texttt{hsep, vsep} allow increased spacing between plots without adding white space around the outer margin. \item Argument \texttt{mar.panel} may have length 1, 2 or 4. \end{itemize} \item \texttt{plot.lpp}: New arguments. Automatically plots a legend. \item \texttt{plot.owin}: \begin{itemize} \item Polygons with holes can now be plotted with filled colours on any device. \item The window can now be filled with one of 8 different textures. \end{itemize} \item \texttt{plot.ppp}: \begin{itemize} \item Automatically plots a legend \item Return value is now a graphics map object \item Now behaves differently if there are multiple columns of marks. Each column of marks is plotted, in a series of separate plots arranged side-by-side. \item An `empty' plot can now be generated by setting \texttt{type="n"}. \item New arguments \code{show.window}, \code{show.all}, \texttt{do.plot}, \texttt{clipwin} \end{itemize} \item \texttt{plot.psp}: Now handles marked line segment patterns and plots the marks as colours. \item \code{plot.scan.test}, \code{as.im.scan.test}: These functions can now give the optimal value of circle radius \code{r}. \item \code{plot.tess}: For a tessellation represented by a pixel image, \code{plot.tess} no longer treats the pixel labels as palette colours. \item \code{plot.ppp}, \code{plot.owin}, \code{plot.im}, \code{plot.psp}, \code{plot.tess}, \code{plot.layered}: New universal argument \code{show.all} determines what happens when a plot is added to an existing plot. If \code{show.all = TRUE} then everything is plotted, including the main title and colour ribbon. \item \code{plot.quadratcount}, \code{plot.quadrattest}: These functions have now been documented. \item \texttt{print.fv}: improved layout. \item \texttt{pool.envelope}: New arguments \texttt{savefuns} and \texttt{savepatterns}. Envelopes generated with \texttt{VARIANCE=TRUE} can now be pooled. The plot settings of the input data are now respected. \item \texttt{ppm}: \begin{itemize} \item The model-fitting function \texttt{ppm} now accepts a syntax similar to \texttt{lm} or \texttt{glm}, for example \verb!ppm(X ~ Z)!, but still accepts the older syntax \verb!ppm(X, ~Z)!. To support both kinds of syntax, the function \texttt{ppm} is now generic, with methods for the classes \texttt{formula}, \texttt{ppp} and \texttt{quad}. The \texttt{formula} method handles a syntax like \verb!ppm(X ~ Z)! while the \texttt{ppp} method handles the old syntax \verb!ppm(X, ~Z)!. \item Covariates no longer have to be passed in the argument \texttt{covariates}: variable names appearing in the model formula can simply be the names of objects in the {\sf R} session. \item Can now fit models with 'hybrid' interactions. \item New option: \texttt{method = "logi"} for the 'logistic likelihood' method. \item New argument \texttt{nd} controls the number of dummy points. \item New argument \texttt{eps} determines the spacing between dummy points. (also works for related functions \texttt{quadscheme}, \texttt{default.dummy}, ...) \item The default quadrature scheme for a point pattern has been improved (in the case of a non-rectangular window) to remove a possible source of bias. \item Confidence intervals for the fitted trend parameters can now be obtained using \texttt{confint} . \item New argument \texttt{project} determines whether the fitted model is forced to be a valid point process. \item Interaction objects may now be 'self-starting' i.e. initial parameter estimates can be computed from the point pattern dataset. \item The spatial trend can now depend on additional parameters. This is done by allowing spatial covariate functions to have additional parameters: \texttt{function(x, y, ...)} where \texttt{...} is controlled by the new argument \texttt{covfunargs} to \texttt{ppm}. \item The print and summary methods for \texttt{ppm} objects now show standard errors for parameter estimates if the model is Poisson. \item Covariates can now be tessellations. \end{itemize} \item \item \texttt{ppp}: New argument \texttt{drop} determines whether a data frame of marks with only one column will be converted to a vector. \item \texttt{ppx}: Arguments have changed. An object of class \texttt{ppx} may now include 'local' coordinates as well as 'spatial' and 'temporal' coordinates, and marks. \item \texttt{predict.ppm}: \begin{itemize} \item Can now calculate the conditional intensity of a model relative to any point pattern \texttt{X} (not just the original data pattern). \item New argument \texttt{correction} allows choice of edge correction when calculating the conditional intensity. \item New argument \texttt{interval} allows confidence intervals or prediction intervals to be calculated. \item New argument \texttt{total} allows for prediction of the total number of points in a specified region. \end{itemize} \item \texttt{predict.slrm}: New argument \texttt{window} \item \texttt{print.ppp}, \texttt{print.summary.ppp}, \texttt{print.owin}, \texttt{print.summary.owin}, \texttt{print.im}, \texttt{print.summary.im}, \texttt{print.fv}, \texttt{print.msr}: These functions now avoid over-running the text margin (i.e.\ they respect \texttt{options('width')} where possible). \item \texttt{print.ppp}, \texttt{summary.ppp}: If the point pattern x was generated by Metropolis-Hastings simulation using \texttt{rmh}, then \texttt{print(x)} and \texttt{summary(x)} show information about the simulation parameters. \item \texttt{print.ppm}: Standard errors for the parameter estimates, and confidence intervals for the parameters, can now be printed for all Gibbs models (but are printed only for Poisson models by default). Now has argument \texttt{what} to allow only selected information to be printed. \item \texttt{print.summary.ppm}: Output gives a more precise description of the fitting method. \item \texttt{profilepl}: Accelerated (typically by a factor of 5). Does not generate warnings when some of the candidate models have zero likelihood - for example when fitting model with a hard core. Can now maximise over trend parameters as well as interaction parameters \item \texttt{progressreport}: Improved output (also affects many functions which print progress reports) \item \texttt{project.ppm}: Improved algorithm. Now handles terms in the trend formula as well as the interaction. The projected point process is now obtained by re-fitting the model, and is guaranteed to be the maximum pseudolikelihood fit. \item \texttt{psp}: A line segment pattern (object of class 'psp') can now have a data frame of marks. \item \texttt{psst}: Argument \texttt{funcorrection} changed to \texttt{funargs} allowing greater flexibility. \item \texttt{quad.ppm}: Now accepts \texttt{kppm} objects. New argument \texttt{clip}. \item \texttt{quadscheme}: Can now generate dummy points from a quasirandom sequence. \item \texttt{quadrat.test.splitppp}: The result is now a single object of class \texttt{quadrattest} \item \texttt{quadrat.test}: Can now perform Monte Carlo test as well (for use in small samples where the $\chi^2$ approximation is inaccurate) New argument \texttt{alternative} allows choice of alternative hypothesis and returns one-sided or two-sided $p$-values as appropriate. The print method for \texttt{quadrattest} objects now displays information about the quadrats. \item \texttt{raster.x}, \texttt{raster.y}, \texttt{raster.xy}: These functions have a new argument \texttt{drop} \item \texttt{rDiggleGratton}, \texttt{rDGS}, \texttt{rHardcore}, \texttt{rStrauss}, \texttt{rStraussHard}: By default the point pattern is now generated on a larger window, and trimmed to the original window. New argument \texttt{expand=TRUE}. \item \texttt{redwoodfull} (Full redwood data): The function \verb!redwoodfull.extra$plot()! has been renamed \texttt{plotit} for conformity with other datasets, and improved. \item \texttt{relrisk}: New argument \texttt{casecontrol} determines whether a bivariate point pattern should be treated as case-control data. New argument \texttt{case} allows the user to specify which mark value corresponds to the cases in a case-control dataset. \item \texttt{rescale}: \begin{itemize} \item If scale argument \texttt{s} is missing, then the data are rescaled to native units. For example if the current unit is 0.1 metres, coordinates will be re-expressed in metres. \item New argument \texttt{unitname} makes it possible to change the name of the unit of length. \end{itemize} \item \texttt{residuals.ppm}: New arguments \texttt{new.coef} and \texttt{quad} for advanced use (make it possible to compute residuals from a modified version of the fitted model.) The value returned by \texttt{residuals.ppm} is now an object of class \texttt{msr}. It can be plotted directly. \item \texttt{rhohat}: Now has arguments \texttt{eps}, \texttt{dimyx} to control pixel resolution. If \texttt{covariate = "x"} or \texttt{"y"}, the resulting object has the same \texttt{unitname} as the original point pattern data. Estimation can now be performed using local likelihood fitting with the \texttt{locfit} package, or using kernel smoothing. \item \texttt{rlabel}: Now works for \texttt{lpp}, \texttt{pp3}, \texttt{ppx} objects. \item \texttt{rlinegrid}, \texttt{rpoisline}: These functions now handle binary mask windows. \item \texttt{rmh}: If \texttt{track=TRUE}, the history of transitions of the Metropolis-Hastings algorithm is saved and returned. \item \texttt{rmh}, \texttt{rmhstart}: The initial state ('\texttt{start}') can now be missing or null. \item \texttt{rmh}, \texttt{rmhcontrol}: It is now possible to save every $k$-th iteration of the Metropolis-Hastings algorithm. The arguments \texttt{nsave} and \texttt{nburn} may be given to \texttt{rmh} or to \texttt{rmhcontrol}. They specify that the point pattern will be saved every \texttt{nsave} iterations, after an initial burn-in of \texttt{nburn} iterations. \item \texttt{rmh.default}: New argument \texttt{snoop} allows the user to activate a visual debugger for the Metropolis-Hastings algorithm. \item \texttt{rmh.default}: \texttt{track} is no longer a formal argument of \texttt{rmh.default}; it is now a parameter of \texttt{rmhcontrol}. However there is no change in usage: the argument \texttt{track} can still be given to \texttt{rmh.default}. \item \texttt{rmh.ppm}, \texttt{simulate.ppm}, \texttt{default.expand}: For point process models which have a trend depending only on x and y, the simulation window is now taken to be the same as the original window containing the data (by default). That is, `expansion' does not take place, by default. (In previous versions of spatstat the simulation window was larger than the original data window.) \item \texttt{rmh.ppm}, \texttt{simulate.ppm}: The argument sequence for these functions has changed. New argument \texttt{expand} allows more explicit control over simulation domain. \item \texttt{rmh.ppm}, \texttt{simulate.ppm}, \texttt{rmhmodel.ppm}: New argument \texttt{new.coef} allows the user to change the parameters of a fitted model before it is simulated. \item \texttt{rmhcontrol}: The default value of the parameters \texttt{periodic} and \texttt{expand} has changed. \item \texttt{rmhcontrol}: The parameter \texttt{expand} can now be in any format acceptable to \texttt{rmhexpand}. \item \texttt{rmh.ppm}, \texttt{rmh.default}, \texttt{simulate.ppm}: Any \texttt{rmhcontrol} parameter can now be given directly as an argument to \texttt{rmh.ppm}, \texttt{rmh.default} or \texttt{simulate.ppm}. \item \texttt{rNeymanScott}: Argument \texttt{rcluster} may now take a different format. \item \texttt{rotate}: The user can specify the centre of rotation. \item \texttt{rotate.owin}, \texttt{affine.owin}: These functions now handle binary pixel masks. New argument \texttt{rescue} determines whether rectangles will be preserved. \item \texttt{rSSI}: Performs 'Random Sequential Packing' if \texttt{n=Inf}. \item \texttt{runifpointOnLines}, \texttt{rpoisppOnLines}, \texttt{runiflpp}, \texttt{rpoislpp}: Can now generate multitype point patterns. \item \texttt{rVarGamma}, \texttt{kppm}, \texttt{vargamma.estK}, \texttt{vargamma.estpcf}: New argument \texttt{nu.pcf} provides an alternative way to specify the kernel shape in the VarGamma model, instead of the existing argument \texttt{nu.ker}. Function calls that use the ambiguous argument name \texttt{nu} will no longer be accepted. \item second order summary functions (\texttt{Kest}, \texttt{Lest}, \texttt{Kinhom}, \texttt{pcf.ppp}, \texttt{Kdot}, \texttt{Kcross}, \texttt{Ldot} etc etc) These functions now accept \texttt{correction="translation"} as an alternative to \texttt{correction = "translate"}, for consistency. \item \code{scan.test}: Now handles multiple values of circle radius 'r'. \item \texttt{setcov}: Can now compute the `cross-covariance' between two regions \item \texttt{shift}: Argument \texttt{vec} can be in various formats. \item \texttt{simulate.ppm}: New argument \texttt{singlerun} determines whether the simulated patterns are generated using independent runs of the Metropolis-Hastings algorithm or are obtained by performing one long run of the algorithm and saving every k-th iteration. \item \texttt{simulate.ppm}, \texttt{simulate.kppm}: The total computation time is also returned. \item \texttt{simulate.kppm}: Now catches errors (such as 'insufficient memory'). \item \texttt{slrm}: Confidence intervals for the fitted trend parameters can now be obtained using \texttt{confint} \item \texttt{smooth.fv}: Further options added. \item \texttt{smooth.ppp}: Accelerated, when there are several columns of marks. Bandwidth \texttt{sigma} is now selected by least-squares cross-validation. \item \texttt{smooth.ppp}: Now handles bandwidths equal to zero (by invoking \texttt{nnmark}) \item \texttt{smooth.ppp}, \texttt{smooth.fv}, \texttt{smooth.msr}: These functions are now 'Deprecated' in favour of the methods \texttt{Smooth.ppp}, \texttt{Smooth.fv}, \texttt{Smooth.msr} respectively. \item \texttt{Smooth.ppp}: \code{sigma} is now a formal argument of \code{Smooth.ppp} \item \texttt{Softcore}: Improved numerical stability. New argument \texttt{sigma0} for manual control over rescaling. \item \texttt{spatstat.options}: \begin{itemize} \item New option \texttt{nvoxel}. \item New option \texttt{print.ppm.SE} controls whether standard errors and confidence intervals are printed for all Gibbs models, for Poisson models only, or are never printed. \item New option \texttt{project.fast} allows a faster shortcut for \texttt{project.ppm}. \item New options \texttt{rmh.p}, \texttt{rmh.q}, \texttt{rmh.nrep} determine the default values of the parameters \texttt{p}, \texttt{q} and \texttt{nrep} of the Metropolis-Hastings algorithm. See \texttt{rmhcontrol}. \item New parameter \texttt{n.bandwidth} \item New parameter \texttt{monochrome} switches all plots from colour to greyscale. \end{itemize} \item \texttt{square} Now accepts vectors of length 2. \item \texttt{str} This generic function (for inspecting the internal structure of an object) now produces sensible output for objects of class \texttt{hyperframe}, \texttt{ppx}, \texttt{lpp}. \item \texttt{stratrand}, \texttt{overlap.owin}, \texttt{update.slrm}, \texttt{edge.Trans}, \texttt{edge.Ripley}: These already-existing functions are now documented. \item \texttt{summary.ppm}: Now reports whether the spatial coordinates have been rounded. Improved behaviour when the model covariates are a data frame. \item \texttt{superimposePSP}: This function is now deprecated in favour of \texttt{superimpose} \item \texttt{superimpose}: Now handles data frames of marks. \item \texttt{union.owin}: It is now guaranteed that if \texttt{A} is a subset of \texttt{B}, then \texttt{union.owin(A,B)=B}. Now handles a single argument: \texttt{union.owin(A)} returns \texttt{A}. \item \code{varblock}: Ugly legends have been repaired. \item \texttt{vcov.ppm}: This function now handles all Gibbs point process models. New argument \texttt{matrix.action} controls what happens when the matrix is ill-conditioned. \item \texttt{vcov.slrm}: Can now calculate correlation matrix or Fisher information matrix as well as variance-covariance matrix. \item \texttt{with.fv}: Argument \texttt{drop} replaced by new argument \texttt{fun} (with different interpretation). New argument \texttt{enclos} allows evaluation in other environments. \item \verb![.fv! Now handles the argument 'drop'. Improved behaviour when plotted. \item \verb![.im! and \verb![<-.im! New argument 'j' allows any type of matrix indexing to be used. \item \verb![.im! Default behaviour changed in the case of a rectangular subset. New argument 'rescue' can be set to TRUE to reinstate previous behaviour. \item \verb![.lpp! Subset index may now be a window (class 'owin') \item \verb![.msr! Now handles character-valued indices. \item \verb![.psp! Now handles binary mask windows. \end{itemize} \section{Serious Bugs Fixed} <>= nbugs <- nrow(news(grepl("^BUG", Category), package="spatstat")) nbugssince <- nrow(news(Version > "1.21-2" & grepl("^BUG", Category), package="spatstat")) @ Hundreds of bugs have been detected and fixed in \spst\ since the version covered by the workshop notes \cite{badd10wshop}. Bugs that may have affected the user are listed in the package \texttt{NEWS} file. To read all these bug reports, type <>= news(grepl("^BUG", Category), package="spatstat") @ which currently produces a list of \Sexpr{nbugs} bugs, of which \Sexpr{nbugssince} were detected after publication of the workshop notes. Following is a list of the {\bf most serious bugs} only, in order of potential impact. \newcommand\bugger[4]{% \\ {} % {\small (Bug introduced in \texttt{spatstat {#1}}, {#2}; % fixed in \texttt{spatstat {#3}}, {#4})}% } \begin{itemize} \item \texttt{nncross.ppp}: Results were completely incorrect if $k > 1$. \bugger{1.31-2}{april 2013}{1.35-0}{december 2013} \item \texttt{nncross.pp3}: Results were completely incorrect in some cases. \bugger{1.32-0}{august 2013}{1.34-0}{october 2013} \item \texttt{rmh}: \begin{itemize} \item Simulation was completely incorrect in the case of a multitype point process with an interaction that does not depend on the marks, such as \verb!ppm(betacells, ~marks, Strauss(60))! due to a coding error in the \texttt{C} interface. \bugger{1.22-3}{march 2010}{1.22-3}{june 2011} \item Simulation of the Area-Interaction model was completely incorrect. \bugger{1.23-6}{october 2011}{1.31-0}{january 2013} \item Simulation of the Geyer saturation process was completely incorrect. \bugger{1.31-0}{january 2013}{1.31-1}{march 2013} \item Simulation of the Strauss-Hard Core process was partially incorrect, giving point patterns with a slightly lower intensity. \bugger{1.31-0}{january 2013}{1.37-0}{may 2014} \item Simulation of the Geyer saturation process was incorrectly initialised, so that the results of a short run (i.e. small value of \texttt{nrep}) were incorrect, while long runs were correct. \bugger{1.17-0}{october 2009}{1.31-1}{march 2013} \end{itemize} \item \texttt{nncross}, \texttt{distfun}, \texttt{AreaInter}: Results of \texttt{nncross} were possibly incorrect when \code{X} and \code{Y} did not have the same window. This bug affected values of \texttt{distfun} and may also have affected ppm objects with interaction \texttt{AreaInter}. \bugger{1.9-4}{june 2006}{1.25-2}{january 2012} \item \texttt{envelope.ppm}: If the model was an inhomogeneous Poisson process, the resulting envelope object was incorrect (the simulations were correct, but the envelopes were calculated assuming the model was CSR). \bugger{1.23-5}{september 2011}{1.23-6}{october 2011} \item \texttt{leverage.ppm}, \texttt{influence.ppm}, \texttt{dfbetas.ppm}: Results were incorrect for non-Poisson processes. \bugger{1.25-0}{december 2011}{1.34-0}{october 2013} \item \texttt{rVarGamma}: Simulations were incorrect; they were generated using the wrong value of the parameter \texttt{nu.ker}. \bugger{1.25-0}{december 2011}{1.35-0}{december 2013} \item \texttt{rCauchy}: Simulations were incorrect; they were generated using the wrong value of the parameter \texttt{omega}. \bugger{1.25-0}{december 2011}{1.25-2}{january 2012} \item \texttt{kppm}, \texttt{matclust.estpcf}, \texttt{pcfmodel}: The pair correlation function of the M\'atern Cluster Process was evaluated incorrectly at distances close to 0. This could have affected the fitted parameters in \texttt{matclust.estpcf()} or \texttt{kppm(clusters="MatClust")}. \bugger{1.20-2}{august 2010}{1.33-0}{september 2013} \item \texttt{ppm}: Results were incorrect for the Geyer saturation model with a non-integer value of the saturation parameter \texttt{sat}. \bugger{1.20-0}{july 2010}{1.31-2}{april 2013} \item \texttt{lppm}: For multitype patterns, the fitted model was completely incorrect due to an error in constructing the quadrature scheme. \bugger{1.23-0}{july 2011}{1.30-0}{december 2012} \item \texttt{Geyer}: For point process models with the \texttt{Geyer} interaction, \texttt{vcov.ppm} and \texttt{suffstat} sometimes gave incorrect answers. \bugger{1.27-0}{may 2012}{1.30-0}{december 2012} \item \texttt{vcov.ppm}, \texttt{suffstat}: These functions sometimes gave incorrect values for marked point process models. \bugger{1.27-0}{may 2012}{1.29-0}{october 2012} \item \texttt{linearK}, \texttt{linearKinhom}: If any data points were located exactly at a vertex of the linear network, the weights for Ang's correction were incorrect, due to numerical error. This sometimes produced infinite or NA values of the linear $K$ function. \bugger{1.23-0}{july 2011}{1.27-0}{may 2012} \item \texttt{Kinhom}: the results were not renormalised (even if \texttt{renormalise=TRUE}) in some cases. \bugger{1.21-0}{december 2010}{1.37-0}{may 2014} \item \texttt{predict.ppm} Calculation of the conditional intensity omitted the edge correction if \texttt{correction='translate'} or \texttt{correction='periodic'}. \bugger{1.17-0}{october 2009}{1.31-3}{may 2013} \item \texttt{scan.test} Results were sometimes incorrect due to numerical instability (a 'Gibbs phenomenon'). \bugger{1.24-1}{october 2011}{1.26-1}{april 2012} \item \texttt{selfcrossing.psp}: $y$ coordinate values were incorrect. \bugger{1.23-2}{august 2011}{1.25-3}{february 2012} \item \texttt{predict.slrm}: Results of \texttt{predict(object, newdata)} were incorrect if the spatial domain of \texttt{newdata} was larger than the original domain. \bugger{1.21-0}{november 2010}{1.25-3}{february 2012} \item \texttt{Lest}: The variance approximations (Lotwick-Silverman and Ripley) obtained with \texttt{var.approx=TRUE} were incorrect for \texttt{Lest} (although they were correct for \texttt{Kest}) due to a coding error. \bugger{1.24-1}{october 2011}{1.24-2}{november 2011} \item \texttt{bw.diggle}: Bandwidth was too large by a factor of 2. \bugger{1.23-4}{september 2011}{1.23-5}{september 2011} \item pair correlation functions (\texttt{pcf.ppp}, \texttt{pcfdot}, \texttt{pcfcross} etc:) The result had a negative bias at the maximum $r$ value, because contributions to the pcf estimate from interpoint distances greater than \texttt{max(r)} were mistakenly omitted. {\small (Bugs fixed in \texttt{spatstat 1.35-0}, december 2013)} \item \texttt{Kest}, \texttt{Lest}: Gave incorrect values in very large datasets, due to numerical overflow. `Very large' typically means about 1 million points in a random pattern, or 100,000 points in a tightly clustered pattern. [Overflow cannot occur unless there are at least 46,341 points.] \item \texttt{bw.relrisk}: Implementation of \texttt{method="weightedleastsquares"} was incorrect and was equivalent to \texttt{method="leastsquares"}. \bugger{1.21-0}{november 2010}{1.23-4}{september 2011} \item \texttt{bdist.tiles} Values were incorrect in some cases due to numerical error. {\small (Bug fixed in \texttt{spatstat 1.29-0}, october 2012)} \item polygon geometry The point-in-polygon test gave the wrong answer in some boundary cases. {\small (Bug fixed in \texttt{spatstat 1.23-2}, august 2011)} \end{itemize} \begin{thebibliography}{1} \bibitem{badd10wshop} A.~Baddeley. \newblock Analysing spatial point patterns in {{R}}. \newblock Technical report, CSIRO, 2010. \newblock Version 4. Available at {\texttt{www.csiro.au/resources/pf16h.html}}. \end{thebibliography} \end{document} spatstat/inst/doc/replicated.R0000644000176000001440000003773012333041747016144 0ustar ripleyusers### R code from vignette source 'replicated.Rnw' ################################################### ### code chunk number 1: replicated.Rnw:29-30 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) ################################################### ### code chunk number 2: replicated.Rnw:35-42 ################################################### library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) ################################################### ### code chunk number 3: replicated.Rnw:189-190 ################################################### waterstriders ################################################### ### code chunk number 4: replicated.Rnw:208-209 ################################################### getOption("SweaveHooks")[["fig"]]() plot(waterstriders, main="") ################################################### ### code chunk number 5: replicated.Rnw:216-217 ################################################### summary(waterstriders) ################################################### ### code chunk number 6: replicated.Rnw:225-226 ################################################### X <- listof(rpoispp(100), rpoispp(100), rpoispp(100)) ################################################### ### code chunk number 7: replicated.Rnw:231-233 ################################################### getOption("SweaveHooks")[["fig"]]() plot(X) X ################################################### ### code chunk number 8: replicated.Rnw:262-263 (eval = FALSE) ################################################### ## hyperframe(...) ################################################### ### code chunk number 9: replicated.Rnw:288-290 ################################################### H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) H ################################################### ### code chunk number 10: replicated.Rnw:298-303 ################################################### G <- hyperframe(X=1:3, Y=letters[1:3], Z=factor(letters[1:3]), W=list(rpoispp(100),rpoispp(100), rpoispp(100)), U=42, V=rpoispp(100), stringsAsFactors=FALSE) G ################################################### ### code chunk number 11: replicated.Rnw:332-333 ################################################### simba ################################################### ### code chunk number 12: replicated.Rnw:346-347 ################################################### pyramidal ################################################### ### code chunk number 13: replicated.Rnw:353-354 ################################################### ws <- hyperframe(Striders=waterstriders) ################################################### ### code chunk number 14: replicated.Rnw:361-363 ################################################### H$X H$Y ################################################### ### code chunk number 15: replicated.Rnw:373-375 ################################################### H$U <- letters[1:3] H ################################################### ### code chunk number 16: replicated.Rnw:380-384 ################################################### G <- hyperframe() G$X <- waterstriders G$Y <- 1:3 G ################################################### ### code chunk number 17: replicated.Rnw:392-396 ################################################### H[,1] H[2,] H[2:3, ] H[1,1] ################################################### ### code chunk number 18: replicated.Rnw:402-405 ################################################### H[,1,drop=TRUE] H[1,1,drop=TRUE] H[1,2,drop=TRUE] ################################################### ### code chunk number 19: replicated.Rnw:418-419 (eval = FALSE) ################################################### ## plot.listof(x, ..., main, arrange = TRUE, nrows = NULL, ncols = NULL) ################################################### ### code chunk number 20: replicated.Rnw:434-435 ################################################### getOption("SweaveHooks")[["fig"]]() plot(waterstriders, pch=16, nrows=1) ################################################### ### code chunk number 21: replicated.Rnw:450-451 ################################################### getOption("SweaveHooks")[["fig"]]() plot(simba) ################################################### ### code chunk number 22: replicated.Rnw:463-465 ################################################### getOption("SweaveHooks")[["fig"]]() H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) plot(H$Y) ################################################### ### code chunk number 23: replicated.Rnw:477-478 (eval = FALSE) ################################################### ## plot(h, e) ################################################### ### code chunk number 24: replicated.Rnw:487-488 ################################################### getOption("SweaveHooks")[["fig"]]() plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) })) ################################################### ### code chunk number 25: replicated.Rnw:500-502 ################################################### getOption("SweaveHooks")[["fig"]]() H <- hyperframe(Bugs=waterstriders) plot(H, quote(plot(Kest(Bugs))), marsize=1) ################################################### ### code chunk number 26: replicated.Rnw:515-517 ################################################### df <- data.frame(A=1:10, B=10:1) with(df, A-B) ################################################### ### code chunk number 27: replicated.Rnw:530-531 (eval = FALSE) ################################################### ## with(h,e) ################################################### ### code chunk number 28: replicated.Rnw:541-544 ################################################### H <- hyperframe(Bugs=waterstriders) with(H, npoints(Bugs)) with(H, distmap(Bugs)) ################################################### ### code chunk number 29: replicated.Rnw:567-568 ################################################### with(simba, npoints(Points)) ################################################### ### code chunk number 30: replicated.Rnw:575-577 ################################################### H <- hyperframe(Bugs=waterstriders) K <- with(H, Kest(Bugs)) ################################################### ### code chunk number 31: replicated.Rnw:585-586 ################################################### getOption("SweaveHooks")[["fig"]]() plot(K) ################################################### ### code chunk number 32: replicated.Rnw:591-593 ################################################### H <- hyperframe(Bugs=waterstriders) with(H, nndist(Bugs)) ################################################### ### code chunk number 33: replicated.Rnw:599-600 ################################################### with(H, min(nndist(Bugs))) ################################################### ### code chunk number 34: replicated.Rnw:612-613 ################################################### simba$Dist <- with(simba, distmap(Points)) ################################################### ### code chunk number 35: replicated.Rnw:626-630 ################################################### getOption("SweaveHooks")[["fig"]]() lambda <- rexp(6, rate=1/50) H <- hyperframe(lambda=lambda) H$Points <- with(H, rpoispp(lambda)) plot(H, quote(plot(Points, main=paste("lambda=", signif(lambda, 4))))) ################################################### ### code chunk number 36: replicated.Rnw:636-637 ################################################### H$X <- with(H, rpoispp(50)) ################################################### ### code chunk number 37: replicated.Rnw:666-667 ################################################### getOption("SweaveHooks")[["fig"]]() plot(simba, quote(plot(density(Points), main="")), nrows=2) ################################################### ### code chunk number 38: replicated.Rnw:686-688 ################################################### getOption("SweaveHooks")[["fig"]]() rhos <- with(demohyper, rhohat(Points, Image)) plot(rhos) ################################################### ### code chunk number 39: replicated.Rnw:705-706 (eval = FALSE) ################################################### ## mppm(formula, data, interaction, ...) ################################################### ### code chunk number 40: replicated.Rnw:716-717 (eval = FALSE) ################################################### ## mppm(Points ~ group, simba, Poisson()) ################################################### ### code chunk number 41: replicated.Rnw:750-751 ################################################### mppm(Points ~ 1, simba) ################################################### ### code chunk number 42: replicated.Rnw:758-759 ################################################### mppm(Points ~ group, simba) ################################################### ### code chunk number 43: replicated.Rnw:765-766 ################################################### mppm(Points ~ id, simba) ################################################### ### code chunk number 44: replicated.Rnw:776-777 ################################################### mppm(Points ~ Image, data=demohyper) ################################################### ### code chunk number 45: replicated.Rnw:795-796 (eval = FALSE) ################################################### ## mppm(Points ~ offset(log(Image)), data=demohyper) ################################################### ### code chunk number 46: replicated.Rnw:808-809 (eval = FALSE) ################################################### ## mppm(Points ~ log(Image), data=demop) ################################################### ### code chunk number 47: replicated.Rnw:826-827 (eval = FALSE) ################################################### ## mppm(formula, data, interaction, ..., iformula=NULL) ################################################### ### code chunk number 48: replicated.Rnw:877-878 ################################################### radii <- with(simba, mean(nndist(Points))) ################################################### ### code chunk number 49: replicated.Rnw:885-887 ################################################### Rad <- hyperframe(R=radii) Str <- with(Rad, Strauss(R)) ################################################### ### code chunk number 50: replicated.Rnw:892-894 ################################################### Int <- hyperframe(str=Str) mppm(Points ~ 1, simba, interaction=Int) ################################################### ### code chunk number 51: replicated.Rnw:921-924 ################################################### h <- hyperframe(Y=waterstriders) g <- hyperframe(po=Poisson(), str4 = Strauss(4), str7= Strauss(7)) mppm(Y ~ 1, data=h, interaction=g, iformula=~str4) ################################################### ### code chunk number 52: replicated.Rnw:935-936 ################################################### fit <- mppm(Points ~ 1, simba, Strauss(0.07), iformula = ~Interaction*group) ################################################### ### code chunk number 53: replicated.Rnw:954-955 ################################################### fit ################################################### ### code chunk number 54: replicated.Rnw:958-960 ################################################### co <- coef(fit) si <- function(x) { signif(x, 4) } ################################################### ### code chunk number 55: replicated.Rnw:971-972 ################################################### coef(fit) ################################################### ### code chunk number 56: replicated.Rnw:1029-1030 (eval = FALSE) ################################################### ## interaction=hyperframe(po=Poisson(), str=Strauss(0.07)) ################################################### ### code chunk number 57: replicated.Rnw:1035-1036 (eval = FALSE) ################################################### ## iformula=~ifelse(group=="control", po, str) ################################################### ### code chunk number 58: replicated.Rnw:1046-1047 (eval = FALSE) ################################################### ## iformula=~I((group=="control")*po) + I((group=="treatment") * str) ################################################### ### code chunk number 59: replicated.Rnw:1057-1062 ################################################### g <- hyperframe(po=Poisson(), str=Strauss(0.07)) fit2 <- mppm(Points ~ 1, simba, g, iformula=~I((group=="control")*po) + I((group=="treatment") * str)) fit2 ################################################### ### code chunk number 60: replicated.Rnw:1085-1088 ################################################### H <- hyperframe(W=waterstriders) fit <- mppm(W ~ 1, H) subfits(fit) ################################################### ### code chunk number 61: replicated.Rnw:1109-1110 (eval = FALSE) ################################################### ## subfits <- subfits.new ################################################### ### code chunk number 62: replicated.Rnw:1122-1124 ################################################### H <- hyperframe(W=waterstriders) with(H, ppm(W)) ################################################### ### code chunk number 63: replicated.Rnw:1147-1149 ################################################### fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) ################################################### ### code chunk number 64: replicated.Rnw:1159-1160 ################################################### getOption("SweaveHooks")[["fig"]]() plot(res) ################################################### ### code chunk number 65: replicated.Rnw:1165-1167 ################################################### getOption("SweaveHooks")[["fig"]]() smor <- with(hyperframe(res=res), Smooth(res, sigma=4)) plot(smor) ################################################### ### code chunk number 66: replicated.Rnw:1179-1182 ################################################### fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) totres <- sapply(res, integral.msr) ################################################### ### code chunk number 67: replicated.Rnw:1188-1195 ################################################### getOption("SweaveHooks")[["fig"]]() fit <- mppm(Points~Image, data=demohyper) resids <- residuals(fit, type="Pearson") totres <- sapply(resids, integral.msr) areas <- with(demohyper, area.owin(as.owin(Points))) df <- as.data.frame(demohyper[, "Group"]) df$resids <- totres/areas plot(resids~Group, df) ################################################### ### code chunk number 68: replicated.Rnw:1216-1219 ################################################### getOption("SweaveHooks")[["fig"]]() fit <- mppm(P ~ 1, hyperframe(P=waterstriders)) sub <- hyperframe(Model=subfits(fit)) plot(sub, quote(diagnose.ppm(Model))) ################################################### ### code chunk number 69: replicated.Rnw:1232-1240 ################################################### H <- hyperframe(P = waterstriders) fitall <- mppm(P ~ 1, H) together <- subfits(fitall) separate <- with(H, ppm(P)) Fits <- hyperframe(Together=together, Separate=separate) dr <- with(Fits, unlist(coef(Separate)) - unlist(coef(Together))) dr exp(dr) ################################################### ### code chunk number 70: replicated.Rnw:1257-1266 ################################################### H <- hyperframe(X=waterstriders) # Poisson with constant intensity for all patterns fit1 <- mppm(X~1, H) quadrat.test(fit1, nx=2) # uniform Poisson with different intensity for each pattern fit2 <- mppm(X ~ id, H) quadrat.test(fit2, nx=2) ################################################### ### code chunk number 71: replicated.Rnw:1295-1296 (eval = FALSE) ################################################### ## 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More generally it applies to data from a designed experiment in which the response from each unit is a spatial point pattern. \end{abstract} \tableofcontents \newpage %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} `Replicated point patterns' are datasets consisting of several point patterns which can be regarded as independent repetitions of the same experiment. For example, three point patterns taken from micrographs of three pipette samples of the same jug of milk, could be assumed to be replicated observations. More generally we could have several experimental groups, with replicated point pattern data in each group. For example there may be two jugs of milk that were treated differently, and we take three pipette samples from each jug. Even more generally our point patterns could be the result of a designed experiment involving control and treatment groups, covariates such as temperature, and even spatial covariates (such as image data). This document describes the capabilities available in the \spst\ package for analysing such data. {\bf These capabilities are still under development and will be extended soon.} Our aim is to fit a model to the data which explains the influence of experimental conditions on the point patterns. The paper \cite{statpaper} outlines a method for fitting such models using maximum product pseudolikelihood. This has been implemented in \spst. This document is an explanation with examples on how to use the code. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Overview of software} The main components needed are: \begin{itemize} \item the model-fitting function \texttt{mppm}, an extension of the \texttt{spatstat} function \texttt{ppm}, that will fit Gibbs point process models to multiple point pattern datasets; \item support for the class \texttt{"mppm"} of point process models fitted by \texttt{mppm} (e.g. functions to print and plot the fitted model, analysis of deviance for Poisson models) \item some tools for exploratory data analysis; \item basic support for the data from such experiments by storing the data in a \emph{``hyperframe''}. A hyperframe is like a data frame, except that each entry in a column can be a point pattern or a pixel image, as well as a single number or categorical value. \item four example datasets. \end{itemize} \section{Formulating the problem} We view the experiment as involving a series of {\em `units'\/}. Each unit is subjected to a known set of experimental conditions (described by the values of the {\em covariates\/}), and each unit yields a {\em response\/} which is a spatial point pattern. The value of a particular covariate for each unit can be either a single value (numerical, logical or factor), or a pixel image. Three important cases are: \begin{description} \item[independent replicates:] We observe $n$ different point patterns that can be regarded as independent replicates, i.e.\ independent realisations of the same point process. The `responses' are the point patterns; there are no covariates. \item[replication in groups:] there are $K$ different experimental groups (e.g. control, aspirin, nurofen). In group $k$ ($k=1,\ldots,K$) we observe $n_k$ point patterns which can be regarded as independent replicates within this group. We regard this as an experiment with $n = \sum_k n_k$ units. The responses are the point patterns; there is one covariate which is a factor (categorical variable) identifying which group each point pattern belongs to. \item[general case:] there are covariates other than factors that influence the response. The point patterns are assumed to be independent, but no two patterns have the same distribution. \end{description} Examples of these three cases are given in the datasets \texttt{waterstriders}, \texttt{pyramidal} and \texttt{demohyper} respectively, which are installed in \spst. \section{Installed datasets} The following datasets are currently installed in \spst. \begin{itemize} \item \texttt{waterstriders}: Penttinen's \cite{pent84} waterstriders data recording the locations of insect larvae on a pond in 3 independent experiments. \item \texttt{pyramidal}: data from Diggle, Lange and Benes \cite{digglangbene91} on the locations of pyramidal neurons in human brain, 31 human subjects grouped into 3 groups (controls, schizoaffective and schizophrenic). \item \texttt{flu}: data from Chen et al \cite{chenetal08} giving the locations of two different virus proteins on the membranes of cells infected with influenza virus; 41 multitype point patterns divided into two virus types (wild and mutant) and two stain types. \item \texttt{simba}: simulated data from an experiment with two groups and 5 replicate point patterns per group. \item \texttt{demohyper}: simulated data from an experiment with two groups in which each experimental unit has a point pattern response and a pixel image covariate. \end{itemize} \section{Lists of point patterns} First we need a convenient way to store the \emph{responses} from all the units in an experiment. An individual point pattern is stored as an object of class \verb!"ppp"!. The easiest way to store all the responses is to form a list of \verb!"ppp"! objects. \subsection{Waterstriders data} The \texttt{waterstriders} data are an example of this type. The data consist of 3 independent point patterns representing the locations of insect larvae on a pond. See \texttt{help(waterstriders)}. <<>>= waterstriders @ The \texttt{waterstriders} dataset is a list of point patterns. It is a list, each of whose entries is a point pattern (object of class \verb!"ppp"!). Note that the observation windows of the three point patterns are {\tt not\/} identical. \subsection{The class \texttt{listof}} For convenience, the \texttt{waterstriders} dataset also belongs to the class \verb!"listof"!. This is a simple mechanism to allow us to handle the list neatly --- for example, we can provide special methods for printing, plotting and summarising the list. \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= plot(waterstriders, main="") @ Notice that the plot method displays each entry of the list in a separate panel. There's also the summary method: <<>>= summary(waterstriders) @ \subsection{Creating a \texttt{listof} object} For example, here is a simulated dataset containing three independent realisations of the Poisson process with intensity 100. <<>>= X <- listof(rpoispp(100), rpoispp(100), rpoispp(100)) @ Then it can be printed and plotted. <>= plot(X) X @ To convert an existing list to the class \code{listof}, use \code{as.listof}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Hyperframes} A \emph{hyperframe} is like a data frame, except that its entries can be objects of any kind. A hyperframe is effectively a two-dimensional array in which each column consists of values of one type (as in a data frame) or consists of objects of one class. The entries in a hyperframe can be point patterns, pixel images, windows, or any other objects. To analyse an experiment, we will store {\bf all} the data from the experiment in a single hyperframe. The rows of the hyperframe will correspond to different experimental units, while the columns represent different variables (response variables or covariates). \subsection{Creating hyperframes} The function \texttt{hyperframe} will create a hyperframe. <>= hyperframe(...) @ The arguments \verb!...! are any number of arguments of the form \texttt{tag=value}. Each \texttt{value} will become a column of the array. The \texttt{tag} determines the name of the column. Each \texttt{value} can be either \begin{itemize} \item an atomic vector or factor (i.e. numeric vector, integer vector, character vector, logical vector, complex vector or factor) \item a list of objects which are all of the same class \item one atomic value, which will be replicated to make an atomic vector or factor \item one object, which will be replicated to make a list of identical objects. \end{itemize} All columns (vectors, factors and lists) must be of the same length, if their length is greater than 1. For example, here is a hyperframe containing a column of numbers and a column of \emph{functions}: <<>>= H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) H @ Note that a column of character strings will be converted to a factor, unless you set \texttt{stringsAsFactors=FALSE} in the call to \code{hyperframe}. This is the same behaviour as for the function \code{data.frame}. <<>>= G <- hyperframe(X=1:3, Y=letters[1:3], Z=factor(letters[1:3]), W=list(rpoispp(100),rpoispp(100), rpoispp(100)), U=42, V=rpoispp(100), stringsAsFactors=FALSE) G @ This hyperframe has 3 rows. The columns named \texttt{U} and \texttt{V} are constant (all entries in a column are the same). The column named \texttt{Y} is a character vector. \subsection{Hyperframes of data} To analyse an experiment, we will store {\bf all} the data from the experiment in a single hyperframe. The rows of the hyperframe will correspond to different experimental units, while the columns represent different variables (response variables or covariates). Several examples of hyperframes are provided with the package, including \texttt{demohyper}, \texttt{flu}, \texttt{simba} and \texttt{pyramidal}, described above. The \texttt{simba} dataset contains simulated data from an experiment with a `control' group and a `treatment' group, each group containing 5 experimental units. The responses in the control group are independent Poisson point patterns with intensity 80. The responses in the treatment group are independent realisations of a Strauss process (see \texttt{help(simba)} for details). The \texttt{simba} dataset is a hyperframe with 10 rows and 2 columns: \texttt{Points} (the point patterns) and \texttt{group} (a factor with levels \texttt{control} and \texttt{treatment}). <<>>= simba @ The \texttt{pyramidal} dataset contains data from Diggle, Lange and Benes \cite{digglangbene91} on the locations of pyramidal neurons in human brain. One point pattern was observed in each of 31 human subjects. The subjects were classified into 3 groups (controls, schizoaffective and schizophrenic). The \texttt{pyramidal} dataset is a hyperframe with 31 rows and 2 columns: \code{Neurons} (the point patterns) and \code{group} (a factor with levels \texttt{control}, \texttt{schizoaffective} and \texttt{schizophrenic}). <<>>= pyramidal @ The \texttt{waterstriders} dataset is not a hyperframe; it's just a list of point patterns. It can easily be converted into a hyperframe: <<>>= ws <- hyperframe(Striders=waterstriders) @ \subsection{Columns of a hyperframe} Individual columns of a hyperframe can be extracted using \verb!$!: <<>>= H$X H$Y @ The result of \verb!$! is a vector or factor if the column contains atomic values; otherwise it is a list of objects (with class \texttt{"listof"} to make it easier to print and plot). Individual columns can also be assigned (overwritten or created) using \verb!$<-!: <<>>= H$U <- letters[1:3] H @ This can be used to build up a hyperframe column-by-column: <<>>= G <- hyperframe() G$X <- waterstriders G$Y <- 1:3 G @ \subsection{Subsets of a hyperframe} Other subsets of a hyperframe can be extracted with \verb![!: <<>>= H[,1] H[2,] H[2:3, ] H[1,1] @ The result of \verb![! is a hyperframe, unless you set \verb!drop=TRUE! and the subset consists of only one element or one column: <<>>= H[,1,drop=TRUE] H[1,1,drop=TRUE] H[1,2,drop=TRUE] @ Currently there is no method for \verb![<-! that would allow you to assign values to a subset of a hyperframe. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Plotting} \subsection{Plotting a \code{listof} object} The plot method for \code{listof} objects has formal arguments <>= plot.listof(x, ..., main, arrange = TRUE, nrows = NULL, ncols = NULL) @ where \code{main} is a title for the entire page. If \code{arrange=TRUE} then the entries of the list are displayed in separate panels on the same page (with \code{nrows} rows and \code{ncols} columns of panels), while if \code{arrange=FALSE} then the entries are just plotted as a series of plot frames. The extra arguments \verb!...! control the individual plot panels. These arguments will be passed to the plot method that displays each entry of the list. Suitable arguments depend on the type of entries. <>= plot(waterstriders, pch=16, nrows=1) @ \subsection{Plotting a hyperframe} \subsubsection{Plotting one column} If \code{h} is a hyperframe, then the default action of \code{plot(h)} is to extract the first column of \code{h} and plot each of the entries in a separate panel on one page (actually using the plot method for class \verb!"listof"!). \SweaveOpts{width=7,height=5} \setkeys{Gin}{width=0.9\textwidth} <>= plot(simba) @ This only works if the entries in the first column are objects for which a plot method is defined (for example, point patterns, images, windows). To select a different column, use \verb!$! or \verb![!: \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= H <- hyperframe(X=1:3, Y=list(sin,cos,tan)) plot(H$Y) @ The plot can be controlled using the arguments for \code{plot.listof} (and, in this case, \code{plot.function}, since \verb!H$Y! consists of functions). \subsubsection{Complex plots} More generally, we can display any kind of higher-order plot involving one or more columns of a hyperframe: <>= plot(h, e) @ where \code{h} is a hyperframe and \code{e} is an \R\ language call or expression that must be evaluated in each row to generate each plot panel. \SweaveOpts{width=9,height=5} \setkeys{Gin}{width=0.9\textwidth} <>= plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) })) @ Note the use of \code{quote}, which prevents the code inside the braces from being evaluated immediately. To plot the $K$-functions of each of the patterns in the \code{waterstriders} dataset, \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= H <- hyperframe(Bugs=waterstriders) plot(H, quote(plot(Kest(Bugs))), marsize=1) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Data analysis} \subsection{Computing with hyperframes} Often we want to perform some computation on each row of a hyperframe. In a data frame, this can be done using the command \code{with}: <<>>= df <- data.frame(A=1:10, B=10:1) with(df, A-B) @ In this example, the expression \code{A-B} is evaluated in each row of the data frame, and the result is a vector containing the computed values for each row. The function \code{with} is generic, and has a method for data frames, \code{with.data.frame}. The computation above was executed by \code{with.data.frame}. The same syntax is available for hyperframes using the method \code{with.hyperframe}: <>= with(h,e) @ Here \code{h} is a hyperframe, and \code{e} is an {\sf R} language construct involving the names of columns in \code{h}. For each row of \code{h}, the expression \code{e} will be evaluated in such a way that each entry in the row is identified by its column name. <<>>= H <- hyperframe(Bugs=waterstriders) with(H, npoints(Bugs)) with(H, distmap(Bugs)) @ The result of \code{with.hyperframe} is a list of objects (of class \verb!"listof"!), or a vector or factor if appropriate. Notice that (unlike the situation for data frames) the operations in the expression \code{e} do not have to be vectorised. For example, \code{distmap} expects a single point pattern, and is not vectorised to deal with a list of point patterns. Instead, the expression \code{distmap(Bugs)} is evaluated separately in each row of the hyperframe. \subsection{Summary statistics} One application of \code{with.hyperframe} is to calculate summary statistics for each row of a hyperframe. For example, the number of points in a point pattern \code{X} is returned by \code{npoints(X)}. To calculate this for each of the responses in the \code{simba} dataset, <<>>= with(simba, npoints(Points)) @ The summary statistic can be any kind of object. For example, to compute the empirical $K$-functions for each of the patterns in the \code{waterstriders} dataset, <<>>= H <- hyperframe(Bugs=waterstriders) K <- with(H, Kest(Bugs)) @ To plot these $K$-functions you can then just type \SweaveOpts{width=6,height=2} \setkeys{Gin}{width=0.9\textwidth} <>= plot(K) @ The summary statistic for each row could be a numeric vector: <<>>= H <- hyperframe(Bugs=waterstriders) with(H, nndist(Bugs)) @ The result is a list, each entry being a vector of nearest neighbour distances. To find the minimum interpoint distance in each pattern: <<>>= with(H, min(nndist(Bugs))) @ \subsection{Generating new columns} New columns of a hyperframe can be created by computation from the existing columns. For example, I can add a new column to the \code{simba} dataset that contains pixel images of the distance maps for each of the point pattern responses. <>= simba$Dist <- with(simba, distmap(Points)) @ \subsection{Simulation} This can be useful for simulation. For example, to generate Poisson point patterns with different intensities, where the intensities are given by a numeric vector \code{lambda}: \SweaveOpts{width=6,height=6} \setkeys{Gin}{width=0.7\textwidth} <>= lambda <- rexp(6, rate=1/50) H <- hyperframe(lambda=lambda) H$Points <- with(H, rpoispp(lambda)) plot(H, quote(plot(Points, main=paste("lambda=", signif(lambda, 4))))) @ It's even simpler to generate 10 independent Poisson point patterns with the \emph{same} intensity 50, say: <>= H$X <- with(H, rpoispp(50)) @ (the expression \code{rpoispp(50)} is evaluated once in each row, yielding a different point pattern in each row because of the randomness). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Exploratory data analysis} Before fitting models to the data, it is prudent to explore the data to detect unusual features and to suggest appropriate models. \subsection{Exploring spatial trend and covariate effects} Points may be distributed non-uniformly either because they are intrinsically non-uniform (``spatial trend'') or because their abundance depends on a spatial covariate (``covariate effects''). Non-uniformity of a point pattern can be investigated using the kernel smoothed intensity. This is the convolution of the point pattern with a smooth density called the kernel. Effectively each point in the pattern is replaced by a copy of the kernel, and the sum of all copies of the kernel is the kernel-smoothed intensity function. It is computed by \texttt{density.ppp} separately for each point pattern. <>= plot(simba, quote(plot(density(Points), main="")), nrows=2) @ Covariate effects due to a real-valued spatial covariate (a real-valued pixel image) can be investigated using the command \code{rhohat}. This uses a kernel smoothing technique to fit a model of the form \[ \lambda(u) = \rho(Z(u)) \] where $\lambda(u)$ is the point process intensity at a location $u$, and $Z(u)$ is the value of the spatial covariate at that location. Here $\rho$ is an unknown, smooth function which is to be estimated. The function $\rho$ expresses the effect of the spatial covariate on the point process intensity. If $\rho$ turns out to be constant, then the covariate has no effect on point process intensity (and the constant value of $\rho$ is the constant intensity of the point process). <>= rhos <- with(demohyper, rhohat(Points, Image)) plot(rhos) @ \SweaveOpts{width=6,height=4} \setkeys{Gin}{width=0.9\textwidth} \subsection{Exploring interpoint interaction} Still to be written. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Fitting models of spatial trend} The command \code{mppm} fits models to multiple point patterns. Its syntax is very similar to that of \code{lm} and \code{glm}: <>= mppm(formula, data, interaction, ...) @ where \code{formula} is a formula describing the systematic trend part of the model, \code{data} is a hyperframe containing all the data (responses and covariates), and \code{interaction} determines the stochastic interpoint interaction part of the model. For example: <>= mppm(Points ~ group, simba, Poisson()) @ Note that the formula has a left hand side, which identifies the response. This should be the name of a column of \code{data}. \subsection{Trend formula} The right side of \code{formula} is an expression for the linear predictor (effectively the {\bf logarithm} of the spatial trend). The variables appearing in the right hand side of \code{formula} should be either \begin{itemize} \item names of columns in \code{data} \item objects in the {\sf R} global environment (such as \code{pi} and \code{log}) \item the reserved names \code{x}, \code{y} (representing Cartesian coordinates), \code{marks} (representing mark values attached to points) or \code{id} (a factor representing the row number in the hyperframe). \end{itemize} \subsubsection{Design covariates} The variables in the trend could be `design covariates'. For example, to fit a model to the \code{simba} dataset in which all patterns are independent replicates of the same uniform Poisson process, with the same constant intensity: <<>>= mppm(Points ~ 1, simba) @ To fit a model in which the two groups of patterns (control and treatment groups) each consist of independent replicates of a uniform Poisson process, but with possibly different intensity in each group: <<>>= mppm(Points ~ group, simba) @ To fit a uniform Poisson process to each pattern, with different intensity for each pattern: <<>>= mppm(Points ~ id, simba) @ \subsubsection{Spatial covariates} The variables in the trend could be `spatial covariates'. For example, the \code{demohyper} dataset has a column \code{Image} containing pixel images. <<>>= mppm(Points ~ Image, data=demohyper) @ This model postulates that each pattern is a Poisson process with intensity of the form \[ \lambda(u) = \exp(\beta_0 + \beta_1 Z(u)) \] at location $u$, where $\beta_0, \beta_1$ are coefficients to be estimated, and $Z(u)$ is the value of the pixel image \code{Image} at location $u$. It may or may not be appropriate to assume that the intensity of the points is an exponential function of the image pixel value $Z$. If instead we wanted the intensity $\lambda(u)$ to be \emph{proportional} to $Z(u)$, the appropriate model is <>= mppm(Points ~ offset(log(Image)), data=demohyper) @ which corresponds to an intensity proportional to \code{Image}, \[ \lambda(u) = \exp(\beta_0 + \log Z(u)) = e^{\beta_0} \; Z(u). \] The \code{offset} indicates that there is no coefficient in front of $\log Z(u)$. Alternatively we could allow a coefficient: <>= mppm(Points ~ log(Image), data=demop) @ which corresponds to a gamma transformation of \code{Image}, \[ \lambda(u) = \exp(\beta_0 + \beta_1 \log Z(u)) = e^{\beta_0} \; Z(u)^{\beta_1}. \] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Interpoint interaction} The stochastic interpoint interaction in a point process model is specified by the arguments \code{interaction} and (optionally) \code{iformula} in <>= mppm(formula, data, interaction, ..., iformula=NULL) @ \subsection{Same interaction for all patterns} In the simplest case, the argument \texttt{interaction} is one of the familiar objects that describe the point process interaction structure. It is an object of class \texttt{"interact"} created by calling one of the functions \begin{center} \begin{tabular}{rl} \texttt{Poisson()} & the Poisson point process\\ \texttt{Hardcore()} & the hard core process \\ \texttt{Strauss()} & the Strauss process \\ \texttt{StraussHard()} & the Strauss/hard core point process\\ \texttt{Softcore()} & pairwise interaction, soft core potential\\ \texttt{PairPiece()} & pairwise interaction, piecewise constant \\ \texttt{DiggleGatesStibbard() } & Diggle-Gates-Stibbard pair potential \\ \texttt{DiggleGratton() } & Diggle-Gratton pair potential \\ \texttt{Fiksel() } & Fiksel pair potential \\ \texttt{LennardJones() } & Lennard-Jones pair potential \\ \texttt{Pairwise()} & pairwise interaction, user-supplied potential\\ \texttt{AreaInter()} & area-interaction potential\\ \texttt{Geyer()} & Geyer's saturation process\\ \texttt{BadGey()} & multiscale Geyer saturation process\\ \texttt{Saturated()} & Saturated pair model, user-supplied potential\\ \texttt{OrdThresh()} & Ord process, threshold potential\\ \texttt{Ord()} & Ord model, user-supplied potential \\ \texttt{MultiStrauss()} & multitype Strauss process \\ \texttt{MultiStraussHard()} & multitype Strauss/hard core process \\ \texttt{Concom()} & connected component interaction \\ \texttt{Hybrid()} & hybrid of several interactions \\ \end{tabular} \end{center} In this `simple' usage of \texttt{mppm}, the point process model assumes that all point patterns have exactly the same interpoint interaction, (with the same interaction parameters), and only differ in their spatial trend. \subsection{Hyperframe of interactions} More generally the argument \code{interaction} can be a hyperframe containing objects of class \texttt{"interact"}. For example, we might want to fit a Strauss process to each point pattern, but with a different Strauss interaction radius for each pattern. <>= radii <- with(simba, mean(nndist(Points))) @ Then \code{radii} is a vector of numbers which we could use as the values of the interaction radius for each case. First we need to make the interaction objects: <<>>= Rad <- hyperframe(R=radii) Str <- with(Rad, Strauss(R)) @ Then we put them into a hyperframe and fit the model: <<>>= Int <- hyperframe(str=Str) mppm(Points ~ 1, simba, interaction=Int) @ An important constraint is that all of the interaction objects in one column must be \emph{instances of the same process} (e.g. Strauss) albeit possibly having different parameter values. For example, you cannot put Poisson and Strauss processes in the same column. \subsection{Interaction formula} If \code{interaction} is a hyperframe, then the additional argument \code{iformula} may be used to fully specify the interaction. (An \code{iformula} is also required if \code{interaction} has more than one column.) The \code{iformula} should be a formula without a left hand side. Variables on the right hand side are typically the names of columns in \code{interaction}. \subsubsection{Selecting one column} If the right hand side of \code{iformula} is a single name, then this identifies the column in \code{interaction} to be used as the interpoint interaction structure. <<>>= h <- hyperframe(Y=waterstriders) g <- hyperframe(po=Poisson(), str4 = Strauss(4), str7= Strauss(7)) mppm(Y ~ 1, data=h, interaction=g, iformula=~str4) @ \subsubsection{Interaction depending on design} The \code{iformula} can also involve columns of \code{data}, but only those columns that are vectors or factors. This allows us to specify an interaction that depends on the experimental design. [This feature is {\bf experimental}.] For example <<>>= fit <- mppm(Points ~ 1, simba, Strauss(0.07), iformula = ~Interaction*group) @ Since \code{Strauss(0.1)} is not a hyperframe, it is first converted to a hyperframe with a single column named \code{Interaction}. The \code{iformula = ~Interaction*group} specifies (since \code{group} is a factor) that the interpoint interaction shall have a different coefficient in each experimental group. That is, we fit a model which has two different values for the Strauss interaction parameter $\gamma$, one for the control group and one for the treatment group. When you print the result of such a fit, the package tries to do `automatic interpretation' of the fitted model (translating the fitted interaction coefficients into meaningful numbers like $\gamma$). This will be successful in \emph{most} cases: <<>>= fit @ <>= co <- coef(fit) si <- function(x) { signif(x, 4) } @ Thus we see that the estimate of the Strauss parameter $\gamma$ for the control group is \Sexpr{si(exp(co[2]))}, and for the treatment group \Sexpr{si(exp(sum(co[c(2,4)])))} (the correct values in this simulated dataset were $1$ and $0.5$). The fitted model can also be interpreted directly from the fitted canonical coefficients: <<>>= coef(fit) @ The last output shows all the coefficients $\beta_j$ in the linear predictor for the (log) conditional intensity. The interpretation of the model coefficients, for any fitted model in \R, depends on the \emph{contrasts} which were applicable when the model was fitted. This is part of the core {\sf R} system: see \code{help(contrasts)} or \code{options(contrasts)}. If you did not specify otherwise, the default is to use \emph{treatment contrasts}. This means that, for an explanatory variable which is a \texttt{factor} with $N$ levels, the first level of the factor is used as a baseline, and the fitted model coefficients represent the factor levels $2, 3, \ldots, N$ relative to this baseline. In the output above, there is a coefficient for \code{(Intercept)} and one for \code{grouptreatment}. These are coefficients related to the \code{group} factor. According to the ``treatment contrasts'' rule, the \code{(Intercept)} coefficient is the estimated effect for the control group, and the \code{grouptreatment} coefficient is the estimated difference between the treatment and control groups. Thus the fitted first order trend is $\exp(\Sexpr{si(co[1])}) = \Sexpr{si(exp(co[1]))}$ for the control group and $\exp(\Sexpr{si(co[1])} + \Sexpr{si(co[3])}) = \Sexpr{si(exp(sum(co[c(1,3)])))}$ for the treatment group. The correct values in this simulated dataset were $80$ and $100$. The remaining coefficients in the output are \code{Interaction} and \code{Interaction:grouptreatment}. Recall that the Strauss process interaction term is $\gamma^{t(u,\xx)} = \exp(t(u,\xx) \log\gamma)$ at a spatial location $u$, for a point pattern $\xx$. Since we're using treatment contrasts, the coefficient \code{Interaction} is the estimate of $\log\gamma$ for the control group. The coefficient \code{Interaction:grouptreatment} is the estimate of the difference in $\log\gamma$ between the treatment and control groups. Thus the estimated Strauss interaction parameter $\gamma$ is $\exp(\Sexpr{si(co[2])}) = \Sexpr{si(exp(co[2]))}$ for the control group and $\exp(\Sexpr{si(co[2])} + (\Sexpr{si(co[4])})) = \Sexpr{si(exp(co[2]+co[4]))}$ for the treatment group. The correct values were $1$ and $0.5$. \subsubsection{Completely different interactions for different cases} In the previous example, when we fitted a Strauss model to all point patterns in the \code{simba} dataset, the fitted model for the patterns in the control group was close to Poisson ($\gamma \approx 1$). Suppose we now want to fit a model which {\it is} Poisson in the control group, and Strauss in the treatment group. The Poisson and Strauss interactions must be given as separate columns in a hyperframe of interactions: <>= interaction=hyperframe(po=Poisson(), str=Strauss(0.07)) @ What do we write for the \code{iformula}? The following \emph{will not} work: <>= iformula=~ifelse(group=="control", po, str) @ This does not work because the Poisson and Strauss models are `incompatible' inside such expressions. The canonical sufficient statistics for the Poisson and Strauss processes do not have the same dimension. Internally in \code{mppm} we translate the symbols \code{po} and \code{str} into matrices; the dimensions of these matrices are different, so the \code{ifelse} expression cannot be evaluated. Instead we need something like the following: <>= iformula=~I((group=="control")*po) + I((group=="treatment") * str) @ The letter \code{I} here is a standard R function that prevents its argument from being interpreted as a formula (thus the \code{*} is interpreted as multiplication instead of a model interaction). The expression \code{(group=="control")} is logical, and when multiplied by the matrix \code{po}, yields a matrix. So the following does work: <<>>= g <- hyperframe(po=Poisson(), str=Strauss(0.07)) fit2 <- mppm(Points ~ 1, simba, g, iformula=~I((group=="control")*po) + I((group=="treatment") * str)) fit2 @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Studying the fitted model} Fitted models produced by \code{mppm} can be examined and validated in many ways. \subsection{Fits for each pattern} \subsubsection{Subfits} The command \code{subfits} takes an \code{mppm} object and extracts, for each individual point pattern, the fitted point process model for that pattern \emph{that is implied by the overall fit}. It returns a list of objects of class \code{ppm}. <<>>= H <- hyperframe(W=waterstriders) fit <- mppm(W ~ 1, H) subfits(fit) @ In this example the result is a list of three \code{ppm} objects representing the implied fits for each of the three point patterns in the \code{waterstriders} dataset. Notice that {\bf the fitted coefficients are the same} in all three models. Note that there are some unresolved difficulties with the implementation of \code{subfits}. Two completely different implementations are supplied in the package; they are called \code{subfits.old} %(used in versions 0.1--1 and earlier) and \code{subfits.new}.% (introduced in 0.1--2). The old version would occasionally crash. Unfortunately the newer version \code{subfits.new} is quite memory-hungry and sometimes causes R to hang. We're still working on this problem. So for the time being, \code{subfits} is the same as \code{subfits.old}. You can change this simply by reassigning, e.g. <>= subfits <- subfits.new @ \subsubsection{Fitting separately to each pattern} For comparison, we could fit a point process model separately to each point pattern dataset using \code{ppm}. The easy way to do this is with \code{with.hyperframe}. To fit a \emph{separate} uniform Poisson point process to each of the three waterstriders patterns, <<>>= H <- hyperframe(W=waterstriders) with(H, ppm(W)) @ The result is again a list of three fitted point process models (objects of class \code{ppm}), but now the fitted coefficients are different. \subsection{Residuals} One standard way to check a fitted model is to examine the residuals. \subsubsection{Point process residuals} Some recent papers \cite{baddetal05,baddmollpake08} have defined residuals for a fitted point process model (fitted to a \emph{single} point pattern). These residuals are implemented in \code{spatstat} as \code{residuals.ppm} and apply to an object of class \code{ppm}, that is, a model fitted to a \emph{single} point pattern. The command \code{residuals.mppm} computes the point process residuals for an \code{mppm} object. <<>>= fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) @ The result is a list, with one entry for each of the point pattern datasets. Each list entry contains the point process residuals for the corresponding point pattern dataset. Each entry in the list is a signed measure (object of class \code{"msr"}) as explained in the help for \code{residuals.ppm}). It can be plotted: <>= plot(res) @ You probably want the smoothed residual field: <>= smor <- with(hyperframe(res=res), Smooth(res, sigma=4)) plot(smor) @ \subsubsection{Sums of residuals} It would be useful to have a residual that is a single value for each point pattern (representing how much that point pattern departs from the model fitted to all the point patterns). That can be computed by \emph{integrating} the residual measures using the function \code{integral.msr}: <<>>= fit <- mppm(P ~ x, hyperframe(P=waterstriders)) res <- residuals(fit) totres <- sapply(res, integral.msr) @ In designed experiments we can plot these total residuals against the design covariates: <>= fit <- mppm(Points~Image, data=demohyper) resids <- residuals(fit, type="Pearson") totres <- sapply(resids, integral.msr) areas <- with(demohyper, area.owin(as.owin(Points))) df <- as.data.frame(demohyper[, "Group"]) df$resids <- totres/areas plot(resids~Group, df) @ \subsubsection{Four-panel diagnostic plots} Sometimes a more useful tool is the function \code{diagnose.ppm} which produces a four-panel diagnostic plot based on the point process residuals. However, it is only available for \code{ppm} objects. To obtain a four-panel diagnostic plot for each of the point patterns, do the following: \begin{enumerate} \item fit a model to multiple point patterns using \code{mppm}. \item extract the individual fits using \code{subfits}. \item plot the residuals of the individual fits. \end{enumerate} For example: <>= fit <- mppm(P ~ 1, hyperframe(P=waterstriders)) sub <- hyperframe(Model=subfits(fit)) plot(sub, quote(diagnose.ppm(Model))) @ (One could also do this for models fitted separately to the individual point patterns.) \subsubsection{Residuals of the parameter estimates} We can also compare the parameter estimates obtained by fitting the model simultaneously to all patterns (using \code{mppm}) with those obtained by fitting the model separately to each pattern (using \code{ppm}). <<>>= H <- hyperframe(P = waterstriders) fitall <- mppm(P ~ 1, H) together <- subfits(fitall) separate <- with(H, ppm(P)) Fits <- hyperframe(Together=together, Separate=separate) dr <- with(Fits, unlist(coef(Separate)) - unlist(coef(Together))) dr exp(dr) @ One could also try deletion residuals, etc. \subsection{Goodness-of-fit tests} \subsubsection{Quadrat count test} The $\chi^2$ goodness-of-fit test based on quadrat counts is implemented for objects of class \code{ppm} (in \code{quadrat.test.ppm}) and also for objects of class \code{mppm} (in \code{quadrat.test.mppm}). This is a goodness-of-fit test for a fitted {\bf Poisson} point process model only. The model could be uniform or non-uniform and the intensity might depend on covariates. <<>>= H <- hyperframe(X=waterstriders) # Poisson with constant intensity for all patterns fit1 <- mppm(X~1, H) quadrat.test(fit1, nx=2) # uniform Poisson with different intensity for each pattern fit2 <- mppm(X ~ id, H) quadrat.test(fit2, nx=2) @ See the help for \code{quadrat.test.ppm} and \code{quadrat.test.mppm} for further details. \subsubsection{Kolmogorov-Smirnov test} The Kolmogorov-Smirnov test of goodness-of-fit of a Poisson point process model compares the observed and predicted distributions of the values of a spatial covariate. We want to test the null hypothesis $H_0$ that the observed point pattern ${\mathbf x}$ is a realisation from the Poisson process with intensity function $\lambda(u)$ (for locations $u$ in the window $W$). Let $Z(u)$ be a given, real-valued covariate defined at each spatial location $u$. Under $H_0$, the \emph{observed} values of $Z$ at the data points, $Z(x_i)$ for each $x_i \in {\mathbf x}$, are independent random variables with common probability distribution function \[ F_0(z) = \frac{\int_W \lambda(u) \indicate{Z(u) \le z} \dee u} {\int_W \lambda(u) \dee u}. \] We can therefore apply the Kolmogorov-Smirnov test of goodness-of-fit. This compares the empirical cumulative distribution of the observed values $Z(x_i)$ to the predicted c.d.f. $F_0$. The test is implemented as \code{kstest.ppm}. The syntax is <>= kstest.mppm(model, covariate) @ where \code{model} is a fitted model (of class \texttt{"mppm"}) and \code{covariate} is either \begin{itemize} \item a \code{function(x,y)} making it possible to compute the value of the covariate at any location \code{(x,y)} \item a pixel image containing the covariate values \item a list of functions, one for each row of the hyperframe of original data \item a list of pixel images, one for each row of the hyperframe of original data \item a hyperframe with one column containing either functions or pixel images. \end{itemize} \newpage \addcontentsline{toc}{section}{Bibliography} %\bibliography{% %extra,% %extra2,% %biblio/badd,% %biblio/bioscience,% %biblio/censoring,% %biblio/mcmc,% %biblio/spatstat,% %biblio/stat,% %biblio/stochgeom% %} \begin{thebibliography}{1} \bibitem{baddmollpake08} A. Baddeley, J. M{\o}ller, and A.G. Pakes. \newblock Properties of residuals for spatial point processes. \newblock {\em Annals of the Institute of Statistical Mathematics}, 60:627--649, 2008. \bibitem{statpaper} A. Baddeley, I. Sintorn, L. Bischof, R. Turner, and S. Heggarty. \newblock Analysing designed experiments where the response is a spatial point pattern. \newblock In preparation. \bibitem{baddetal05} A. Baddeley, R. Turner, J. M{\o}ller, and M. Hazelton. \newblock Residual analysis for spatial point processes (with discussion). \newblock {\em Journal of the Royal Statistical Society, series B}, 67(5):617--666, 2005. \bibitem{chenetal08} B.J. Chen, G.P. Leser, D. Jackson, and R.A. Lamb. \newblock The influenza virus {M2} protein cytoplasmic tail interacts with the {M1} protein and influences virus assembly at the site of virus budding. \newblock {\em Journal of Virology}, 82:10059--10070, 2008. \bibitem{digglangbene91} P.J. Diggle, N. Lange, and F. M. Benes. \newblock Analysis of variance for replicated spatial point patterns in clinical neuroanatomy. \newblock {\em Journal of the {A}merican {S}tatistical {A}ssociation}, 86:618--625, 1991. \bibitem{pent84} A. Penttinen. \newblock {\em Modelling Interaction in Spatial Point Patterns: Parameter Estimation by the Maximum Likelihood Method}. \newblock Number 7 in {Jyv\"askyl\"a} Studies in Computer Science, Economics and Statistics. University of {Jyv\"askyl\"a}, 1984. \end{thebibliography} %\addcontentsline{toc}{section}{Index} %\printindex \end{document} spatstat/inst/doc/shapefiles.pdf0000644000176000001440000023074312333041757016523 0ustar ripleyusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 3451 /Filter /FlateDecode /N 79 /First 641 >> stream x[[S#~?BoI*5:ڪpx=fƞpSu nI_A2i&d))e:v1#c3vϜRLpǜ rLH 闣]&ס2&bḡB&9t &u0i zaNF;H=&$-SBL:̒1S)2V b`Z2"~Ì3hX/3k1.X9)p09&9>~c!*b,6X -WLgb~;0=MrɐL@ZK,86@"LI"b,C VP˷ CEHZ5TpKF HΣ+dS+4KrTФV%yGJGs T7JC=rtC0Rl4e#pYqot-NjuM8 jj,e:Y?`c Bᑷ@SIdN"Y?I1ī<͈%n:ϒ9d:I@E-*X-@Y4Y71ﲃaFrh}E:&xF'{[r˗ibXf/e/ KJPчH`7MW/.AQ@PrtOg{ ouy1Er[.u24G/KEQEhE+Y,88?c@ӗu&DUijhhX2ބ-W a9ףlu51%xoLTW1icțt~FYκtIGUc17n%UXxtEIt'hfmXpհtqPׄxl} 6習1Dl\!f')ѥ"<*"+R Uk@&T]rUh1JXK'sQ<\^s9;ݜ}&Tݜ}+ewԚp5HE&LV{&Ḫp 51jp|Oξ^.#'+bq bbQz`Sb6Eel3ɧjS 6UDQʧS$|b:Y;|.uHj']L|b!%/+!pMg֎);9PV= Qo>gA.h}f<$Z`Br F!v,*`ԹT1_ I~%7P)tj~%Awp C*_QnчbYZ6?~z7XggrnXO "<|Q Pl]zΟM\.7acxGnK] "\h]~LUF<|:N PLnѕE"I$+)KX)U 4`X UVR8s~6c7tf 3l`&6pRB>R*pJv#\w=xM详P_M#ٲD\gZ,mۓAWW |D?G9WWܯ\]'nkܬ`*`6.̨JOqid?>g*;hѴШ:=l1Ƙ$ [+:atQ}3!ڒ!Gg6B Rs᠗EA災@oY qpGá K!rꩀ=,2b Ԯ<-:lIIm'TNBf'{(5:N`9CcnAY:XR`%W2J^u66[ vU76lKϒ,/-b;&͒Y (+9MEgg9[:aݑq,׼<IöUӶ׶`WgT>S9.,[lYh-e9KMMmi5b~Z@@Myk<%~2h%rq:o,{_amM5-bEHSJ_$ uoHuâ":۷A|sη6viiZV,p ZlF,Jcj쌶HtF(Ʀz>'I%OwDURz01E(iVDYWMhmatП=|-`tAxs[0M07w_P缦#=>{˧״ }SB73^rں Օ⠓W{5 +:m&b-"Oq3-b+ Ӷf"v# n0 wrGxaNlTL8z"'3*|JqAURv:l^l9|q-}n:uiWq@!ᡉ1Va.< {pq;49Y!SVs[ ΛxhH%{88kxTٴdSM nk?ؘ69VvmǤOO\(S|RFLYG,s#wޞ7_nƑTis,ym2 H~yTOO[=<*l̔ᜳs~gg?Ϣjed0۴^ #Bzy[ٵqfa\c`f: *DΗЈTPɛ8*z:}ņ.x|qMSʂ 2KoTϮ6xJ_ Γx` ޺io4`a2k5n7gmNO){S3ĸq !SӖOYV0^T<唐y: jIA&+ZUy'Hj#N Ά`1eN֕H,}Fxc G.I'_'$G/vd+V|P*;t?ӻݎc^~klշĖDboz~@$?Br~Y-jטtUP M3/?̬~ +?c_Jendstream endobj 81 0 obj << /Subtype /XML /Type /Metadata /Length 1337 >> stream 2014-05-09T10:56:46+08:00 2014-05-09T10:56:46+08:00 David M. 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mydata <- read.csv("myfile.csv") ################################################### ### code chunk number 7: getstart.Rnw:165-166 ################################################### head(mydata) ################################################### ### code chunk number 8: getstart.Rnw:181-182 (eval = FALSE) ################################################### ## mypattern <- ppp(mydata[,3], mydata[,7], c(100,200), c(10,90)) ################################################### ### code chunk number 9: getstart.Rnw:185-186 (eval = FALSE) ################################################### ## ppp(x.coordinates, y.coordinates, x.range, y.range) ################################################### ### code chunk number 10: getstart.Rnw:195-196 ################################################### getOption("SweaveHooks")[["fig"]]() plot(mypattern) ################################################### ### code chunk number 11: getstart.Rnw:203-204 (eval = FALSE) ################################################### ## summary(mypattern) ################################################### ### code chunk number 12: getstart.Rnw:208-209 ################################################### options(SweaveHooks=list(fig=function() par(mar=rep(4,4)+0.1))) ################################################### ### code chunk number 13: getstart.Rnw:211-212 ################################################### getOption("SweaveHooks")[["fig"]]() plot(Kest(mypattern)) ################################################### ### code chunk number 14: getstart.Rnw:218-219 (eval = FALSE) ################################################### ## plot(envelope(mypattern,Kest)) ################################################### ### code chunk number 15: getstart.Rnw:221-222 ################################################### env <- envelope(mypattern,Kest, nsim=39) ################################################### ### code chunk number 16: getstart.Rnw:224-225 ################################################### getOption("SweaveHooks")[["fig"]]() plot(env, main="envelope(mypattern, Kest)") ################################################### ### code chunk number 17: getstart.Rnw:227-228 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) ################################################### ### code chunk number 18: getstart.Rnw:234-235 ################################################### getOption("SweaveHooks")[["fig"]]() plot(density(mypattern)) ################################################### ### code chunk number 19: getstart.Rnw:245-246 (eval = FALSE) ################################################### ## marks(mypattern) <- mydata[, c(5,9)] ################################################### ### code chunk number 20: getstart.Rnw:248-249 ################################################### mypattern <-finpines ################################################### ### code chunk number 21: getstart.Rnw:252-253 (eval = FALSE) ################################################### ## plot(Smooth(mypattern)) ################################################### ### code chunk number 22: getstart.Rnw:256-257 ################################################### getOption("SweaveHooks")[["fig"]]() plot(Smooth(mypattern, sigma=1.2), main="Smooth(mypattern)") spatstat/inst/doc/BEGINNER.txt0000644000176000001440000000151012324667727015636 0ustar ripleyusers -== Welcome to the 'spatstat' package! ==- For a friendly introduction to spatstat, type the command vignette('getstart') which displays the document "Getting Started with Spatstat". For an overview of all capabilities, type help(spatstat) View the documentation for any command/function 'foo' by typing help(foo) Activate the graphical help interface by typing help.start() To handle spatial data in the 'shapefile' format, see the document "Handling shapefiles in the spatstat package", by typing vignette('shapefiles') For a complete two-day workshop on spatstat, please download the workshop notes at For news about new features in the latest version of spatstat, type latest.news or vignette('updates') (Press 'Q' to exit, on some computers) spatstat/inst/doc/shapefiles.R0000644000176000001440000001307012333041747016142 0ustar ripleyusers### R code from vignette source 'shapefiles.Rnw' ################################################### ### code chunk number 1: shapefiles.Rnw:7-8 ################################################### options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) ################################################### ### code chunk number 2: shapefiles.Rnw:23-29 ################################################### library(spatstat) options(useFancyQuotes=FALSE) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") ################################################### ### code chunk number 3: shapefiles.Rnw:104-105 (eval = FALSE) ################################################### ## library(maptools) ################################################### ### code chunk number 4: shapefiles.Rnw:109-110 (eval = FALSE) ################################################### ## x <- readShapeSpatial("mydata.shp") ################################################### ### code chunk number 5: shapefiles.Rnw:115-116 (eval = FALSE) ################################################### ## class(x) ################################################### ### code chunk number 6: shapefiles.Rnw:133-137 ################################################### baltim <- columbus <- fylk <- list() class(baltim) <- "SpatialPointsDataFrame" class(columbus) <- "SpatialPolygonsDataFrame" class(fylk) <- "SpatialLinesDataFrame" ################################################### ### code chunk number 7: shapefiles.Rnw:139-143 (eval = FALSE) ################################################### ## setwd(system.file("shapes", package="maptools")) ## baltim <- readShapeSpatial("baltim.shp") ## columbus <- readShapeSpatial("columbus.shp") ## fylk <- readShapeSpatial("fylk-val.shp") ################################################### ### code chunk number 8: shapefiles.Rnw:145-148 ################################################### class(baltim) class(columbus) class(fylk) ################################################### ### code chunk number 9: shapefiles.Rnw:170-171 (eval = FALSE) ################################################### ## X <- X[W] ################################################### ### code chunk number 10: shapefiles.Rnw:188-189 (eval = FALSE) ################################################### ## y <- as(x, "ppp") ################################################### ### code chunk number 11: shapefiles.Rnw:200-202 (eval = FALSE) ################################################### ## balt <- as(baltim, "ppp") ## bdata <- slot(baltim, "data") ################################################### ### code chunk number 12: shapefiles.Rnw:250-251 (eval = FALSE) ################################################### ## out <- lapply(x@lines, function(z) { lapply(z@Lines, as.psp) }) ################################################### ### code chunk number 13: shapefiles.Rnw:260-261 (eval = FALSE) ################################################### ## curvegroup <- lapply(out, function(z) { do.call("superimposePSP", z)}) ################################################### ### code chunk number 14: shapefiles.Rnw:301-305 (eval = FALSE) ################################################### ## out <- lapply(x@lines, function(z) { lapply(z@Lines, as.psp) }) ## dat <- x@data ## for(i in seq(nrow(dat))) ## out[[i]] <- lapply(out[[i]], "marks<-", value=dat[i, , drop=FALSE]) ################################################### ### code chunk number 15: shapefiles.Rnw:326-328 ################################################### getOption("SweaveHooks")[["fig"]]() data(chorley) plot(as.owin(chorley), lwd=3, main="polygon") ################################################### ### code chunk number 16: shapefiles.Rnw:341-343 ################################################### getOption("SweaveHooks")[["fig"]]() data(demopat) plot(as.owin(demopat), col="blue", main="polygonal region") ################################################### ### code chunk number 17: shapefiles.Rnw:379-382 (eval = FALSE) ################################################### ## regions <- slot(x, "polygons") ## regions <- lapply(regions, function(x) { SpatialPolygons(list(x)) }) ## windows <- lapply(regions, as.owin) ################################################### ### code chunk number 18: shapefiles.Rnw:387-388 (eval = FALSE) ################################################### ## te <- tess(tiles=windows) ################################################### ### code chunk number 19: shapefiles.Rnw:424-425 (eval = FALSE) ################################################### ## y <- as(x, "SpatialPolygons") ################################################### ### code chunk number 20: shapefiles.Rnw:435-439 (eval = FALSE) ################################################### ## cp <- as(columbus, "SpatialPolygons") ## cregions <- slot(cp, "polygons") ## cregions <- lapply(cregions, function(x) { SpatialPolygons(list(x)) }) ## cwindows <- lapply(cregions, as.owin) ################################################### ### code chunk number 21: shapefiles.Rnw:449-451 (eval = FALSE) ################################################### ## ch <- hyperframe(window=cwindows) ## ch <- cbind.hyperframe(ch, columbus@data) ################################################### ### code chunk number 22: shapefiles.Rnw:471-473 (eval = FALSE) ################################################### ## y <- as(x, "im") ## ylist <- lapply(slot(x, "data"), function(z, y) { y[,] <- z; y }, y=y) spatstat/inst/doc/getstart.Rnw0000644000176000001440000003077312333041747016232 0ustar ripleyusers\documentclass[11pt]{article} % \VignetteIndexEntry{Getting Started with Spatstat} <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ \usepackage{graphicx} \newcommand{\pkg}[1]{\texttt{#1}} \newcommand{\bold}[1]{{\textbf {#1}}} \newcommand{\R}{{\sf R}} \newcommand{\spst}{\pkg{spatstat}} \newcommand{\Spst}{\pkg{Spatstat}} \begin{document} \bibliographystyle{plain} \thispagestyle{empty} \SweaveOpts{eps=TRUE} \setkeys{Gin}{width=0.6\textwidth} <>= library(spatstat) spatstat.options(image.colfun=function(n) { grey(seq(0,1,length=n)) }) sdate <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Date") sversion <- read.dcf(file = system.file("DESCRIPTION", package = "spatstat"), fields = "Version") options(useFancyQuotes=FALSE) @ \title{Getting started with \texttt{spatstat}} \author{Adrian Baddeley, Rolf Turner and Ege Rubak} \date{For \spst\ version \texttt{\Sexpr{sversion}}} \maketitle Welcome to \spst, a package in the \R\ language for analysing spatial point patterns. This document will help you to get started with \spst. It gives you a quick overview of \spst, and some cookbook recipes for doing basic calculations. \section*{What kind of data does \spst\ handle?} \Spst\ is mainly designed for analysing \emph{spatial point patterns}. For example, suppose you are an ecologist studying plant seedlings. You have pegged out a $10 \times 10$ metre rectangle for your survey. Inside the rectangle you identify all the seedlings of the species you want, and record their $(x,y)$ locations. You can plot the $(x,y)$ locations: <>= data(redwood) plot(redwood, pch=16, main="") @ This is a \emph{spatial point pattern} dataset. Methods for analysing this kind of data are summarised in the highly recommended book by Diggle \cite{digg03} and other references in the bibliography. \nocite{handbook10,bivapebegome08} Alternatively the points could be locations in one dimension (such as road accidents recorded on a road network) or in three dimensions (such as cells observed in 3D microscopy). You might also have recorded additional information about each seedling, such as its height, or the number of fronds. Such information, attached to each point in the point pattern, is called a \emph{mark} variable. For example, here is a stand of pine trees, with each tree marked by its diameter at breast height (dbh). The circle radii represent the dbh values (not to scale). <>= data(longleaf) plot(longleaf, main="") @ You might also have recorded supplementary data, such as the terrain elevation, which might serve as explanatory variables. These data can be in any format. \Spst\ does not usually provide capabilities for analysing such data in their own right, but \spst\ does allow such explanatory data to be taken into account in the analysis of a spatial point pattern. \Spst\ is \underline{\bf not} designed to handle point data where the $(x,y)$ locations are fixed (e.g.\ temperature records from the state capital cities in Australia) or where the different $(x,y)$ points represent the same object at different times (e.g.\ hourly locations of a tiger shark with a GPS tag). These are different statistical problems, for which you need different methodology. \section*{What can \spst\ do?} \Spst\ supports a very wide range of popular techniques for statistical analysis for spatial point patterns, for example \begin{itemize} \item kernel estimation of density/intensity \item quadrat counting and clustering indices \item detection of clustering using Ripley's $K$-function \item spatial logistic regression \item model-fitting \item Monte Carlo tests \end{itemize} as well as some advanced statistical techniques. \Spst\ is one of the largest packages available for \R, containing over 1000 commands. It is the product of 15 years of software development by leading researchers in spatial statistics. \section*{How do I start using \spst?} \begin{enumerate} \item Install \R\ on your computer \begin{quote} Go to \texttt{r-project.org} and follow the installation instructions. \end{quote} \item Install the \spst\ package in your \R\ system \begin{quote} Start \R\ and type \verb!install.packages("spatstat")!. If that doesn't work, go to \texttt{r-project.org} to learn how to install Contributed Packages. \end{quote} \item Start \R\ \item Type \texttt{library(spatstat)} to load the package. \item Type \texttt{help(spatstat)} for information. \end{enumerate} \section*{How do I get my data into \spst?} <>= data(finpines) mypattern <- unmark(finpines) mydata <- round(as.data.frame(finpines), 2) @ Here is a cookbook example. Suppose you've recorded the $(x,y)$ locations of seedlings, in an Excel spreadsheet. You should also have recorded the dimensions of the survey area in which the seedlings were mapped. \begin{enumerate} \item In Excel, save the spreadsheet into a comma-separated values (CSV) file. \item Start \R\ \item Read your data into \R\ using \texttt{read.csv}. \begin{quote} If your CSV file is called \texttt{myfile.csv} then you could type something like <>= mydata <- read.csv("myfile.csv") @ to read the data from the file and save them in an object called \texttt{mydata} (or whatever you want to call it). You may need to set various options to get this to work for your file format: type \texttt{help(read.csv)} for information. \end{quote} \item Check that \texttt{mydata} contains the data you expect. \begin{quote} For example, to see the first few rows of data from the spreadsheet, type <<>>= head(mydata) @ To select a particular column of data, you can type \texttt{mydata[,3]} to extract the third column, or \verb!mydata$x! to extract the column labelled \texttt{x}. \end{quote} \item Type \texttt{library(spatstat)} to load the \spst\ package \item Now convert the data to a point pattern object using the \spst\ command \texttt{ppp}. \begin{quote} Suppose that the \texttt{x} and \texttt{y} coordinates were stored in columns 3 and 7 of the spreadsheet. Suppose that the sampling plot was a rectangle, with the $x$ coordinates ranging from 100 to 200, and the $y$ coordinates ranging from 10 to 90. Then you would type <>= mypattern <- ppp(mydata[,3], mydata[,7], c(100,200), c(10,90)) @ The general form is <>= ppp(x.coordinates, y.coordinates, x.range, y.range) @ Note that this only stores the seedling locations. If you have additional columns of data (such as seedling height, seedling sex, etc) these can be added as \emph{marks}, later. \end{quote} \item Check that the point pattern looks right by plotting it: <>= plot(mypattern) @ \item Now you are ready to do some statistical analysis. Try the following: \begin{itemize} \item Basic summary of data: type <>= summary(mypattern) @ \item Ripley's $K$-function: <>= options(SweaveHooks=list(fig=function() par(mar=rep(4,4)+0.1))) @ <>= plot(Kest(mypattern)) @ For more information, type \texttt{help(Kest)} \item Envelopes of $K$-function: <>= plot(envelope(mypattern,Kest)) @ <>= env <- envelope(mypattern,Kest, nsim=39) @ <>= plot(env, main="envelope(mypattern, Kest)") @ <>= options(SweaveHooks=list(fig=function() par(mar=c(1,1,1,1)))) @ For more information, type \texttt{help(envelope)} \item kernel smoother of point density: <>= plot(density(mypattern)) @ For more information, type \texttt{help(density.ppp)} \end{itemize} \item Next if you have additional columns of data recording (for example) the seedling height and seedling sex, you can add these data as \emph{marks}. Suppose that columns 5 and 9 of the spreadsheet contained such values. Then do something like <>= marks(mypattern) <- mydata[, c(5,9)] @ <>= mypattern <-finpines @ Now you can try things like the kernel smoother of mark values: <>= plot(Smooth(mypattern)) @ \setkeys{Gin}{width=0.8\textwidth} <>= plot(Smooth(mypattern, sigma=1.2), main="Smooth(mypattern)") @ \setkeys{Gin}{width=0.4\textwidth} \item You are airborne! Now look at the workshop notes \cite{badd10wshop} for more hints. \end{enumerate} \section*{How do I find out which command to use?} Information sources for \spst\ include: \begin{itemize} \item the Quick Reference guide: a list of the most useful commands. \begin{quote} To view the quick reference guide, start \R, then type \texttt{library(spatstat)} and then \texttt{help(spatstat)}. Alternatively you can download a pdf of the Quick Reference guide from the website \texttt{www.spatstat.org} \end{quote} \item online help: \begin{quote} The online help files are useful --- they give detailed information and advice about each command. They are available when you are running \spst. To get help about a particular command \texttt{blah}, type \texttt{help(blah)}. There is a graphical help interface, which you can start by typing \texttt{help.start()}. Alternatively you can download a pdf of the entire manual (1000 pages!) from the website \texttt{www.spatstat.org}. \end{quote} \item workshop notes: \begin{quote} A complete set of notes from an Introductory Workshop is available from \texttt{www.csiro.au/resources/pf16h.html} or by visiting \texttt{www.spatstat.org} \end{quote} \item vignettes: \begin{quote} \Spst\ comes installed with several `vignettes' (introductory documents with examples) which can be accessed using the graphical help interface. They include a document about \texttt{Handling shapefiles}. \end{quote} \item website: \begin{quote} Visit the \spst\ package website \texttt{www.spatstat.org} \end{quote} \item forums: \begin{quote} Join the forum \texttt{R-sig-geo} by visiting \texttt{r-project.org}. Then email your questions to the forum. Alternatively you can ask the authors of the \spst\ package (their email addresses are given in the package documentation). \end{quote} \end{itemize} % The following is inserted from refs.bbl originally generated from refs.tex \begin{thebibliography}{10} \bibitem{badd10wshop} A. Baddeley. \newblock Analysing spatial point patterns in {{R}}. \newblock Technical report, CSIRO, 2010. \newblock Version 4. Available at {\texttt{www.csiro.au/resources/pf16h.html}}. \bibitem{bivapebegome08} R. Bivand, E.J. Pebesma, and V. G{\'{o}}mez-Rubio. \newblock {\em Applied spatial data analysis with {R}}. \newblock Springer, 2008. \bibitem{cres93} N.A.C. Cressie. \newblock {\em Statistics for Spatial Data}. \newblock {John Wiley and Sons}, {New York}, second edition, 1993. \bibitem{digg03} P.J. Diggle. \newblock {\em Statistical Analysis of Spatial Point Patterns}. \newblock Hodder Arnold, London, second edition, 2003. \bibitem{fortdale05} M.J. Fortin and M.R.T. Dale. \newblock {\em Spatial analysis: a guide for ecologists}. \newblock Cambridge University Press, Cambridge, UK, 2005. \bibitem{fothroge09handbook} A.S. Fotheringham and P.A. Rogers, editors. \newblock {\em The {SAGE} {H}andbook on {S}patial {A}nalysis}. \newblock SAGE Publications, London, 2009. \bibitem{gaetguyo09} C. Gaetan and X. Guyon. \newblock {\em Spatial statistics and modeling}. \newblock Springer, 2009. \newblock Translated by Kevin Bleakley. \bibitem{handbook10} A.E. Gelfand, P.J. Diggle, M. Fuentes, and P. Guttorp, editors. \newblock {\em Handbook of Spatial Statistics}. \newblock CRC Press, 2010. \bibitem{illietal08} J. Illian, A. Penttinen, H. Stoyan, and D. Stoyan. \newblock {\em Statistical Analysis and Modelling of Spatial Point Patterns}. \newblock John Wiley and Sons, Chichester, 2008. \bibitem{mollwaag04} J. M{\o}ller and R.P. Waagepetersen. \newblock {\em Statistical Inference and Simulation for Spatial Point Processes}. \newblock Chapman and Hall/CRC, Boca Raton, 2004. \bibitem{pfeietal08} D.U. Pfeiffer, T. Robinson, M. Stevenson, K. Stevens, D. Rogers, and A. Clements. \newblock {\em Spatial analysis in epidemiology}. \newblock Oxford University Press, Oxford, UK, 2008. \bibitem{wallgotw04} L.A. Waller and C.A. Gotway. \newblock {\em Applied spatial statistics for public health data}. \newblock Wiley, 2004. \end{thebibliography} \end{document} spatstat/inst/ratfor/0000755000176000001440000000000012324667727014437 5ustar ripleyusersspatstat/inst/ratfor/Makefile0000755000176000001440000000244412324667727016106 0ustar ripleyusers RATFOR = /home/adrian/bin/ratfor77 #RATFOR = /usr/local/bin/ratfor CPP = /usr/bin/cpp ########################################################## # Sources actually written by humans: RAT_SRC = dppll.r inxypOld.r C_DOMINIC = dinfty.c dwpure.c C_MISC = raster.h areadiff.c closepair.c connect.c corrections.c \ discarea.c distances.c distmapbin.c distseg.c \ exactdist.c exactPdist.c \ massdisthack.c poly2im.c trigraf.c utils.c xyseg.c C_MH = methas.h dist2.h areaint.c badgey.c dgs.c \ diggra.c dist2.c fexitc.c getcif.c geyer.c \ lookup.c methas.c stfcr.c \ straush.c straushm.c strauss.c straussm.c C_KEST = Kloop.h Kborder.c C_SRC = $(C_DOMINIC) $(C_MISC) $(C_MH) $(C_KEST) CC_SRC = PerfectStrauss.cc HUMAN = $(RAT_SRC) $(C_SRC) $(CC_SRC) Makefile ########################################################## # Source to be generated automatically: RAT_FOR = dppll.f inxypOld.f GENERATED = $(RAT_FOR) ###################################################### ########### TARGETS ################################ target: $(GENERATED) @echo -- Done ------- tar: tar cvf src.tar $(HUMAN) clean: rm $(GENERATED) -rm src.tar ####################################################### ######### RULES ################################## .r.f: $(RATFOR) -o $@ $? spatstat/inst/ratfor/inxypOld.r0000755000176000001440000000216312324667727016435 0ustar ripleyuserssubroutine inxyp(x,y,xp,yp,npts,nedges,score,onbndry) implicit double precision(a-h,o-z) dimension x(npts), y(npts), xp(nedges), yp(nedges), score(npts) logical first, onbndry(npts) zero = 0.0d0 half = 0.5d0 one = 1.0d0 do i = 1,nedges { x0 = xp(i) y0 = yp(i) if(i == nedges) { x1 = xp(1) y1 = yp(1) } else { x1 = xp(i+1) y1 = yp(i+1) } dx = x1 - x0 dy = y1 - y0 do j = 1,npts { xcrit = (x(j) - x0)*(x(j) - x1) if(xcrit <= zero) { if(xcrit == zero) { contrib = half } else { contrib = one } ycrit = y(j)*dx - x(j)*dy + x0*dy - y0*dx if(dx < 0) { if(ycrit >= zero) { score(j) = score(j) + contrib } onbndry(j) = onbndry(j) | (ycrit == zero) } else if(dx > zero) { if(ycrit < zero) { score(j) = score(j) - contrib } onbndry(j) = onbndry(j) | (ycrit == zero) } else { if(x(j) == x0) { ycrit = (y(j) - y0)*(y(j) - y1) } onbndry(j) = onbndry(j) | (ycrit <= zero) } } } } return end spatstat/inst/ratfor/dppll.r0000755000176000001440000000203312324667727015736 0ustar ripleyuserssubroutine dppll(x,y,l1,l2,l3,l4,np,nl,eps,mint,rslt,xmin,jmin) implicit double precision(a-h,o-z) dimension x(np), y(np), rslt(np,nl), xmin(np), jmin(np) double precision l1(nl), l2(nl), l3(nl), l4(nl) one = 1.d0 zero = 0.d0 do j = 1,nl { dx = l3(j) - l1(j) dy = l4(j) - l2(j) alen = sqrt(dx**2 + dy**2) if(alen .gt. eps) { co = dx/alen si = dy/alen } else { co = 0.5 si = 0.5 } do i = 1, np { xpx1 = x(i) - l1(j) ypy1 = y(i) - l2(j) xpx2 = x(i) - l3(j) ypy2 = y(i) - l4(j) d1 = xpx1**2 + ypy1**2 d2 = xpx2**2 + ypy2**2 dd = min(d1,d2) if(alen .gt. eps) { xpr = xpx1*co + ypy1*si if(xpr .lt. zero .or. xpr .gt. alen) { d3 = -one } else { ypr = - xpx1*si + ypy1*co d3 = ypr**2 } } else { d3 = -one } if(d3 .ge. zero) { dd = min(dd,d3) } sd =sqrt(dd) rslt(i,j) = sd if(mint.gt.0) { if(sd .lt. xmin(i)) { xmin(i) = sd if(mint.gt.1) { jmin(i) = j } } } } } return end spatstat/tests/0000755000176000001440000000000012324667730013321 5ustar ripleyusersspatstat/tests/badwindow.txt0000644000176000001440000005451112324667730016046 0ustar ripleyusers x y i 486959 6497047 1 487223 6497012 1 487293 6497170 1 487434 6497187 1 487504 6497047 1 487539 6496959 1 487557 6496889 1 488875 6496924 1 488945 6496643 1 490808 6496643 1 490737 6496854 1 490298 6497644 1 490140 6498541 1 490298 6498857 1 490491 6497855 1 490948 6496854 1 491036 6496555 1 491950 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6 489916 6503146 6 489916 6503056 6 489736 6503056 6 489736 6503206 6 489706 6503206 6 489706 6503266 6 489676 6503266 6 489676 6503356 6 489796 6503356 6 489796 6503596 6 489916 6503596 6 489916 6503386 6 490006 6505666 7 489916 6505666 7 489916 6505756 7 490006 6505756 7 487426 6504856 8 487396 6504796 8 487276 6504676 8 490786 6505366 9 490786 6505336 9 491176 6505336 9 491176 6505276 9 491236 6505276 9 491236 6505126 9 491266 6505126 9 491266 6504976 9 491236 6504976 9 491236 6504916 9 491206 6504916 9 491206 6504886 9 491176 6504886 9 491176 6504856 9 491086 6504856 9 491086 6504886 9 490996 6504886 9 490996 6504916 9 490966 6504916 9 490966 6504946 9 490936 6504946 9 490936 6505006 9 490876 6505006 9 490876 6505186 9 490846 6505186 9 490846 6505246 9 490726 6505246 9 490726 6505276 9 490696 6505276 9 490696 6505366 9 487906 6505066 10 487906 6505036 10 487936 6505036 10 487936 6505006 10 487966 6505006 10 487966 6504616 10 487906 6504616 10 487906 6504586 10 487846 6504586 10 487846 6504556 10 487756 6504556 10 487756 6504526 10 487606 6504526 10 487606 6504646 10 487636 6504646 10 487636 6504766 10 487666 6504766 10 487666 6504886 10 487726 6504886 10 487726 6504976 10 487756 6504976 10 487756 6505006 10 487786 6505006 10 487786 6505036 10 487816 6505036 10 487816 6505066 10 491416 6504856 11 491326 6504856 11 491326 6505006 11 491416 6505006 11 491386 6504736 12 491266 6504736 12 491266 6504826 12 491386 6504826 12 487456 6504586 13 487456 6504436 13 487366 6504436 13 487366 6504466 13 487306 6504466 13 487306 6504556 13 487336 6504556 13 487336 6504586 13 487396 6504586 13 487396 6504676 13 487486 6504676 13 487486 6504586 13 489226 6504646 14 489226 6504616 14 489256 6504616 14 489256 6504556 14 489286 6504556 14 489286 6504466 14 489106 6504466 14 489106 6504586 14 489136 6504586 14 489136 6504646 14 488296 6504406 15 488296 6504316 15 488206 6504316 15 488206 6504376 15 488176 6504376 15 488176 6504466 15 488206 6504466 15 488206 6504496 15 488236 6504496 15 488236 6504526 15 488326 6504526 15 488326 6504406 15 490666 6504466 16 490666 6504376 16 490696 6504376 16 490696 6504316 16 490756 6504316 16 490756 6504256 16 490786 6504256 16 490786 6504166 16 490696 6504166 16 490696 6504226 16 490576 6504226 16 490576 6504286 16 490546 6504286 16 490546 6504466 16 489346 6503986 17 489346 6504076 17 489406 6504076 17 489406 6504166 17 489526 6504166 17 489526 6504256 17 489496 6504256 17 489496 6504346 17 489586 6504346 17 489586 6504256 17 489646 6504256 17 489646 6504196 17 489706 6504196 17 489706 6504016 17 489676 6504016 17 489676 6503896 17 489586 6503896 17 489586 6503956 17 489496 6503956 17 489496 6503986 17 489346 6503986 17 489346 6503986 17 489346 6503836 17 489376 6503836 17 489376 6503746 17 489346 6503746 17 489346 6503566 17 489256 6503566 17 489256 6503506 17 489226 6503506 17 489226 6503416 17 489196 6503416 17 489196 6503386 17 489076 6503386 17 489076 6503446 17 489046 6503446 17 489046 6503566 17 489016 6503566 17 489016 6503626 17 488986 6503626 17 488986 6503836 17 489106 6503836 17 489106 6504106 17 489076 6504106 17 489076 6504226 17 489196 6504226 17 489196 6504196 17 489226 6504196 17 489226 6504076 17 489256 6504076 17 489256 6503986 17 487936 6504166 18 487936 6504136 18 487966 6504136 18 487966 6504016 18 487846 6504016 18 487846 6504046 18 487816 6504046 18 487816 6504136 18 487846 6504136 18 487846 6504166 18 488596 6504046 19 488596 6503986 19 488626 6503986 19 488626 6503896 19 488596 6503896 19 488596 6503716 19 488506 6503716 19 488506 6503806 19 488476 6503806 19 488476 6503986 19 488506 6503986 19 488506 6504046 19 487396 6503896 20 487486 6503836 20 487516 6503806 20 487126 6503956 20 487216 6503986 20 488296 6503806 21 488296 6503776 21 488326 6503776 21 488326 6503746 21 488356 6503746 21 488356 6503626 21 488236 6503626 21 488236 6503656 21 488206 6503656 21 488206 6503686 21 488146 6503686 21 488146 6503776 21 488176 6503776 21 488176 6503806 21 491146 6503686 22 491146 6503626 22 491176 6503626 22 491176 6503536 22 491146 6503536 22 491146 6503476 22 491026 6503476 22 491026 6503656 22 491056 6503656 22 491056 6503686 22 487816 6503506 23 487816 6503476 23 487846 6503476 23 487846 6503386 23 487936 6503386 23 487936 6503356 23 487966 6503356 23 487966 6503296 23 488026 6503296 23 488026 6503236 23 488086 6503236 23 488086 6503116 23 487936 6503116 23 487936 6503146 23 487846 6503146 23 487846 6503176 23 487816 6503176 23 487816 6503206 23 487786 6503206 23 487786 6503386 23 487696 6503386 23 487696 6503356 23 487606 6503356 23 487606 6503506 23 490036 6503506 24 490036 6503386 24 490096 6503386 24 490096 6503266 24 490066 6503266 24 490066 6503176 24 490096 6503176 24 490096 6503026 24 489976 6503026 24 489976 6503086 24 489946 6503086 24 489946 6503146 24 489916 6503146 24 489916 6503386 24 489946 6503386 24 489946 6503506 24 489496 6503356 25 489406 6503356 25 489406 6503446 25 489496 6503446 25 488386 6503356 26 488386 6503326 26 488416 6503326 26 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6497026 63 spatstat/tests/alltests.R0000644000176000001440000015455612330130002015265 0ustar ripleyusers# nndist.R # Check that nndist and nnwhich give # results consistent with direct calculation from pairdist # Similarly for nncross and distfun # Also test whether minnndist(X) == min(nndist(X)) require(spatstat) local({ eps <- sqrt(.Machine$double.eps) f <- function(mat,k) { apply(mat, 1, function(z,n) { sort(z)[n] }, n=k+1) } g <- function(mat,k) { apply(mat, 1, function(z,n) { order(z)[n] }, n=k+1) } # Two dimensions X <- runifpoint(42) nn <- nndist(X) nnP <- f(pairdist(X), 1) if(any(abs(nn - nnP) > eps)) stop("nndist.ppp does not agree with pairdist") nn5 <- nndist(X, k=5) nn5P <- f(pairdist(X), 5) if(any(abs(nn5 - nn5P) > eps)) stop("nndist.ppp(k=5) does not agree with pairdist") nw <- nnwhich(X) nwP <- g(pairdist(X), 1) if(any(nw != nwP)) stop("nnwhich.ppp does not agree with pairdist") nw5 <- nnwhich(X, k=5) nw5P <- g(pairdist(X), 5) if(any(nw5 != nw5P)) stop("nnwhich.ppp(k=5) does not agree with pairdist") # Three dimensions X <- runifpoint3(42) nn <- nndist(X) nnP <- f(pairdist(X), 1) if(any(abs(nn - nnP) > eps)) stop("nndist.pp3 does not agree with pairdist") nn5 <- nndist(X, k=5) nn5P <- f(pairdist(X), 5) if(any(abs(nn5 - nn5P) > eps)) stop("nndist.pp3(k=5) does not agree with pairdist") nw <- nnwhich(X) nwP <- g(pairdist(X), 1) if(any(nw != nwP)) stop("nnwhich.pp3 does not agree with pairdist") nw5 <- nnwhich(X, k=5) nw5P <- g(pairdist(X), 5) if(any(nw5 != nw5P)) stop("nnwhich.pp3(k=5) does not agree with pairdist") # m dimensions X <- runifpointx(42, boxx(c(0,1),c(0,1),c(0,1),c(0,1))) nn <- nndist(X) nnP <- f(pairdist(X), 1) if(any(abs(nn - nnP) > eps)) stop("nndist.ppx does not agree with pairdist") nn5 <- nndist(X, k=5) nn5P <- f(pairdist(X), 5) if(any(abs(nn5 - nn5P) > eps)) stop("nndist.ppx(k=5) does not agree with pairdist") nw <- nnwhich(X) nwP <- g(pairdist(X), 1) if(any(nw != nwP)) stop("nnwhich.ppx does not agree with pairdist") nw5 <- nnwhich(X, k=5) nw5P <- g(pairdist(X), 5) if(any(nw5 != nw5P)) stop("nnwhich.ppx(k=5) does not agree with pairdist") #### nncross in two dimensions X <- runifpoint(42) Y <- runifpoint(42, win=owin(c(1,2),c(1,2))) # default nncross nc <- nncross(X,Y) ncd <- nc$dist ncw <- nc$which cd <- crossdist(X,Y) cdd <- apply(cd, 1, min) cdw <- apply(cd, 1, which.min) if(any(abs(ncd - cdd) > eps)) stop("nncross()$dist does not agree with apply(crossdist(), 1, min)") if(any(ncw != cdw)) stop("nncross()$which does not agree with apply(crossdist(), 1, which.min)") # sort on x nc <- nncross(X,Y, sortby="x") ncd <- nc$dist ncw <- nc$which if(any(abs(ncd - cdd) > eps)) stop("nncross(sortby=x)$dist does not agree with apply(crossdist(), 1, min)") if(any(ncw != cdw)) stop("nncross(sortby=x)$which does not agree with apply(crossdist(), 1, which.min)") # pre-sorted on x Y <- Y[order(Y$x)] nc <- nncross(X,Y, is.sorted.Y=TRUE, sortby="x") ncd <- nc$dist ncw <- nc$which cd <- crossdist(X,Y) cdd <- apply(cd, 1, min) cdw <- apply(cd, 1, which.min) if(any(abs(ncd - cdd) > eps)) stop("For sorted data, nncross()$dist does not agree with apply(crossdist(), 1, min)") if(any(ncw != cdw)) stop("For sorted data, nncross()$which does not agree with apply(crossdist(), 1, which.min)") # sanity check for nncross with k > 1 ndw <- nncross(X, Y, k=1:4)$which if(any(is.na(ndw))) stop("NA's returned by nncross.ppp(k > 1)$which") ndw <- nncross(X, Y, k=1:4, what="which") if(any(is.na(ndw))) stop("NA's returned by nncross.ppp(k > 1, what='which')") # test of correctness for nncross with k > 1 flipcells <- flipxy(cells) calcwhich <- nncross(cells, flipcells, k=1:4, what="which") truewhich <- t(apply(crossdist(cells,flipcells), 1, order))[,1:4] if(any(calcwhich != truewhich)) stop("nncross(k > 1) gives wrong answer") # test of agreement between nngrid.h and knngrid.h # dimyx=23 (found by trial-and-error) ensures that there are no ties a <- as.matrix(nnmap(cells, what="which", dimyx=23)) b <- as.matrix(nnmap(cells, what="which", dimyx=23, k=1:2)[[1]]) if(any(a != b)) stop("algorithms in nngrid.h and knngrid.h disagree") ## minnndist mfast <- minnndist(X) mslow <- min(nndist(X)) if(abs(mfast-mslow) > eps) stop("minnndist(X) disagrees with min(nndist(X))") mfast <- maxnndist(X) mslow <- max(nndist(X)) if(abs(mfast-mslow) > eps) stop("maxnndist(X) disagrees with max(nndist(X))") }) require(spatstat) local({ Y <- split(urkiola) B <- Y$birch O <- Y$oak B.lam <- predict (ppm(B, ~polynom(x,y,2)), type="trend") O.lam <- predict (ppm(O, ~polynom(x,y,2)), type="trend") Kinhom(B, lambda=B.lam, correction="iso") Kinhom(B, lambda=B.lam, correction="border") Kcross.inhom(urkiola, i="birch", j="oak", B.lam, O.lam) Kcross.inhom(urkiola, i="birch", j="oak", B.lam, O.lam, correction = "iso") Kcross.inhom(urkiola, i="birch", j="oak", B.lam, O.lam, correction = "border") }) require(spatstat) local({ # critical R values that provoke GCC bug #323 a <- marktable(lansing, R=0.25) a <- marktable(lansing, R=0.21) a <- marktable(lansing, R=0.20) a <- marktable(lansing, R=0.10) }) # tests/NAinCov.R # Testing the response to the presence of NA's in covariates require(spatstat) local({ X <- runifpoint(42) Y <- as.im(function(x,y) { x+y }, owin()) Y[owin(c(0.2,0.4),c(0.2,0.4))] <- NA # fit model: should produce a warning but no failure misfit <- ppm(X, ~Y, covariates=list(Y=Y)) # prediction Z <- predict(misfit, type="trend") Z <- predict(misfit, type="se") # covariance matrix: all should be silent v <- vcov(misfit) ss <- vcov(misfit, what="internals") NULL }) # tests for agreement between C and interpreted code # for interpoint distances require(spatstat) local({ eps <- .Machine$double.eps * 4 # pairdist.ppp X <- rpoispp(42) dC <- pairdist(X, method="C") dR <- pairdist(X, method="interpreted") if(any(abs(dC - dR) > eps)) stop("Algorithms for pairdist() do not agree") dC <- pairdist(X, periodic=TRUE, method="C") dR <- pairdist(X, periodic=TRUE, method="interpreted") if(any(abs(dC - dR) > eps)) stop("Algorithms for pairdist(periodic=TRUE) do not agree") # crossdist.ppp Y <- rpoispp(42) dC <- crossdist(X, Y, method="C") dR <- crossdist(X, Y, method="interpreted") if(any(abs(dC - dR) > eps)) stop("Algorithms for crossdist() do not agree") dC <- crossdist(X, Y, periodic=TRUE, method="C") dR <- crossdist(X, Y, periodic=TRUE, method="interpreted") if(any(abs(dC - dR) > eps)) stop("Algorithms for crossdist(periodic=TRUE) do not agree") # nndist.ppp nnC <- nndist(X, method="C") nnI <- nndist(X, method="interpreted") if(any(abs(nnC - nnI) > eps)) stop("Algorithms for nndist() do not agree") nn3C <- nndist(X, k=3, method="C") nn3I <- nndist(X, k=3, method="interpreted") if(any(abs(nn3C - nn3I) > eps)) stop("Algorithms for nndist(k=3) do not agree") # nnwhich.ppp nwC <- nnwhich(X, method="C") nwI <- nnwhich(X, method="interpreted") if(any(nwC != nwI)) stop("Algorithms for nnwhich() do not agree") nw3C <- nnwhich(X, k=3, method="C") nw3I <- nnwhich(X, k=3, method="interpreted") if(any(nw3C != nw3I)) stop("Algorithms for nnwhich(k=3) do not agree") }) # ppmBadData.R # $Revision: 1.4 $ $Date: 2011/12/05 07:29:16 $ # Testing robustness of ppm and support functions # when data are rubbish require(spatstat) local({ # --------------------------------------------------- # from Rolf: very large proportion of data is NA SEED <- 42 K <- 101 A <- 500 X <- seq(0, A, length=K) G <- expand.grid(x=X, y=X) FOO <- function(x,y) { sin(x)^2 + cos(y)^2 } M1 <- im(matrix(FOO(G$x, G$y), K, K), xcol=X, yrow=X) M <- im(matrix(FOO(G$x, G$y), K, K)) BAR <- function(x) { exp(-6.618913 + 5.855337 * x - 8.432483 * x^2) } V <- im(BAR(M$v), xcol=X, yrow=X) # V <- eval.im(exp(-6.618913 + 5.855337 * M - 8.432483 * M^2)) set.seed(SEED) Y <- rpoispp(V) fY <- ppm(Y, ~cv + I(cv^2), covariates=list(cv=M), correction="translate") diagnose.ppm(fY) lurking(fY, covariate=as.im(function(x,y){x}, square(A)), type="raw") }) # -------------------------------------------------------- # from Andrew Bevan: numerical overflow, ill-conditioned Fisher information local({ SEED <- 42 nongranite<- owin(poly = list(x = c(0, 8500, 7000, 6400, 6400, 6700, 7000, 7200, 7300, 8000, 8100, 8800, 9500, 10000, 10000, 0), y = c(0, 0, 2000, 3800, 4000, 5000, 6500, 7400, 7500, 8000, 8100, 9000, 9500, 9600, 10000, 10000))) #Trend on raster grid rain <- as.im(X=function(x,y) { x^2 + y^2 }, W=nongranite, dimyx=100) #Generate a point pattern via a Lennard-Jones process set.seed(SEED) mod4<- rmhmodel(cif="lennard", par=list(beta=1, sigma=250, epsilon=2.2), trend=rain, w=nongranite) ljtr<- rmh(mod4, start=list(n.start=80), control=list(p=1, nrep=1e5)) #Fit a point process model to the pattern with rain as a covariate # NOTE INCORRECT TREND FORMULA ljtrmod <- ppm(ljtr, trend= ~ Z, interaction=NULL, covariates=list(Z=rain)) ss <- summary(ljtrmod) }) local({ # From Ege # Degenerate but non-null argument 'covariates' xx <- list() names(xx) <- character(0) fit <- ppm(cells, ~x, covariates = xx) st <- summary(fit) }) # # tests/ppmgam.R # # Test ppm with use.gam=TRUE # # $Revision: 1.2 $ $Date: 2013/03/01 08:46:34 $ # require(spatstat) local({ fit <- ppm(nztrees, ~s(x,y), use.gam=TRUE) mm <- model.matrix(fit) mf <- model.frame(fit) v <- vcov(fit) }) # # ppmlogi.R # # Tests of ppm(method='logi') # # $Revision: 1.2 $ Date$ # require(spatstat) local({ fit <- ppm(cells, ~x, method="logi") f <- fitted(fit) p <- predict(fit) fitS <- ppm(cells, ~x, Strauss(0.08), method="logi") fS <- fitted(fitS) pS <- predict(fitS) if(spatstat.options("allow.logi.influence")) { a <- leverage(fit) b <- influence(fit) d <- dfbetas(fit) aS <- leverage(fitS) bS <- influence(fitS) dS <- dfbetas(fitS) } }) # ppmmarkorder.R # $Revision: 1.2 $ $Date: 2011/12/05 07:29:16 $ # Test that predict.ppm, plot.ppm and plot.fitin # tolerate marks with levels that are not in alpha order # require(spatstat) local({ X <- amacrine levels(marks(X)) <- c("ZZZ", "AAA") fit <- ppm(X, ~marks, MultiStrauss(c("ZZZ","AAA"), matrix(0.06, 2, 2))) aa <- predict(fit, type="trend") bb <- predict(fit, type="cif") plot(fit) plot(fitin(fit)) }) # # tests/ppmscope.R # # Test things that might corrupt the internal format of ppm objects # # $Revision: 1.4 $ $Date: 2014/04/05 02:45:41 $ # require(spatstat) local({ ## (1) Scoping problem that can arise when ppm splits the data fit <- ppm(bei, ~elev, covariates=bei.extra) mm <- model.matrix(fit) ## (2) Fast update mechanism fit1 <- ppm(cells, ~x+y, Strauss(0.07)) fit2 <- update(fit1, ~y) fit3 <- update(fit2, ~x) ## (3) New formula-based syntax attach(bei.extra) slfit <- ppm(bei ~ grad) sl2fit <- update(slfit, ~grad + I(grad^2)) slfitup <- update(slfit, use.internal=TRUE) sl2fitup <- update(sl2fit, use.internal=TRUE) }) # # tests/ppmtricks.R # # Test backdoor exits and hidden options in ppm # # $Revision: 1.1 $ $Date: 2013/06/17 06:54:51 $ # require(spatstat) local({ # (1) skip.border fit <- ppm(cells, ~1, Strauss(0.1), skip.border=TRUE) }) # # tests/prediction.R # # Things that might go wrong with predict() # # $Revision: 1.1 $ $Date: 2013/11/12 16:06:11 $ # require(spatstat) local({ # test of 'covfunargs' f <- function(x,y,a){ y - a } fit <- ppm(cells, ~x + f, covariates=list(f=f), covfunargs=list(a=1/2)) p <- predict(fit) }) # # tests/rmhAux.R # # $Revision: 1.1 $ $Date: 2013/02/18 10:41:27 $ # # For interactions which maintain 'auxiliary data', # verify that the auxiliary data are correctly updated. # # To do this we run rmh with nsave=1 so that the point pattern state # is saved after every iteration, then the algorithm is restarted, # and the auxiliary data are re-initialised. The final state must agree with # the result of simulation without saving. # ---------------------------------------------------- require(spatstat) local({ # Geyer: mod <- list(cif="geyer", par=list(beta=1.25,gamma=1.6,r=0.2,sat=4.5), w=square(10)) set.seed(42) X.nosave <- rmh(model=mod, start=list(n.start=50), control=list(nrep=1e3, periodic=FALSE, expand=1)) set.seed(42) X.save <- rmh(model=mod, start=list(n.start=50), control=list(nrep=1e3, periodic=FALSE, expand=1, nburn=0, nsave=1)) stopifnot(npoints(X.save) == npoints(X.nosave)) stopifnot(max(nncross(X.save, X.nosave)$dist) == 0) stopifnot(max(nncross(X.nosave, X.save)$dist) == 0) }) # Test examples for rmh.default # run to reasonable length # and with tests for validity added # ---------------------------------------------------- require(spatstat) local({ if(!exists("nr")) nr <- 5e3 spatstat.options(expand=1.1) # Strauss process. mod01 <- list(cif="strauss",par=list(beta=2,gamma=0.2,r=0.7), w=c(0,10,0,10)) X1.strauss <- rmh(model=mod01,start=list(n.start=80), control=list(nrep=nr)) # Strauss process, conditioning on n = 80: X2.strauss <- rmh(model=mod01,start=list(n.start=80), control=list(p=1,nrep=nr)) stopifnot(X2.strauss$n == 80) # test tracking mechanism X1.strauss <- rmh(model=mod01,start=list(n.start=80), control=list(nrep=nr), track=TRUE) X2.strauss <- rmh(model=mod01,start=list(n.start=80), control=list(p=1,nrep=nr), track=TRUE) # Hard core process: mod02 <- list(cif="hardcore",par=list(beta=2,hc=0.7),w=c(0,10,0,10)) X3.hardcore <- rmh(model=mod02,start=list(n.start=60), control=list(nrep=nr)) # Strauss process equal to pure hardcore: mod02 <- list(cif="strauss",par=list(beta=2,gamma=0,r=0.7),w=c(0,10,0,10)) X3.strauss <- rmh(model=mod02,start=list(n.start=60), control=list(nrep=nr)) # Strauss process in a polygonal window. x <- c(0.55,0.68,0.75,0.58,0.39,0.37,0.19,0.26,0.42) y <- c(0.20,0.27,0.68,0.99,0.80,0.61,0.45,0.28,0.33) mod03 <- list(cif="strauss",par=list(beta=2000,gamma=0.6,r=0.07), w=owin(poly=list(x=x,y=y))) X4.strauss <- rmh(model=mod03,start=list(n.start=90), control=list(nrep=nr)) # Strauss process in a polygonal window, conditioning on n = 42. X5.strauss <- rmh(model=mod03,start=list(n.start=42), control=list(p=1,nrep=nr)) stopifnot(X5.strauss$n == 42) # Strauss process, starting off from X4.strauss, but with the # polygonal window replace by a rectangular one. At the end, # the generated pattern is clipped to the original polygonal window. xxx <- X4.strauss xxx$window <- as.owin(c(0,1,0,1)) X6.strauss <- rmh(model=mod03,start=list(x.start=xxx), control=list(nrep=nr)) # Strauss with hardcore: mod04 <- list(cif="straush",par=list(beta=2,gamma=0.2,r=0.7,hc=0.3), w=c(0,10,0,10)) X1.straush <- rmh(model=mod04,start=list(n.start=70), control=list(nrep=nr)) # Another Strauss with hardcore (with a perhaps surprising result): mod05 <- list(cif="straush",par=list(beta=80,gamma=0.36,r=45,hc=2.5), w=c(0,250,0,250)) X2.straush <- rmh(model=mod05,start=list(n.start=250), control=list(nrep=nr)) # Pure hardcore (identical to X3.strauss). mod06 <- list(cif="straush",par=list(beta=2,gamma=1,r=1,hc=0.7), w=c(0,10,0,10)) X3.straush <- rmh(model=mod06,start=list(n.start=60), control=list(nrep=nr)) # Area-interaction, inhibitory mod.area <- list(cif="areaint",par=list(beta=2,eta=0.5,r=0.5), w=square(10)) X.area <- rmh(model=mod.area,start=list(n.start=60), control=list(nrep=nr)) # Area-interaction, clustered mod.area2 <- list(cif="areaint",par=list(beta=2,eta=1.5,r=0.5), w=square(10)) X.area2 <- rmh(model=mod.area2,start=list(n.start=60), control=list(nrep=nr)) # Area-interaction close to hard core set.seed(42) mod.area0 <- list(cif="areaint",par=list(beta=2,eta=1e-300,r=0.35), w=square(10)) X.area0 <- rmh(model=mod.area0,start=list(x.start=X3.hardcore), control=list(nrep=nr)) stopifnot(nndist(X.area0) > 0.6) # Soft core: w <- c(0,10,0,10) mod07 <- list(cif="sftcr",par=list(beta=0.8,sigma=0.1,kappa=0.5), w=c(0,10,0,10)) X.sftcr <- rmh(model=mod07,start=list(n.start=70), control=list(nrep=nr)) # Diggle, Gates, and Stibbard: mod12 <- list(cif="dgs",par=list(beta=3600,rho=0.08),w=c(0,1,0,1)) X.dgs <- rmh(model=mod12,start=list(n.start=300), control=list(nrep=nr)) # Diggle-Gratton: mod13 <- list(cif="diggra", par=list(beta=1800,kappa=3,delta=0.02,rho=0.04), w=square(1)) X.diggra <- rmh(model=mod13,start=list(n.start=300), control=list(nrep=nr)) # Geyer: mod14 <- list(cif="geyer",par=list(beta=1.25,gamma=1.6,r=0.2,sat=4.5), w=c(0,10,0,10)) X1.geyer <- rmh(model=mod14,start=list(n.start=200), control=list(nrep=nr)) # Geyer; same as a Strauss process with parameters # (beta=2.25,gamma=0.16,r=0.7): mod15 <- list(cif="geyer",par=list(beta=2.25,gamma=0.4,r=0.7,sat=10000), w=c(0,10,0,10)) X2.geyer <- rmh(model=mod15,start=list(n.start=200), control=list(nrep=nr)) mod16 <- list(cif="geyer",par=list(beta=8.1,gamma=2.2,r=0.08,sat=3)) data(redwood) X3.geyer <- rmh(model=mod16,start=list(x.start=redwood), control=list(periodic=TRUE,nrep=nr)) # Geyer, starting from the redwood data set, simulating # on a torus, and conditioning on n: X4.geyer <- rmh(model=mod16,start=list(x.start=redwood), control=list(p=1,periodic=TRUE,nrep=nr)) # Lookup (interaction function h_2 from page 76, Diggle (2003)): r <- seq(from=0,to=0.2,length=101)[-1] # Drop 0. h <- 20*(r-0.05) h[r<0.05] <- 0 h[r>0.10] <- 1 mod17 <- list(cif="lookup",par=list(beta=4000,h=h,r=r),w=c(0,1,0,1)) X.lookup <- rmh(model=mod17,start=list(n.start=100), control=list(nrep=nr)) # Strauss with trend tr <- function(x,y){x <- x/250; y <- y/250; exp((6*x + 5*y - 18*x^2 + 12*x*y - 9*y^2)/6) } beta <- 0.3 gmma <- 0.5 r <- 45 tr3 <- function(x,y){x <- x/250; y <- y/250; exp((6*x + 5*y - 18*x^2 + 12*x*y - 9*y^2)/6) } # log quadratic trend mod17 <- list(cif="strauss",par=list(beta=beta,gamma=gmma,r=r),w=c(0,250,0,250), trend=tr3) X1.strauss.trend <- rmh(model=mod17,start=list(n.start=90), control=list(nrep=nr)) }) # Things which should cause an error require(spatstat) local({ if(!exists("nv")) nv <- 0 if(!exists("nr")) nr <- 1e3 # Strauss with zero intensity and p = 1 mod0S <- list(cif="strauss",par=list(beta=0,gamma=0.6,r=0.7), w = square(3)) out <- try(X0S <- rmh(model=mod0S,start=list(n.start=80), control=list(p=1,nrep=nr,nverb=nv),verbose=FALSE)) if(!inherits(out, "try-error")) stop("Error not trapped (Strauss with zero intensity and p = 1) in tests/rmhErrors.R") }) # # tests/rmhExpand.R # # test decisions about expansion of simulation window # # $Revision: 1.2 $ $Date: 2011/12/05 07:29:16 $ # require(spatstat) local({ fit <- ppm(cells, ~x) # check rmhmodel.ppm mod <- rmhmodel(fit) wsim <- as.rectangle(mod$trend) if(!identical(wsim, as.owin(cells))) stop("Expansion occurred improperly in rmhmodel.ppm") }) # # tests of rmh, running multitype point processes # require(spatstat) local({ if(!exists("nr")) nr <- 5e3 if(!exists("nv")) nv <- 0 spatstat.options(expand=1.1) # Multitype Poisson modp2 <- list(cif="poisson", par=list(beta=2), types=letters[1:3], w = square(10)) Xp2 <- rmh(modp2, start=list(n.start=0), control=list(p=1)) # Multitype Strauss: beta <- c(0.027,0.008) gmma <- matrix(c(0.43,0.98,0.98,0.36),2,2) r <- matrix(c(45,45,45,45),2,2) mod08 <- list(cif="straussm",par=list(beta=beta,gamma=gmma,radii=r), w=c(0,250,0,250)) X1.straussm <- rmh(model=mod08,start=list(n.start=80), control=list(ptypes=c(0.75,0.25),nrep=nr,nverb=nv)) # Multitype Strauss conditioning upon the total number # of points being 80: X2.straussm <- rmh(model=mod08,start=list(n.start=80), control=list(p=1,ptypes=c(0.75,0.25),nrep=nr, nverb=nv)) stopifnot(X2.straussm$n == 80) # Conditioning upon the number of points of type 1 being 60 # and the number of points of type 2 being 20: X3.straussm <- rmh(model=mod08,start=list(n.start=c(60,20)), control=list(fixall=TRUE,p=1,ptypes=c(0.75,0.25), nrep=nr,nverb=nv)) stopifnot(all(table(X3.straussm$marks) == c(60,20))) # Multitype Strauss hardcore: rhc <- matrix(c(9.1,5.0,5.0,2.5),2,2) mod09 <- list(cif="straushm",par=list(beta=beta,gamma=gmma, iradii=r,hradii=rhc),w=c(0,250,0,250)) X.straushm <- rmh(model=mod09,start=list(n.start=80), control=list(ptypes=c(0.75,0.25),nrep=nr,nverb=nv)) # Multitype Strauss hardcore with trends for each type: beta <- c(0.27,0.08) tr3 <- function(x,y){x <- x/250; y <- y/250; exp((6*x + 5*y - 18*x^2 + 12*x*y - 9*y^2)/6) } # log quadratic trend tr4 <- function(x,y){x <- x/250; y <- y/250; exp(-0.6*x+0.5*y)} # log linear trend mod10 <- list(cif="straushm",par=list(beta=beta,gamma=gmma, iradii=r,hradii=rhc),w=c(0,250,0,250), trend=list(tr3,tr4)) X1.straushm.trend <- rmh(model=mod10,start=list(n.start=350), control=list(ptypes=c(0.75,0.25), nrep=nr,nverb=nv)) # Multitype Strauss hardcore with trends for each type, given as images: bigwin <- square(250) i1 <- as.im(tr3, bigwin) i2 <- as.im(tr4, bigwin) mod11 <- list(cif="straushm",par=list(beta=beta,gamma=gmma, iradii=r,hradii=rhc),w=bigwin, trend=list(i1,i2)) X2.straushm.trend <- rmh(model=mod11,start=list(n.start=350), control=list(ptypes=c(0.75,0.25),expand=1, nrep=nr,nverb=nv)) ####################################################################### ############ checks on distribution of output ####################### ####################################################################### checkp <- function(p, context, testname, failmessage, pcrit=0.01) { if(missing(failmessage)) failmessage <- paste("output failed", testname) if(p < pcrit) warning(paste(context, ",", failmessage), call.=FALSE) cat(paste("\n", context, ",", testname, "has p-value", signif(p,4), "\n")) } # Multitype Strauss code; output is multitype Poisson beta <- 100 * c(1,1) ri <- matrix(0.07, 2, 2) gmma <- matrix(1, 2, 2) # no interaction tr1 <- function(x,y){ rep(1, length(x)) } tr2 <- function(x,y){ rep(2, length(x)) } mod <- rmhmodel(cif="straussm", par=list(beta=beta,gamma=gmma,radii=ri), w=owin(), trend=list(tr1,tr2)) X <- rmh(mod, start=list(n.start=0), control=list(nrep=1e6)) # The model is Poisson with intensity 100 for type 1 and 200 for type 2. # Total number of points is Poisson (300) # Marks are i.i.d. with P(type 1) = 1/3, P(type 2) = 2/3. # Test whether the total intensity looks right # p <- ppois(X$n, 300) p.val <- 2 * min(p, 1-p) checkp(p.val, "In multitype Poisson simulation", "test whether total number of points has required mean value") # Test whether the mark distribution looks right ta <- table(X$marks) cat("Frequencies of marks:") print(ta) checkp(chisq.test(ta, p = c(1,2)/3)$p.value, "In multitype Poisson simulation", "chi-squared goodness-of-fit test for mark distribution (1/3, 2/3)") ##### #### multitype Strauss code; fixall=TRUE; #### output is multinomial process with nonuniform locations #### the.context <- "In nonuniform multinomial simulation" beta <- 100 * c(1,1) ri <- matrix(0.07, 2, 2) gmma <- matrix(1, 2, 2) # no interaction tr1 <- function(x,y){ ifelse(x < 0.5, 0, 2) } tr2 <- function(x,y){ ifelse(y < 0.5, 1, 3) } # cdf of these distributions Fx1 <- function(x) { ifelse(x < 0.5, 0, ifelse(x < 1, 2 * x - 1, 1)) } Fy2 <- function(y) { ifelse(y < 0, 0, ifelse(y < 0.5, y/2, ifelse(y < 1, (1/2 + 3 * (y-1/2))/2, 1))) } mod <- rmhmodel(cif="straussm", par=list(beta=beta,gamma=gmma,radii=ri), w=owin(), trend=list(tr1,tr2)) X <- rmh(mod, start=list(n.start=c(50,50)), control=list(nrep=1e6, expand=1, p=1, fixall=TRUE)) # The model is Poisson # Mean number of type 1 points = 100 # Mean number of type 2 points = 200 # Total intensity = 300 # Marks are i.i.d. with P(type 1) = 1/3, P(type 2) = 2/3 # Test whether the coordinates look OK Y <- split(X) X1 <- Y[[names(Y)[1]]] X2 <- Y[[names(Y)[2]]] checkp(ks.test(X1$y, "punif")$p.value, the.context, "Kolmogorov-Smirnov test of uniformity of y coordinates of type 1 points") if(any(X1$x < 0.5)) { warning(paste(the.context, ",", "x-coordinates of type 1 points are IMPOSSIBLE"), call.=FALSE) } else { checkp(ks.test(Fx1(X1$x), "punif")$p.value, the.context, "Kolmogorov-Smirnov test of uniformity of transformed x coordinates of type 1 points") } checkp(ks.test(X2$x, "punif")$p.value, the.context, "Kolmogorov-Smirnov test of uniformity of x coordinates of type 2 points") checkp(ks.test(Fy2(X2$y), "punif")$p.value, the.context, "Kolmogorov-Smirnov test of uniformity of transformed y coordinates of type 2 points") }) # # tests/rmhTrend.R # # Problems with trend images (rmhmodel.ppm or rmhEngine) # require(spatstat) local({ set.seed(42) # Bug folder 37 of 8 feb 2011 # rmhmodel.ppm -> predict.ppm # + rmhResolveTypes -> is.subset.owin data(demopat) Z <- rescale(demopat, 7000) X <- unmark(Z) X1 <- split(Z)[[1]] Int <- density(X,dimyx=200) Lint <- eval.im(log(npoints(X1)*Int/npoints(X))) M <- as.owin(Int) MR <- intersect.owin(M,scalardilate(M,0.5,origin="midpoint")) X1 <- X1[MR] Fut <- ppm(X1,~offset(Lint),covariates=list(Lint=Lint), inter=BadGey(r=c(0.03,0.05),sat=3)) Y <- rmh(Fut,control=list(expand=M,nrep=1e3), verbose=FALSE) }) # strange boundary cases require(spatstat) local({ if(!exists("nv")) nv <- 0 if(!exists("nr")) nr <- 5e3 # Poisson process cat("Poisson\n") modP <- list(cif="poisson",par=list(beta=10), w = square(3)) XP <- rmh(model = modP, start = list(n.start=25), control=list(nrep=nr,nverb=nv)) # Poisson process case of Strauss cat("\nPoisson case of Strauss\n") modPS <- list(cif="strauss",par=list(beta=10,gamma=1,r=0.7), w = square(3)) XPS <- rmh(model=modPS, start=list(n.start=25), control=list(nrep=nr,nverb=nv)) # Strauss with zero intensity cat("\nStrauss with zero intensity\n") mod0S <- list(cif="strauss",par=list(beta=0,gamma=0.6,r=0.7), w = square(3)) X0S <- rmh(model=mod0S,start=list(n.start=80), control=list(nrep=nr,nverb=nv)) stopifnot(X0S$n == 0) # Poisson with zero intensity cat("\nPoisson with zero intensity\n") mod0P <- list(cif="poisson",par=list(beta=0), w = square(3)) X0P <- rmh(model = mod0P, start = list(n.start=25), control=list(nrep=nr,nverb=nv)) # Poisson conditioned on zero points cat("\nPoisson conditioned on zero points\n") modp <- list(cif="poisson", par=list(beta=2), w = square(10)) Xp <- rmh(modp, start=list(n.start=0), control=list(p=1, nrep=nr)) stopifnot(Xp$n == 0) # Multitype Poisson conditioned on zero points cat("\nMultitype Poisson conditioned on zero points\n") modp2 <- list(cif="poisson", par=list(beta=2), types=letters[1:3], w = square(10)) Xp2 <- rmh(modp2, start=list(n.start=0), control=list(p=1, nrep=nr)) stopifnot(is.marked(Xp2)) stopifnot(Xp2$n == 0) # Multitype Poisson conditioned on zero points of each type cat("\nMultitype Poisson conditioned on zero points of each type\n") Xp2fix <- rmh(modp2, start=list(n.start=c(0,0,0)), control=list(p=1, fixall=TRUE, nrep=nr)) stopifnot(is.marked(Xp2fix)) stopifnot(Xp2fix$n == 0) }) # # tests of rmhmodel.ppm # require(spatstat) local({ f <- ppm(cells) m <- rmhmodel(f) f <- ppm(cells, ~x) m <- rmhmodel(f) f <- ppm(cells, ~1, Strauss(0.1)) m <- rmhmodel(f) f <- ppm(cells, ~1, StraussHard(r=0.1,hc=0.05)) m <- rmhmodel(f) f <- ppm(cells, ~1, Hardcore(0.07)) m <- rmhmodel(f) f <- ppm(cells, ~1, DiggleGratton(0.05,0.1)) m <- rmhmodel(f) f <- ppm(cells, ~1, Softcore(0.5), correction="isotropic") m <- rmhmodel(f) f <- ppm(cells, ~1, Geyer(0.07,2)) m <- rmhmodel(f) f <- ppm(cells, ~1, BadGey(c(0.07,0.1,0.13),2)) m <- rmhmodel(f) f <- ppm(cells, ~1, PairPiece(r = c(0.05, 0.1, 0.2))) m <- rmhmodel(f) f <- ppm(cells, ~1, AreaInter(r=0.06)) m <- rmhmodel(f) # multitype r <- matrix(0.07, 2, 2) f <- ppm(amacrine, ~1, MultiStrauss(c("off","on"),r)) m <- rmhmodel(f) h <- matrix(min(nndist(amacrine))/2, 2, 2) f <- ppm(amacrine, ~1, MultiStraussHard(c("off","on"),r, h)) m <- rmhmodel(f) diag(r) <- NA diag(h) <- NA f <- ppm(amacrine, ~1, MultiStrauss(c("off","on"),r)) m <- rmhmodel(f) f <- ppm(amacrine, ~1, MultiStraussHard(c("off","on"),r, h)) m <- rmhmodel(f) # multitype data, interaction not dependent on type f <- ppm(amacrine, ~marks, Strauss(0.05)) m <- rmhmodel(f) # trends f <- ppm(cells, ~x, Strauss(0.1)) m <- rmhmodel(f) f <- ppm(cells, ~y, StraussHard(r=0.1,hc=0.05)) m <- rmhmodel(f) f <- ppm(cells, ~x+y, Hardcore(0.07)) m <- rmhmodel(f) f <- ppm(cells, ~polynom(x,y,2), Softcore(0.5), correction="isotropic") m <- rmhmodel(f) # covariates Z <- as.im(function(x,y){ x^2+y^2 }, as.owin(cells)) f <- ppm(cells, ~z, covariates=list(z=Z)) m <- rmhmodel(f) m <- rmhmodel(f, control=list(p=1)) Zim <- as.im(Z, as.owin(cells)) f <- ppm(cells, ~z, covariates=list(z=Zim)) m <- rmhmodel(f) Z <- as.im(function(x,y){ x^2+y }, as.owin(amacrine)) f <- ppm(amacrine, ~z + marks, covariates=list(z=Z)) m <- rmhmodel(f) m <- rmhmodel(f, control=list(p=1)) m <- rmhmodel(f, control=list(p=1,fixall=TRUE)) Zim <- as.im(Z, as.owin(amacrine)) f <- ppm(amacrine, ~z + marks, covariates=list(z=Zim)) m <- rmhmodel(f) }) # # tests/rmhmodelHybrids.R # # Test that rmhmodel.ppm and rmhmodel.default # work on Hybrid interaction models # # $Revision: 1.3 $ $Date: 2013/01/29 02:12:13 $ # require(spatstat) local({ # ......... rmhmodel.ppm ....................... fit1 <- ppm(redwood, ~1, Hybrid(A=Strauss(0.02), B=Geyer(0.1, 2), C=Geyer(0.15, 1))) m1 <- rmhmodel(fit1) m1 reach(m1) # Test of handling 'IsOffset' data(cells) fit2 <- ppm(cells, ~1, Hybrid(H=Hardcore(0.05), G=Geyer(0.15, 2))) rmhmodel(fit2) # Test of handling Poisson components fit3 <- ppm(cells, ~1, Hybrid(P=Poisson(), S=Strauss(0.05))) X3 <- rmh(fit3, control=list(nrep=1e3,expand=1), verbose=FALSE) # ............ rmhmodel.default ............................ modH <- list(cif=c("strauss","geyer"), par=list(list(beta=50,gamma=0.5, r=0.1), list(beta=1, gamma=0.7, r=0.2, sat=2)), w = square(1)) rmodH <- rmhmodel(modH) rmodH reach(rmodH) # test handling of Poisson components modHP <- list(cif=c("poisson","strauss"), par=list(list(beta=5), list(beta=10,gamma=0.5, r=0.1)), w = square(1)) rmodHP <- rmhmodel(modHP) rmodHP reach(rmodHP) modPP <- list(cif=c("poisson","poisson"), par=list(list(beta=5), list(beta=10)), w = square(1)) rmodPP <- rmhmodel(modPP) rmodPP reach(rmodPP) }) # # tests/rmh.ppm.R # # $Revision: 1.1 $ $Date: 2012/10/14 07:24:21 $ # # Examples removed from rmh.ppm.Rd # stripped down to minimal tests of validity # require(spatstat) local({ op <- spatstat.options() spatstat.options(rmh.nrep=10, npixel=10, ndummy.min=10) spatstat.options(project.fast=TRUE) Nrep <- 10 X <- swedishpines # Poisson process fit <- ppm(X, ~1, Poisson()) Xsim <- rmh(fit) # Strauss process fit <- ppm(X, ~1, Strauss(r=7)) Xsim <- rmh(fit) # Strauss process simulated on a larger window # then clipped to original window Xsim <- rmh(fit, control=list(nrep=Nrep, expand=1.1, periodic=TRUE)) # Strauss - hard core process # fit <- ppm(X, ~1, StraussHard(r=7,hc=2)) # Xsim <- rmh(fit, start=list(n.start=X$n)) # Geyer saturation process # fit <- ppm(X, ~1, Geyer(r=7,sat=2)) # Xsim <- rmh(fit, start=list(n.start=X$n)) # Area-interaction process fit <- ppm(X, ~1, AreaInter(r=7)) Xsim <- rmh(fit, start=list(n.start=X$n)) # soft core interaction process # X <- quadscheme(X, nd=50) # fit <- ppm(X, ~1, Softcore(kappa=0.1), correction="isotropic") # Xsim <- rmh(fit, start=list(n.start=X$n)) # Diggle-Gratton pairwise interaction model # fit <- ppm(cells, ~1, DiggleGratton(0.05, 0.1)) # Xsim <- rmh(fit, start=list(n.start=cells$n)) # plot(Xsim, main="simulation from fitted Diggle-Gratton model") X <- rSSI(0.05, 100) # piecewise-constant pairwise interaction function fit <- ppm(X, ~1, PairPiece(seq(0.02, 0.1, by=0.01))) Xsim <- rmh(fit) # marked point pattern Y <- amacrine # marked Poisson models fit <- ppm(Y) Ysim <- rmh(fit) fit <- ppm(Y,~marks) Ysim <- rmh(fit) fit <- ppm(Y,~x) Ysim <- rmh(fit) # fit <- ppm(Y,~polynom(x,2)) # Ysim <- rmh(fit) fit <- ppm(Y,~marks+x) Ysim <- rmh(fit) # fit <- ppm(Y,~marks+polynom(x,2)) # Ysim <- rmh(fit) # multitype Strauss models MS <- MultiStrauss(types = levels(Y$marks), radii=matrix(0.07, ncol=2, nrow=2)) # fit <- ppm(Y,~marks*polynom(x,2), MS) fit <- ppm(Y,~marks*x, MS) Ysim <- rmh(fit) spatstat.options(op) }) # fvproblems.R require(spatstat) # This appears in the workshop notes # Problem detected by Martin Bratschi local({ Jdif <- function(X, ..., i) { Jidot <- Jdot(X, ..., i=i) J <- Jest(X, ...) dif <- eval.fv(Jidot - J) return(dif) } Z <- Jdif(amacrine, i="on") }) # # Test mathlegend code # local({ K <- Kest(cells) plot(K) plot(K, . ~ r) plot(K, . - theo ~ r) plot(K, sqrt(./pi) ~ r) plot(K, cbind(iso, theo) ~ r) plot(K, cbind(iso, theo) - theo ~ r) plot(K, sqrt(cbind(iso, theo)/pi) ~ r) plot(K, cbind(iso/2, -theo) ~ r) plot(K, cbind(iso/2, trans/2) - theo ~ r) # test expansion of .x and .y plot(K, . ~ .x) plot(K, . - theo ~ .x) plot(K, .y - theo ~ .x) plot(K, sqrt(.y) - sqrt(theo) ~ .x) # problems with parsing weird strings in levels(marks(X)) # noted by Ulf Mehlig levels(marks(amacrine)) <- c("Nastricreechia krorluppia", "Homo habilis") plot(Kcross(amacrine)) plot(alltypes(amacrine, "K")) plot(alltypes(amacrine, "J")) plot(alltypes(amacrine, pcfcross)) }) ## tests involving strange mark values ## $Revision: 1.2 $ $Date: 2014/01/08 11:15:05 $ require(spatstat) local({ ## ppm() where mark levels contain illegal characters hyphenated <- c("a", "not-a") spaced <- c("U", "non U") suffixed <- c("a+", "a*") charred <- c("+", "*") irad <- matrix(0.1, 2,2) hrad <- matrix(0.005, 2, 2) tryit <- function(types, X, irad, hrad) { levels(marks(X)) <- types fit <- ppm(X, ~marks + polynom(x,y,2), MultiStraussHard(types=types,iradii=irad,hradii=hrad)) print(fit) print(coef(fit)) val <- fitted(fit) pred <- predict(fit) return(invisible(NULL)) } tryit(hyphenated, amacrine, irad, hrad) tryit(spaced, amacrine, irad, hrad) tryit(suffixed, amacrine, irad, hrad) tryit(charred, amacrine, irad, hrad) ## marks which are dates X <- cells n <- npoints(X) endoftime <- rep(ISOdate(2001,1,1), n) eotDate <- rep(as.Date("2001-01-01"), n) markformat(endoftime) markformat(eotDate) marks(X) <- endoftime print(X) Y <- X %mark% data.frame(id=1:42, date=endoftime, dd=eotDate) print(Y) }) # # test cases where there are no (rows or columns of) marks # require(spatstat) local({ n <- npoints(cells) df <- data.frame(x=1:n, y=factor(sample(letters, n, replace=TRUE))) nocolumns <- c(FALSE, FALSE) norows <- rep(FALSE, n) X <- cells marks(X) <- df marks(X) <- df[,1] marks(X) <- df[,nocolumns] Z <- Y <- X[integer(0)] marks(Y) <- df[norows,] stopifnot(is.marked(Y)) marks(Z) <- df[norows,nocolumns] stopifnot(!is.marked(Z)) }) # checks validity of fast C implementation of Geyer interaction require(spatstat) local({ X <- redwood Q <- quadscheme(X) U <- union.quad(Q) EP <- equalpairs.quad(Q) G <- Geyer(0.11, 2) # The value r=0.11 is chosen to avoid hardware numerical effects (gcc bug 323). # It avoids being close any value of pairdist(redwood). # The nearest such values are 0.1077.. and 0.1131.. # By contrast if r = 0.1 there are values differing from 0.1 by 3e-17 a <- pairsat.family$eval(X,U,EP,G$pot,G$par,"border") b <- G$fasteval(X,U,EP,G$pot,G$par,"border") if(!all(a==b)) stop("Results of Geyer()$fasteval and pairsat.family$eval do not match") # ... # and again for a non-integer value of 'sat' # (spotted by Thordis Linda Thorarinsdottir) G <- Geyer(0.11, 2.5) a <- pairsat.family$eval(X,U,EP,G$pot,G$par,"border") b <- G$fasteval(X,U,EP,G$pot,G$par,"border") if(!all(a==b)) stop("Results of Geyer()$fasteval and pairsat.family$eval do not match when sat is not an integer") }) require(spatstat) local({ co <- as.ppp(corners(letterR), letterR, check=FALSE) co[letterR] }) # tests/segments.R # $Revision: 1.7 $ $Date: 2011/12/05 07:29:16 $ require(spatstat) local({ # pointed out by Jeff Laake W <- owin() X <- psp(x0=.25,x1=.25,y0=0,y1=1,window=W) X[W] # migrated from 'lpp' X <- psp(runif(10),runif(10),runif(10),runif(10), window=owin()) Z <- as.mask.psp(X) Z <- pixellate(X) # test of distppll pointed out by Ang Qi Wei p <- matrix(c(1.5, 0), 1, 2) l <- matrix(c(0,0,1,0,1,0,2,0), 2, 4, byrow=T) a <- distppll(p, l, mintype=2, method="interpreted") b <- distppll(p, l, mintype=2, method="Fortran") d <- distppll(p, l, mintype=2, method="C") if(a$min.which != b$min.which) stop("conflict between Fortran and interpreted code in distppll") if(a$min.which != d$min.which) stop("conflict between C and interpreted code in distppll") # tests of pixellate.psp -> seg2pixL ns <- 50 out <- numeric(ns) for(i in 1:ns) { X <- psp(runif(1), runif(1), runif(1), runif(1), window=owin()) len <- lengths.psp(X) dlen <- sum(pixellate(X)$v) out[i] <- if(len > 1e-7) dlen/len else 1 } if(diff(range(out)) > 0.01) stop(paste( "pixellate.psp test 1: relative error [", paste(diff(range(out)), collapse=", "), "]")) # Michael Sumner's test examples set.seed(33) n <- 2001 co <- cbind(runif(n), runif(n)) ow <- owin() X <- psp(co[-n,1], co[-n,2], co[-1,1], co[-1,2], window=ow) s1 <- sum(pixellate(X)) s2 <- sum(lengths.psp(X)) if(abs(s1 - s2)/s2 > 0.01) { stop(paste("pixellate.psp test 2:", "sum(pixellate(X)) = ", s1, "!=", s2, "= sum(lengths.psp(X))")) } wts <- 1/(lengths.psp(X) * X$n) s1 <- sum(pixellate(X, weights=wts)) if(abs(s1-1) > 0.01) { stop(paste("pixellate.psp test 3:", "sum(pixellate(X, weights))=", s1, " (should be 1)")) } X <- psp(0, 0, 0.01, 0.001, window=owin()) s1 <- sum(pixellate(X)) s2 <- sum(lengths.psp(X)) if(abs(s1 - s2)/s2 > 0.01) { stop(paste("pixellate.psp test 4:", "sum(pixellate(X)) = ", s1, "!=", s2, "= sum(lengths.psp(X))")) } X <- psp(0, 0, 0.001, 0.001, window=owin()) s1 <- sum(pixellate(X)) s2 <- sum(lengths.psp(X)) if(abs(s1 - s2)/s2 > 0.01) { stop(paste("pixellate.psp test 5:", "sum(pixellate(X)) = ", s1, "!=", s2, "= sum(lengths.psp(X))")) } }) # test for step() operation # require(spatstat) local({ Z <- as.im(function(x,y){ x^3 - y^2 }, nztrees$window) fitP <- ppm(nztrees, ~x+y+Z, covariates=list(Z=Z)) step(fitP) fitS <- update(fitP, Strauss(7)) step(fitS) fitM <- ppm(amacrine, ~ marks*(x+y), MultiStrauss(types=levels(marks(amacrine)), radii=matrix(0.04, 2, 2))) step(fitM) }) ## badwindowcheck.R ## $Revision: 1.2 $ $Date: 2014/01/27 07:18:41 $ ## require(spatstat) local({ ## Simple example of self-crossing polygon x <- read.table("selfcross.txt", header=TRUE) ## Auto-repair w <- owin(poly=x) ## Real data involving various quirks b <- read.table("badwindow.txt", header=TRUE) b <- split(b, factor(b$i)) b <- lapply(b, function(z) { as.list(z[,-3]) }) ## make owin without checking W <- owin(poly=b, check=FALSE) ## Apply stringent checks owinpolycheck(W,verbose=FALSE) ## Auto-repair W2 <- owin(poly=b) }) # # tests/hobjects.R # # Validity of methods for ppm(... method="ho") # require(spatstat) local({ fit <- ppm(cells, ~1, Strauss(0.1), method="ho", nsim=10) fitx <- ppm(cells, ~offset(x), Strauss(0.1), method="ho", nsim=10) a <- AIC(fit) ax <- AIC(fitx) f <- fitted(fit) fx <- fitted(fitx) p <- predict(fit) px <- predict(fitx) }) # check kstest with strange data require(spatstat) local({ # Marked point patterns with some marks not represented AC <- split(ants, un=FALSE)$Cataglyphis AM <- split(ants, un=FALSE)$Messor DM <- distmap(AM) # should produce a warning, rather than a crash: kstest(AC, DM) # should be OK: kstest(unmark(AC), DM) # linear networks X <- runiflpp(20, simplenet) fit <- lppm(X, ~1) kstest(fit, "y") }) # # tests/kppm.R # # $Revision: 1.6 $ $Date: 2012/04/08 03:22:20 $ # # Test functionality of kppm that depends on RandomFields # require(spatstat) local({ if(require(RandomFields) && RandomFieldsSafe()) { fit0 <- kppm(redwood, ~1, "LGCP") simulate(fit0) fit <- kppm(redwood, ~x, "LGCP", covmodel=list(model="matern", nu=0.3), control=list(maxit=5)) simulate(fit) # ... and Abdollah's code fit <- kppm(redwood, ~x, cluster="Cauchy", statistic="K") simulate(fit) } }) # temporary test file for localpcfmatrix require(spatstat) local({ a <- localpcfmatrix(redwood) a plot(a) a[, 3:5] }) # check for various bugs related to factor conversions require(spatstat) local({ # make a factor image m <- factor(rep(letters[1:4], 4)) Z <- im(m, xcol=1:4, yrow=1:4) # make a point pattern set.seed(42) X <- runifpoint(20, win=as.owin(Z)) # look up the image at the points of X # (a) internal ans1 <- lookup.im(Z, X$x, X$y) stopifnot(is.factor(ans1)) # (b) user level ans2 <- Z[X] stopifnot(is.factor(ans2)) # (c) turn the image into a tessellation # and apply quadratcount V <- tess(image = Z) quadratcount(X, tess=V) }) # # tests/splitpea.R # # Check behaviour of split.ppp etc # # Thanks to Marcelino de la Cruz # # $Revision: 1.9 $ $Date: 2014/02/05 09:30:38 $ # require(spatstat) local({ W <- square(8) X <- ppp(c(2.98, 4.58, 7.27, 1.61, 7.19), c(7.56, 5.29, 5.03, 0.49, 1.65), window=W) Z <- quadrats(W, 4, 4) Yall <- split(X, Z, drop=FALSE) Ydrop <- split(X, Z, drop=TRUE) P <- Yall[[1]] if(!all(inside.owin(P$x, P$y, P$window))) stop("Black hole detected when drop=FALSE") P <- Ydrop[[1]] if(!all(inside.owin(P$x, P$y, P$window))) stop("Black hole detected when drop=TRUE") Ydrop[[1]] <- P[1] split(X, Z, drop=TRUE) <- Ydrop # test NA handling Zbad <- quadrats(square(4), 2, 2) Ybdrop <- split(X, Zbad, drop=TRUE) Yball <- split(X, Zbad, drop=FALSE) # From Marcelino set.seed(1) W<- square(10) # the big window puntos<- rpoispp(0.5, win=W) data(letterR) r00 <- letterR r05 <- shift(letterR,c(0,5)) r50 <- shift(letterR,c(5,0)) r55 <- shift(letterR,c(5,5)) tessr4 <- tess(tiles=list(r00, r05,r50,r55)) puntosr4 <- split(puntos, tessr4, drop=TRUE) split(puntos, tessr4, drop=TRUE) <- puntosr4 ## More headaches with mark format A <- runifpoint(10) B <- runifpoint(10) AB <- split(superimpose(A=A, B=B)) }) # # tests/imageops.R # # $Revision: 1.6 $ $Date: 2014/03/20 04:02:20 $ # require(spatstat) local({ A <- as.im(owin()) B <- as.im(owin(c(1.1, 1.9), c(0,1))) Z <- imcov(A, B) stopifnot(abs(max(Z) - 0.8) < 0.1) ## handling images with 1 row or column ycov <- function(x, y) y E <- as.im(ycov, owin(), dimyx = c(2,1)) G <- cut(E, 2) H <- as.tess(G) E12 <- as.im(ycov, owin(), dimyx = c(1,2)) G12 <- cut(E12, 2) H12 <- as.tess(G12) }) # tests/triplets.R # test code for triplet interaction # $Revision: 1.4 $ $Date: 2012/07/12 02:43:32 $ require(spatstat) local({ fit <- ppm(redwood, ~1, Triplets(0.1)) fit suffstat(fit) # hard core (zero triangles, coefficient is NA) fit0 <- ppm(cells, ~1, Triplets(0.05)) fit0 suffstat(fit0) # bug case (1 triangle in data) fit1 <- ppm(cells, ~1, Triplets(0.15)) fit1 suffstat(fit1) }) # # tests/project.ppm.R # # $Revision: 1.3 $ $Date: 2012/10/22 03:12:08 $ # # Tests of projection mechanism # require(spatstat) local({ # a very unidentifiable model fit <- ppm(cells, ~Z, Strauss(1e-06), covariates=list(Z=0)) project.ppm(fit) # multitype fit2 <- ppm(amacrine, ~1, MultiStrauss(types=c("off", "on"), radii=matrix(1e-06, 2, 2))) project.ppm(fit2) # hybrids r0 <- min(nndist(redwood)) ra <- 1.25 * r0 rb <- 0.8 * r0 f <- ppm(redwood, ~1, Hybrid(A=Strauss(ra), B=Geyer(0.1, 2)), project=TRUE) f <- ppm(redwood, ~1, Hybrid(A=Strauss(rb), B=Geyer(0.1, 2)), project=TRUE) f <- ppm(redwood, ~1, Hybrid(A=Strauss(ra), B=Strauss(0.1)), project=TRUE) f <- ppm(redwood, ~1, Hybrid(A=Strauss(rb), B=Strauss(0.1)), project=TRUE) f <- ppm(redwood, ~1, Hybrid(A=Hardcore(rb), B=Strauss(0.1)), project=TRUE) f <- ppm(redwood, ~1, Hybrid(A=Hardcore(rb), B=Geyer(0.1, 2)), project=TRUE) f <- ppm(redwood, ~1, Hybrid(A=Geyer(rb, 1), B=Strauss(0.1)), project=TRUE) }) # # tests/hyperframe.R # # test "[.hyperframe" etc # # $Revision: 1.2 $ $Date: 2012/01/31 11:04:44 $ # lambda <- runif(4, min=50, max=100) X <- lapply(as.list(lambda), function(x) { rpoispp(x) }) h <- hyperframe(lambda=lambda, X=X) h$lambda2 <- lambda^2 h[, "lambda3"] <- lambda^3 h[, "Y"] <- X h[, "X"] <- lapply(X, flipxy) h[, c("X", "Y")] <- hyperframe(X=X, Y=X) # check fast code for Kest require(spatstat) local({ Kb <- Kest(cells, nlarge=0) Ku <- Kest(cells, correction="none") Kbu <- Kest(cells, correction=c("none", "border")) }) # # tests/vcovppm.R # # Check validity of vcov.ppm algorithms # # Thanks to Ege Rubak # # $Revision: 1.5 $ $Date: 2014/01/24 05:59:31 $ # require(spatstat) local({ set.seed(42) X <- rStrauss(200, .5, .05) model <- ppm(X, inter = Strauss(.05)) b <- vcov(model, generic = TRUE, algorithm = "basic") v <- vcov(model, generic = TRUE, algorithm = "vector") vc <- vcov(model, generic = TRUE, algorithm = "vectorclip") vn <- vcov(model, generic = FALSE) disagree <- function(x, y, tol=1e-7) { max(abs(x-y)) > tol } asymmetric <- function(x) { disagree(x, t(x)) } if(asymmetric(b)) stop("Non-symmetric matrix produced by vcov.ppm 'basic' algorithm") if(asymmetric(v)) stop("Non-symmetric matrix produced by vcov.ppm 'vector' algorithm") if(asymmetric(vc)) stop("Non-symmetric matrix produced by vcov.ppm 'vectorclip' algorithm") if(asymmetric(vn)) stop("Non-symmetric matrix produced by vcov.ppm Strauss algorithm") if(disagree(v, b)) stop("Disagreement between vcov.ppm algorithms 'vector' and 'basic' ") if(disagree(v, vc)) stop("Disagreement between vcov.ppm algorithms 'vector' and 'vectorclip' ") if(disagree(vn, vc)) stop("Disagreement between vcov.ppm generic and Strauss algorithms") # Geyer code xx <- c(0.7375956, 0.6851697, 0.6399788, 0.6188382) yy <- c(0.5816040, 0.6456319, 0.5150633, 0.6191592) Y <- ppp(xx, yy, window=square(1)) modelY <- ppm(Y, ~1, Geyer(0.1, 1)) b <- vcov(modelY, generic = TRUE, algorithm = "basic") v <- vcov(modelY, generic = TRUE, algorithm = "vector") vc <- vcov(modelY, generic = TRUE, algorithm = "vectorclip") if(asymmetric(b)) stop("Non-symmetric matrix produced by vcov.ppm 'basic' algorithm for Geyer model") if(asymmetric(v)) stop("Non-symmetric matrix produced by vcov.ppm 'vector' algorithm for Geyer model") if(asymmetric(vc)) stop("Non-symmetric matrix produced by vcov.ppm 'vectorclip' algorithm for Geyer model") if(disagree(v, b)) stop("Disagreement between vcov.ppm algorithms 'vector' and 'basic' for Geyer model") if(disagree(v, vc)) stop("Disagreement between vcov.ppm algorithms 'vector' and 'vectorclip' for Geyer model") ## tests of 'deltasuffstat' code ## Handling of offset terms modelH <- ppm(cells, ~x, Hardcore(0.05)) a <- vcov(modelH, generic=TRUE) ## may fall over b <- vcov(modelH, generic=FALSE) if(disagree(a, b)) stop("Disagreement between vcov.ppm algorithms for Hardcore model") ## Correctness of pairwise.family$delta2 modelZ <- ppm(amacrine, ~1, MultiStrauss(radii=matrix(0.1, 2, 2))) b <- vcov(modelZ, generic=FALSE) g <- vcov(modelZ, generic=TRUE) if(disagree(b, g)) stop("Disagreement between vcov.ppm algorithms for MultiStrauss model") }) # tests/windows.R # Tests of owin geometry code require(spatstat) local({ # Ege Rubak spotted this problem in 1.28-1 A <- as.owin(ants) B <- dilation(A, 140) if(!is.subset.owin(A, B)) stop("is.subset.owin fails in polygonal case") # thanks to Tom Rosenbaum A <- shift(square(3), origin="midpoint") B <- shift(square(1), origin="midpoint") AB <- setminus.owin(A, B) D <- shift(square(2), origin="midpoint") if(is.subset.owin(D,AB)) stop("is.subset.owin fails for polygons with holes") }) # # test addvar options # X <- rpoispp(function(x,y){exp(3+3*x)}) model <- ppm(X, ~y) addvar(model, "x", crosscheck=TRUE) addvar(model, "x", bw.input="quad") w <- square(0.5) addvar(model, "x", subregion=w) addvar(model, "x", subregion=w, bw.input="points") # additional test of parres X <- rpoispp(function(x,y){exp(3+x+2*x^2)}) model <- ppm(X, ~x+y) # options in parres parres(model, "x") parres(model, "x", bw.input="quad") w <- square(0.5) parres(model, "x", subregion=w) parres(model, "x", subregion=w, bw.input="quad") # check whether 'update.ppm' has messed up internals mod2 <- update(model, ~x) parres(mod2, "x") # # tests/lppstuff.R # # Tests for lpp code # # $Revision: 1.2 $ $Date: 2013/01/23 08:20:41 $ require(spatstat) local({ # check 'normalise' option in linearKinhom X <- rpoislpp(5, simplenet) fit <- lppm(X, ~x) K <- linearKinhom(X, lambda=fit, normalise=FALSE) plot(K) g <- linearpcfinhom(X, lambda=fit, normalise=FALSE) plot(g) K <- linearKinhom(X, lambda=fit, normalise=TRUE) plot(K) g <- linearpcfinhom(X, lambda=fit, normalise=TRUE) plot(g) # check empty patterns OK X <- runiflpp(0, simplenet) print(X) }) # # tests/density.R # # Test behaviour of density methods and inhomogeneous summary functions # # $Revision: 1.1 $ $Date: 2013/02/26 09:13:52 $ # require(spatstat) local({ lam <- density(redwood) K <- Kinhom(redwood, lam) lamX <- density(redwood, at="points") KX <- Kinhom(redwood, lamX) }) # # tests/slrm.R # # $Revision: 1.1 $ $Date: 2013/04/19 10:14:52 $ # # Test slrm fitting and prediction when there are NA's # require(spatstat) local({ X <- copper$SouthPoints W <- owin(poly=list(x=c(0,35,35,1),y=c(1,1,150,150))) Y <- X[W] fit <- slrm(Y ~ x+y) pred <- predict(fit) }) # tests/linalgeb.R # checks validity of linear algebra code # $Revision: 1.2 $ $Date: 2013/04/18 09:14:37 $ require(spatstat) local({ p <- 3 n <- 4 x <- array(as.numeric(1:(p * n * n)), dim=c(p, n, n)) w <- matrix(1:(n*n), n, n) y <- matrix(numeric(p * p), p, p) for(i in 1:n) for(j in (1:n)[-i]) y <- y + w[i,j] * outer(x[,i,j], x[,j,i]) z <- sumsymouter(x, w) if(!identical(y,z)) stop("sumsymouter gives incorrect result") }) # # tests/undoc.R # # $Revision: 1.1 $ $Date: 2013/07/25 10:26:09 $ # # Test undocumented hacks, etc require(spatstat) local({ # pixellate.ppp accepts a data frame of weights pixellate(cells, weights=data.frame(a=1:42, b=42:1)) }) # # tests/mppm.R # # Basic tests of mppm # # $Revision: 1.1 $ $Date: 2013/11/10 08:59:08 $ # require(spatstat) local({ # test interaction formulae and subfits fit1 <- mppm(Points ~ group, simba, hyperframe(po=Poisson(), str=Strauss(0.1)), iformula=~ifelse(group=="control", po, str)) fit2 <- mppm(Points ~ group, simba, hyperframe(po=Poisson(), str=Strauss(0.1)), iformula=~id * str) fit3 <- mppm(Points ~ group, simba, hyperframe(po=Poisson(), pie=PairPiece(c(0.05,0.1))), iformula=~I((group=="control") * po) + I((group=="treatment") * pie)) fit1 fit2 fit3 subfits(fit1) subfits(fit2) subfits(fit3) # test handling of offsets and zero cif values in mppm data(waterstriders) H <- hyperframe(Y = waterstriders) mppm(Y ~ 1, data=H, Hardcore(1.5)) mppm(Y ~ 1, data=H, StraussHard(7, 1.5)) }) # tests/ppx.R # # Test operations for ppx objects # # $Revision: 1.1 $ $Date: 2013/11/19 03:36:27 $ # require(spatstat) local({ df <- data.frame(x=c(1,2,2,1), y=c(1,2,3,1), z=c(2,3,4,2)) X <- ppx(data=df, coord.type=rep("s", 3), domain=box3()) unique(X) duplicated(X) multiplicity(X) }) ## tests/percy.R ## Tests of Percus-Yevick approximations ## $Revision: 1.1 $ $Date: 2014/01/31 11:49:46 $ require(spatstat) local({ fit <- ppm(swedishpines, ~1, DiggleGatesStibbard(6)) K <- Kmodel(fit) }) ## legacy.R ## Test that current version of spatstat is compatible with outmoded usage ## $Revision: 1.1 $ $Date: 2014/04/30 08:05:57 $ local({ require(spatstat) ## (1) Old syntax of ppm ppm(cells, ~x) ## (2) Old syntax of MultiStrauss etc. r <- matrix(3, 2, 2) a <- MultiStrauss( , r) a <- MultiStrauss(NULL, r) a <- MultiHard(, r) h <- r/2 a <- MultiStraussHard( , r, h) NULL }) spatstat/tests/selfcross.txt0000644000176000001440000000071512324667730016070 0ustar ripleyusers x y 0.3057897 0.1518920 0.6038506 0.3132859 0.6343093 0.2740279 0.5364061 0.2936569 0.8170620 0.4681368 0.8083595 0.6535217 0.6125531 0.6796937 0.6103774 0.6360737 0.4363273 0.6338927 0.4689617 0.6927797 0.6538900 0.7560286 0.6169043 0.7756576 0.5994993 0.7276756 0.3514779 0.7363996 0.3123166 0.6622457 0.1447933 0.4877658 0.2274671 0.4332408 0.1578471 0.3721728 0.2753309 0.4027068 0.1817790 0.4136118 0.2100621 0.3067429 spatstat/src/0000755000176000001440000000000012333041761012734 5ustar ripleyusersspatstat/src/linnncross.h0000644000176000001440000000625712333041761015307 0ustar ripleyusers/* linnncross.h Function body definitions with macros $Revision: 1.1 $ $Date: 2013/10/21 02:01:39 $ Macros used: FNAME name of function EXCLU whether serial numbers are provided WHICH whether 'nnwhich' is required */ void FNAME(np, xp, yp, /* data points 'from' */ nq, xq, yq, /* data points 'to' */ nv, xv, yv, /* network vertices */ ns, from, to, /* segments */ dpath, /* shortest path distances between vertices */ psegmap, /* map from data points to segments */ qsegmap, /* map from data points to segments */ #ifdef EXCLU idP, idQ, /* serial numbers for patterns p and q */ #endif huge, /* value taken as infinity */ /* OUTPUT */ #ifdef WHICH nndist, /* nearest neighbour distance for each point */ nnwhich /* identifies nearest neighbour */ #else nndist, /* nearest neighbour distance for each point */ #endif ) int *np, *nq, *nv, *ns; int *from, *to, *psegmap, *qsegmap; /* integer vectors (mappings) */ #ifdef EXCLU int *idP, *idQ; #endif double *xp, *yp, *xq, *yq, *xv, *yv; /* vectors of coordinates */ double *huge; double *dpath; /* matrix */ double *nndist; /* nearest neighbour distance for each point */ #ifdef WHICH int *nnwhich; /* identifies nearest neighbour */ #endif { int Np, Nq, Nv, i, j; int segPi, segQj, nbi1, nbi2, nbj1, nbj2; double d, xpi, ypi, xqj, yqj, dXi1, dXi2, d1Xj, d2Xj, d11, d12, d21, d22; double dmin, hugevalue; #ifdef EXCLU int idPi; #endif #ifdef WHICH int whichmin; #endif Np = *np; Nq = *nq; Nv = *nv; hugevalue = *huge; /* initialise nn distances */ for(i = 0; i < Np; i++) { nndist[i] = hugevalue; #ifdef WHICH nnwhich[i] = -1; #endif } /* main loop */ for(i = 0; i < Np; i++) { xpi = xp[i]; ypi = yp[i]; #ifdef EXCLU idPi = idP[i]; #endif segPi = psegmap[i]; nbi1 = from[segPi]; nbi2 = to[segPi]; dXi1 = EUCLID(xpi, ypi, xv[nbi1], yv[nbi1]); dXi2 = EUCLID(xpi, ypi, xv[nbi2], yv[nbi2]); dmin = nndist[i]; #ifdef WHICH whichmin = nnwhich[i]; #endif for(j = 0; j < Nq; j++) { #ifdef EXCLU if(idQ[j] != idPi) { #endif xqj = xq[j]; yqj = yq[j]; segQj = qsegmap[j]; /* compute path distance between i and j */ if(segPi == segQj) { /* points i and j lie on the same segment; use Euclidean distance */ d = EUCLID(xpi, ypi, xqj, yqj); } else { /* Shortest path from i to j passes through ends of segments; Calculate shortest of 4 possible paths from i to j */ nbj1 = from[segQj]; nbj2 = to[segQj]; d1Xj = EUCLID(xv[nbj1], yv[nbj1], xqj, yqj); d2Xj = EUCLID(xv[nbj2], yv[nbj2], xqj, yqj); d11 = dXi1 + DPATH(nbi1,nbj1) + d1Xj; d12 = dXi1 + DPATH(nbi1,nbj2) + d2Xj; d21 = dXi2 + DPATH(nbi2,nbj1) + d1Xj; d22 = dXi2 + DPATH(nbi2,nbj2) + d2Xj; d = d11; if(d12 < d) d = d12; if(d21 < d) d = d21; if(d22 < d) d = d22; } /* OK, distance between i and j is d */ /* update nn for point i */ if(d < dmin) { dmin = d; #ifdef WHICH whichmin = j; #endif } #ifdef EXCLU } #endif } /* commit nn distance for point i */ nndist[i] = dmin; #ifdef WHICH nnwhich[i] = whichmin; #endif } } spatstat/src/linnndist.c0000755000176000001440000001167412333041761015116 0ustar ripleyusers#include /* linnndist.c Shortest-path distances between nearest neighbours in linear network $Revision: 1.1 $ $Date: 2013/10/21 02:01:14 $ linnndist linnnwhich */ #define DPATH(I,J) dpath[(J) + Nv * (I)] #define ANSWER(I,J) answer[(J) + Np * (I)] #define EUCLID(X,Y,U,V) sqrt(pow((X)-(U),2)+pow((Y)-(V),2)) void linnndist(np, xp, yp, /* data points */ nv, xv, yv, /* network vertices */ ns, from, to, /* segments */ dpath, /* shortest path distances between vertices */ segmap, /* map from data points to segments */ huge, /* value taken as infinity */ /* OUTPUT */ answer /* nearest neighbour distance for each point */ ) int *np, *nv, *ns; int *from, *to, *segmap; /* integer vectors (mappings) */ double *xp, *yp, *xv, *yv; /* vectors of coordinates */ double *huge; double *dpath; /* matrix */ double *answer; /* vector of output values */ { int Np, Nv, i, j, Np1; int segi, segj, nbi1, nbi2, nbj1, nbj2; double d, xpi, ypi, xpj, ypj, dXi1, dXi2, d1Xj, d2Xj, d11, d12, d21, d22; double dmin, hugevalue; Np = *np; Nv = *nv; Np1 = Np - 1; hugevalue = *huge; /* initialise nn distances */ for(i = 0; i < Np; i++) answer[i] = hugevalue; /* main loop */ for(i = 0; i < Np1; i++) { xpi = xp[i]; ypi = yp[i]; segi = segmap[i]; nbi1 = from[segi]; nbi2 = to[segi]; dXi1 = EUCLID(xpi, ypi, xv[nbi1], yv[nbi1]); dXi2 = EUCLID(xpi, ypi, xv[nbi2], yv[nbi2]); dmin = answer[i]; for(j = i+1; j < Np; j++) { xpj = xp[j]; ypj = yp[j]; segj = segmap[j]; /* compute path distance between i and j */ if(segi == segj) { /* points i and j lie on the same segment; use Euclidean distance */ d = sqrt(pow(xpi - xpj, 2) + pow(ypi - ypj, 2)); } else { /* Shortest path from i to j passes through ends of segments; Calculate shortest of 4 possible paths from i to j */ nbj1 = from[segj]; nbj2 = to[segj]; d1Xj = EUCLID(xv[nbj1], yv[nbj1], xpj, ypj); d2Xj = EUCLID(xv[nbj2], yv[nbj2], xpj, ypj); d11 = dXi1 + DPATH(nbi1,nbj1) + d1Xj; d12 = dXi1 + DPATH(nbi1,nbj2) + d2Xj; d21 = dXi2 + DPATH(nbi2,nbj1) + d1Xj; d22 = dXi2 + DPATH(nbi2,nbj2) + d2Xj; d = d11; if(d12 < d) d = d12; if(d21 < d) d = d21; if(d22 < d) d = d22; } /* OK, distance between i and j is d */ /* update nn distance for point i */ if(d < dmin) dmin = d; /* update nn distance for point j */ if(d < answer[j]) answer[j] = d; } /* commit nn distance for point i */ answer[i] = dmin; } } void linnnwhich(np, xp, yp, /* data points */ nv, xv, yv, /* network vertices */ ns, from, to, /* segments */ dpath, /* shortest path distances between vertices */ segmap, /* map from data points to segments */ huge, /* value taken as infinity */ /* OUTPUT */ nndist, /* nearest neighbour distance for each point */ nnwhich /* identifies nearest neighbour */ ) int *np, *nv, *ns; int *from, *to, *segmap; /* integer vectors (mappings) */ double *xp, *yp, *xv, *yv; /* vectors of coordinates */ double *huge; double *dpath; /* matrix */ double *nndist; /* vector of output values */ int *nnwhich; /* vector of output values */ { int Np, Nv, i, j, Np1; int segi, segj, nbi1, nbi2, nbj1, nbj2; double d, xpi, ypi, xpj, ypj, dXi1, dXi2, d1Xj, d2Xj, d11, d12, d21, d22; double dmin, hugevalue; int whichmin; Np = *np; Nv = *nv; Np1 = Np - 1; hugevalue = *huge; /* initialise nn distances and identifiers */ for(i = 0; i < Np; i++) { nndist[i] = hugevalue; nnwhich[i] = -1; } /* main loop */ for(i = 0; i < Np1; i++) { xpi = xp[i]; ypi = yp[i]; segi = segmap[i]; nbi1 = from[segi]; nbi2 = to[segi]; dXi1 = EUCLID(xpi, ypi, xv[nbi1], yv[nbi1]); dXi2 = EUCLID(xpi, ypi, xv[nbi2], yv[nbi2]); dmin = nndist[i]; whichmin = nnwhich[i]; for(j = i+1; j < Np; j++) { xpj = xp[j]; ypj = yp[j]; segj = segmap[j]; if(segi == segj) { /* points i and j lie on the same segment; use Euclidean distance */ d = sqrt(pow(xpi - xpj, 2) + pow(ypi - ypj, 2)); } else { /* Shortest path from i to j passes through ends of segments; Calculate shortest of 4 possible paths from i to j */ nbj1 = from[segj]; nbj2 = to[segj]; d1Xj = EUCLID(xv[nbj1], yv[nbj1], xpj, ypj); d2Xj = EUCLID(xv[nbj2], yv[nbj2], xpj, ypj); d11 = dXi1 + DPATH(nbi1,nbj1) + d1Xj; d12 = dXi1 + DPATH(nbi1,nbj2) + d2Xj; d21 = dXi2 + DPATH(nbi2,nbj1) + d1Xj; d22 = dXi2 + DPATH(nbi2,nbj2) + d2Xj; d = d11; if(d12 < d) d = d12; if(d21 < d) d = d21; if(d22 < d) d = d22; } /* OK, distance between i and j is d */ /* update nn for point i */ if(d < dmin) { dmin = d; whichmin = j; } /* update nn for point j */ if(d < nndist[j]) { nndist[j] = d; nnwhich[j] = i; } } /* commit nn for point i */ nndist[i] = dmin; nnwhich[i] = whichmin; } } spatstat/src/linpairdist.c0000755000176000001440000000430112333041761015423 0ustar ripleyusers#include #include #include "chunkloop.h" /* linpairdist.c Shortest-path distances between each pair of points in linear network $Revision: 1.5 $ $Date: 2012/10/12 10:21:46 $ linpairdist */ #define DPATH(I,J) dpath[(I) + Nv * (J)] #define ANSWER(I,J) answer[(I) + Np * (J)] #define EUCLID(X,Y,U,V) sqrt(pow((X)-(U),2)+pow((Y)-(V),2)) void linpairdist(np, xp, yp, /* data points */ nv, xv, yv, /* network vertices */ ns, from, to, /* segments */ dpath, /* shortest path distances between vertices */ segmap, /* map from data points to segments */ /* OUTPUT */ answer /* shortest path distances between points */ ) int *np, *nv, *ns; int *from, *to, *segmap; /* integer vectors (mappings) */ double *xp, *yp, *xv, *yv; /* vectors of coordinates */ double *dpath, *answer; /* matrices */ { int Np, Nv, i, j, Np1, maxchunk; int segi, segj, nbi1, nbi2, nbj1, nbj2; double d, xpi, ypi, xpj, ypj, dXi1, dXi2, d1Xj, d2Xj, d11, d12, d21, d22; Np = *np; Nv = *nv; Np1 = Np - 1; OUTERCHUNKLOOP(i, Np1, maxchunk, 1024) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Np1, maxchunk, 1024) { xpi = xp[i]; ypi = yp[i]; segi = segmap[i]; nbi1 = from[segi]; nbi2 = to[segi]; dXi1 = EUCLID(xpi, ypi, xv[nbi1], yv[nbi1]); dXi2 = EUCLID(xpi, ypi, xv[nbi2], yv[nbi2]); for(j = i+1; j < Np; j++) { xpj = xp[j]; ypj = yp[j]; segj = segmap[j]; if(segi == segj) { /* points i and j lie on the same segment; use Euclidean distance */ d = sqrt(pow(xpi - xpj, 2) + pow(ypi - ypj, 2)); } else { /* Shortest path from i to j passes through ends of segments; Calculate shortest of 4 possible paths from i to j */ nbj1 = from[segj]; nbj2 = to[segj]; d1Xj = EUCLID(xv[nbj1], yv[nbj1], xpj, ypj); d2Xj = EUCLID(xv[nbj2], yv[nbj2], xpj, ypj); d11 = dXi1 + DPATH(nbi1,nbj1) + d1Xj; d12 = dXi1 + DPATH(nbi1,nbj2) + d2Xj; d21 = dXi2 + DPATH(nbi2,nbj1) + d1Xj; d22 = dXi2 + DPATH(nbi2,nbj2) + d2Xj; d = d11; if(d12 < d) d = d12; if(d21 < d) d = d21; if(d22 < d) d = d22; } /* write */ ANSWER(i,j) = ANSWER(j,i) = d; } ANSWER(i,i) = 0; } } } spatstat/src/closepair.c0000755000176000001440000002322412333041761015067 0ustar ripleyusers/* closepair.c $Revision: 1.31 $ $Date: 2013/11/22 01:01:12 $ Assumes point pattern is sorted in increasing order of x coordinate paircount() count the total number of pairs (i, j) with distance < rmax Cclosepaircounts count for each i the number of j with distance < rmax crosscount() count number of close pairs in two patterns (note: Ccrosspaircounts is defined in Estrauss.c) duplicatedxy() find duplicated (x,y) pairs Fclosepairs() extract close pairs of coordinates .C interface - output vectors have Fixed length Fcrosspairs() extract close pairs in two patterns .C interface - output vectors have Fixed length Vclosepairs() extract close pairs of coordinates .Call interface - output vectors have Variable length Vcrosspairs() extract close pairs in two patterns .Call interface - output vectors have Variable length */ #include #include #include #define OK 0 #define ERR_OVERFLOW 1 #define ERR_ALLOC 2 #define FAILED(X) ((void *)(X) == (void *)NULL) #define intRealloc(PTR, OLDLENGTH, NEWLENGTH) \ (int *) S_realloc((char *) PTR, NEWLENGTH, OLDLENGTH, sizeof(int)) #define dblRealloc(PTR, OLDLENGTH, NEWLENGTH) \ (double *) S_realloc((char *) PTR, NEWLENGTH, OLDLENGTH, sizeof(double)) double sqrt(); /* count TOTAL number of close pairs */ void paircount(nxy, x, y, rmaxi, count) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *rmaxi; /* maximum distance */ /* output */ int *count; { int n, maxchunk, i, j, counted; double xi, yi, rmax, r2max, dx, dy, a; n = *nxy; rmax = *rmaxi; r2max = rmax * rmax; *count = counted = 0; if(n == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < n) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n) maxchunk = n; for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; if(i > 0) { /* scan backwards from i */ for(j = i - 1; j >= 0; j--) { dx = x[j] - xi; a = r2max - dx * dx; if(a < 0) break; dy = y[j] - yi; a -= dy * dy; if(a >= 0) ++counted; } } if(i + 1 < n) { /* scan forwards from i */ for(j = i + 1; j < n; j++) { dx = x[j] - xi; a = r2max - dx * dx; if(a < 0) break; dy = y[j] - yi; a -= dy * dy; if(a >= 0) ++counted; } } /* end loop over i */ } } *count = counted; } /* count for each i the number of j closer than distance r */ void Cclosepaircounts(nxy, x, y, rmaxi, counts) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *rmaxi; /* maximum distance */ /* output VECTOR, assumed initialised to 0 */ int *counts; { int n, maxchunk, i, j; double xi, yi, rmax, r2max, dx, dy, a; n = *nxy; rmax = *rmaxi; r2max = rmax * rmax; if(n == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < n) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n) maxchunk = n; for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; if(i > 0) { /* scan backwards from i */ for(j = i - 1; j >= 0; j--) { dx = x[j] - xi; a = r2max - dx * dx; if(a < 0) break; dy = y[j] - yi; a -= dy * dy; if(a >= 0) (counts[i])++; } } if(i + 1 < n) { /* scan forwards from i */ for(j = i + 1; j < n; j++) { dx = x[j] - xi; a = r2max - dx * dx; if(a < 0) break; dy = y[j] - yi; a -= dy * dy; if(a >= 0) (counts[i])++; } } /* end loop over i */ } } } /* analogue for two different point patterns */ void crosscount(nn1, x1, y1, nn2, x2, y2, rmaxi, count) /* inputs */ int *nn1, *nn2; double *x1, *y1, *x2, *y2, *rmaxi; /* output */ int *count; { int n1, n2, maxchunk, i, j, jleft, counted; double x1i, y1i, rmax, r2max, xleft, dx, dy, a; n1 = *nn1; n2 = *nn2; rmax = *rmaxi; r2max = rmax * rmax; *count = counted = 0; if(n1 == 0 || n2 == 0) return; jleft = 0; i = 0; maxchunk = 0; while(i < n1) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n1) maxchunk = n1; for(; i < maxchunk; i++) { x1i = x1[i]; y1i = y1[i]; /* adjust starting index */ xleft = x1i - rmax; while((x2[jleft] < xleft) && (jleft+1 < n2)) ++jleft; /* process from j=jleft until dx > rmax */ for(j=jleft; j < n2; j++) { dx = x2[j] - x1i; a = r2max - dx * dx; if(a < 0) break; dy = y2[j] - y1i; a -= dy * dy; if(a > 0) ++counted; } } } *count = counted; } /* Find duplicated locations xx, yy are not sorted */ void duplicatedxy(n, x, y, out) /* inputs */ int *n; double *x, *y; /* output */ int *out; /* logical vector */ { int m, i, j; double xi, yi; m = *n; for(i = 1; i < m; i++) { R_CheckUserInterrupt(); xi = x[i]; yi = y[i]; for(j = 0; j < i; j++) if((x[j] == xi) && (y[j] == yi)) break; if(j == i) out[i] = 0; else out[i] = 1; } } /* ............... fixed output length .............. */ void Fclosepairs(nxy, x, y, r, noutmax, nout, iout, jout, xiout, yiout, xjout, yjout, dxout, dyout, dout, status) /* inputs */ int *nxy, *noutmax; double *x, *y, *r; /* outputs */ int *nout, *iout, *jout; double *xiout, *yiout, *xjout, *yjout, *dxout, *dyout, *dout; int *status; { int n, k, kmax, maxchunk, i, j; double xi, yi, rmax, r2max, dx, dy, dx2, d2; n = *nxy; rmax = *r; r2max = rmax * rmax; *status = OK; *nout = 0; k = 0; /* k is the next available storage location and also the current length of the list */ kmax = *noutmax; if(n == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < n) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n) maxchunk = n; for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; if(i > 0) { /* scan backwards */ for(j = i - 1; j >= 0; j++) { dx = x[j] - xi; dx2 = dx * dx; if(dx2 > r2max) break; dy = y[j] - yi; d2 = dx2 + dy * dy; if(d2 <= r2max) { /* add this (i, j) pair to output */ if(k >= kmax) { *nout = k; *status = ERR_OVERFLOW; return; } jout[k] = j + 1; /* R indexing */ iout[k] = i + 1; xiout[k] = xi; yiout[k] = yi; xjout[k] = x[j]; yjout[k] = y[j]; dxout[k] = dx; dyout[k] = dy; dout[k] = sqrt(d2); ++k; } } } if(i + 1 < n) { /* scan forwards */ for(j = i + 1; j < n; j++) { dx = x[j] - xi; dx2 = dx * dx; if(dx2 > r2max) break; dy = y[j] - yi; d2 = dx2 + dy * dy; if(d2 <= r2max) { /* add this (i, j) pair to output */ if(k >= kmax) { *nout = k; *status = ERR_OVERFLOW; return; } jout[k] = j + 1; /* R indexing */ iout[k] = i + 1; xiout[k] = xi; yiout[k] = yi; xjout[k] = x[j]; yjout[k] = y[j]; dxout[k] = dx; dyout[k] = dy; dout[k] = sqrt(d2); ++k; } } } } } *nout = k; } void Fcrosspairs(nn1, x1, y1, nn2, x2, y2, rmaxi, noutmax, nout, iout, jout, xiout, yiout, xjout, yjout, dxout, dyout, dout, status) /* inputs */ int *nn1, *nn2, *noutmax; double *x1, *y1, *x2, *y2, *rmaxi; /* outputs */ int *nout, *iout, *jout; double *xiout, *yiout, *xjout, *yjout, *dxout, *dyout, *dout; int *status; { int n1, n2, maxchunk, k, kmax, i, j, jleft; double x1i, y1i, rmax, r2max, xleft, dx, dy, dx2, d2; n1 = *nn1; n2 = *nn2; rmax = *rmaxi; r2max = rmax * rmax; *status = OK; *nout = 0; k = 0; /* k is the next available storage location and also the current length of the list */ kmax = *noutmax; if(n1 == 0 || n2 == 0) return; jleft = 0; i = 0; maxchunk = 0; while(i < n1) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n1) maxchunk = n1; for(; i < maxchunk; i++) { x1i = x1[i]; y1i = y1[i]; /* adjust starting position jleft */ xleft = x1i - rmax; while((x2[jleft] < xleft) && (jleft+1 < n2)) ++jleft; /* process from j=jleft until dx > rmax */ for(j=jleft; j < n2; j++) { dx = x2[j] - x1i; dx2 = dx * dx; if(dx2 > r2max) break; dy = y2[j] - y1i; d2 = dx2 + dy * dy; if(d2 <= r2max) { /* add this (i, j) pair to output */ if(k >= kmax) { *nout = k; *status = ERR_OVERFLOW; return; } jout[k] = j + 1; /* R indexing */ iout[k] = i + 1; xiout[k] = x1i; yiout[k] = y1i; xjout[k] = x2[j]; yjout[k] = y2[j]; dxout[k] = dx; dyout[k] = dy; dout[k] = sqrt(d2); ++k; } } } } *nout = k; } /* ........ versions that return variable-length vectors ......... */ /* return i, j only */ #define CLOSEFUN VcloseIJpairs #define CROSSFUN VcrossIJpairs #undef THRESH #undef COORDS #include "closefuns.h" #undef CLOSEFUN #undef CROSSFUN #undef THRESH #undef COORDS /* return i, j, xi, yi, xj, yj, dx, dy, d */ #define CLOSEFUN Vclosepairs #define CROSSFUN Vcrosspairs #undef THRESH #define COORDS #include "closefuns.h" #undef CLOSEFUN #undef CROSSFUN #undef THRESH #undef COORDS /* return i, j, t where t = 1{d < s} */ #define CLOSEFUN Vclosethresh #define CROSSFUN Vcrossthresh #define THRESH #undef COORDS #include "closefuns.h" #undef CLOSEFUN #undef CROSSFUN #undef THRESH #undef COORDS spatstat/src/mhloop.h0000755000176000001440000002342512333041761014414 0ustar ripleyusers /* mhloop.h This file contains the iteration loop for the Metropolis-Hastings algorithm methas.c It is #included several times in methas.c with different #defines for the following variables MH_MARKED whether the simulation is marked (= the variable 'marked' is TRUE) MH_SINGLE whether there is a single interaction (as opposed to a hybrid of several interactions) MH_TRACKING whether to save transition history MH_DEBUG whether to print debug information MH_SNOOP whether to run visual debugger $Revision: 1.18 $ $Date: 2013/05/27 02:09:10 $ */ #ifndef MH_DEBUG #define MH_DEBUG NO #endif OUTERCHUNKLOOP(irep, algo.nrep, maxchunk, 1024) { R_CheckUserInterrupt(); INNERCHUNKLOOP(irep, algo.nrep, maxchunk, 1024) { #if MH_DEBUG Rprintf("iteration %d\n", irep); #endif if(verb) { /* print progress message every nverb iterations */ iverb = irep + 1 + algo.nrep0; if((iverb % algo.nverb) == 0) Rprintf("iteration %d\n", iverb); } itype = REJECT; nfree = state.npts - algo.ncond; /* number of 'free' points */ /* ................ generate proposal ..................... */ /* Shift or birth/death: */ if(unif_rand() > algo.p) { #if MH_DEBUG Rprintf("propose birth or death\n"); #endif /* Birth/death: */ if(unif_rand() > algo.q) { /* Propose birth: */ birthprop.u = xpropose[irep]; birthprop.v = ypropose[irep]; #if MH_MARKED birthprop.mrk = mpropose[irep]; #endif #if MH_DEBUG #if MH_MARKED Rprintf("propose birth at (%lf, %lf) with mark %d\n", birthprop.u, birthprop.v, birthprop.mrk); #else Rprintf("propose birth at (%lf, %lf)\n", birthprop.u, birthprop.v); #endif #endif /* evaluate conditional intensity */ #if MH_MARKED betavalue = model.beta[birthprop.mrk]; #endif #if MH_SINGLE anumer = betavalue * (*(thecif.eval))(birthprop, state, thecdata); #else anumer = betavalue; for(k = 0; k < Ncif; k++) anumer *= (*(cif[k].eval))(birthprop, state, cdata[k]); #endif adenom = qnodds*(nfree+1); #if MH_DEBUG Rprintf("cif = %lf, Hastings ratio = %lf\n", anumer, anumer/adenom); #endif /* accept/reject */ if(unif_rand() * adenom < anumer) { #if MH_DEBUG Rprintf("accepted birth\n"); #endif itype = BIRTH; /* Birth proposal accepted. */ } #if MH_SNOOP /* visual debug */ mhsnoop(&snooper, irep, &algo, &state, &birthprop, anumer, adenom, &itype); #endif #if MH_TRACKING /* save transition history */ if(irep < history.nmax) { history.n++; history.proptype[irep] = BIRTH; history.accepted[irep] = (itype == REJECT) ? 0 : 1; #ifdef HISTORY_INCLUDES_RATIO history.numerator[irep] = anumer; history.denominator[irep] = adenom; #endif } #endif } else if(nfree > 0) { /* Propose death: */ ix = floor(nfree * unif_rand()); if(ix < 0) ix = 0; ix = algo.ncond + ix; if(ix >= state.npts) ix = state.npts - 1; deathprop.ix = ix; deathprop.u = state.x[ix]; deathprop.v = state.y[ix]; #if MH_MARKED deathprop.mrk = state.marks[ix]; #endif #if MH_DEBUG #if MH_MARKED Rprintf("propose death of point %d = (%lf, %lf) with mark %d\n", ix, deathprop.u, deathprop.v, deathprop.mrk); #else Rprintf("propose death of point %d = (%lf, %lf)\n", ix, deathprop.u, deathprop.v); #endif #endif /* evaluate conditional intensity */ #if MH_MARKED betavalue = model.beta[deathprop.mrk]; #endif #if MH_SINGLE adenom = betavalue * (*(thecif.eval))(deathprop, state, thecdata); #else adenom = betavalue; for(k = 0; k < Ncif; k++) adenom *= (*(cif[k].eval))(deathprop, state, cdata[k]); #endif anumer = qnodds * nfree; #if MH_DEBUG Rprintf("cif = %lf, Hastings ratio = %lf\n", adenom, anumer/adenom); #endif /* accept/reject */ if(unif_rand() * adenom < anumer) { #if MH_DEBUG Rprintf("accepted death\n"); #endif itype = DEATH; /* Death proposal accepted. */ } #if MH_SNOOP /* visual debug */ mhsnoop(&snooper, irep, &algo, &state, &deathprop, anumer, adenom, &itype); #endif #if MH_TRACKING /* save transition history */ if(irep < history.nmax) { history.n++; history.proptype[irep] = DEATH; history.accepted[irep] = (itype == REJECT) ? 0 : 1; #ifdef HISTORY_INCLUDES_RATIO history.numerator[irep] = anumer; history.denominator[irep] = adenom; #endif } #endif } } else if(nfree > 0) { /* Propose shift: */ /* point to be shifted */ ix = floor(nfree * unif_rand()); if(ix < 0) ix = 0; ix = algo.ncond + ix; if(ix >= state.npts) ix = state.npts - 1; deathprop.ix = ix; deathprop.u = state.x[ix]; deathprop.v = state.y[ix]; #if MH_MARKED deathprop.mrk = state.marks[ix]; #endif /* where to shift */ shiftprop.u = xpropose[irep]; shiftprop.v = ypropose[irep]; #if MH_MARKED shiftprop.mrk = (algo.fixall) ? deathprop.mrk : mpropose[irep]; #endif shiftprop.ix = ix; #if MH_DEBUG #if MH_MARKED Rprintf("propose shift of point %d = (%lf, %lf)[mark %d] to (%lf, %lf)[mark %d]\n", ix, deathprop.u, deathprop.v, deathprop.mrk, shiftprop.u, shiftprop.v, shiftprop.mrk); #else Rprintf("propose shift of point %d = (%lf, %lf) to (%lf, %lf)\n", ix, deathprop.u, deathprop.v, shiftprop.u, shiftprop.v); #endif #endif /* evaluate cif in two stages */ #if MH_SINGLE cvd = (*(thecif.eval))(deathprop, state, thecdata); cvn = (*(thecif.eval))(shiftprop, state, thecdata); #else cvd = cvn = 1.0; for(k = 0; k < Ncif; k++) { cvd *= (*(cif[k].eval))(deathprop, state, cdata[k]); cvn *= (*(cif[k].eval))(shiftprop, state, cdata[k]); } #endif #if MH_MARKED if(!algo.fixall) { cvn *= model.beta[shiftprop.mrk]; cvd *= model.beta[deathprop.mrk]; } #endif #if MH_DEBUG Rprintf("cif[old] = %lf, cif[new] = %lf, Hastings ratio = %lf\n", cvd, cvn, cvn/cvd); #endif /* accept/reject */ if(unif_rand() * cvd < cvn) { #if MH_DEBUG Rprintf("accepted shift\n"); #endif itype = SHIFT; /* Shift proposal accepted . */ } #if MH_SNOOP /* visual debug */ mhsnoop(&snooper, irep, &algo, &state, &shiftprop, cvn, cvd, &itype); #endif #if MH_TRACKING /* save transition history */ if(irep < history.nmax) { history.n++; history.proptype[irep] = SHIFT; history.accepted[irep] = (itype == REJECT) ? 0 : 1; #ifdef HISTORY_INCLUDES_RATIO history.numerator[irep] = cvn; history.denominator[irep] = cvd; #endif } #endif } if(itype != REJECT) { /* ....... implement the transition ............ */ if(itype == BIRTH) { /* Birth transition */ /* add point at (u,v) */ #if MH_DEBUG #if MH_MARKED Rprintf("implementing birth at (%lf, %lf) with mark %d\n", birthprop.u, birthprop.v, birthprop.mrk); #else Rprintf("implementing birth at (%lf, %lf)\n", birthprop.u, birthprop.v); #endif #endif if(state.npts + 1 > state.npmax) { #if MH_DEBUG Rprintf("!!!!!!!!!!! storage overflow !!!!!!!!!!!!!!!!!\n"); #endif /* storage overflow; allocate more storage */ Nmore = 2 * state.npmax; state.x = (double *) S_realloc((char *) state.x, Nmore, state.npmax, sizeof(double)); state.y = (double *) S_realloc((char *) state.y, Nmore, state.npmax, sizeof(double)); #if MH_MARKED state.marks = (int *) S_realloc((char *) state.marks, Nmore, state.npmax, sizeof(int)); #endif state.npmax = Nmore; /* call the initialiser again, to allocate additional space */ #if MH_SINGLE thecdata = (*(thecif.init))(state, model, algo); #else model.ipar = iparvector; for(k = 0; k < Ncif; k++) { if(k > 0) model.ipar += plength[k-1]; cdata[k] = (*(cif[k].init))(state, model, algo); } #endif #if MH_DEBUG Rprintf("........... storage extended .................\n"); #endif } if(mustupdate) { /* Update auxiliary variables first */ #if MH_SINGLE (*(thecif.update))(state, birthprop, thecdata); #else for(k = 0; k < Ncif; k++) { if(needupd[k]) (*(cif[k].update))(state, birthprop, cdata[k]); } #endif } /* Now add point */ state.x[state.npts] = birthprop.u; state.y[state.npts] = birthprop.v; #if MH_MARKED state.marks[state.npts] = birthprop.mrk; #endif state.npts = state.npts + 1; #if MH_DEBUG Rprintf("\tnpts=%d\n", state.npts); #endif } else if(itype==DEATH) { /* Death transition */ /* delete point x[ix], y[ix] */ if(mustupdate) { /* Update auxiliary variables first */ #if MH_SINGLE (*(thecif.update))(state, deathprop, thecdata); #else for(k = 0; k < Ncif; k++) { if(needupd[k]) (*(cif[k].update))(state, deathprop, cdata[k]); } #endif } ix = deathprop.ix; state.npts = state.npts - 1; #if MH_DEBUG Rprintf("implementing death of point %d\n", ix); Rprintf("\tnpts=%d\n", state.npts); #endif if(ix < state.npts) { for(j = ix; j < state.npts; j++) { state.x[j] = state.x[j+1]; state.y[j] = state.y[j+1]; #if MH_MARKED state.marks[j] = state.marks[j+1]; #endif } } } else { /* Shift transition */ /* Shift (x[ix], y[ix]) to (u,v) */ #if MH_DEBUG #if MH_MARKED Rprintf("implementing shift from %d = (%lf, %lf)[%d] to (%lf, %lf)[%d]\n", deathprop.ix, deathprop.u, deathprop.v, deathprop.mrk, shiftprop.u, shiftprop.v, shiftprop.mrk); #else Rprintf("implementing shift from %d = (%lf, %lf) to (%lf, %lf)\n", deathprop.ix, deathprop.u, deathprop.v, shiftprop.u, shiftprop.v); Rprintf("\tnpts=%d\n", state.npts); #endif #endif if(mustupdate) { /* Update auxiliary variables first */ #if MH_SINGLE (*(thecif.update))(state, shiftprop, thecdata); #else for(k = 0; k < Ncif; k++) { if(needupd[k]) (*(cif[k].update))(state, shiftprop, cdata[k]); } #endif } ix = shiftprop.ix; state.x[ix] = shiftprop.u; state.y[ix] = shiftprop.v; #if MH_MARKED state.marks[ix] = shiftprop.mrk; #endif } #if MH_DEBUG } else { Rprintf("rejected\n"); #endif } } } spatstat/src/dppll.f0000755000176000001440000000244112333041761014222 0ustar ripleyusersC Output from Public domain Ratfor, version 1.0 subroutine dppll(x,y,l1,l2,l3,l4,np,nl,eps,mint,rslt,xmin,jmin) implicit double precision(a-h,o-z) dimension x(np), y(np), rslt(np,nl), xmin(np), jmin(np) double precision l1(nl), l2(nl), l3(nl), l4(nl) one = 1.d0 zero = 0.d0 do23000 j = 1,nl dx = l3(j) - l1(j) dy = l4(j) - l2(j) alen = sqrt(dx**2 + dy**2) if(alen .gt. eps)then co = dx/alen si = dy/alen else co = 0.5 si = 0.5 endif do23004 i = 1, np xpx1 = x(i) - l1(j) ypy1 = y(i) - l2(j) xpx2 = x(i) - l3(j) ypy2 = y(i) - l4(j) d1 = xpx1**2 + ypy1**2 d2 = xpx2**2 + ypy2**2 dd = min(d1,d2) if(alen .gt. eps)then xpr = xpx1*co + ypy1*si if(xpr .lt. zero .or. xpr .gt. alen)then d3 = -one else ypr = - xpx1*si + ypy1*co d3 = ypr**2 endif else d3 = -one endif if(d3 .ge. zero)then dd = min(dd,d3) endif sd =sqrt(dd) rslt(i,j) = sd if(mint.gt.0)then if(sd .lt. xmin(i))then xmin(i) = sd if(mint.gt.1)then jmin(i) = j endif endif endif 23004 continue 23005 continue 23000 continue 23001 continue return end spatstat/src/distances.c0000755000176000001440000002327412333041761015070 0ustar ripleyusers/* distances.c Distances between pairs of points $Revision: 1.30 $ $Date: 2013/11/03 03:35:34 $ Cpairdist Pairwise distances Cpair2dist Pairwise distances squared CpairPdist Pairwise distances with periodic correction CpairP2dist Pairwise distances squared, with periodic correction Ccrossdist Pairwise distances for two sets of points Ccross2dist Pairwise distances squared, for two sets of points CcrossPdist Pairwise distances for two sets of points, periodic correction Cmatchxy Find matches between two sets of points */ #include #include #include "chunkloop.h" double sqrt(); void Cpairdist(n, x, y, squared, d) /* inputs */ int *n; double *x, *y; int *squared; /* output */ double *d; { void Cpair1dist(), Cpair2dist(); if(*squared == 0) { Cpair1dist(n, x, y, d); } else { Cpair2dist(n, x, y, d); } } void Cpair1dist(n, x, y, d) /* inputs */ int *n; double *x, *y; /* output */ double *d; { int i, j, npoints, maxchunk; double *dp; double xi, yi, dx, dy, dist; npoints = *n; /* set d[0,0] = 0 */ *d = 0.0; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { xi = x[i]; yi = y[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dist = sqrt( dx * dx + dy * dy ); /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } } /* squared distances */ void Cpair2dist(n, x, y, d) /* inputs */ int *n; double *x, *y; /* output */ double *d; { int i, j, npoints, maxchunk; double *dp; double xi, yi, dx, dy, dist; npoints = *n; /* set d[0,0] = 0 */ *d = 0.0; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { xi = x[i]; yi = y[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dist = dx * dx + dy * dy; /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } } void Ccrossdist(nfrom, xfrom, yfrom, nto, xto, yto, squared, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *xto, *yto; int *squared; /* output */ double *d; { void Ccross1dist(), Ccross2dist(); if(*squared == 0) { Ccross1dist(nfrom, xfrom, yfrom, nto, xto, yto, d); } else { Ccross2dist(nfrom, xfrom, yfrom, nto, xto, yto, d); } } void Ccross1dist(nfrom, xfrom, yfrom, nto, xto, yto, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *xto, *yto; /* output */ double *d; { int i, j, nf, nt, maxchunk; double *dptr; double xj, yj, dx, dy; nf = *nfrom; nt = *nto; dptr = d; OUTERCHUNKLOOP(j, nt, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nt, maxchunk, 16384) { xj = xto[j]; yj = yto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; *dptr = sqrt( dx * dx + dy * dy ); } } } } /* squared distances */ void Ccross2dist(nfrom, xfrom, yfrom, nto, xto, yto, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *xto, *yto; /* output */ double *d; { int i, j, nf, nt, maxchunk; double *dptr; double xj, yj, dx, dy; nf = *nfrom; nt = *nto; dptr = d; OUTERCHUNKLOOP(j, nt, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nt, maxchunk, 16384) { xj = xto[j]; yj = yto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; *dptr = dx * dx + dy * dy; } } } } /* distances with periodic correction */ void CpairPdist(n, x, y, xwidth, yheight, squared, d) /* inputs */ int *n; double *x, *y, *xwidth, *yheight; int *squared; /* output */ double *d; { void CpairP1dist(), CpairP2dist(); if(*squared == 0) { CpairP1dist(n, x, y, xwidth, yheight, d); } else { CpairP2dist(n, x, y, xwidth, yheight, d); } } void CpairP1dist(n, x, y, xwidth, yheight, d) /* inputs */ int *n; double *x, *y, *xwidth, *yheight; /* output */ double *d; { int i, j, npoints, maxchunk; double *dp; double xi, yi, dx, dy, dx2, dy2, dx2p, dy2p, dist, wide, high; npoints = *n; wide = *xwidth; high = *yheight; /* set d[0,0] = 0 */ *d = 0.0; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { xi = x[i]; yi = y[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dx2p = dx * dx; dy2p = dy * dy; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; dist = sqrt( dx2p + dy2p ); /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } } /* same function without the sqrt */ void CpairP2dist(n, x, y, xwidth, yheight, d) /* inputs */ int *n; double *x, *y, *xwidth, *yheight; /* output */ double *d; { int i, j, npoints, maxchunk; double *dp; double xi, yi, dx, dy, dx2, dy2, dx2p, dy2p, dist, wide, high; npoints = *n; wide = *xwidth; high = *yheight; /* set d[0,0] = 0 */ *d = 0.0; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { xi = x[i]; yi = y[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dx2p = dx * dx; dy2p = dy * dy; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; dist = dx2p + dy2p; /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } } void CcrossPdist(nfrom, xfrom, yfrom, nto, xto, yto, xwidth, yheight, squared, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *xto, *yto, *xwidth, *yheight; int *squared; /* output */ double *d; { void CcrossP1dist(), CcrossP2dist(); if(*squared == 0) { CcrossP1dist(nfrom, xfrom, yfrom, nto, xto, yto, xwidth, yheight, d); } else { CcrossP2dist(nfrom, xfrom, yfrom, nto, xto, yto, xwidth, yheight, d); } } void CcrossP1dist(nfrom, xfrom, yfrom, nto, xto, yto, xwidth, yheight, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *xto, *yto, *xwidth, *yheight; /* output */ double *d; { int i, j, nf, nt, maxchunk; double *dptr; double xj, yj, dx, dy, dx2, dy2, dx2p, dy2p, wide, high; nf = *nfrom; nt = *nto; wide = *xwidth; high = *yheight; dptr = d; OUTERCHUNKLOOP(j, nt, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nt, maxchunk, 16384) { xj = xto[j]; yj = yto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; dx2p = dx * dx; dy2p = dy * dy; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; *dptr = sqrt( dx2p + dy2p ); } } } } void CcrossP2dist(nfrom, xfrom, yfrom, nto, xto, yto, xwidth, yheight, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *xto, *yto, *xwidth, *yheight; /* output */ double *d; { int i, j, nf, nt, maxchunk; double *dptr; double xj, yj, dx, dy, dx2, dy2, dx2p, dy2p, wide, high; nf = *nfrom; nt = *nto; wide = *xwidth; high = *yheight; dptr = d; OUTERCHUNKLOOP(j, nt, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nt, maxchunk, 16384) { xj = xto[j]; yj = yto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; dx2p = dx * dx; dy2p = dy * dy; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; *dptr = dx2p + dy2p; } } } } /* matchxy Find matches between two lists of points */ void Cmatchxy(na, xa, ya, nb, xb, yb, match) /* inputs */ int *na, *nb; double *xa, *ya, *xb, *yb; /* output */ int *match; /* match[i] = j+1 if xb[j], yb[j] matches xa[i], ya[i] */ /* match[i] = 0 if no such point matches xa[i], ya[i] */ { int i, j, Na, Nb, maxchunk; double xai, yai; Na = *na; Nb = *nb; OUTERCHUNKLOOP(i, Na, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Na, maxchunk, 16384) { xai = xa[i]; yai = ya[i]; match[i] = 0; for (j=0; j < Nb; j++) { if(xai == xb[j] && yai == yb[j]) { match[i] = j+1; break; } } } } } spatstat/src/functable.h0000755000176000001440000000310212333041761015047 0ustar ripleyusers/* $Revision: 1.1 $ $Date: 2009/11/04 23:54:15 $ Definitions of C structures for spatial statistics function estimates. Usually the estimates are of the form f^(x) = a^(x)/b^(x); we store f^ and also a^ and b^ to cater for applications with replicated data. # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ typedef struct Ftable { /* double precision function table */ double t0; double t1; int n; /* number of entries */ double *f; double *num; /* f[i] = num[i]/denom[i] */ double *denom; } Ftable; typedef struct Itable { /* integer count table e.g for histograms */ double t0; double t1; int n; int *num; int *denom; /* usually p[i] = num[i]/denom[i] */ } Itable; typedef struct H4table { /* Four histograms, for censored data */ double t0; double t1; int n; int *obs; /* observed lifetimes: o_i = min(t_i, c_i) */ int *nco; /* uncensored lifetimes: o_i for which t_i <= c_i */ int *cen; /* censoring times: c_i */ int *ncc; /* censor times of uncensored data: c_i for which t_i <= c_i */ int upperobs; /* number of o_i that exceed t1 */ int uppercen; /* number of c_i that exceed t1 */ } H4table; spatstat/src/triplets.c0000644000176000001440000000615512333041761014755 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Triplets process */ /* Format for storage of parameters and precomputed/auxiliary data */ typedef struct Triplets { double gamma; double r; double loggamma; double r2; double *period; int hard; int per; int *neighbour; /* scratch list of neighbours of current point */ int Nmax; /* length of scratch space allocated */ } Triplets; /* initialiser function */ Cdata *tripletsinit(state, model, algo) State state; Model model; Algor algo; { /* create storage for model parameters */ Triplets *triplets; triplets = (Triplets *) R_alloc(1, sizeof(Triplets)); /* create scratch space */ triplets->Nmax = 1024; triplets->neighbour = (int *) R_alloc(1024, sizeof(int)); /* Interpret model parameters*/ triplets->gamma = model.ipar[0]; triplets->r = model.ipar[1]; /* No longer passed as r^2 */ triplets->r2 = triplets->r * triplets->r; triplets->period = model.period; #ifdef MHDEBUG Rprintf("Initialising Triplets gamma=%lf, r=%lf\n", triplets->gamma, triplets->r); #endif /* is the model numerically equivalent to hard core ? */ triplets->hard = (triplets->gamma < DOUBLE_EPS); triplets->loggamma = (triplets->hard) ? 0 : log(triplets->gamma); /* periodic boundary conditions? */ triplets->per = (model.period[0] > 0.0); return((Cdata *) triplets); } /* conditional intensity evaluator */ double tripletscif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, kount, ix, j, k, nj, nk, N, Nmax, Nmore, N1; int *neighbour; double *x, *y; double u, v; double r2, d2, cifval; Triplets *triplets; triplets = (Triplets *) cdata; r2 = triplets->r2; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; if(npts == 0) return((double) 1.0); neighbour = triplets->neighbour; Nmax = triplets->Nmax; N = 0; /* compile list of neighbours */ for(j=0; j < npts; j++) { if(j != ix) { d2 = dist2either(u,v,x[j],y[j],triplets->period); if(d2 < r2) { /* add j to list of neighbours of current point */ if(N >= Nmax) { /* storage space overflow: reallocate */ Nmore = 2 * Nmax; triplets->neighbour = neighbour = (int *) S_realloc((char *) triplets->neighbour, Nmore, Nmax, sizeof(int)); triplets->Nmax = Nmax = Nmore; } neighbour[N] = j; N++; } } } /* count r-close (ordered) pairs of neighbours */ kount = 0; if(N > 1) { N1 = N - 1; for(j = 0; j < N1; j++) { nj = neighbour[j]; for(k = j+1; k < N; k++) { nk = neighbour[k]; if(nj != nk) { d2 = dist2either(x[nj],y[nj],x[nk],y[nk],triplets->period); if(d2 < r2) kount++; } } } } if(triplets->hard) { if(kount > 0) cifval = 0.0; else cifval = 1.0; } else cifval = exp((triplets->loggamma) * kount); #ifdef MHDEBUG Rprintf("triplet count=%d cif=%lf\n", kount, cifval); #endif return cifval; } Cifns TripletsCifns = { &tripletsinit, &tripletscif, (updafunptr) NULL, NO}; spatstat/src/KrectV3.h0000644000176000001440000000025312333041761014366 0ustar ripleyusers/* KrectV4.h with or without border correction */ if((*doBord) == 1) { #define BORDER #include "KrectV4.h" } else { #undef BORDER #include "KrectV4.h" } spatstat/src/loccumx.h0000644000176000001440000000371012333041761014560 0ustar ripleyusers/* loccumx.h C template for loccum.c grid-to-data functions $Revision: 1.5 $ $Date: 2012/11/10 06:13:52 $ macros: FNAME function name NULVAL initial value (empty sum = 0, empty product = 1) INC(A,B) increment operation A += B or A *= B */ void FNAME(ntest, xtest, ytest, ndata, xdata, ydata, vdata, nr, rmax, ans) /* inputs */ int *ntest, *ndata, *nr; double *xtest, *ytest, *xdata, *ydata, *vdata; double *rmax; /* output */ double *ans; /* matrix of column vectors of functions for each point of first pattern */ { int Ntest, Ndata, Nr, Nans; double Rmax; int i, j, k, jleft, kmin, maxchunk, columnstart; double Rmax2, rstep, xtesti, ytesti, xleft; double dx, dy, dx2, d2, d, contrib; Ntest = *ntest; Ndata = *ndata; Nr = *nr; Rmax = *rmax; if(Ntest == 0) return; Nans = Nr * Ntest; /* initialise products to 1 */ OUTERCHUNKLOOP(k, Nans, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(k, Nans, maxchunk, 8196) { ans[k] = NULVAL; } } if(Ndata == 0) return; rstep = Rmax/(Nr-1); Rmax2 = Rmax * Rmax; jleft = 0; OUTERCHUNKLOOP(i, Ntest, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Ntest, maxchunk, 8196) { xtesti = xtest[i]; ytesti = ytest[i]; columnstart = Nr * i; /* start position for f_i(.) in 'ans' */ /* adjust starting point */ xleft = xtesti - Rmax; while((xdata[jleft] < xleft) && (jleft+1 < Ndata)) ++jleft; /* process from jleft until |dx| > Rmax */ for(j=jleft; j < Ndata; j++) { dx = xdata[j] - xtesti; dx2 = dx * dx; if(dx2 > Rmax2) break; dy = ydata[j] - ytesti; d2 = dx2 + dy * dy; if(d2 <= Rmax2) { d = sqrt(d2); kmin = (int) ceil(d/rstep); if(kmin < Nr) { contrib = vdata[j]; for(k = kmin; k < Nr; k++) INC(ans[columnstart + k] , contrib); } } } } } } spatstat/src/constants.h0000644000176000001440000000051512333041761015122 0ustar ripleyusers/* constants.h Ensure that required constants are defined (Insurance against flaky installations) $Revision: 1.1 $ $Date: 2013/08/09 08:14:15 $ */ #ifndef M_PI_2 #define M_PI_2 1.570796326794897 #endif #ifndef M_PI #define M_PI 3.141592653589793 #endif #ifndef M_2PI #define M_2PI 6.283185307179586 #endif spatstat/src/inxypOld.f0000755000176000001440000000273412333041761014722 0ustar ripleyusersC Output from Public domain Ratfor, version 1.0 subroutine inxyp(x,y,xp,yp,npts,nedges,score,onbndry) implicit double precision(a-h,o-z) dimension x(npts), y(npts), xp(nedges), yp(nedges), score(npts) logical first, onbndry(npts) zero = 0.0d0 half = 0.5d0 one = 1.0d0 do23000 i = 1,nedges x0 = xp(i) y0 = yp(i) if(i .eq. nedges)then x1 = xp(1) y1 = yp(1) else x1 = xp(i+1) y1 = yp(i+1) endif dx = x1 - x0 dy = y1 - y0 do23004 j = 1,npts xcrit = (x(j) - x0)*(x(j) - x1) if(xcrit .le. zero)then if(xcrit .eq. zero)then contrib = half else contrib = one endif ycrit = y(j)*dx - x(j)*dy + x0*dy - y0*dx if(dx .lt. 0)then if(ycrit .ge. zero)then score(j) = score(j) + contrib endif onbndry(j) = onbndry(j) .or. (ycrit .eq. zero) else if(dx .gt. zero)then if(ycrit .lt. zero)then score(j) = score(j) - contrib endif onbndry(j) = onbndry(j) .or. (ycrit .eq. zero) else if(x(j) .eq. x0)then ycrit = (y(j) - y0)*(y(j) - y1) endif onbndry(j) = onbndry(j) .or. (ycrit .le. zero) endif endif endif 23004 continue 23005 continue 23000 continue 23001 continue return end spatstat/src/Kborder.c0000755000176000001440000000155112333041761014475 0ustar ripleyusers#include #include #include /* Kborder.c Efficient computation of border-corrected estimates of K for large datasets KborderI() Estimates K function, returns integer numerator & denominator KborderD() Estimates K function, returns double precision numerator & denominator Kwborder() Estimates Kinhom. Functions require (x,y) data to be sorted in ascending order of x and expect r values to be equally spaced and starting at zero $Revision: 1.4 $ $Date: 2013/05/27 02:09:10 $ */ #undef WEIGHTED #define FNAME KborderI #define OUTTYPE int #include "Kborder.h" #undef FNAME #undef OUTTYPE #define FNAME KborderD #define OUTTYPE double #include "Kborder.h" #undef FNAME #undef OUTTYPE #define FNAME Kwborder #define WEIGHTED #define OUTTYPE double #include "Kborder.h" spatstat/src/knnXdist.h0000644000176000001440000001501312333041761014707 0ustar ripleyusers #if (1 == 0) /* knnXdist.h Code template for C functions supporting nncross for k-nearest neighbours (k > 1) THE FOLLOWING CODE ASSUMES THAT LISTS ARE SORTED IN ASCENDING ORDER OF y COORDINATE This code is #included multiple times in knndistance.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour EXCLUDE #defined if exclusion mechanism is used Either or both DIST and WHICH may be defined. When EXCLUDE is defined, code numbers id1, id2 are attached to the patterns X and Y respectively, such that x1[i], y1[i] and x2[j], y2[j] are the same point iff id1[i] = id2[j]. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2013 Licence: GPL >= 2 $Revision: 1.10 $ $Date: 2013/12/10 03:29:55 $ */ #endif void FNAME(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge) /* inputs */ int *n1, *n2; double *x1, *y1, *x2, *y2, *huge; int *id1, *id2; int *kmax; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { int npoints1, npoints2, nk, nk1; int maxchunk, i, jleft, jright, jwhich, lastjwhich, unsorted, k, k1; double d2, d2minK, x1i, y1i, dx, dy, dy2, hu, hu2, tmp; double *d2min; #ifdef WHICH int *which; int itmp; #endif #ifdef EXCLUDE int id1i; #endif #ifdef TRACER int kk; #endif npoints1 = *n1; npoints2 = *n2; nk = *kmax; nk1 = nk - 1; hu = *huge; hu2 = hu * hu; if(npoints1 == 0 || npoints2 == 0) return; lastjwhich = 0; /* create space to store the nearest neighbour distances and indices for the current point */ d2min = (double *) R_alloc((size_t) nk, sizeof(double)); #ifdef WHICH which = (int *) R_alloc((size_t) nk, sizeof(int)); #endif /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints1) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints1) maxchunk = npoints1; for(; i < maxchunk; i++) { /* initialise nn distances and indices */ d2minK = hu2; jwhich = -1; for(k = 0; k < nk; k++) { d2min[k] = hu2; #ifdef WHICH which[k] = -1; #endif } x1i = x1[i]; y1i = y1[i]; #ifdef EXCLUDE id1i = id1[i]; #endif #ifdef TRACER Rprintf("i=%d : (%lf, %lf) ..................... \n", i, x1i, y1i); #endif if(lastjwhich < npoints2) { #ifdef TRACER Rprintf("\tForward search from lastjwhich=%d:\n", lastjwhich); #endif /* search forward from previous nearest neighbour */ for(jright = lastjwhich; jright < npoints2; ++jright) { #ifdef TRACER Rprintf("\tjright=%d \t (%lf, %lf)\n", jright, x2[jright], y2[jright]); #endif dy = y2[jright] - y1i; dy2 = dy * dy; #ifdef TRACER Rprintf("\t\t dy2=%lf,\t d2minK=%lf\n", dy2, d2minK); #endif if(dy2 > d2minK) /* note that dy2 >= d2minK could break too early */ break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[jright] != id1i) { #ifdef TRACER Rprintf("\t\t %d and %d are not identical\n", i, jright); #endif #endif dx = x2[jright] - x1i; d2 = dx * dx + dy2; #ifdef TRACER Rprintf("\t\t d2=%lf\n", d2); #endif if (d2 < d2minK) { /* overwrite last entry in list of neighbours */ #ifdef TRACER Rprintf("\t\t overwrite d2min[nk1]=%lf by d2=%lf\n", d2min[nk1], d2); #endif d2min[nk1] = d2; jwhich = jright; #ifdef WHICH which[nk1] = jright; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } #ifdef TRACER Rprintf("\t\t sorted nn distances:\n"); for(kk = 0; kk < nk; kk++) Rprintf("\t\t d2min[%d] = %lf\n", kk, d2min[kk]); #endif /* adjust maximum distance */ d2minK = d2min[nk1]; #ifdef TRACER Rprintf("\t\t d2minK=%lf\n", d2minK); #endif } #ifdef EXCLUDE } #endif } /* end forward search */ #ifdef TRACER Rprintf("\tEnd forward search\n"); #endif } if(lastjwhich > 0) { #ifdef TRACER Rprintf("\tBackward search from lastjwhich=%d:\n", lastjwhich); #endif /* search backward from previous nearest neighbour */ for(jleft = lastjwhich - 1; jleft >= 0; --jleft) { #ifdef TRACER Rprintf("\tjleft=%d \t (%lf, %lf)\n", jleft, x2[jleft], y2[jleft]); #endif dy = y1i - y2[jleft]; dy2 = dy * dy; #ifdef TRACER Rprintf("\t\t dy2=%lf,\t d2minK=%lf\n", dy2, d2minK); #endif if(dy2 > d2minK) /* note that dy2 >= d2minK could break too early */ break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[jleft] != id1i) { #ifdef TRACER Rprintf("\t\t %d and %d are not identical\n", i, jleft); #endif #endif dx = x2[jleft] - x1i; d2 = dx * dx + dy2; #ifdef TRACER Rprintf("\t\t d2=%lf\n", d2); #endif if (d2 < d2minK) { /* overwrite last entry in list of neighbours */ #ifdef TRACER Rprintf("\t\t overwrite d2min[nk1]=%lf by d2=%lf\n", d2min[nk1], d2); #endif d2min[nk1] = d2; jwhich = jleft; #ifdef WHICH which[nk1] = jleft; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } #ifdef TRACER Rprintf("\t\t sorted nn distances:\n"); for(kk = 0; kk < nk; kk++) Rprintf("\t\t d2min[%d] = %lf\n", kk, d2min[kk]); #endif /* adjust maximum distance */ d2minK = d2min[nk1]; #ifdef TRACER Rprintf("\t\t d2minK=%lf\n", d2minK); #endif } #ifdef EXCLUDE } #endif } /* end backward search */ #ifdef TRACER Rprintf("\tEnd backward search\n"); #endif } /* copy nn distances for point i to output matrix in ROW MAJOR order */ for(k = 0; k < nk; k++) { #ifdef DIST nnd[nk * i + k] = sqrt(d2min[k]); #endif #ifdef WHICH nnwhich[nk * i + k] = which[k] + 1; /* R indexing */ #endif } /* save index of last neighbour encountered */ lastjwhich = jwhich; /* end of loop over points i */ } } } spatstat/src/nn3Ddist.h0000644000176000001440000000364412333041761014602 0ustar ripleyusers/* nn3Ddist.h Code template for nearest-neighbour algorithms for 3D point patterns Input is a single point pattern - supports 'nndist' and 'nnwhich' This code is #included multiple times in nn3Ddist.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour Either or both DIST and WHICH may be defined. THE FOLLOWING CODE ASSUMES THAT THE POINT PATTERN IS SORTED IN ASCENDING ORDER OF THE z COORDINATE $Revision: 1.5 $ $Date: 2013/06/28 10:38:46 $ */ void FNAME(n, x, y, z, nnd, nnwhich, huge) /* inputs */ int *n; double *x, *y, *z, *huge; /* outputs */ double *nnd; int *nnwhich; { int npoints, i, j, maxchunk; double d2, d2min, xi, yi, zi, dx, dy, dz, dz2, hu, hu2; #ifdef WHICH int which; #endif hu = *huge; hu2 = hu * hu; npoints = *n; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { d2min = hu2; #ifdef WHICH which = -1; #endif xi = x[i]; yi = y[i]; zi = z[i]; /* search backward */ if(i > 0){ for(j = i - 1; j >= 0; --j) { dz = z[j] - zi; dz2 = dz * dz; if(dz2 > d2min) break; dx = x[j] - xi; dy = y[j] - yi; d2 = dx * dx + dy * dy + dz2; if (d2 < d2min) { d2min = d2; #ifdef WHICH which = j; #endif } } } /* search forward */ if(i < npoints - 1) { for(j = i + 1; j < npoints; ++j) { dz = z[j] - zi; dz2 = dz * dz; if(dz2 > d2min) break; dx = x[j] - xi; dy = y[j] - yi; d2 = dx * dx + dy * dy + dz2; if (d2 < d2min) { d2min = d2; #ifdef WHICH which = j; #endif } } } #ifdef DIST nnd[i] = sqrt(d2min); #endif #ifdef WHICH /* convert to R indexing */ nnwhich[i] = which + 1; #endif } } } spatstat/src/Kborder.h0000755000176000001440000001066312333041761014506 0ustar ripleyusers/* Kborder.h Code template for K function estimators in Kborder.c Variables: FNAME function name OUTTYPE storage type of the output vectors ('int' or 'double') WEIGHTED #defined for weighted (inhom) K function Copyright (C) Adrian Baddeley, Julian Gilbey and Rolf Turner 2000-2013 Licence: GPL >= 2 $Revision: 1.11 $ $Date: 2013/09/18 04:06:59 $ */ void FNAME( nxy, x, y, #ifdef WEIGHTED w, #endif b, nr, rmax, numer, denom) /* inputs */ int *nxy, *nr; double *x, *y, *b, *rmax; #ifdef WEIGHTED double *w; #endif /* outputs */ OUTTYPE *numer, *denom; { int i, j, l, n, nt, n1, nt1, lmin, lmax, maxchunk; double dt, tmax, xi, yi, bi, maxsearch, max2search; double bratio, dratio, dij, dij2, dx, dy, dx2; OUTTYPE *numerLowAccum, *numerHighAccum, *denomAccum; OUTTYPE naccum, daccum; #ifdef WEIGHTED double wi, wj, wij; #endif #ifdef WEIGHTED #define ZERO 0.0 #define WI wi #define WJ wj #define WIJ wij #else #define ZERO 0 #define WI 1 #define WJ 1 #define WIJ 1 #endif n = *nxy; nt = *nr; n1 = n - 1; nt1 = nt - 1; dt = (*rmax)/(nt-1); tmax = *rmax; /* initialise */ numerLowAccum = (OUTTYPE *) R_alloc(nt, sizeof(OUTTYPE)); numerHighAccum = (OUTTYPE *) R_alloc(nt, sizeof(OUTTYPE)); denomAccum = (OUTTYPE *) R_alloc(nt, sizeof(OUTTYPE)); for(l = 0; l < nt; l++) numer[l] = denom[l] = numerLowAccum[l] = numerHighAccum[l] = denomAccum[l] = ZERO; if(n == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < n) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n) maxchunk = n; for(; i < maxchunk; i++) { /* -------- DENOMINATOR -------------*/ bi = b[i]; #ifdef WEIGHTED wi = w[i]; #endif /* increment denominator for all r < b[i] */ bratio = bi/dt; /* lmax is the largest integer STRICTLY less than bratio */ lmax = (int) ceil(bratio) - 1; lmax = (lmax <= nt1) ? lmax : nt1; /* effectively increment entries 0 to lmax */ if(lmax >= 0) denomAccum[lmax] += WI; /* ---------- NUMERATOR -----------*/ /* scan through points (x[j],y[j]) */ xi = x[i]; yi = y[i]; maxsearch = (bi < tmax) ? bi : tmax; max2search = maxsearch * maxsearch; /* scan backward from i-1 until |x[j]-x[i]| > maxsearch or until we run out */ if(i > 0) { for(j=i-1; j >= 0; j--) { /* squared interpoint distance */ dx = x[j] - xi; dx2 = dx * dx; if(dx2 >= max2search) break; dy = y[j] - yi; dij2 = dx2 + dy * dy; if(dij2 < max2search) { #ifdef WEIGHTED wj = w[j]; #endif /* increment numerator for all r such that dij <= r < bi */ dij = (double) sqrt(dij2); dratio = dij/dt; /* smallest integer greater than or equal to dratio */ lmin = (int) ceil(dratio); /* increment entries lmin to lmax inclusive */ if(lmax >= lmin) { #ifdef WEIGHTED wij = wi * wj; #endif numerLowAccum[lmin] += WIJ; numerHighAccum[lmax] += WIJ; } } } } /* scan forward from i+1 until x[j]-x[i] > maxsearch or until we run out */ if(i < n1) { for(j=i+1; j < n; j++) { /* squared interpoint distance */ dx = x[j] - xi; dx2 = dx * dx; if(dx2 >= max2search) break; dy = y[j] - yi; dij2 = dx2 + dy * dy; if(dij2 < max2search) { #ifdef WEIGHTED wj = w[j]; #endif /* increment numerator for all r such that dij <= r < bi */ dij = (double) sqrt(dij2); dratio = dij/dt; /* smallest integer greater than or equal to dratio */ lmin = (int) ceil(dratio); /* increment entries lmin to lmax inclusive */ if(lmax >= lmin) { #ifdef WEIGHTED wij = wi * wj; #endif numerLowAccum[lmin] += WIJ; numerHighAccum[lmax] += WIJ; } } } } } } /* Now use the accumulated values to compute the numerator and denominator. The value of denomAccum[l] should be added to denom[k] for all k <= l. numerHighAccum[l] should be added to numer[k] for all k <=l numerLowAccum[l] should then be subtracted from numer[k] for k <= l. */ for(l=nt1, naccum=daccum=ZERO; l>=0; l--) { daccum += denomAccum[l]; denom[l] = daccum; naccum += numerHighAccum[l]; numer[l] = naccum; naccum -= numerLowAccum[l]; } } #undef ZERO #undef WI #undef WJ #undef WIJ spatstat/src/Knone.h0000644000176000001440000000567212333041761014171 0ustar ripleyusers/* Knone.h Code template for K function estimators in Knone.c Variables: FNAME function name OUTTYPE storage type of the output 'numer' ('int' or 'double') WEIGHTED #defined for weighted (inhom) K function Copyright (C) Adrian Baddeley, Julian Gilbey and Rolf Turner 2000-2013 Licence: GPL >= 2 $Revision: 1.6 $ $Date: 2013/09/18 04:08:26 $ */ void FNAME( nxy, x, y, #ifdef WEIGHTED w, #endif nr, rmax, numer) /* inputs */ int *nxy, *nr; double *x, *y, *rmax; #ifdef WEIGHTED double *w; #endif /* output */ OUTTYPE *numer; { int i, j, l, n, nt, n1, lmin, lmax, maxchunk; double dt, tmax, tmax2, xi, yi; double dratio, dij, dij2, dx, dy, dx2; #ifdef WEIGHTED double wi, wj, wij; #endif #ifdef WEIGHTED #define ZERO 0.0 #define WI wi #define WJ wj #define WIJ wij #else #define ZERO 0 #define WI 1 #define WJ 1 #define WIJ 1 #endif n = *nxy; nt = *nr; n1 = n - 1; lmax = nt - 1; dt = (*rmax)/(nt-1); tmax = *rmax; tmax2 = tmax * tmax; /* initialise */ for(l = 0; l < nt; l++) numer[l] = ZERO; if(n == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < n) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n) maxchunk = n; for(; i < maxchunk; i++) { #ifdef WEIGHTED wi = w[i]; #endif xi = x[i]; yi = y[i]; /* scan backward from i-1 until x[j] < x[i] -tmax or until we run out */ if(i > 0) { for(j=i-1; j >= 0; j--) { dx = x[j] - xi; dx2 = dx * dx; if(dx2 >= tmax2) break; dy = y[j] - yi; dij2 = dx2 + dy * dy; if(dij2 < tmax2) { #ifdef WEIGHTED wj = w[j]; #endif /* increment numerator for all r >= dij */ dij = (double) sqrt(dij2); dratio = dij/dt; /* smallest integer greater than or equal to dratio */ lmin = (int) ceil(dratio); /* effectively increment entries lmin to lmax inclusive */ if(lmin <= lmax) { #ifdef WEIGHTED wij = wi * wj; #endif numer[lmin] += WIJ; } } } } /* scan forward from i+1 until x[j] > x[i] + tmax or until we run out */ if(i < n1) { for(j=i+1; j < n; j++) { /* squared interpoint distance */ dx = x[j] - xi; dx2 = dx * dx; if(dx2 >= tmax2) break; dy = y[j] - yi; dij2 = dx2 + dy * dy; if(dij2 < tmax2) { #ifdef WEIGHTED wj = w[j]; #endif /* increment numerator for all r >= dij */ dij = (double) sqrt(dij2); dratio = dij/dt; /* smallest integer greater than or equal to dratio */ lmin = (int) ceil(dratio); /* increment entries lmin to lmax inclusive */ if(lmin <= lmax) { #ifdef WEIGHTED wij = wi * wj; #endif numer[lmin] += WIJ; } } } } } } /* Now accumulate the numerator. */ if(nt > 1) for(l=1; l < nt; l++) numer[l] += numer[l-1]; } #undef ZERO #undef WI #undef WJ #undef WIJ spatstat/src/KrectV1.h0000644000176000001440000000026312333041761014365 0ustar ripleyusers/* KrectV2.h with or without isotropic correction */ if((*doIso) == 1) { #define ISOTROPIC #include "KrectV2.h" } else { #undef ISOTROPIC #include "KrectV2.h" } spatstat/src/linalg.c0000755000176000001440000000716012333041761014355 0ustar ripleyusers/* linalg.c Home made linear algebra Yes, really $Revision: 1.9 $ $Date: 2013/09/25 06:07:24 $ Csumouter Cwsumouter Cquadform Csumsymouter Cwsumsymouter */ #include #include #include "chunkloop.h" /* ............... matrices ..............................*/ /* Csumouter computes the sum of outer products of columns of x y = sum[j] (x[,j] %o% x[,j]) */ void Csumouter(x, n, p, y) double *x; /* p by n matrix */ int *n, *p; double *y; /* output matrix p by p, initialised to zero */ { int N, P; register int i, j, k, maxchunk; register double xij, xkj; register double *xcolj; N = *n; P = *p; OUTERCHUNKLOOP(j, N, maxchunk, 2048) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, N, maxchunk, 2048) { xcolj = x + j * P; for(i = 0; i < P; i++) { xij = xcolj[i]; for(k = 0; k < P; k++) { xkj = xcolj[k]; y[k * P + i] += xij * xkj; } } } } } /* Cwsumouter computes the weighted sum of outer products of columns of x y = sum[j] (w[j] * x[,j] %o% x[,j]) */ void Cwsumouter(x, n, p, w, y) double *x; /* p by n matrix */ int *n, *p; double *w; /* weight vector, length n */ double *y; /* output matrix p by p, initialised to zero */ { int N, P; register int i, j, k, maxchunk; register double wj, xij, wjxij, xkj; register double *xcolj; N = *n; P = *p; OUTERCHUNKLOOP(j, N, maxchunk, 2048) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, N, maxchunk, 2048) { wj = w[j]; xcolj = x + j * P; for(i = 0; i < P; i++) { xij = xcolj[i]; wjxij = wj * xij; for(k = 0; k < P; k++) { xkj = xcolj[k]; y[k * P + i] += wjxij * xkj; } } } } } /* computes the quadratic form values y[j] = x[,j] %*% v %*% t(x[,j]) */ void Cquadform(x, n, p, v, y) double *x; /* p by n matrix */ int *n, *p; double *v; /* p by p matrix */ double *y; /* output vector, length n */ { int N, P; register int i, j, k, maxchunk; register double xij, xkj, vik, yj; register double *xcolj; N = *n; P = *p; OUTERCHUNKLOOP(j, N, maxchunk, 2048) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, N, maxchunk, 2048) { xcolj = x + j * P; yj = 0; for(i = 0; i < P; i++) { xij = xcolj[i]; for(k = 0; k < P; k++) { xkj = xcolj[k]; vik = v[k * P + i]; yj += xij * vik * xkj; } } y[j] = yj; } } } /* computes the bilinear form values z[j] = x[,j] %*% v %*% t(y[,j]) */ void Cbiform(x, y, n, p, v, z) double *x, *y; /* p by n matrices */ int *n, *p; double *v; /* p by p matrix */ double *z; /* output vector, length n */ { int N, P; register int i, j, k, maxchunk; register double xij, vik, ykj, zj; register double *xcolj, *ycolj; N = *n; P = *p; OUTERCHUNKLOOP(j, N, maxchunk, 2048) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, N, maxchunk, 2048) { xcolj = x + j * P; ycolj = y + j * P; zj = 0; for(i = 0; i < P; i++) { xij = xcolj[i]; for(k = 0; k < P; k++) { ykj = ycolj[k]; vik = v[k * P + i]; zj += xij * vik * ykj; } } z[j] = zj; } } } /* ............... 3D arrays ...................... */ #undef FNAME #undef WEIGHTED /* sumsymouter computes the sum of outer products x[,i,j] %o% x[,j,i] over all pairs i, j */ #define FNAME Csumsymouter #include "sumsymouter.h" #undef FNAME /* wsumsymouter computes the weighted sum of outer products w[i,j] * (x[,i,j] %o% x[,j,i]) over all pairs i, j */ #define FNAME Cwsumsymouter #define WEIGHTED #include "sumsymouter.h" #undef FNAME #undef WEIGHTED spatstat/src/nn3DdistX.h0000644000176000001440000000520412333041761014724 0ustar ripleyusers/* nn3DdistX.h Code template for nearest-neighbour algorithms for 3D point patterns Input is two point patterns - supports 'nncross' This code is #included multiple times in nn3Ddist.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour EXCLUDE #defined if the two patterns may include common points (which are not to be counted as neighbours) Either or both DIST and WHICH may be defined. THE FOLLOWING CODE ASSUMES THAT BOTH POINT PATTERNS ARE SORTED IN ASCENDING ORDER OF THE z COORDINATE If EXCLUDE is #defined, Code numbers id1, id2 are attached to the patterns X and Y respectively, such that x1[i], y1[i] and x2[j], y2[j] are the same point iff id1[i] = id2[j]. $Revision: 1.5 $ $Date: 2013/09/20 10:01:25 $ */ void FNAME(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge) /* inputs */ int *n1, *n2, *id1, *id2; double *x1, *y1, *z1, *x2, *y2, *z2, *huge; /* outputs */ double *nnd; int *nnwhich; { int npoints1, npoints2, i, j, jwhich, lastjwhich; double d2, d2min, x1i, y1i, z1i, dx, dy, dz, dz2, hu, hu2; #ifdef EXCLUDE int id1i; #endif hu = *huge; hu2 = hu * hu; npoints1 = *n1; npoints2 = *n2; if(npoints1 == 0 || npoints2 == 0) return; lastjwhich = 0; for(i = 0; i < npoints1; i++) { R_CheckUserInterrupt(); d2min = hu2; jwhich = -1; x1i = x1[i]; y1i = y1[i]; z1i = z1[i]; #ifdef EXCLUDE id1i = id1[i]; #endif /* search backward from previous nearest neighbour */ if(lastjwhich > 0) { for(j = lastjwhich - 1; j >= 0; --j) { dz = z2[j] - z1i; dz2 = dz * dz; if(dz2 > d2min) break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[j] != id1i) { #endif dx = x2[j] - x1i; dy = y2[j] - y1i; d2 = dx * dx + dy * dy + dz2; if (d2 < d2min) { d2min = d2; jwhich = j; } #ifdef EXCLUDE } #endif } } /* search forward from previous nearest neighbour */ if(lastjwhich < npoints2) { for(j = lastjwhich; j < npoints2; ++j) { dz = z2[j] - z1i; dz2 = dz * dz; if(dz2 > d2min) break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[j] != id1i) { #endif dx = x2[j] - x1i; dy = y2[j] - y1i; d2 = dx * dx + dy * dy + dz2; if (d2 < d2min) { d2min = d2; jwhich = j; } #ifdef EXCLUDE } #endif } } #ifdef DIST nnd[i] = sqrt(d2min); #endif #ifdef WHICH /* convert to R indexing */ nnwhich[i] = jwhich + 1; #endif lastjwhich = jwhich; } } spatstat/src/closefuns.h0000644000176000001440000003154112333041761015112 0ustar ripleyusers/* closefuns.h Function definitions to be #included in closepair.c several times with different values of macros. Macros used: CLOSEFUN name of function for 'closepairs' CROSSFUN name of function for 'crosspairs' COORDS if defined, also return xi, yi, xj, yj, dx, dy, d THRESH if defined, also return 1(d < s) $Revision: 1.3 $ $Date: 2013/05/22 10:21:28 $ */ SEXP CLOSEFUN(SEXP xx, SEXP yy, SEXP rr, #ifdef THRESH SEXP ss, #endif SEXP nguess) { double *x, *y; double xi, yi, rmax, r2max, dx, dy, d2; int n, k, kmax, kmaxold, maxchunk, i, j, m; /* local storage */ int *iout, *jout; /* R objects in return value */ SEXP Out, iOut, jOut; /* external storage pointers */ int *iOutP, *jOutP; #ifdef COORDS double *xiout, *yiout, *xjout, *yjout, *dxout, *dyout, *dout; SEXP xiOut, yiOut, xjOut, yjOut, dxOut, dyOut, dOut; double *xiOutP, *yiOutP, *xjOutP, *yjOutP, *dxOutP, *dyOutP, *dOutP; #endif #ifdef THRESH double s, s2; int *tout; SEXP tOut; int *tOutP; #endif /* protect R objects from garbage collector */ PROTECT(xx = AS_NUMERIC(xx)); PROTECT(yy = AS_NUMERIC(yy)); PROTECT(rr = AS_NUMERIC(rr)); PROTECT(nguess = AS_INTEGER(nguess)); #ifdef THRESH PROTECT(ss = AS_NUMERIC(ss)); #define NINPUTS 5 #else #define NINPUTS 4 #endif /* Translate arguments from R to C */ x = NUMERIC_POINTER(xx); y = NUMERIC_POINTER(yy); n = LENGTH(xx); rmax = *(NUMERIC_POINTER(rr)); kmax = *(INTEGER_POINTER(nguess)); r2max = rmax * rmax; #ifdef THRESH s = *(NUMERIC_POINTER(ss)); s2 = s * s; #endif k = 0; /* k is the next available storage location and also the current length of the list */ if(n > 0 && kmax > 0) { /* allocate space */ iout = (int *) R_alloc(kmax, sizeof(int)); jout = (int *) R_alloc(kmax, sizeof(int)); #ifdef COORDS xiout = (double *) R_alloc(kmax, sizeof(double)); yiout = (double *) R_alloc(kmax, sizeof(double)); xjout = (double *) R_alloc(kmax, sizeof(double)); yjout = (double *) R_alloc(kmax, sizeof(double)); dxout = (double *) R_alloc(kmax, sizeof(double)); dyout = (double *) R_alloc(kmax, sizeof(double)); dout = (double *) R_alloc(kmax, sizeof(double)); #endif #ifdef THRESH tout = (int *) R_alloc(kmax, sizeof(int)); #endif /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < n) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n) maxchunk = n; for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; if(i > 0) { /* scan backward */ for(j = i - 1; j >= 0; j--) { dx = x[j] - xi; if(dx < -rmax) break; dy = y[j] - yi; d2 = dx * dx + dy * dy; if(d2 <= r2max) { /* add this (i, j) pair to output */ if(k >= kmax) { /* overflow; allocate more space */ kmaxold = kmax; kmax = 2 * kmax; iout = intRealloc(iout, kmaxold, kmax); jout = intRealloc(jout, kmaxold, kmax); #ifdef COORDS xiout = dblRealloc(xiout, kmaxold, kmax); yiout = dblRealloc(yiout, kmaxold, kmax); xjout = dblRealloc(xjout, kmaxold, kmax); yjout = dblRealloc(yjout, kmaxold, kmax); dxout = dblRealloc(dxout, kmaxold, kmax); dyout = dblRealloc(dyout, kmaxold, kmax); dout = dblRealloc(dout, kmaxold, kmax); #endif #ifdef THRESH tout = intRealloc(tout, kmaxold, kmax); #endif } jout[k] = j + 1; /* R indexing */ iout[k] = i + 1; #ifdef COORDS xiout[k] = xi; yiout[k] = yi; xjout[k] = x[j]; yjout[k] = y[j]; dxout[k] = dx; dyout[k] = dy; dout[k] = sqrt(d2); #endif #ifdef THRESH tout[k] = (d2 <= s2) ? 1 : 0; #endif ++k; } } } if(i + 1 < n) { /* scan forward */ for(j = i + 1; j < n; j++) { dx = x[j] - xi; if(dx > rmax) break; dy = y[j] - yi; d2 = dx * dx + dy * dy; if(d2 <= r2max) { /* add this (i, j) pair to output */ if(k >= kmax) { /* overflow; allocate more space */ kmaxold = kmax; kmax = 2 * kmax; iout = intRealloc(iout, kmaxold, kmax); jout = intRealloc(jout, kmaxold, kmax); #ifdef COORDS xiout = dblRealloc(xiout, kmaxold, kmax); yiout = dblRealloc(yiout, kmaxold, kmax); xjout = dblRealloc(xjout, kmaxold, kmax); yjout = dblRealloc(yjout, kmaxold, kmax); dxout = dblRealloc(dxout, kmaxold, kmax); dyout = dblRealloc(dyout, kmaxold, kmax); dout = dblRealloc(dout, kmaxold, kmax); #endif #ifdef THRESH tout = intRealloc(tout, kmaxold, kmax); #endif } jout[k] = j + 1; /* R indexing */ iout[k] = i + 1; #ifdef COORDS xiout[k] = xi; yiout[k] = yi; xjout[k] = x[j]; yjout[k] = y[j]; dxout[k] = dx; dyout[k] = dy; dout[k] = sqrt(d2); #endif #ifdef THRESH tout[k] = (d2 <= s2) ? 1 : 0; #endif ++k; } } } /* end of i loop */ } } } /* return a list of vectors */ PROTECT(iOut = NEW_INTEGER(k)); PROTECT(jOut = NEW_INTEGER(k)); #ifdef COORDS PROTECT(xiOut = NEW_NUMERIC(k)); PROTECT(yiOut = NEW_NUMERIC(k)); PROTECT(xjOut = NEW_NUMERIC(k)); PROTECT(yjOut = NEW_NUMERIC(k)); PROTECT(dxOut = NEW_NUMERIC(k)); PROTECT(dyOut = NEW_NUMERIC(k)); PROTECT(dOut = NEW_NUMERIC(k)); #endif #ifdef THRESH PROTECT(tOut = NEW_INTEGER(k)); #endif if(k > 0) { iOutP = INTEGER_POINTER(iOut); jOutP = INTEGER_POINTER(jOut); #ifdef COORDS xiOutP = NUMERIC_POINTER(xiOut); yiOutP = NUMERIC_POINTER(yiOut); xjOutP = NUMERIC_POINTER(xjOut); yjOutP = NUMERIC_POINTER(yjOut); dxOutP = NUMERIC_POINTER(dxOut); dyOutP = NUMERIC_POINTER(dyOut); dOutP = NUMERIC_POINTER(dOut); #endif #ifdef THRESH tOutP = INTEGER_POINTER(tOut); #endif for(m = 0; m < k; m++) { iOutP[m] = iout[m]; jOutP[m] = jout[m]; #ifdef COORDS xiOutP[m] = xiout[m]; yiOutP[m] = yiout[m]; xjOutP[m] = xjout[m]; yjOutP[m] = yjout[m]; dxOutP[m] = dxout[m]; dyOutP[m] = dyout[m]; dOutP[m] = dout[m]; #endif #ifdef THRESH tOutP[m] = tout[m]; #endif } } #define HEAD 2 #ifdef THRESH #define MIDDLE 1 #else #define MIDDLE 0 #endif #ifdef COORDS #define TAIL 7 #else #define TAIL 0 #endif PROTECT(Out = NEW_LIST(HEAD+MIDDLE+TAIL)); SET_VECTOR_ELT(Out, 0, iOut); SET_VECTOR_ELT(Out, 1, jOut); #ifdef THRESH SET_VECTOR_ELT(Out, HEAD, tOut); #endif #ifdef COORDS SET_VECTOR_ELT(Out, HEAD+MIDDLE, xiOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+1, yiOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+2, xjOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+3, yjOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+4, dxOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+5, dyOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+6, dOut); #endif UNPROTECT(NINPUTS+1+HEAD+MIDDLE+TAIL); /* 1 is for 'Out' itself */ return(Out); } #undef NINPUTS #undef HEAD #undef MIDDLE #undef TAIL SEXP CROSSFUN(SEXP xx1, SEXP yy1, SEXP xx2, SEXP yy2, SEXP rr, #ifdef THRESH SEXP ss, #endif SEXP nguess) { /* input vectors */ double *x1, *y1, *x2, *y2; /* lengths */ int n1, n2, nout, noutmax, noutmaxold, maxchunk; /* distance parameter */ double rmax, r2max; /* indices */ int i, j, jleft, m; /* temporary values */ double x1i, y1i, xleft, dx, dy, dx2, d2; /* local storage */ int *iout, *jout; /* R objects in return value */ SEXP Out, iOut, jOut; /* external storage pointers */ int *iOutP, *jOutP; #ifdef COORDS SEXP xiOut, yiOut, xjOut, yjOut, dxOut, dyOut, dOut; double *xiOutP, *yiOutP, *xjOutP, *yjOutP, *dxOutP, *dyOutP, *dOutP; double *xiout, *yiout, *xjout, *yjout, *dxout, *dyout, *dout; #endif #ifdef THRESH double s, s2; int *tout; SEXP tOut; int *tOutP; #endif /* protect R objects from garbage collector */ PROTECT(xx1 = AS_NUMERIC(xx1)); PROTECT(yy1 = AS_NUMERIC(yy1)); PROTECT(xx2 = AS_NUMERIC(xx2)); PROTECT(yy2 = AS_NUMERIC(yy2)); PROTECT(rr = AS_NUMERIC(rr)); PROTECT(nguess = AS_INTEGER(nguess)); #ifdef THRESH PROTECT(ss = AS_NUMERIC(ss)); #define NINPUTS 7 #else #define NINPUTS 6 #endif /* Translate arguments from R to C */ x1 = NUMERIC_POINTER(xx1); y1 = NUMERIC_POINTER(yy1); x2 = NUMERIC_POINTER(xx2); y2 = NUMERIC_POINTER(yy2); n1 = LENGTH(xx1); n2 = LENGTH(xx2); rmax = *(NUMERIC_POINTER(rr)); noutmax = *(INTEGER_POINTER(nguess)); r2max = rmax * rmax; #ifdef THRESH s = *(NUMERIC_POINTER(ss)); s2 = s * s; #endif nout = 0; /* nout is the next available storage location and also the current length of the list */ if(n1 > 0 && n2 > 0 && noutmax > 0) { /* allocate space */ iout = (int *) R_alloc(noutmax, sizeof(int)); jout = (int *) R_alloc(noutmax, sizeof(int)); #ifdef COORDS xiout = (double *) R_alloc(noutmax, sizeof(double)); yiout = (double *) R_alloc(noutmax, sizeof(double)); xjout = (double *) R_alloc(noutmax, sizeof(double)); yjout = (double *) R_alloc(noutmax, sizeof(double)); dxout = (double *) R_alloc(noutmax, sizeof(double)); dyout = (double *) R_alloc(noutmax, sizeof(double)); dout = (double *) R_alloc(noutmax, sizeof(double)); #endif #ifdef THRESH tout = (int *) R_alloc(noutmax, sizeof(int)); #endif jleft = 0; i = 0; maxchunk = 0; while(i < n1) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > n1) maxchunk = n1; for( ; i < maxchunk; i++) { x1i = x1[i]; y1i = y1[i]; /* adjust starting point jleft */ xleft = x1i - rmax; while((x2[jleft] < xleft) && (jleft+1 < n2)) ++jleft; /* process from j = jleft until dx > rmax */ for(j=jleft; j < n2; j++) { /* squared interpoint distance */ dx = x2[j] - x1i; if(dx > rmax) break; dx2 = dx * dx; dy = y2[j] - y1i; d2 = dx2 + dy * dy; if(d2 <= r2max) { /* add this (i, j) pair to output */ if(nout >= noutmax) { /* overflow; allocate more space */ noutmaxold = noutmax; noutmax = 2 * noutmax; iout = intRealloc(iout, noutmaxold, noutmax); jout = intRealloc(jout, noutmaxold, noutmax); #ifdef COORDS xiout = dblRealloc(xiout, noutmaxold, noutmax); yiout = dblRealloc(yiout, noutmaxold, noutmax); xjout = dblRealloc(xjout, noutmaxold, noutmax); yjout = dblRealloc(yjout, noutmaxold, noutmax); dxout = dblRealloc(dxout, noutmaxold, noutmax); dyout = dblRealloc(dyout, noutmaxold, noutmax); dout = dblRealloc(dout, noutmaxold, noutmax); #endif #ifdef THRESH tout = intRealloc(tout, noutmaxold, noutmax); #endif } iout[nout] = i + 1; /* R indexing */ jout[nout] = j + 1; #ifdef COORDS xiout[nout] = x1i; yiout[nout] = y1i; xjout[nout] = x2[j]; yjout[nout] = y2[j]; dxout[nout] = dx; dyout[nout] = dy; dout[nout] = sqrt(d2); #endif #ifdef THRESH tout[nout] = (d2 <= s2) ? 1 : 0; #endif ++nout; } } } } } /* return a list of vectors */ PROTECT(iOut = NEW_INTEGER(nout)); PROTECT(jOut = NEW_INTEGER(nout)); #ifdef COORDS PROTECT(xiOut = NEW_NUMERIC(nout)); PROTECT(yiOut = NEW_NUMERIC(nout)); PROTECT(xjOut = NEW_NUMERIC(nout)); PROTECT(yjOut = NEW_NUMERIC(nout)); PROTECT(dxOut = NEW_NUMERIC(nout)); PROTECT(dyOut = NEW_NUMERIC(nout)); PROTECT(dOut = NEW_NUMERIC(nout)); #endif #ifdef THRESH PROTECT(tOut = NEW_INTEGER(nout)); #endif if(nout > 0) { iOutP = INTEGER_POINTER(iOut); jOutP = INTEGER_POINTER(jOut); #ifdef COORDS xiOutP = NUMERIC_POINTER(xiOut); yiOutP = NUMERIC_POINTER(yiOut); xjOutP = NUMERIC_POINTER(xjOut); yjOutP = NUMERIC_POINTER(yjOut); dxOutP = NUMERIC_POINTER(dxOut); dyOutP = NUMERIC_POINTER(dyOut); dOutP = NUMERIC_POINTER(dOut); #endif #ifdef THRESH tOutP = INTEGER_POINTER(tOut); #endif for(m = 0; m < nout; m++) { iOutP[m] = iout[m]; jOutP[m] = jout[m]; #ifdef COORDS xiOutP[m] = xiout[m]; yiOutP[m] = yiout[m]; xjOutP[m] = xjout[m]; yjOutP[m] = yjout[m]; dxOutP[m] = dxout[m]; dyOutP[m] = dyout[m]; dOutP[m] = dout[m]; #endif #ifdef THRESH tOutP[m] = tout[m]; #endif } } #define HEAD 2 #ifdef THRESH #define MIDDLE 1 #else #define MIDDLE 0 #endif #ifdef COORDS #define TAIL 7 #else #define TAIL 0 #endif PROTECT(Out = NEW_LIST(HEAD+MIDDLE+TAIL)); SET_VECTOR_ELT(Out, 0, iOut); SET_VECTOR_ELT(Out, 1, jOut); #ifdef THRESH SET_VECTOR_ELT(Out, HEAD, tOut); #endif #ifdef COORDS SET_VECTOR_ELT(Out, HEAD+MIDDLE, xiOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+1, yiOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+2, xjOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+3, yjOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+4, dxOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+5, dyOut); SET_VECTOR_ELT(Out, HEAD+MIDDLE+6, dOut); #endif UNPROTECT(NINPUTS+1+HEAD+MIDDLE+TAIL); /* 1 is for 'Out' itself */ return(Out); } #undef NINPUTS #undef HEAD #undef MIDDLE #undef TAIL spatstat/src/dist2.c0000755000176000001440000000377712333041761014146 0ustar ripleyusers# include #include #include "yesno.h" /* dist2: squared distance in torus dist2thresh: faster code for testing whether dist2 < r2 dist2Mthresh: same as dist2thresh, but does not assume the points are within one period of each other. */ double dist2(u,v,x,y,period) double u, v, x, y; double *period; { double wide, high, dx, dy, dxp, dyp, a, b, d2; /* points are assumed to lie within one period of each other */ wide = period[0]; high = period[1]; dx = u - x; if(dx < 0.0) dx = -dx; dxp = wide - dx; a = (dx < dxp)? dx : dxp; dy = v - y; if(dy < 0.0) dy = -dy; dyp = high - dy; b = (dy < dyp)? dy : dyp; d2 = a * a + b * b; return d2; } double dist2either(u,v,x,y,period) double u, v, x, y; double *period; { if(period[0] < 0.0) return pow(u-x,2) + pow(v-y,2); return(dist2(u,v,x,y,period)); } int dist2thresh(u,v,x,y,period,r2) double u, v, x, y, r2; double *period; { double wide, high, dx, dy, dxp, dyp, a, b, residue; /* points are assumed to lie within one period of each other */ wide = period[0]; high = period[1]; dx = u - x; if(dx < 0.0) dx = -dx; dxp = wide - dx; a = (dx < dxp) ? dx : dxp; residue = r2 - a * a; if(residue <= 0.0) return NO; dy = v - y; if(dy < 0.0) dy = -dy; dyp = high - dy; b = (dy < dyp) ? dy : dyp; if (residue > b * b) return YES; return NO; } int dist2Mthresh(u,v,x,y,period,r2) double u, v, x, y, r2; double *period; { double wide, high, dx, dy, dxp, dyp, a, b, residue; /* points are NOT assumed to lie within one period of each other */ wide = period[0]; high = period[1]; dx = u - x; if(dx < 0.0) dx = -dx; while(dx > wide) dx -= wide; dxp = wide - dx; a = (dx < dxp) ? dx : dxp; residue = r2 - a * a; if(residue < 0.0) return NO; dy = v - y; if(dy < 0.0) dy = -dy; while(dy > high) dy -= high; dyp = high - dy; b = (dy < dyp) ? dy : dyp; if (residue >= b * b) return YES; return NO; } spatstat/src/utils.c0000755000176000001440000000073612333041761014251 0ustar ripleyusers/* utils.c $Revision: 1.2 $ $Date: 2006/10/19 10:22:21 $ Small utilities */ void drevcumsum(double *x, int *nx) { int i; double sumx; double *xp; i = *nx - 1; xp = x + i; sumx = *xp; while(i > 0) { --i; --xp; sumx += *xp; *xp = sumx; } } void irevcumsum(int *x, int *nx) { int i; int sumx; int *xp; i = *nx - 1; xp = x + i; sumx = *xp; while(i > 0) { --i; --xp; sumx += *xp; *xp = sumx; } } spatstat/src/nndistX.h0000644000176000001440000000606512333041761014543 0ustar ripleyusers #if (1 == 0) /* nndistX.h Code template for C functions supporting nncross THE FOLLOWING CODE ASSUMES THAT LISTS ARE SORTED IN ASCENDING ORDER OF y COORDINATE This code is #included multiple times in nndistance.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour EXCLUDE #defined if exclusion mechanism is used Either or both DIST and WHICH may be defined. When EXCLUDE is defined, code numbers id1, id2 are attached to the patterns X and Y respectively, such that x1[i], y1[i] and x2[j], y2[j] are the same point iff id1[i] = id2[j]. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2012 Licence: GPL >= 2 $Revision: 1.5 $ $Date: 2013/09/18 04:49:18 $ */ #endif void FNAME(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge) /* inputs */ int *n1, *n2; double *x1, *y1, *x2, *y2, *huge; int *id1, *id2; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { int npoints1, npoints2, maxchunk, i, jleft, jright, jwhich, lastjwhich; double d2, d2min, x1i, y1i, dx, dy, dy2, hu, hu2; #ifdef EXCLUDE int id1i; #endif hu = *huge; hu2 = hu * hu; npoints1 = *n1; npoints2 = *n2; if(npoints1 == 0 || npoints2 == 0) return; lastjwhich = 0; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints1) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints1) maxchunk = npoints1; for(; i < maxchunk; i++) { d2min = hu2; jwhich = -1; x1i = x1[i]; y1i = y1[i]; #ifdef EXCLUDE id1i = id1[i]; #endif if(lastjwhich < npoints2) { /* search forward from previous nearest neighbour */ for(jright = lastjwhich; jright < npoints2; ++jright) { dy = y2[jright] - y1i; dy2 = dy * dy; if(dy2 > d2min) /* note that dy2 >= d2min could break too early */ break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[jright] != id1i) { #endif dx = x2[jright] - x1i; d2 = dx * dx + dy2; if (d2 < d2min) { d2min = d2; jwhich = jright; } #ifdef EXCLUDE } #endif } /* end forward search */ } if(lastjwhich > 0) { /* search backward from previous nearest neighbour */ for(jleft = lastjwhich - 1; jleft >= 0; --jleft) { dy = y1i - y2[jleft]; dy2 = dy * dy; if(dy2 > d2min) /* note that dy2 >= d2min could break too early */ break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[jleft] != id1i) { #endif dx = x2[jleft] - x1i; d2 = dx * dx + dy2; if (d2 < d2min) { d2min = d2; jwhich = jleft; } #ifdef EXCLUDE } #endif } /* end backward search */ } /* commit values */ #ifdef DIST nnd[i] = sqrt(d2min); #endif #ifdef WHICH nnwhich[i] = jwhich + 1; /* R indexing */ #endif lastjwhich = jwhich; } } } spatstat/src/exactdist.c0000755000176000001440000001460512333041761015101 0ustar ripleyusers/* exactdist.c Exact distance transform of a point pattern (used to estimate the empty space function F) $Revision: 1.12 $ $Date: 2011/09/20 07:36:17 $ Author: Adrian Baddeley Sketch of functionality: the 'data' are a finite list of points in R^2 (x,y coordinates) and the 'output' is a real valued image whose entries are distances, with the value for each pixel equalling the distance from that pixel to the nearest point of the data pattern. Routines: exact_dt_R() interface to R exact_dt() implementation of distance transform dist_to_bdry() compute distance to edge of image frame shape_raster() initialise a Raster structure The appropriate calling sequence for exact_dt_R() is exemplified in 'exactdt.R' */ #undef DEBUG #include #include "raster.h" #ifdef DEBUG #include #endif void shape_raster(ras,data,xmin,ymin,xmax,ymax,nrow,ncol,mrow,mcol) Raster *ras; /* the raster structure to be initialised */ void *data; int nrow, ncol; /* absolute dimensions of storage array */ int mrow, mcol; /* margins clipped off */ /* e.g. valid width is ncol - 2*mcol columns */ double xmin, ymin, /* image dimensions in R^2 after clipping */ xmax, ymax; { ras->data = data; ras->nrow = nrow; ras->ncol = ncol; ras->length = nrow * ncol; ras->rmin = mrow; ras->rmax = nrow - mrow - 1; ras->cmin = mcol; ras->cmax = ncol - mcol - 1; ras->x0 = ras->xmin = xmin; ras->x1 = ras->xmax = xmax; ras->y0 = ras->ymin = ymin; ras->y1 = ras->ymax = ymax; ras->xstep = (xmax-xmin)/(ncol - 2 * mcol - 1); ras->ystep = (ymax-ymin)/(nrow - 2 * mrow - 1); /* Rprintf("xstep,ystep = %lf,%lf\n", ras->xstep,ras->ystep); */ } void exact_dt(x, y, npt, dist, index) double *x, *y; /* data points */ int npt; Raster *dist; /* exact distance to nearest point */ Raster *index; /* which point x[i],y[i] is closest */ { int i,j,k,l,m; double d; int ii; double dd; /* double bdiag; */ /* initialise rasters */ #define UNDEFINED -1 #define Is_Defined(I) (I >= 0) #define Is_Undefined(I) (I < 0) Clear(*index,int,UNDEFINED) d = 2.0 * DistanceSquared(dist->xmin,dist->ymin,dist->xmax,dist->ymax); Clear(*dist,double,d) /* If the list of data points is empty, ... exit now */ if(npt == 0) return; for(i = 0; i < npt; i++) { /* Rprintf("%ld -> (%lf,%lf)\n", i, x[i], y[i]); */ j = RowIndex(*dist,y[i]); k = ColIndex(*dist,x[i]); /* if(!Inside(*dist,j,k)) Rprintf("(%ld,%ld) out of bounds\n",j,k); else if (!Inside(*dist,j+1,k+1)) Rprintf("(%ld+1,%ld+1) out of bounds\n",j,k); */ for(l = j; l <= j+1; l++) for(m = k; m <= k+1; m++) { d = DistanceToSquared(x[i],y[i],*index,l,m); if( Is_Undefined(Entry(*index,l,m,int)) || Entry(*dist,l,m,double) > d) { /* Rprintf("writing (%ld,%ld) -> %ld\t%lf\n", l,m,i,d); */ Entry(*index,l,m,int) = i; Entry(*dist,l,m,double) = d; /* Rprintf("checking: %ld, %lf\n", Entry(*index,l,m,int), Entry(*dist,l,m,double)); */ } } } /* for(j = 0; j <= index->nrow; j++) for(k = 0; k <= index->ncol; k++) Rprintf("[%ld,%ld] %ld\t%lf\n", j,k,Entry(*index,j,k,int),Entry(*dist,j,k,double)); */ /* how to update the distance values */ #define COMPARE(ROW,COL,RR,CC) \ d = Entry(*dist,ROW,COL,double); \ ii = Entry(*index,RR,CC,int); \ /* Rprintf(" %lf\t (%ld,%ld) |-> %ld\n", d, RR, CC, ii); */ \ if(Is_Defined(ii) /* && ii < npt */ \ && Entry(*dist,RR,CC,double) < d) { \ dd = DistanceSquared(x[ii],y[ii],Xpos(*index,COL),Ypos(*index,ROW)); \ if(dd < d) { \ /* Rprintf("(%ld,%ld) <- %ld\n", ROW, COL, ii); */ \ Entry(*index,ROW,COL,int) = ii; \ Entry(*dist,ROW,COL,double) = dd; \ /* Rprintf("checking: %ld, %lf\n", Entry(*index,ROW,COL,int), Entry(*dist,ROW,COL,double)); */\ } \ } /* bound on diagonal step distance */ /* bdiag = sqrt(index->xstep * index->xstep + index->ystep * index->ystep); */ /* forward pass */ for(j = index->rmin; j <= index->rmax; j++) for(k = index->cmin; k <= index->cmax; k++) { /* Rprintf("Neighbourhood of (%ld,%ld):\n", j,k); */ COMPARE(j,k, j-1,k-1) COMPARE(j,k, j-1, k) COMPARE(j,k, j-1,k+1) COMPARE(j,k, j, k-1) } /* backward pass */ for(j = index->rmax; j >= index->rmin; j--) for(k = index->cmax; k >= index->cmin; k--) { COMPARE(j,k, j+1,k+1) COMPARE(j,k, j+1, k) COMPARE(j,k, j+1,k-1) COMPARE(j,k, j, k+1) } /* take square roots of the distances^2 */ for(j = index->rmin; j <= index->rmax; j++) for(k = index->cmin; k <= index->cmax; k++) Entry(*dist,j,k,double) = sqrt(Entry(*dist,j,k,double)); } #define MIN(A,B) (((A) < (B)) ? (A) : (B)) void dist_to_bdry(d) /* compute distance to boundary from each raster point */ Raster *d; /* of course this is easy for a rectangular grid but we implement it in C for ease of future modification */ { int j, k; double x, y, xd, yd; for(j = d->rmin; j <= d->rmax;j++) { y = Ypos(*d,j); yd = MIN(y - d->ymin, d->ymax - y); for(k = d->cmin; k <= d->cmax;k++) { x = Xpos(*d,k); xd = MIN(x - d->xmin, d->xmax - x); Entry(*d,j,k,double) = MIN(xd,yd); } } } /* R interface */ void exact_dt_R(x, y, npt, xmin, ymin, xmax, ymax, nr, nc, mr, mc, distances, indices, boundary) double *x, *y; /* input data points */ int *npt; double *xmin, *ymin, *xmax, *ymax; /* guaranteed bounding box */ int *nr, *nc; /* desired raster dimensions EXCLUDING margins */ int *mr, *mc; /* margins */ /* output arrays */ double *distances; /* distance to nearest point */ int *indices; /* index to nearest point */ double *boundary; /* distance to boundary */ { Raster dist, index, bdist; int mrow, mcol, nrow, ncol; mrow = *mr; mcol = *mc; /* full dimensions */ nrow = *nr + 2 * mrow; ncol = *nc + 2 * mcol; shape_raster( &dist, (void *) distances,*xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); shape_raster( &index, (void *) indices, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); shape_raster( &bdist, (void *) boundary, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); exact_dt(x, y, (int) *npt, &dist, &index); dist_to_bdry(&bdist); } spatstat/src/corrections.c0000755000176000001440000002203612333041761015440 0ustar ripleyusers/* corrections.c Edge corrections $Revision: 1.12 $ $Date: 2013/05/27 02:09:10 $ */ #include #include #include #include #include "chunkloop.h" #include "yesno.h" #include "constants.h" #undef DEBUG /* This constant is defined in Rmath.h */ #define TWOPI M_2PI #define MIN(A,B) (((A) < (B)) ? (A) : (B)) #define BETWEEN(X,X0,X1) (((X) - (X0)) * ((X) - (X1)) <= 0) #define UNDER(X,Y,X0,Y0,X1,Y1) \ (((Y1) - (Y0)) * ((X) - (X0)) >= ((Y) - (Y0)) * ((X1)- (X0))) #define UNDERNEATH(X,Y,X0,Y0,X1,Y1) \ (((X0) < (X1)) ? UNDER(X,Y,X0,Y0,X1,Y1) : UNDER(X,Y,X1,Y1,X0,Y0)) #define TESTINSIDE(X,Y,X0,Y0,X1,Y1) \ (BETWEEN(X,X0,X1) && UNDERNEATH(X, Y, X0, Y0, X1, Y1)) void ripleybox(nx, x, y, rmat, nr, xmin, ymin, xmax, ymax, epsilon, out) /* inputs */ int *nx, *nr; /* dimensions */ double *x, *y; /* coordinate vectors of length nx */ double *rmat; /* matrix nx by nr */ double *xmin, *ymin, *xmax, *ymax; /* box dimensions */ double *epsilon; /* threshold for proximity to corner */ /* output */ double *out; /* output matrix nx by nr */ { int i, j, n, m, ijpos, ncor, maxchunk; double xx, yy, x0, y0, x1, y1, dL, dR, dU, dD, aL, aU, aD, aR, rij; double cL, cU, cD, cR, bLU, bLD, bRU, bRD, bUL, bUR, bDL, bDR; double corner, extang; double eps; n = *nx; m = *nr; x0 = *xmin; y0 = *ymin; x1 = *xmax; y1 = *ymax; eps = *epsilon; OUTERCHUNKLOOP(i, n, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, n, maxchunk, 16384) { xx = x[i]; yy = y[i]; /* perpendicular distance from point to each edge of rectangle L = left, R = right, D = down, U = up */ dL = xx - x0; dR = x1 - xx; dD = yy - y0; dU = y1 - yy; /* test for corner of the rectangle */ #define ABS(X) (((X) >= 0) ? (X) : (-X)) #define SMALL(X) ((ABS(X) < eps) ? 1 : 0) ncor = SMALL(dL) + SMALL(dR) + SMALL(dD) + SMALL(dU); corner = (ncor >= 2) ? YES : NO; /* angle between - perpendicular to edge of rectangle and - line from point to corner of rectangle */ bLU = atan2(dU, dL); bLD = atan2(dD, dL); bRU = atan2(dU, dR); bRD = atan2(dD, dR); bUL = atan2(dL, dU); bUR = atan2(dR, dU); bDL = atan2(dL, dD); bDR = atan2(dR, dD); for(j = 0; j < m; j++) { ijpos = j * n + i; rij = rmat[ijpos]; #ifdef DEBUG Rprintf("rij = %lf\n", rij); #endif /* half the angle subtended by the intersection between the circle of radius r[i,j] centred on point i and each edge of the rectangle (prolonged to an infinite line) */ aL = (dL < rij) ? acos(dL/rij) : 0.0; aR = (dR < rij) ? acos(dR/rij) : 0.0; aD = (dD < rij) ? acos(dD/rij) : 0.0; aU = (dU < rij) ? acos(dU/rij) : 0.0; #ifdef DEBUG Rprintf("aL = %lf\n", aL); Rprintf("aR = %lf\n", aR); Rprintf("aD = %lf\n", aD); Rprintf("aU = %lf\n", aU); #endif /* apply maxima */ cL = MIN(aL, bLU) + MIN(aL, bLD); cR = MIN(aR, bRU) + MIN(aR, bRD); cU = MIN(aU, bUL) + MIN(aU, bUR); cD = MIN(aD, bDL) + MIN(aD, bDR); #ifdef DEBUG Rprintf("cL = %lf\n", cL); Rprintf("cR = %lf\n", cR); Rprintf("cD = %lf\n", cD); Rprintf("cU = %lf\n", cU); #endif /* total exterior angle over 2 pi */ extang = (cL + cR + cU + cD)/TWOPI; /* add pi/2 for corners */ if(corner) extang += 1/4; #ifdef DEBUG Rprintf("extang = %lf\n", extang); #endif /* OK, now compute weight */ out[ijpos] = 1 / (1 - extang); } } } } void ripleypoly(nc, xc, yc, nr, rmat, nseg, x0, y0, x1, y1, out) /* inputs */ int *nc, *nr, *nseg; double *xc, *yc, *rmat; double *x0, *y0, *x1, *y1; /* output */ double *out; { int n, m, i, j, k, l, nradperpt, ncut, nchanges, maxchunk; double xcentre, ycentre, xx0, yy0, xx1, yy1, xx01, yy01; double x, y, radius, radius2, dx0, dx1, dy0; double a, b, c, t, det, sqrtdet, tmp; double theta[6], delta[7], tmid[7]; double xtest, ytest, contrib, total; n = *nc; nradperpt = *nr; m = *nseg; OUTERCHUNKLOOP(i, n, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, n, maxchunk, 16384) { xcentre = xc[i]; ycentre = yc[i]; #ifdef DEBUG Rprintf("centre = (%lf, %lf)\n", xcentre, ycentre); #endif for(j = 0; j < nradperpt; j++) { radius = rmat[ j * n + i]; radius2 = radius * radius; #ifdef DEBUG Rprintf("radius = %lf\n", radius); #endif total = 0.0; for(k=0; k < m; k++) { #ifdef DEBUG Rprintf("k = %d\n", k); #endif ncut = 0; xx0 = x0[k]; yy0 = y0[k]; xx1 = x1[k]; yy1 = y1[k]; #ifdef DEBUG Rprintf("(%lf,%lf) to (%lf,%lf)\n", xx0, yy0, xx1, yy1); #endif /* intersection with left edge */ dx0 = xx0 - xcentre; det = radius2 - dx0 * dx0; if(det > 0) { sqrtdet = sqrt(det); y = ycentre + sqrtdet; if(y < yy0) { theta[ncut] = atan2(y - ycentre, dx0); #ifdef DEBUG Rprintf("cut left at theta= %lf\n", theta[ncut]); #endif ncut++; } y = ycentre - sqrtdet; if(y < yy0) { theta[ncut] = atan2(y-ycentre, dx0); #ifdef DEBUG Rprintf("cut left at theta= %lf\n", theta[ncut]); #endif ncut++; } } else if(det == 0) { if(ycentre < yy0) { theta[ncut] = atan2(0.0, dx0); #ifdef DEBUG Rprintf("tangent left at theta= %lf\n", theta[ncut]); #endif ncut++; } } /* intersection with right edge */ dx1 = xx1 - xcentre; det = radius2 - dx1 * dx1; if(det > 0) { sqrtdet = sqrt(det); y = ycentre + sqrtdet; if(y < yy1) { theta[ncut] = atan2(y - ycentre, dx1); #ifdef DEBUG Rprintf("cut right at theta= %lf\n", theta[ncut]); #endif ncut++; } y = ycentre - sqrtdet; if(y < yy1) { theta[ncut] = atan2(y - ycentre, dx1); #ifdef DEBUG Rprintf("cut right at theta= %lf\n", theta[ncut]); #endif ncut++; } } else if(det == 0) { if(ycentre < yy1) { theta[ncut] = atan2(0.0, dx1); #ifdef DEBUG Rprintf("tangent right at theta= %lf\n", theta[ncut]); #endif ncut++; } } /* intersection with top segment */ xx01 = xx1 - xx0; yy01 = yy1 - yy0; dy0 = yy0 - ycentre; a = xx01 * xx01 + yy01 * yy01; b = 2 * (xx01 * dx0 + yy01 * dy0); c = dx0 * dx0 + dy0 * dy0 - radius2; det = b * b - 4 * a * c; if(det > 0) { sqrtdet = sqrt(det); t = (sqrtdet - b)/(2 * a); if(t >= 0 && t <= 1) { x = xx0 + t * xx01; y = yy0 + t * yy01; theta[ncut] = atan2(y - ycentre, x - xcentre); #ifdef DEBUG Rprintf("hits segment: t = %lf, theta = %lf\n", t, theta[ncut]); #endif ++ncut; } t = (-sqrtdet - b)/(2 * a); if(t >= 0 && t <= 1) { x = xx0 + t * xx01; y = yy0 + t * yy01; theta[ncut] = atan2(y - ycentre, x - xcentre); #ifdef DEBUG Rprintf("hits segment: t = %lf, theta = %lf\n", t, theta[ncut]); #endif ++ncut; } } else if(det == 0) { t = - b/(2 * a); if(t >= 0 && t <= 1) { x = xx0 + t * xx01; y = yy0 + t * yy01; theta[ncut] = atan2(y - ycentre, x - xcentre); #ifdef DEBUG Rprintf("tangent to segment: t = %lf, theta = %lf\n", t, theta[ncut]); #endif ++ncut; } } /* for safety, force all angles to be in range [0, 2 * pi] */ if(ncut > 0) for(l = 0; l < ncut; l++) if(theta[l] < 0) theta[l] += TWOPI; /* sort angles */ if(ncut > 1) { do { nchanges = 0; for(l = 0; l < ncut - 1; l++) { if(theta[l] > theta[l+1]) { /* swap */ ++nchanges; tmp = theta[l]; theta[l] = theta[l+1]; theta[l+1] = tmp; } } } while(nchanges > 0); } #ifdef DEBUG if(ncut > 0) { for(l = 0; l < ncut; l++) Rprintf("theta[%d] = %lf\n", l, theta[l]); } #endif /* compute length of circumference inside polygon */ if(ncut == 0) { /* entire circle is either in or out */ xtest = xcentre + radius; ytest = ycentre; if(TESTINSIDE(xtest, ytest, xx0, yy0, xx1, yy1)) contrib = TWOPI; else contrib = 0.0; } else { /* find midpoints and lengths of pieces (adding theta = ) */ delta[0] = theta[0]; tmid[0] = theta[0]/2; if(ncut > 1) { for(l = 1; l < ncut; l++) { delta[l] = theta[l] - theta[l-1]; tmid[l] = (theta[l] + theta[l-1])/2; } } delta[ncut] = TWOPI - theta[ncut - 1]; tmid[ncut] = (TWOPI + theta[ncut-1])/2; contrib = 0.0; for(l = 0; l <= ncut; l++) { #ifdef DEBUG Rprintf("delta[%d] = %lf\n", l, delta[l]); #endif xtest = xcentre + radius * cos(tmid[l]); ytest = ycentre + radius * sin(tmid[l]); if(TESTINSIDE(xtest, ytest, xx0, yy0, xx1, yy1)) { contrib += delta[l]; #ifdef DEBUG Rprintf("... inside\n"); } else { Rprintf("... outside\n"); #endif } } } /* multiply by sign of trapezium */ if(xx0 < xx1) contrib *= -1; #ifdef DEBUG Rprintf("contrib = %lf\n", contrib); #endif total += contrib; } out[ j * n + i] = total; #ifdef DEBUG Rprintf("total = %lf\n", total); #endif } } } } spatstat/src/PerfectDiggleGratton.h0000644000176000001440000001342112333041761017151 0ustar ripleyusers // ........................... Diggle-Gratton process .......................... // $Revision: 1.5 $ $Date: 2012/03/10 11:22:56 $ class DiggleGrattonProcess : public PointProcess { public: double beta, delta, rho, kappa, rhominusdelta, deltasquared, rhosquared; DiggleGrattonProcess(double xmin, double xmax, double ymin, double ymax, double b, double d, double r, double k); ~DiggleGrattonProcess(){} void NewEvent(double *x, double *y, char *InWindow); void GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP); double Interaction(double dsquared); }; DiggleGrattonProcess::DiggleGrattonProcess(double xmin, double xmax, double ymin, double ymax, double b, double d, double r, double k) : PointProcess(xmin, xmax, ymin, ymax){ beta = b; delta = d; rho = r; kappa = k; deltasquared = delta * delta; rhosquared = rho * rho; rhominusdelta = rho - delta; InteractionRange = rho; TotalBirthRate = beta*(xmax-xmin)*(ymax-ymin); } double DiggleGrattonProcess::Interaction(double dsquared) { double rtn, dist, t; rtn = 1; if(dsquared < rhosquared) { if(dsquared < deltasquared) { rtn = 0; } else { dist = sqrt(dsquared); t = (dist - delta)/rhominusdelta; rtn = pow(t, kappa); } } return(rtn); } void DiggleGrattonProcess::NewEvent(double *x, double *y, char *InWindow) { double Xdim, Ydim; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; *x = slumptal()*Xdim+Xmin; *y = slumptal()*Ydim+Ymin; *InWindow = 1; } void DiggleGrattonProcess::GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP) { int i; double xtemp, ytemp, L, Xdim, Ydim; struct Point *TempPoint; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; L = beta*Xdim*Ydim; *GeneratedPoints = poisson(L); *LivingPoints = *GeneratedPoints; for (i=1; i<=*GeneratedPoints ; i++){ //Rprintf("Generating DiggleGrattonProcess Poisson 3\n"); //scanf("%f",&f1); xtemp = slumptal()*Xdim+Xmin; ytemp = slumptal()*Ydim+Ymin; // //Rprintf("Generating DiggleGrattonProcess Poisson 3.2\n"); TempPoint = ALLOCATE(struct Point); // TempPoint->X = xtemp; TempPoint->Y = ytemp; TempPoint->No = i; TempPoint->R = slumptal(); //Rprintf("Generating DiggleGrattonProcess Poisson 3.6\n"); TempPoint->next = headPoint->next; headPoint->next = TempPoint; *NoP = *NoP + 1; } } // ........................... Interface to R .......................... extern "C" { SEXP PerfectDiggleGratton(SEXP beta, SEXP delta, SEXP rho, SEXP kappa, SEXP xrange, SEXP yrange) { // input parameters double Beta, Delta, Rho, Kappa, Xmin, Xmax, Ymin, Ymax; double *Xrange, *Yrange; // internal int xcells, ycells; long int StartTime, EndTime; // output int noutmax; SEXP xout, yout, nout, out; double *xx, *yy; int *nn; // protect arguments from garbage collector PROTECT(beta = AS_NUMERIC(beta)); PROTECT(delta = AS_NUMERIC(delta)); PROTECT(rho = AS_NUMERIC(rho)); PROTECT(kappa = AS_NUMERIC(kappa)); PROTECT(xrange = AS_NUMERIC(xrange)); PROTECT(yrange = AS_NUMERIC(yrange)); // that's 6 protected objects // extract arguments Beta = *(NUMERIC_POINTER(beta)); Delta = *(NUMERIC_POINTER(delta)); Rho = *(NUMERIC_POINTER(rho)); Kappa = *(NUMERIC_POINTER(kappa)); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ Rho); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ Rho); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ Rho); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ Rho); if(ycells > 9) ycells = 9; if(ycells < 1) ycells = 1; #ifdef DBGS Rprintf("xcells %d ycells %d\n",xcells,ycells); Rprintf("Initialising\n"); #endif // Initialise DiggleGratton point process DiggleGrattonProcess ExampleProcess(Xmin,Xmax,Ymin,Ymax,Beta,Delta,Rho,Kappa); // Initialise point pattern Point2Pattern ExamplePattern(Xmin,Xmax,Ymin,Ymax, xcells, ycells); // parameters: min x, max x, min y, max y, "cells" in x and y direction // used for speeding up neighbour counting, 9 is max here #ifdef DBGS Rprintf("Initialisation complete\n"); #endif // Synchronise random number generator GetRNGstate(); // Initialise perfect sampler Sampler PerfectSampler(&ExampleProcess); // Perform perfect sampling PerfectSampler.Sim(&ExamplePattern, &StartTime, &EndTime); // Synchronise random number generator PutRNGstate(); // Get upper estimate of number of points noutmax = ExamplePattern.UpperCount() + 1; // Allocate space for output PROTECT(xout = NEW_NUMERIC(noutmax)); PROTECT(yout = NEW_NUMERIC(noutmax)); PROTECT(nout = NEW_INTEGER(1)); xx = NUMERIC_POINTER(xout); yy = NUMERIC_POINTER(yout); nn = INTEGER_POINTER(nout); // copy data into output storage ExamplePattern.Return(xx, yy, nn, noutmax); // pack up into output list PROTECT(out = NEW_LIST(3)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, nout); // return UNPROTECT(10); // 6 arguments plus xout, yout, nout, out return(out); } } spatstat/src/Ediggra.c0000755000176000001440000000354412333041761014453 0ustar ripleyusers#include #include #include "chunkloop.h" /* Ediggra.c $Revision: 1.4 $ $Date: 2012/03/28 05:55:50 $ C implementation of 'eval' for DiggleGratton interaction (exponentiated) Assumes point patterns are sorted in increasing order of x coordinate */ double sqrt(); void Ediggra(nnsource, xsource, ysource, idsource, nntarget, xtarget, ytarget, idtarget, ddelta, rrho, values) /* inputs */ int *nnsource, *nntarget; double *xsource, *ysource, *xtarget, *ytarget; int *idsource, *idtarget; double *ddelta, *rrho; /* output */ double *values; { int nsource, ntarget, maxchunk, j, i, ileft, idsourcej; double xsourcej, ysourcej, xleft, dx, dy, dx2, d2; double delta, rho, delta2, rho2, rhominusdelta; double product; nsource = *nnsource; ntarget = *nntarget; delta = *ddelta; rho = *rrho; rho2 = rho * rho; delta2 = delta * delta; rhominusdelta = rho - delta; if(nsource == 0 || ntarget == 0) return; ileft = 0; OUTERCHUNKLOOP(j, nsource, maxchunk, 65536) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nsource, maxchunk, 65536) { product = 1; xsourcej = xsource[j]; ysourcej = ysource[j]; idsourcej = idsource[j]; /* adjust starting point */ xleft = xsourcej - rho; while((xtarget[ileft] < xleft) && (ileft+1 < ntarget)) ++ileft; /* process until dx > rho (or until product is zero) */ for(i=ileft; i < ntarget; i++) { dx = xtarget[i] - xsourcej; dx2 = dx * dx; if(dx2 > rho2) break; if(idtarget[i] != idsourcej) { dy = ytarget[i] - ysourcej; d2 = dx2 + dy * dy; if(d2 <= rho2) { if(d2 <= delta2) { product = 0; break; } else product *= (sqrt(d2) - delta)/rhominusdelta; } } } values[j] = product; } } } spatstat/src/nndist.h0000644000176000001440000000405112333041761014404 0ustar ripleyusers/* nndist.h Code template for C functions supporting nndist and nnwhich (k=1) THE FOLLOWING CODE ASSUMES THAT y IS SORTED IN ASCENDING ORDER This code is #included multiple times in nndistance.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour Either or both DIST and WHICH may be defined. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2012 Licence: GPL >= 2 $Revision: 1.2 $ $Date: 2012/03/14 02:37:27 $ */ void FNAME(n, x, y, #ifdef DIST nnd, #endif #ifdef WHICH nnwhich, #endif huge) /* inputs */ int *n; double *x, *y, *huge; /* outputs */ #ifdef DIST double *nnd; #endif #ifdef WHICH int *nnwhich; #endif { int npoints, i, maxchunk, left, right; double d2, d2min, xi, yi, dx, dy, dy2, hu, hu2; #ifdef WHICH int which; #endif hu = *huge; hu2 = hu * hu; npoints = *n; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints) maxchunk = npoints; for(; i < maxchunk; i++) { d2min = hu2; #ifdef WHICH which = -1; #endif xi = x[i]; yi = y[i]; if(i < npoints - 1) { /* search forward */ for(right = i + 1; right < npoints; ++right) { dy = y[right] - yi; dy2 = dy * dy; if(dy2 > d2min) break; dx = x[right] - xi; d2 = dx * dx + dy2; if (d2 < d2min) { d2min = d2; #ifdef WHICH which = right; #endif } } } if(i > 0){ /* search backward */ for(left = i - 1; left >= 0; --left) { dy = yi - y[left]; dy2 = dy * dy; if(dy2 > d2min) break; dx = x[left] - xi; d2 = dx * dx + dy2; if (d2 < d2min) { d2min = d2; #ifdef WHICH which = left; #endif } } } #ifdef DIST nnd[i] = sqrt(d2min); #endif #ifdef WHICH nnwhich[i] = which + 1; /* R indexing */ #endif } } } spatstat/src/localpcf.h0000755000176000001440000000453512333041761014702 0ustar ripleyusers/* localpcf.h Source template for versions of local pair correlation Requires variable: WEIGHTED Assumes point patterns are sorted in increasing order of x coordinate $Revision: 1.5 $ $Date: 2012/03/27 04:50:04 $ */ #ifdef WEIGHTED #define FNAME locWpcfx #else #define FNAME locpcfx #endif void FNAME(nn1, x1, y1, id1, nn2, x2, y2, id2, #ifdef WEIGHTED w2, #endif nnr, rmaxi, del, pcf) /* inputs */ int *nn1, *nn2, *nnr; double *x1, *y1, *x2, *y2; int *id1, *id2; double *rmaxi, *del; #ifdef WEIGHTED double *w2; #endif /* output */ double *pcf; /* matrix of column vectors of pcf's for each point of first pattern */ { int n1, n2, nr, i, j, k, jleft, kmin, kmax, id1i, maxchunk; double x1i, y1i, rmax, delta, xleft, dx, dy, dx2; double d2, d2max, dmax, d; double rstep, rvalue, frac, contrib, weight, coef; n1 = *nn1; n2 = *nn2; nr = *nnr; rmax = *rmaxi; delta = *del; dmax = rmax + delta; /* maximum relevant value of interpoint distance */ d2max = dmax * dmax; rstep = rmax/(nr-1); coef = 3.0 /(4.0 * delta); if(n1 == 0 || n2 == 0) return; jleft = 0; OUTERCHUNKLOOP(i, n1, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, n1, maxchunk, 8196) { x1i = x1[i]; y1i = y1[i]; id1i = id1[i]; /* adjust starting point */ xleft = x1i - dmax; while((x2[jleft] < xleft) && (jleft+1 < n2)) ++jleft; /* process from jleft until |dx| > dmax */ for(j=jleft; j < n2; j++) { dx = x2[j] - x1i; dx2 = dx * dx; if(dx2 > d2max) break; dy = y2[j] - y1i; d2 = dx2 + dy * dy; if(d2 <= d2max && id2[j] != id1i) { d = sqrt(d2); kmin = (int) floor((d-delta)/rstep); kmax = (int) ceil((d+delta)/rstep); if(kmin <= nr-1 && kmax >= 0) { /* nonempty intersection with range of r values */ /* compute intersection */ if(kmin < 0) kmin = 0; if(kmax >= nr) kmax = nr-1; /* */ weight = coef/d; #ifdef WEIGHTED weight = weight * w2[j]; #endif for(k = kmin; k <= kmax; k++) { rvalue = k * rstep; frac = (d - rvalue)/delta; /* Epanechnikov kernel with halfwidth delta */ contrib = (1 - frac * frac); if(contrib > 0) pcf[k + nr * i] += contrib * weight; } } } } } } } #undef FNAME spatstat/src/PerfectStraussHard.h0000644000176000001440000001272612333041761016671 0ustar ripleyusers // ..................... Strauss-Hardcore process .......................... // $Revision: 1.3 $ $Date: 2014/02/18 10:42:53 $ class StraussHardProcess : public PointProcess { public: double beta, gamma, H, R, Hsquared, Rsquared; StraussHardProcess(double xmin, double xmax, double ymin, double ymax, double b, double g, double Ri, double Hc); ~StraussHardProcess(){} void NewEvent(double *x, double *y, char *InWindow); void GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP); double Interaction(double dsquared); // void CalcBeta(long int xsidepomm, long int ysidepomm, // double *betapomm); // void CheckBeta(long int xsidepomm, long int ysidepomm, // double *betapomm); // double lnCondInt(struct Point2 *TempCell, Point2Pattern *p2p); // void Beta(struct Point2 *TempCell); // void CalcBeta(Point2Pattern *p2p); }; StraussHardProcess::StraussHardProcess(double xmin, double xmax, double ymin, double ymax, double b, double g, double Ri, double Hc) : PointProcess(xmin, xmax, ymin, ymax){ beta = b; gamma = g; R = Ri; H = Hc; Rsquared = R * R; Hsquared = H * H; InteractionRange = R; TotalBirthRate = beta*(xmax-xmin)*(ymax-ymin); } double StraussHardProcess::Interaction(double dsquared) { if(dsquared >= Rsquared) return(1.0); if(dsquared >= Hsquared) return(gamma); return(0.0); } void StraussHardProcess::NewEvent(double *x, double *y, char *InWindow) { double Xdim, Ydim; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; *x = slumptal()*Xdim+Xmin; *y = slumptal()*Ydim+Ymin; *InWindow = 1; } void StraussHardProcess::GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP) { int i; double xtemp, ytemp, L, Xdim, Ydim; struct Point *TempPoint; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; L = beta*Xdim*Ydim; *GeneratedPoints = poisson(L); *LivingPoints = *GeneratedPoints; for (i=1; i<=*GeneratedPoints ; i++){ //Rprintf("Generating StraussHardProcess Poisson 3\n"); //scanf("%f",&f1); xtemp = slumptal()*Xdim+Xmin; ytemp = slumptal()*Ydim+Ymin; // //Rprintf("Generating StraussHardProcess Poisson 3.2\n"); TempPoint = ALLOCATE(struct Point); // TempPoint->X = xtemp; TempPoint->Y = ytemp; TempPoint->No = i; TempPoint->R = slumptal(); //Rprintf("Generating StraussHardProcess Poisson 3.6\n"); TempPoint->next = headPoint->next; headPoint->next = TempPoint; *NoP = *NoP + 1; } } // ........................... Interface to R .......................... extern "C" { SEXP PerfectStraussHard(SEXP beta, SEXP gamma, SEXP r, SEXP hc, SEXP xrange, SEXP yrange) { // input parameters double Beta, Gamma, R, H, Xmin, Xmax, Ymin, Ymax; double *Xrange, *Yrange; // internal int xcells, ycells; long int StartTime, EndTime; // output int noutmax; SEXP xout, yout, nout, out; double *xx, *yy; int *nn; // protect arguments from garbage collector PROTECT(beta = AS_NUMERIC(beta)); PROTECT(gamma = AS_NUMERIC(gamma)); PROTECT(r = AS_NUMERIC(r)); PROTECT(hc = AS_NUMERIC(hc)); PROTECT(xrange = AS_NUMERIC(xrange)); PROTECT(yrange = AS_NUMERIC(yrange)); // that's 6 protected objects // extract arguments Beta = *(NUMERIC_POINTER(beta)); Gamma = *(NUMERIC_POINTER(gamma)); R = *(NUMERIC_POINTER(r)); H = *(NUMERIC_POINTER(hc)); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ R); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ R); if(ycells > 9) ycells = 9; if(ycells < 1) ycells = 1; #ifdef DBGS Rprintf("xcells %d ycells %d\n",xcells,ycells); Rprintf("Initialising\n"); #endif // Initialise StraussHard point process StraussHardProcess ExampleProcess(Xmin,Xmax,Ymin,Ymax, Beta, Gamma, R, H); // Initialise point pattern Point2Pattern ExamplePattern(Xmin,Xmax,Ymin,Ymax, xcells, ycells); // parameters: min x, max x, min y, max y, "cells" in x and y direction // used for speeding up neighbour counting, 9 is max here #ifdef DBGS Rprintf("Initialisation complete\n"); #endif // Synchronise random number generator GetRNGstate(); // Initialise perfect sampler Sampler PerfectSampler(&ExampleProcess); // Perform perfect sampling PerfectSampler.Sim(&ExamplePattern, &StartTime, &EndTime); // Synchronise random number generator PutRNGstate(); // Get upper estimate of number of points noutmax = ExamplePattern.UpperCount() + 1; // Allocate space for output PROTECT(xout = NEW_NUMERIC(noutmax)); PROTECT(yout = NEW_NUMERIC(noutmax)); PROTECT(nout = NEW_INTEGER(1)); xx = NUMERIC_POINTER(xout); yy = NUMERIC_POINTER(yout); nn = INTEGER_POINTER(nout); // copy data into output storage ExamplePattern.Return(xx, yy, nn, noutmax); // pack up into output list PROTECT(out = NEW_LIST(3)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, nout); // return UNPROTECT(10); // 6 arguments plus xout, yout, nout, out return(out); } } spatstat/src/massdisthack.c0000755000176000001440000000355612333041761015572 0ustar ripleyusers/* HACKED from R-2.0.1/src/appl/massdist.c by Adrian Baddeley Changes indicated by 'AB' */ /* * R : A Computer Language for Statistical Data Analysis * Copyright (C) 1996-2004 Robert Gentleman and Ross Ihaka and the * R Development Core Team * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifdef HAVE_CONFIG_H #include #endif #include #include void massdisthack(double *x, int *nx, double *xmass, /* AB: new variable */ double *xlow, double *xhigh, double *y, int *ny) { double fx, xdelta, xmi, xpos; /* AB */ int i, ix, ixmax, ixmin; ixmin = 0; ixmax = *ny - 2; /* AB: line deleted */ xdelta = (*xhigh - *xlow) / (*ny - 1); for(i=0; i < *ny ; i++) y[i] = 0; for(i=0; i < *nx ; i++) { if(R_FINITE(x[i])) { xpos = (x[i] - *xlow) / xdelta; ix = floor(xpos); fx = xpos - ix; xmi = xmass[i]; /* AB: new line */ if(ixmin <= ix && ix <= ixmax) { y[ix] += (1 - fx) * xmi; /* AB */ y[ix + 1] += fx * xmi; /* AB */ } else if(ix == -1) { y[0] += fx * xmi; /* AB */ } else if(ix == ixmax + 1) { y[ix] += (1 - fx) * xmi; /* AB */ } } } /* AB: lines deleted */ } spatstat/src/minnnd.c0000644000176000001440000000544412333041761014372 0ustar ripleyusers/* minnnd.c Minimum/Maximum Nearest Neighbour Distance $Revision: 1.2 $ $Date: 2014/03/25 02:18:31 $ */ #include #include #include /* THE FOLLOWING CODE ASSUMES THAT y IS SORTED IN ASCENDING ORDER */ void minnnd2(n, x, y, huge, result) /* inputs */ int *n; double *x, *y, *huge; /* outputs */ double *result; { int npoints, i, maxchunk, left, right; double d2, d2min, xi, yi, dx, dy, dy2, hu, hu2; hu = *huge; hu2 = hu * hu; npoints = *n; d2min = hu2; if(npoints == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints) maxchunk = npoints; for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; if(i < npoints - 1) { /* search forward */ for(right = i + 1; right < npoints; ++right) { dy = y[right] - yi; dy2 = dy * dy; if(dy2 > d2min) break; dx = x[right] - xi; d2 = dx * dx + dy2; if (d2 < d2min) { d2min = d2; } } } if(i > 0){ /* search backward */ for(left = i - 1; left >= 0; --left) { dy = yi - y[left]; dy2 = dy * dy; if(dy2 > d2min) break; dx = x[left] - xi; d2 = dx * dx + dy2; if (d2 < d2min) { d2min = d2; } } } } } *result = d2min; } void maxnnd2(n, x, y, huge, result) /* inputs */ int *n; double *x, *y, *huge; /* outputs */ double *result; { int npoints, i, maxchunk, left, right; double d2, d2mini, d2max, xi, yi, dx, dy, dy2, hu, hu2; hu = *huge; hu2 = hu * hu; npoints = *n; /* maximum (over all i) nearest-neighbour distance, squared */ d2max = 0.0; if(npoints == 0) return; /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints) maxchunk = npoints; for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; /* nearest-neighbour distance for point i, squared */ d2mini = hu2; if(i < npoints - 1) { /* search forward */ for(right = i + 1; right < npoints; ++right) { dy = y[right] - yi; dy2 = dy * dy; if(dy2 > d2mini) break; dx = x[right] - xi; d2 = dx * dx + dy2; if (d2 < d2mini) { d2mini = d2; if(d2mini <= d2max) break; } } } if(i > 0 && d2mini > d2max){ /* search backward */ for(left = i - 1; left >= 0; --left) { dy = yi - y[left]; dy2 = dy * dy; if(dy2 > d2mini) break; dx = x[left] - xi; d2 = dx * dx + dy2; if (d2 < d2mini) { d2mini = d2; if(d2mini <= d2max) break; } } } if(d2mini > d2max) d2max = d2mini; } } *result = d2max; } spatstat/src/mhsnoop.c0000644000176000001440000001064712333041761014573 0ustar ripleyusers#include #include #include #include "methas.h" #include "mhsnoopdef.h" /* mhsnoop.c $Revision: 1.8 $ $Date: 2013/05/27 02:09:10 $ support for visual debugger in RMH */ /* To switch on debugging code, insert the line: #define MH_DEBUG YES */ #ifndef MH_DEBUG #define MH_DEBUG NO #endif void initmhsnoop(Snoop *s, SEXP env) { s->active = isEnvironment(env); s->nextstop = 0; /* stop at iteration 0 */ s->nexttype = NO_TYPE; /* deactivated */ if(s->active) { s->env = env; s->expr = findVar(install("callbackexpr"), env); } else { s->env = s->expr = R_NilValue; } } void mhsnoop(Snoop *s, int irep, Algor *algo, State *state, Propo *prop, double numer, double denom, int *itype) { SEXP e; int npts, j; /* passed from C to R before debugger */ SEXP Sirep, Sx, Sy, Sm, Sproptype, Sproplocn, Spropmark, Spropindx; SEXP Snumer, Sdenom, Sitype; double *Px, *Py, *Pproplocn; int *Pm; /* passed from R to C after debugger */ SEXP Sinxt, Stnxt, SitypeUser; #if MH_DEBUG Rprintf("mhsnoop called at iteration %d\n", irep); #endif if(!(s->active)) return; #if MH_DEBUG Rprintf("mhsnoop is active\n"); #endif /* execute when the simulation reaches the next stopping time: a specified iteration number 'nextstop' or a specified proposal type 'nexttype' */ if(irep != s->nextstop && prop->itype != s->nexttype) return; #if MH_DEBUG Rprintf("debug triggered\n"); #endif /* environment for communication with R */ e = s->env; /* copy data to R */ /* copy iteration number */ PROTECT(Sirep = NEW_INTEGER(1)); *(INTEGER_POINTER(Sirep)) = irep; setVar(install("irep"), Sirep, e); UNPROTECT(1); /* copy (x,y) coordinates */ npts = state->npts; PROTECT(Sx = NEW_NUMERIC(npts)); PROTECT(Sy = NEW_NUMERIC(npts)); Px = NUMERIC_POINTER(Sx); Py = NUMERIC_POINTER(Sy); for(j = 0; j < npts; j++) { Px[j] = state->x[j]; Py[j] = state->y[j]; } setVar(install("xcoords"), Sx, e); setVar(install("ycoords"), Sy, e); UNPROTECT(2); /* copy marks */ if(state->ismarked) { PROTECT(Sm = NEW_INTEGER(npts)); Pm = INTEGER_POINTER(Sm); for(j = 0; j < npts; j++) { Pm[j] = state->marks[j]; } setVar(install("mcodes"), Sm, e); UNPROTECT(1); } /* proposal type */ PROTECT(Sproptype = NEW_INTEGER(1)); *(INTEGER_POINTER(Sproptype)) = prop->itype; setVar(install("proptype"), Sproptype, e); UNPROTECT(1); /* proposal coordinates */ PROTECT(Sproplocn = NEW_NUMERIC(2)); Pproplocn = NUMERIC_POINTER(Sproplocn); Pproplocn[0] = prop->u; Pproplocn[1] = prop->v; setVar(install("proplocn"), Sproplocn, e); UNPROTECT(1); /* proposal mark value */ if(state->ismarked) { PROTECT(Spropmark = NEW_INTEGER(1)); *(INTEGER_POINTER(Spropmark)) = prop->mrk; setVar(install("propmark"), Spropmark, e); UNPROTECT(1); } /* proposal point index */ PROTECT(Spropindx = NEW_INTEGER(1)); *(INTEGER_POINTER(Spropindx)) = prop->ix; setVar(install("propindx"), Spropindx, e); UNPROTECT(1); /* Metropolis-Hastings numerator and denominator */ PROTECT(Snumer = NEW_NUMERIC(1)); PROTECT(Sdenom = NEW_NUMERIC(1)); *(NUMERIC_POINTER(Snumer)) = numer; *(NUMERIC_POINTER(Sdenom)) = denom; setVar(install("numerator"), Snumer, e); setVar(install("denominator"), Sdenom, e); UNPROTECT(2); /* tentative outcome of proposal */ PROTECT(Sitype = NEW_INTEGER(1)); *(INTEGER_POINTER(Sitype)) = *itype; setVar(install("itype"), Sitype, e); UNPROTECT(1); /* ..... call visual debugger */ #if MH_DEBUG Rprintf("executing callback\n"); #endif eval(s->expr, s->env); /* update outcome of proposal */ SitypeUser = findVar(install("itype"), e); *itype = *(INTEGER_POINTER(SitypeUser)); #if MH_DEBUG Rprintf("Assigning itype = %d\n", *itype); #endif /* update stopping time */ Sinxt = findVar(install("inxt"), e); s->nextstop = *(INTEGER_POINTER(Sinxt)); Stnxt = findVar(install("tnxt"), e); s->nexttype = *(INTEGER_POINTER(Stnxt)); #if MH_DEBUG if(s->nextstop >= 0) Rprintf("Next stop: iteration %d\n", s->nextstop); if(s->nexttype >= 0) { if(s->nexttype == BIRTH) Rprintf("Next stop: first birth proposal\n"); if(s->nexttype == DEATH) Rprintf("Next stop: first death proposal\n"); if(s->nexttype == SHIFT) Rprintf("Next stop: first shift proposal\n"); } #endif return; } spatstat/src/xyseg.c0000755000176000001440000005263612333041761014256 0ustar ripleyusers/* xyseg.c Computation with line segments xysegint compute intersections between line segments $Revision: 1.19 $ $Date: 2013/09/18 04:59:17 $ */ #include #include #include #include #include #include "chunkloop.h" #define NIETS -1.0 #undef DEBUG #define INSIDE01(X,E) (X * (1.0 - X) >= -E) /* --------------- PAIRS OF PSP OBJECTS ---------------------- */ /* xysegint Determines intersections between each pair of line segments drawn from two lists of line segments. Line segments are given as x0, y0, dx, dy where (x0,y0) is the first endpoint and (dx, dy) is the vector from the first to the second endpoint. Points along a line segment are represented in parametric coordinates, (x,y) = (x0, y0) + t * (dx, dy). Output from xysegint() consists of five matrices xx, yy, ta, tb, ok. The (i,j)-th entries in these matrices give information about the intersection between the i-th segment in list 'a' and the j-th segment in list 'b'. The information is ok[i,j] = 1 if there is an intersection = 0 if not xx[i,j] = x coordinate of intersection yy[i,j] = y coordinate of intersection ta[i,j] = parameter of intersection point relative to i-th segment in list 'a' tb[i,j] = parameter of intersection point relative to j-th segment in list 'b' */ void xysegint(na, x0a, y0a, dxa, dya, nb, x0b, y0b, dxb, dyb, eps, xx, yy, ta, tb, ok) /* inputs (vectors of coordinates) */ int *na, *nb; double *x0a, *y0a, *dxa, *dya, *x0b, *y0b, *dxb, *dyb; /* input (tolerance for determinant) */ double *eps; /* outputs (matrices) */ double *xx, *yy, *ta, *tb; int *ok; { int i, j, ma, mb, ijpos, maxchunk; double determinant, absdet, diffx, diffy, tta, ttb, epsilon; ma = *na; mb = *nb; epsilon = *eps; OUTERCHUNKLOOP(j, mb, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mb, maxchunk, 8196) { for(i = 0; i < ma; i++) { ijpos = j * ma + i; ok[ijpos] = 0; xx[ijpos] = yy[ijpos] = ta[ijpos] = tb[ijpos] = NIETS; determinant = dxb[j] * dya[i] - dyb[j] * dxa[i]; absdet = (determinant > 0) ? determinant : -determinant; #ifdef DEBUG Rprintf("i = %d, j = %d\n", i, j); Rprintf("segment A[i]: (%lf, %lf) to (%lf, %lf)\n", x0a[i], y0a[i], x0a[i] + dxa[i], y0a[i] + dya[i]); Rprintf("segment B[j]: (%lf, %lf) to (%lf, %lf)\n", x0b[j], y0b[j], x0b[j] + dxb[j], y0b[j] + dyb[j]); Rprintf("determinant=%lf\n", determinant); #endif if(absdet > epsilon) { diffx = (x0b[j] - x0a[i])/determinant; diffy = (y0b[j] - y0a[i])/determinant; ta[ijpos] = tta = - dyb[j] * diffx + dxb[j] * diffy; tb[ijpos] = ttb = - dya[i] * diffx + dxa[i] * diffy; #ifdef DEBUG Rprintf("ta = %lf, tb = %lf\n", tta, ttb); #endif if(INSIDE01(tta, epsilon) && INSIDE01(ttb, epsilon)) { /* intersection */ ok[ijpos] = 1; xx[ijpos] = x0a[i] + tta * dxa[i]; yy[ijpos] = y0a[i] + tta * dya[i]; #ifdef DEBUG Rprintf("segments intersect at (%lf, %lf)\n", xx[ijpos], yy[ijpos]); #endif } } } } } } /* Stripped-down version of xysegint that just returns logical matrix */ void xysi(na, x0a, y0a, dxa, dya, nb, x0b, y0b, dxb, dyb, eps, ok) /* inputs (vectors of coordinates) */ int *na, *nb; double *x0a, *y0a, *dxa, *dya, *x0b, *y0b, *dxb, *dyb; /* input (tolerance for determinant) */ double *eps; /* outputs (matrices) */ int *ok; { int i, j, ma, mb, ijpos, maxchunk; double determinant, absdet, diffx, diffy, tta, ttb, epsilon; ma = *na; mb = *nb; epsilon = *eps; OUTERCHUNKLOOP(j, mb, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mb, maxchunk, 8196) { for(i = 0; i < ma; i++) { ijpos = j * ma + i; ok[ijpos] = 0; determinant = dxb[j] * dya[i] - dyb[j] * dxa[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = (x0b[j] - x0a[i])/determinant; diffy = (y0b[j] - y0a[i])/determinant; tta = - dyb[j] * diffx + dxb[j] * diffy; ttb = - dya[i] * diffx + dxa[i] * diffy; if(INSIDE01(tta, epsilon) && INSIDE01(ttb, epsilon)) { /* intersection */ ok[ijpos] = 1; } } } } } } /* Test whether there is at least one intersection */ void xysiANY(na, x0a, y0a, dxa, dya, nb, x0b, y0b, dxb, dyb, eps, ok) /* inputs (vectors of coordinates) */ int *na, *nb; double *x0a, *y0a, *dxa, *dya, *x0b, *y0b, *dxb, *dyb; /* input (tolerance for determinant) */ double *eps; /* output (single logical value) */ int *ok; { int i, j, ma, mb, maxchunk; double determinant, absdet, diffx, diffy, tta, ttb, epsilon; *ok = 0; ma = *na; mb = *nb; epsilon = *eps; OUTERCHUNKLOOP(j, mb, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mb, maxchunk, 8196) { for(i = 0; i < ma; i++) { determinant = dxb[j] * dya[i] - dyb[j] * dxa[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = (x0b[j] - x0a[i])/determinant; diffy = (y0b[j] - y0a[i])/determinant; tta = - dyb[j] * diffx + dxb[j] * diffy; ttb = - dya[i] * diffx + dxa[i] * diffy; if(INSIDE01(tta, epsilon) && INSIDE01(ttb, epsilon)) { /* intersection */ *ok = 1; return; } } } } } } /* Analogue of xysegint when segments in list 'a' are infinite vertical lines */ void xysegVslice(na, xa, nb, x0b, y0b, dxb, dyb, eps, yy, ok) /* inputs (vectors of coordinates) */ int *na, *nb; double *xa, *x0b, *y0b, *dxb, *dyb; /* input (tolerance for determinant) */ double *eps; /* outputs (matrices) */ double *yy; int *ok; { int i, j, ma, mb, ijpos, maxchunk; double diffx0, diffx1, width, abswidth, epsilon; int notvertical; ma = *na; mb = *nb; epsilon = *eps; OUTERCHUNKLOOP(j, mb, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mb, maxchunk, 8196) { /* determine whether segment j is nearly vertical */ width = dxb[j]; abswidth = (width > 0) ? width : -width; notvertical = (abswidth <= epsilon); for(i = 0; i < ma; i++) { ijpos = j * ma + i; ok[ijpos] = 0; yy[ijpos] = NIETS; /* test whether vertical line i separates endpoints of segment j */ diffx0 = xa[i] - x0b[j]; diffx1 = diffx0 - width; if(diffx0 * diffx1 <= 0) { /* intersection */ ok[ijpos] = 1; /* compute y-coordinate of intersection point */ if(notvertical) { yy[ijpos] = y0b[j] + diffx0 * dyb[j]/width; } else { /* vertical or nearly-vertical segment: pick midpoint */ yy[ijpos] = y0b[j] + dyb[j]/2.0; } } } } } } /* -------------- ONE PSP OBJECT ---------------------------- */ /* Similar to xysegint, but computes intersections between all pairs of segments in a single list, excluding the diagonal comparisons of course */ void xysegXint(n, x0, y0, dx, dy, eps, xx, yy, ti, tj, ok) /* inputs (vectors of coordinates) */ int *n; double *x0, *y0, *dx, *dy; /* input (tolerance for determinant) */ double *eps; /* outputs (matrices) */ double *xx, *yy, *ti, *tj; int *ok; { int i, j, m, mm1, ijpos, jipos, iipos, maxchunk; double determinant, absdet, diffx, diffy, tti, ttj, epsilon; m = *n; epsilon = *eps; mm1 = m - 1; OUTERCHUNKLOOP(j, mm1, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mm1, maxchunk, 8196) { for(i = j+1; i < m; i++) { ijpos = j * m + i; jipos = i * m + j; ok[ijpos] = ok[jipos] = 0; xx[ijpos] = yy[ijpos] = ti[ijpos] = ti[jipos] = NIETS; xx[jipos] = yy[jipos] = tj[ijpos] = tj[jipos] = NIETS; determinant = dx[j] * dy[i] - dy[j] * dx[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = (x0[j] - x0[i])/determinant; diffy = (y0[j] - y0[i])/determinant; ti[ijpos] = tti = - dy[j] * diffx + dx[j] * diffy; tj[ijpos] = ttj = - dy[i] * diffx + dx[i] * diffy; tj[jipos] = ti[ijpos]; ti[jipos] = tj[ijpos]; if(INSIDE01(tti, epsilon) && INSIDE01(ttj, epsilon)) { ok[ijpos] = ok[jipos] = 1; xx[ijpos] = xx[jipos] = x0[i] + tti * dx[i]; yy[ijpos] = yy[jipos] = y0[i] + tti * dy[i]; } } } } } /* assign diagonal */ for(i = 0; i < m; i++) { iipos = i * m + i; ok[iipos] = 0; xx[iipos] = yy[iipos] = ti[iipos] = tj[iipos] = NIETS; } } /* Reduced version of xysegXint that returns logical matrix 'ok' only */ void xysxi(n, x0, y0, dx, dy, eps, ok) /* inputs (vectors of coordinates) */ int *n; double *x0, *y0, *dx, *dy; /* input (tolerance for determinant) */ double *eps; /* outputs (matrices) */ int *ok; { int i, j, m, mm1, ijpos, jipos, iipos, maxchunk; double determinant, absdet, diffx, diffy, tti, ttj, epsilon; m = *n; epsilon = *eps; mm1 = m - 1; OUTERCHUNKLOOP(j, mm1, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mm1, maxchunk, 8196) { for(i = j+1; i < m; i++) { ijpos = j * m + i; jipos = i * m + j; ok[ijpos] = ok[jipos] = 0; determinant = dx[j] * dy[i] - dy[j] * dx[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = (x0[j] - x0[i])/determinant; diffy = (y0[j] - y0[i])/determinant; tti = - dy[j] * diffx + dx[j] * diffy; ttj = - dy[i] * diffx + dx[i] * diffy; if(INSIDE01(tti, epsilon) && INSIDE01(ttj, epsilon)) { ok[ijpos] = ok[jipos] = 1; } } } } } /* assign diagonal */ for(i = 0; i < m; i++) { iipos = i * m + i; ok[iipos] = 0; } } /* ---------------------- ONE CLOSED POLYGON ------------------------ */ /* Identify self-intersections in a closed polygon (Similar to xysegXint, but does not compare segments which are cyclically adjacent in the list) */ void Cxypolyselfint(n, x0, y0, dx, dy, eps, xx, yy, ti, tj, ok) /* inputs (vectors of coordinates) */ int *n; double *x0, *y0, *dx, *dy; /* input (tolerance for determinant) */ double *eps; /* outputs (matrices) */ double *xx, *yy, *ti, *tj; int *ok; { int i, j, k, m, m2, mm1, mm2, mstop, ijpos, jipos, maxchunk; double determinant, absdet, diffx, diffy, tti, ttj, epsilon; m = *n; epsilon = *eps; m2 = m * m; /* initialise matrices */ for(k = 0; k < m2; k++) { ok[k] = 0; xx[k] = yy[k] = ti[k] = tj[k] = NIETS; } if(m <= 2) return; /* Compare j with j+2, j+3, ...., m-1 Don't compare 0 with m-1 */ mm1 = m - 1; mm2 = m - 2; OUTERCHUNKLOOP(j, mm2, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mm2, maxchunk, 8196) { mstop = (j > 0) ? m : mm1; for(i = j+2; i < mstop; i++) { ijpos = j * m + i; jipos = i * m + j; determinant = dx[j] * dy[i] - dy[j] * dx[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = (x0[j] - x0[i])/determinant; diffy = (y0[j] - y0[i])/determinant; ti[ijpos] = tti = - dy[j] * diffx + dx[j] * diffy; tj[ijpos] = ttj = - dy[i] * diffx + dx[i] * diffy; tj[jipos] = ti[ijpos]; ti[jipos] = tj[ijpos]; if(INSIDE01(tti, epsilon) && INSIDE01(ttj, epsilon)) { ok[ijpos] = ok[jipos] = 1; xx[ijpos] = xx[jipos] = x0[i] + tti * dx[i]; yy[ijpos] = yy[jipos] = y0[i] + tti * dy[i]; } } } } } } /* Just determines whether there is self-intersection (exits quicker & uses less space) */ void xypsi(n, x0, y0, dx, dy, xsep, ysep, eps, proper, answer) /* inputs (vectors of coordinates) */ int *n; double *x0, *y0, *dx, *dy; /* inputs (distances beyond which intersection is impossible) */ double *xsep, *ysep; /* input (tolerance for determinant) */ double *eps; /* input (flag) */ int *proper; /* output */ int *answer; { int i, j, m, mm1, mm2, mstop, prop, maxchunk; double determinant, absdet, diffx, diffy, tti, ttj, epsilon; double Xsep, Ysep; m = *n; prop = *proper; Xsep = *xsep; Ysep = *ysep; epsilon = *eps; *answer = 0; if(m <= 2) return; /* Compare j with j+2, j+3, ...., m-1 Don't compare 0 with m-1 */ mm1 = m - 1; mm2 = m - 2; OUTERCHUNKLOOP(j, mm2, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, mm2, maxchunk, 8196) { mstop = (j > 0) ? m : mm1; for(i = j+2; i < mstop; i++) { diffx = x0[j] - x0[i]; diffy = y0[j] - y0[i]; if(diffx < Xsep && diffx > -Xsep && diffy < Ysep && diffy > -Ysep) { determinant = dx[j] * dy[i] - dy[j] * dx[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = diffx/determinant; diffy = diffy/determinant; tti = - dy[j] * diffx + dx[j] * diffy; ttj = - dy[i] * diffx + dx[i] * diffy; if(INSIDE01(tti, epsilon) && INSIDE01(ttj, epsilon)) { /* intersection occurs */ if(prop == 0 || (tti != 0.0 && tti != 1.0) || (ttj != 0.0 && ttj != 1.0)) { /* proper intersection */ *answer = 1; return; } } } } } } } } /* ---------------- .Call INTERFACE --------------------------- Analogues of functions above, but using the .Call interface and dynamic storage allocation, to save space. */ SEXP Cxysegint(SEXP x0a, SEXP y0a, SEXP dxa, SEXP dya, SEXP x0b, SEXP y0b, SEXP dxb, SEXP dyb, SEXP eps) { int i, j, k, na, nb; double determinant, absdet, diffx, diffy, tta, ttb; int nout, noutmax, newmax, maxchunk; double epsilon; double *x0A, *y0A, *dxA, *dyA, *x0B, *y0B, *dxB, *dyB; double *ta, *tb, *x, *y; int *ia, *jb; SEXP out, iAout, jBout, tAout, tBout, xout, yout; double *tAoutP, *tBoutP, *xoutP, *youtP; int *iAoutP, *jBoutP; PROTECT(x0a = AS_NUMERIC(x0a)); PROTECT(y0a = AS_NUMERIC(y0a)); PROTECT(dxa = AS_NUMERIC(dxa)); PROTECT(dya = AS_NUMERIC(dya)); PROTECT(x0b = AS_NUMERIC(x0b)); PROTECT(y0b = AS_NUMERIC(y0b)); PROTECT(dxb = AS_NUMERIC(dxb)); PROTECT(dyb = AS_NUMERIC(dyb)); PROTECT(eps = AS_NUMERIC(eps)); /* that's 9 protected */ /* get pointers */ x0A = NUMERIC_POINTER(x0a); y0A = NUMERIC_POINTER(y0a); dxA = NUMERIC_POINTER(dxa); dyA = NUMERIC_POINTER(dya); x0B = NUMERIC_POINTER(x0b); y0B = NUMERIC_POINTER(y0b); dxB = NUMERIC_POINTER(dxb); dyB = NUMERIC_POINTER(dyb); /* determine length of vectors */ na = LENGTH(x0a); nb = LENGTH(x0b); epsilon = *(NUMERIC_POINTER(eps)); /* guess amount of storage required for output */ noutmax = (na > nb) ? na : nb; nout = 0; ia = (int *) R_alloc(noutmax, sizeof(int)); jb = (int *) R_alloc(noutmax, sizeof(int)); ta = (double *) R_alloc(noutmax, sizeof(double)); tb = (double *) R_alloc(noutmax, sizeof(double)); x = (double *) R_alloc(noutmax, sizeof(double)); y = (double *) R_alloc(noutmax, sizeof(double)); /* scan data and collect intersections */ OUTERCHUNKLOOP(j, nb, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nb, maxchunk, 8196) { for(i = 0; i < na; i++) { determinant = dxB[j] * dyA[i] - dyB[j] * dxA[i]; absdet = (determinant > 0) ? determinant : -determinant; #ifdef DEBUG Rprintf("i = %d, j = %d\n", i, j); Rprintf("segment A[i]: (%lf, %lf) to (%lf, %lf)\n", x0A[i], y0A[i], x0A[i] + dxA[i], y0A[i] + dyA[i]); Rprintf("segment B[j]: (%lf, %lf) to (%lf, %lf)\n", x0B[j], y0B[j], x0B[j] + dxB[j], y0B[j] + dyB[j]); Rprintf("determinant=%lf\n", determinant); #endif if(absdet > epsilon) { diffx = (x0B[j] - x0A[i])/determinant; diffy = (y0B[j] - y0A[i])/determinant; tta = - dyB[j] * diffx + dxB[j] * diffy; ttb = - dyA[i] * diffx + dxA[i] * diffy; #ifdef DEBUG Rprintf("ta = %lf, tb = %lf\n", tta, ttb); #endif if(INSIDE01(tta, epsilon) && INSIDE01(ttb, epsilon)) { /* intersection */ if(nout >= noutmax) { /* storage overflow - increase space */ newmax = 4 * noutmax; ia = (int *) S_realloc((char *) ia, newmax, noutmax, sizeof(int)); jb = (int *) S_realloc((char *) jb, newmax, noutmax, sizeof(int)); ta = (double *) S_realloc((char *) ta, newmax, noutmax, sizeof(double)); tb = (double *) S_realloc((char *) tb, newmax, noutmax, sizeof(double)); x = (double *) S_realloc((char *) x, newmax, noutmax, sizeof(double)); y = (double *) S_realloc((char *) y, newmax, noutmax, sizeof(double)); noutmax = newmax; } ta[nout] = tta; tb[nout] = ttb; ia[nout] = i; jb[nout] = j; x[nout] = x0A[i] + tta * dxA[i]; y[nout] = y0A[i] + tta * dyA[i]; #ifdef DEBUG Rprintf("segments intersect at (%lf, %lf)\n", x[nout], y[nout]); #endif ++nout; } } } } } /* pack up */ PROTECT(iAout = NEW_INTEGER(nout)); PROTECT(jBout = NEW_INTEGER(nout)); PROTECT(tAout = NEW_NUMERIC(nout)); PROTECT(tBout = NEW_NUMERIC(nout)); PROTECT(xout = NEW_NUMERIC(nout)); PROTECT(yout = NEW_NUMERIC(nout)); /* 9 + 6 = 15 protected */ iAoutP = INTEGER_POINTER(iAout); jBoutP = INTEGER_POINTER(jBout); tAoutP = NUMERIC_POINTER(tAout); tBoutP = NUMERIC_POINTER(tBout); xoutP = NUMERIC_POINTER(xout); youtP = NUMERIC_POINTER(yout); for(k = 0; k < nout; k++) { iAoutP[k] = ia[k]; jBoutP[k] = jb[k]; tAoutP[k] = ta[k]; tBoutP[k] = tb[k]; xoutP[k] = x[k]; youtP[k] = y[k]; } PROTECT(out = NEW_LIST(6)); /* 15 + 1 = 16 protected */ SET_VECTOR_ELT(out, 0, iAout); SET_VECTOR_ELT(out, 1, jBout); SET_VECTOR_ELT(out, 2, tAout); SET_VECTOR_ELT(out, 3, tBout); SET_VECTOR_ELT(out, 4, xout); SET_VECTOR_ELT(out, 5, yout); UNPROTECT(16); return(out); } /* Similar to Cxysegint, but computes intersections between all pairs of segments in a single list, excluding the diagonal comparisons of course */ SEXP CxysegXint(SEXP x0, SEXP y0, SEXP dx, SEXP dy, SEXP eps) { int i, j, k, n, n1; double determinant, absdet, diffx, diffy, tti, ttj; int nout, noutmax, newmax, maxchunk; double epsilon; double *X0, *Y0, *Dx, *Dy; double *ti, *tj, *x, *y; int *ii, *jj; SEXP out, iout, jout, tiout, tjout, xout, yout; double *tioutP, *tjoutP, *xoutP, *youtP; int *ioutP, *joutP; PROTECT(x0 = AS_NUMERIC(x0)); PROTECT(y0 = AS_NUMERIC(y0)); PROTECT(dx = AS_NUMERIC(dx)); PROTECT(dy = AS_NUMERIC(dy)); PROTECT(eps = AS_NUMERIC(eps)); /* that's 5 protected */ /* get pointers */ X0 = NUMERIC_POINTER(x0); Y0 = NUMERIC_POINTER(y0); Dx = NUMERIC_POINTER(dx); Dy = NUMERIC_POINTER(dy); /* determine length of vectors */ n = LENGTH(x0); epsilon = *(NUMERIC_POINTER(eps)); /* guess amount of storage required for output */ noutmax = n; nout = 0; ii = (int *) R_alloc(noutmax, sizeof(int)); jj = (int *) R_alloc(noutmax, sizeof(int)); ti = (double *) R_alloc(noutmax, sizeof(double)); tj = (double *) R_alloc(noutmax, sizeof(double)); x = (double *) R_alloc(noutmax, sizeof(double)); y = (double *) R_alloc(noutmax, sizeof(double)); /* scan data */ n1 = n - 1; OUTERCHUNKLOOP(j, n1, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, n1, maxchunk, 8196) { for(i = j+1; i < n; i++) { determinant = Dx[j] * Dy[i] - Dy[j] * Dx[i]; absdet = (determinant > 0) ? determinant : -determinant; if(absdet > epsilon) { diffx = (X0[j] - X0[i])/determinant; diffy = (Y0[j] - Y0[i])/determinant; tti = - Dy[j] * diffx + Dx[j] * diffy; ttj = - Dy[i] * diffx + Dx[i] * diffy; if(INSIDE01(tti,epsilon) && INSIDE01(ttj,epsilon)) { /* intersection */ if(nout >= noutmax) { /* storage overflow - increase space */ newmax = 4 * noutmax; ii = (int *) S_realloc((char *) ii, newmax, noutmax, sizeof(int)); jj = (int *) S_realloc((char *) jj, newmax, noutmax, sizeof(int)); ti = (double *) S_realloc((char *) ti, newmax, noutmax, sizeof(double)); tj = (double *) S_realloc((char *) tj, newmax, noutmax, sizeof(double)); x = (double *) S_realloc((char *) x, newmax, noutmax, sizeof(double)); y = (double *) S_realloc((char *) y, newmax, noutmax, sizeof(double)); noutmax = newmax; } ti[nout] = tti; tj[nout] = ttj; ii[nout] = i; jj[nout] = j; x[nout] = X0[i] + tti * Dx[i]; y[nout] = Y0[i] + tti * Dy[i]; ++nout; } } } } } /* pack up */ PROTECT(iout = NEW_INTEGER(nout)); PROTECT(jout = NEW_INTEGER(nout)); PROTECT(tiout = NEW_NUMERIC(nout)); PROTECT(tjout = NEW_NUMERIC(nout)); PROTECT(xout = NEW_NUMERIC(nout)); PROTECT(yout = NEW_NUMERIC(nout)); /* 5 + 6 = 11 protected */ ioutP = INTEGER_POINTER(iout); joutP = INTEGER_POINTER(jout); tioutP = NUMERIC_POINTER(tiout); tjoutP = NUMERIC_POINTER(tjout); xoutP = NUMERIC_POINTER(xout); youtP = NUMERIC_POINTER(yout); for(k = 0; k < nout; k++) { ioutP[k] = ii[k]; joutP[k] = jj[k]; tioutP[k] = ti[k]; tjoutP[k] = tj[k]; xoutP[k] = x[k]; youtP[k] = y[k]; } PROTECT(out = NEW_LIST(6)); /* 11 + 1 = 12 protected */ SET_VECTOR_ELT(out, 0, iout); SET_VECTOR_ELT(out, 1, jout); SET_VECTOR_ELT(out, 2, tiout); SET_VECTOR_ELT(out, 3, tjout); SET_VECTOR_ELT(out, 4, xout); SET_VECTOR_ELT(out, 5, yout); UNPROTECT(12); return(out); } spatstat/src/knndistance.c0000644000176000001440000001043312333041761015402 0ustar ripleyusers/* knndistance.c K-th Nearest Neighbour Distances between points Copyright (C) Adrian Baddeley, Jens Oehlschlaegel and Rolf Turner 2000-2013 Licence: GNU Public Licence >= 2 $Revision: 1.8 $ $Date: 2013/12/10 03:29:45 $ Function definitions are #included from knndist.h and knnXdist.h THE FOLLOWING FUNCTIONS ASSUME THAT y IS SORTED IN ASCENDING ORDER SINGLE LIST: knndsort k-th nearest neighbour distances knnwhich k-th nearest neighbours knnsort k-th nearest neighbours and their distances ONE LIST TO ANOTHER LIST: knnXdist Nearest neighbour distance from one list to another knnXwhich Nearest neighbour ID from one list to another knnX Nearest neighbour ID & distance from one list to another ONE LIST TO ANOTHER OVERLAPPING LIST: knnXEdist Nearest neighbour distance from one list to another, overlapping knnXEwhich Nearest neighbour ID from one list to another, overlapping knnXE Nearest neighbour ID & distance */ #undef SPATSTAT_DEBUG #include #include #include #include "yesno.h" double sqrt(); /* THE FOLLOWING CODE ASSUMES THAT y IS SORTED IN ASCENDING ORDER */ /* ------------------- one point pattern X --------------------- */ /* knndsort nearest neighbours 1:kmax returns distances only */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knndsort #define DIST #include "knndist.h" /* knnwhich nearest neighbours 1:kmax returns identifiers only */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnwhich #define WHICH #include "knndist.h" /* knnsort nearest neighbours 1:kmax returns distances and indices */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnsort #define DIST #define WHICH #include "knndist.h" /* --------------- two distinct point patterns X and Y --------------- */ /* general interface */ void knnXinterface(n1, x1, y1, id1, n2, x2, y2, id2, kmax, exclude, wantdist, wantwhich, nnd, nnwhich, huge) /* inputs */ int *n1, *n2; double *x1, *y1, *x2, *y2, *huge; int *id1, *id2; int *kmax; /* options */ int *exclude, *wantdist, *wantwhich; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { void knnX(), knnXdist(), knnXwhich(); void knnXE(), knnXEdist(), knnXEwhich(); int ex, di, wh; ex = (*exclude != 0); di = (*wantdist != 0); wh = (*wantwhich != 0); if(!ex) { if(di && wh) { knnX(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge); } else if(di) { knnXdist(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge); } else if(wh) { knnXwhich(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge); } } else { if(di && wh) { knnXE(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge); } else if(di) { knnXEdist(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge); } else if(wh) { knnXEwhich(n1, x1, y1, id1, n2, x2, y2, id2, kmax, nnd, nnwhich, huge); } } } /* Turn off the debugging tracer in knnXdist.h */ #undef TRACER /* knnXdist returns distances only */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnXdist #define DIST #include "knnXdist.h" /* knnXwhich returns identifiers only */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnXwhich #define WHICH #include "knnXdist.h" /* knnX returns distances and indices */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnX #define DIST #define WHICH #include "knnXdist.h" /* --------------- overlapping point patterns X and Y --------------- */ /* knnXEdist returns distances only */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnXEdist #define DIST #define EXCLUDE #include "knnXdist.h" /* knnXEwhich returns identifiers only */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnXEwhich #define WHICH #define EXCLUDE #include "knnXdist.h" /* knnXE returns distances and indices */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME knnXE #define DIST #define WHICH #define EXCLUDE #include "knnXdist.h" spatstat/src/geom3.h0000755000176000001440000000041012333041761014115 0ustar ripleyusers/* $Revision: 1.1 $ $Date: 2009/11/04 23:54:15 $ Definitions for 3D geometrical structures */ typedef struct Point { double x; double y; double z; } Point; typedef struct Box { double x0; double x1; double y0; double y1; double z0; double z1; } Box; spatstat/src/lineardisc.c0000755000176000001440000001732612333041761015231 0ustar ripleyusers#include #include #include "chunkloop.h" /* lineardisc.c Disc of radius r in linear network $Revision: 1.10 $ $Date: 2013/05/27 02:09:10 $ */ #define DPATH(I,J) dpath[(J) + Nv * (I)] #include "yesno.h" #undef DEBUG void lineardisc(f, seg, /* centre of disc (local coords) */ r, /* radius of disc */ nv, xv, yv, /* network vertices */ ns, from, to, /* segments */ dpath, /* shortest path distances between vertices */ lengths, /* segment lengths */ allinside, boundary, dxv, nendpoints) int *nv, *ns; int *from, *to; /* integer vectors (mappings) */ double *f, *r; int *seg; double *xv, *yv; /* vectors of coordinates of vertices */ double *dpath; /* matrix of shortest path distances between vertices */ double *lengths; /* vector of segment lengths */ /* OUTPUTS */ int *allinside, *boundary; /* vectors of status for each segment */ double *dxv; /* vector of distances for each vertex */ int *nendpoints; { int Nv, Ns; double f0, rad; int seg0; int i, A, B, fromi, toi, allin, bdry, reachable, nends, maxchunk; double length0, dxA, dxB, dxAvi, dxBvi, residue; double *resid; int *covered; Nv = *nv; Ns = *ns; f0 = *f; seg0 = *seg; rad = *r; /* endpoints of segment containing centre */ A = from[seg0]; B = to[seg0]; /* distances from x to A and B */ length0 = lengths[seg0]; dxA = f0 * length0; dxB = (1-f0) * length0; /* visit vertices */ covered = (int *) R_alloc((size_t) Nv, sizeof(int)); resid = (double *) R_alloc((size_t) Nv, sizeof(double)); OUTERCHUNKLOOP(i, Nv, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Nv, maxchunk, 16384) { /* distance going through A */ dxAvi = dxA + DPATH(A,i); /* distance going through B */ dxBvi = dxB + DPATH(B,i); /* shortest path distance to this vertex */ dxv[i] = (dxAvi < dxBvi) ? dxAvi : dxBvi; /* distance left to 'spend' from this vertex */ residue = rad - dxv[i]; resid[i] = (residue > 0)? residue : 0; /* determine whether vertex i is inside the disc of radius r */ covered[i] = (residue >= 0); } } /* Now visit line segments. */ nends = 0; OUTERCHUNKLOOP(i, Ns, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Ns, maxchunk, 16384) { /* Determine which line segments are completely inside the disc, and which cross the boundary. */ if(i == seg0) { /* initial segment: disc starts from centre (x, y) */ allin = covered[A] && covered[B]; bdry = !allin; if(bdry) { if(!covered[A]) nends++; if(!covered[B]) nends++; } } else { /* another segment: disc extends in from either endpoint */ fromi = from[i]; toi = to[i]; reachable = (covered[fromi] || covered[toi]); if(reachable) { allin = covered[fromi] && covered[toi] && (resid[fromi] + resid[toi] >= lengths[i]); bdry = !allin; } else allin = bdry = NO; if(bdry) { if(covered[fromi]) nends++; if(covered[toi]) nends++; } } allinside[i] = allin; boundary[i] = bdry; } } *nendpoints = nends; } /* ------------------------------------------------- */ /* count endpoints of several discs in a network */ /* ------------------------------------------------- */ void Ccountends(np, f, seg, /* centres of discs (local coords) */ r, /* radii of discs */ nv, xv, yv, /* network vertices */ ns, from, to, /* network segments */ dpath, /* shortest path distances between vertices */ lengths, /* segment lengths */ toler, /* tolerance */ nendpoints /* output counts of endpoints */ ) int *np, *nv, *ns; int *from, *to; /* integer vectors (mappings) */ double *f, *r; int *seg; double *xv, *yv; /* vectors of coordinates of vertices */ double *dpath; /* matrix of shortest path distances between vertices */ double *lengths; /* vector of segment lengths */ double *toler; /* tolerance for merging endpoints and vertices */ /* OUTPUT */ int *nendpoints; { int Np, Nv, Ns; double f0, rad; int seg0; int i, m, A, B, fromi, toi, reachable, nends, maxchunk, covfrom, covto, allin; double length0, dxA, dxB, dxAvi, dxBvi, dxvi, residue, resfrom, resto, tol; double *resid; int *covered, *terminal; Np = *np; Nv = *nv; Ns = *ns; tol = *toler; covered = (int *) R_alloc((size_t) Nv, sizeof(int)); terminal = (int *) R_alloc((size_t) Nv, sizeof(int)); resid = (double *) R_alloc((size_t) Nv, sizeof(double)); /* loop over centre points */ OUTERCHUNKLOOP(m, Np, maxchunk, 256) { R_CheckUserInterrupt(); INNERCHUNKLOOP(m, Np, maxchunk, 256) { f0 = f[m]; seg0 = seg[m]; rad = r[m]; #ifdef DEBUG Rprintf("\nCentre point %d lies in segment %d\n", m, seg0); #endif /* endpoints of segment containing centre */ A = from[seg0]; B = to[seg0]; /* distances from centre to A and B */ length0 = lengths[seg0]; dxA = f0 * length0; dxB = (1-f0) * length0; nends = 0; /* visit vertices */ for(i = 0; i < Nv; i++) { /* distance going through A */ dxAvi = dxA + DPATH(A,i); /* distance going through B */ dxBvi = dxB + DPATH(B,i); /* shortest path distance to this vertex */ dxvi = (dxAvi < dxBvi) ? dxAvi : dxBvi; /* distance left to 'spend' from this vertex */ residue = rad - dxvi; if(residue > tol) { resid[i] = residue; covered[i] = YES; terminal[i] = NO; } else if(residue < -tol) { resid[i] = 0; covered[i] = terminal[i] = NO; } else { /* vertex is within 'tol' of an endpoint - deem it to be one */ resid[i] = 0; covered[i] = terminal[i] = YES; /* vertex is an endpoint of disc */ ++nends; } } #ifdef DEBUG Rprintf("%d terminal endpoints\n", nends); #endif /* Now visit line segments to count any endpoints that are interior to the segments. */ for(i = 0; i < Ns; i++) { /* Determine which line segments are completely inside the disc, and which cross the boundary. */ if(i == seg0) { /* initial segment: disc starts from (x0, y0) */ if(!covered[A]) nends++; if(!covered[B]) nends++; #ifdef DEBUG if(!covered[A]) Rprintf("A not covered\n"); if(!covered[B]) Rprintf("B not covered\n"); #endif } else { /* another segment: disc extends in from either endpoint */ fromi = from[i]; toi = to[i]; covfrom = covered[fromi]; covto = covered[toi]; resfrom = resid[fromi]; resto = resid[toi]; reachable = covfrom || covto; #ifdef DEBUG residue = resfrom + resto - lengths[i]; Rprintf("%d: %s %s: %lf + %lf - %lf = %lf sign %s\n", i, (terminal[fromi]) ? "T" : ((covfrom) ? "Y" : "N"), (terminal[toi]) ? "T" : ((covto) ? "Y" : "N"), resfrom, resto, lengths[i], residue, (residue < 0) ? "-" : ((residue > 0) ? "+" : "0")); #endif if(reachable) { residue = resfrom + resto - lengths[i]; allin = covfrom && covto && (residue >= 0); #ifdef DEBUG if(allin) { Rprintf("Covered\n"); } else if((terminal[fromi] || terminal[toi]) && (residue >= - tol * lengths[i])) { Rprintf("Deemed to be covered\n"); } else Rprintf("Reachable\n"); #endif allin = allin || ((terminal[fromi] || terminal[toi]) && (residue >= - tol)); if(!allin) { /* segment is not entirely covered by disc - infer endpoint(s) in interior of segment */ if(covfrom && !terminal[fromi]) nends++; if(covto && !terminal[toi]) nends++; #ifdef DEBUG if(covfrom && !terminal[fromi]) Rprintf("fromi => end\n"); if(covto && !terminal[toi]) Rprintf("toi => end\n"); #endif } } } } nendpoints[m] = nends; } } } spatstat/src/nndistance.c0000755000176000001440000001054512333041761015236 0ustar ripleyusers/* nndistance.c Nearest Neighbour Distances between points Copyright (C) Adrian Baddeley, Jens Oehlschlaegel and Rolf Turner 2000-2012 Licence: GNU Public Licence >= 2 $Revision: 1.21 $ $Date: 2013/11/03 03:36:27 $ THE FOLLOWING FUNCTIONS ASSUME THAT y IS SORTED IN ASCENDING ORDER SINGLE LIST: nndistsort Nearest neighbour distances nnwhichsort Nearest neighbours nnsort Nearest neighbours & distances ONE LIST TO ANOTHER LIST: nnXdist Nearest neighbour distance from one list to another nnXwhich Nearest neighbour ID from one list to another nnX Nearest neighbour ID & distance from one list to another ONE LIST TO ANOTHER OVERLAPPING LIST: nnXEdist Nearest neighbour distance from one list to another, overlapping nnXEwhich Nearest neighbour ID from one list to another, overlapping nnXE Nearest neighbour ID & distance */ #undef SPATSTAT_DEBUG #include #include #include #include "yesno.h" double sqrt(); /* THE FOLLOWING CODE ASSUMES THAT y IS SORTED IN ASCENDING ORDER */ /* ------------------- one point pattern X --------------------- */ /* nndistsort: nearest neighbour distances */ #undef FNAME #undef DIST #undef WHICH #define FNAME nndistsort #define DIST #include "nndist.h" /* nnwhichsort: id of nearest neighbour */ #undef FNAME #undef DIST #undef WHICH #define FNAME nnwhichsort #define WHICH #include "nndist.h" /* nnsort: distance & id of nearest neighbour */ #undef FNAME #undef DIST #undef WHICH #define FNAME nnsort #define DIST #define WHICH #include "nndist.h" /* --------------- two distinct point patterns X and Y ----------------- */ /* general interface */ void nnXinterface(n1, x1, y1, id1, n2, x2, y2, id2, exclude, wantdist, wantwhich, nnd, nnwhich, huge) /* inputs */ int *n1, *n2; double *x1, *y1, *x2, *y2, *huge; int *id1, *id2; /* options */ int *exclude, *wantdist, *wantwhich; /* outputs */ double *nnd; int *nnwhich; { void nnX(), nnXdist(), nnXwhich(); void nnXE(), nnXEdist(), nnXEwhich(); int ex, di, wh; ex = (*exclude != 0); di = (*wantdist != 0); wh = (*wantwhich != 0); if(!ex) { if(di && wh) { nnX(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge); } else if(di) { nnXdist(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge); } else if(wh) { nnXwhich(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge); } } else { if(di && wh) { nnXE(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge); } else if(di) { nnXEdist(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge); } else if(wh) { nnXEwhich(n1, x1, y1, id1, n2, x2, y2, id2, nnd, nnwhich, huge); } } } /* nnXdist: nearest neighbour distance (from each point of X to the nearest point of Y) */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME nnXdist #define DIST #include "nndistX.h" /* nnXwhich: nearest neighbour id */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME nnXwhich #define WHICH #include "nndistX.h" /* nnX: nearest neighbour distance and id */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME nnX #define DIST #define WHICH #include "nndistX.h" /* --------------- two point patterns X and Y with common points --------- */ /* Code numbers id1, id2 are attached to the patterns X and Y respectively, such that x1[i], y1[i] and x2[j], y2[j] are the same point iff id1[i] = id2[j]. */ /* nnXEdist: similar to nnXdist but allows X and Y to include common points (which are not to be counted as neighbours) */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME nnXEdist #define DIST #define EXCLUDE #include "nndistX.h" /* nnXEwhich: similar to nnXwhich but allows X and Y to include common points (which are not to be counted as neighbours) */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME nnXEwhich #define WHICH #define EXCLUDE #include "nndistX.h" /* nnXE: similar to nnX but allows X and Y to include common points (which are not to be counted as neighbours) */ #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE #define FNAME nnXE #define DIST #define WHICH #define EXCLUDE #include "nndistX.h" spatstat/src/dinfty.c0000755000176000001440000000677612333041761014420 0ustar ripleyusers/* dinfty.c $Revision: 1.6 $ $Date: 2011/09/20 07:42:18 $ Code by Dominic Schuhmacher Modified by Adrian Baddeley */ #include #include #define COST(I,J) (d)[n * (J) + (I)] int arraymax(int *a, int n); void swap(int i, int j, int *a); int largestmobpos(int *mobile, int *current, int *collectvals, int n); /* ------------ The main function ----------------------------- */ void dinfty_R(int *d, int *num, int *assignment) { int i,j; /* indices */ int lmp, lmq; /* largest mobile position and its neighbor */ int newmax; int n, currmin; int *current, *travel, *mobile, *assig, *distrelev, *collectvals; n = *num; /* scratch space */ assig = (int *) R_alloc((long) n, sizeof(int)); travel = (int *) R_alloc((long) n, sizeof(int)); mobile = (int *) R_alloc((long) n, sizeof(int)); current = (int *) R_alloc((long) n, sizeof(int)); distrelev = (int *) R_alloc((long) n, sizeof(int)); collectvals = (int *) R_alloc((long) (n * n), sizeof(int)); /* */ /* We use the Johnson-Trotter Algorithm for listing permutations */ /* */ /* Initialize the algorithm */ for (i = 0; i < n; i++) { travel[i] = -1; /* all numbers traveling to the left */ mobile[i] = 1; /* all numbers mobile */ current[i] = i; /* current permutation is the identity */ assig[i] = i; /* best permutation up to now is the identity */ distrelev[i] = COST(i, i); /* pick relevant entries in the cost matrix */ } currmin = arraymax(distrelev, n); /* minimal max up to now */ /* The main loop */ while(arraymax(mobile, n) == 1) { lmp = largestmobpos(mobile, current, collectvals, n); lmq = lmp + travel[lmp]; swap(lmp, lmq, current); swap(lmp, lmq, travel); for (i = 0; i < n; i++) { if (current[i] > current[lmq]) travel[i] = -travel[i]; j = i + travel[i]; if (j < 0 || j > n-1 || current[i] < current[j]) mobile[i] = 0; else mobile[i] = 1; distrelev[i] = COST(i, current[i]); } /* Calculation of new maximal value */ newmax = arraymax(distrelev, n); if (newmax < currmin) { currmin = newmax; for (i = 0; i < n; i++) { assig[i] = current[i]; } } } /* For testing: print distance from within C program Rprintf("Prohorov distance is %d\n", currmin); */ /* "Return" the final assignment */ for (i = 0; i < n; i++) { assignment[i] = assig[i] + 1; } } /* ------------------------------------------------------------*/ /* Maximal element of an integer array */ int arraymax(int *a, int n) { int i, amax; if(n < 1) return(-1); amax = a[0]; if(n > 1) for(i = 0; i < n; i++) if(a[i] > amax) amax = a[i]; return(amax); } /* Swap elements i and j in array a */ void swap(int i, int j, int *a) { int v; v = a[i]; a[i] = a[j]; a[j] = v; } /* Return index of largest mobile number in current */ int largestmobpos(int *mobile, int *current, int *collectvals, int n) { int i,j, maxval; j = 0; for (i = 0; i < n; i++) { if (mobile[i] == 1) { collectvals[j] = current[i]; j++; } } maxval = arraymax(collectvals, j); for (i = 0; i < n; i++) { if (current[i] == maxval) { return(i); } } error("Internal error: largestmobpos failed"); return(0); } spatstat/src/PerfectHardcore.h0000644000176000001440000001137712333041761016156 0ustar ripleyusers // ........................... Hardcore process .......................... // $Revision: 1.4 $ $Date: 2012/03/10 11:23:09 $ class HardcoreProcess : public PointProcess { public: double beta, R, Rsquared; HardcoreProcess(double xmin, double xmax, double ymin, double ymax, double b, double Ri); ~HardcoreProcess(){} void NewEvent(double *x, double *y, char *InWindow); void GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP); double Interaction(double dsquared); }; HardcoreProcess::HardcoreProcess(double xmin, double xmax, double ymin, double ymax, double b, double Ri) : PointProcess(xmin, xmax, ymin, ymax){ beta = b; R = Ri; Rsquared = R * R; InteractionRange = R; TotalBirthRate = beta*(xmax-xmin)*(ymax-ymin); } double HardcoreProcess::Interaction(double dsquared) { double rtn; rtn = 1; if(dsquared < Rsquared) rtn = 0; return(rtn); } void HardcoreProcess::NewEvent(double *x, double *y, char *InWindow) { double Xdim, Ydim; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; *x = slumptal()*Xdim+Xmin; *y = slumptal()*Ydim+Ymin; *InWindow = 1; } void HardcoreProcess::GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP) { int i; double xtemp, ytemp, L, Xdim, Ydim; struct Point *TempPoint; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; L = beta*Xdim*Ydim; *GeneratedPoints = poisson(L); *LivingPoints = *GeneratedPoints; for (i=1; i<=*GeneratedPoints ; i++){ //Rprintf("Generating HardcoreProcess Poisson 3\n"); //scanf("%f",&f1); xtemp = slumptal()*Xdim+Xmin; ytemp = slumptal()*Ydim+Ymin; // //Rprintf("Generating HardcoreProcess Poisson 3.2\n"); TempPoint = ALLOCATE(struct Point); // TempPoint->X = xtemp; TempPoint->Y = ytemp; TempPoint->No = i; TempPoint->R = slumptal(); //Rprintf("Generating HardcoreProcess Poisson 3.6\n"); TempPoint->next = headPoint->next; headPoint->next = TempPoint; *NoP = *NoP + 1; } } // ........................... Interface to R .......................... extern "C" { SEXP PerfectHardcore(SEXP beta, SEXP r, SEXP xrange, SEXP yrange) { // input parameters double Beta, R, Xmin, Xmax, Ymin, Ymax; double *Xrange, *Yrange; // internal int xcells, ycells; long int StartTime, EndTime; // output int noutmax; SEXP xout, yout, nout, out; double *xx, *yy; int *nn; // protect arguments from garbage collector PROTECT(beta = AS_NUMERIC(beta)); PROTECT(r = AS_NUMERIC(r)); PROTECT(xrange = AS_NUMERIC(xrange)); PROTECT(yrange = AS_NUMERIC(yrange)); // that's 4 protected objects // extract arguments Beta = *(NUMERIC_POINTER(beta)); R = *(NUMERIC_POINTER(r)); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ R); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ R); if(ycells > 9) ycells = 9; if(ycells < 1) ycells = 1; #ifdef DBGS Rprintf("xcells %d ycells %d\n",xcells,ycells); Rprintf("Initialising\n"); #endif // Initialise Hardcore point process HardcoreProcess ExampleProcess(Xmin,Xmax,Ymin,Ymax, Beta, R); // Initialise point pattern Point2Pattern ExamplePattern(Xmin,Xmax,Ymin,Ymax, xcells, ycells); // parameters: min x, max x, min y, max y, "cells" in x and y direction // used for speeding up neighbour counting, 9 is max here #ifdef DBGS Rprintf("Initialisation complete\n"); #endif // Synchronise random number generator GetRNGstate(); // Initialise perfect sampler Sampler PerfectSampler(&ExampleProcess); // Perform perfect sampling PerfectSampler.Sim(&ExamplePattern, &StartTime, &EndTime); // Synchronise random number generator PutRNGstate(); // Get upper estimate of number of points noutmax = ExamplePattern.UpperCount() + 1; // Allocate space for output PROTECT(xout = NEW_NUMERIC(noutmax)); PROTECT(yout = NEW_NUMERIC(noutmax)); PROTECT(nout = NEW_INTEGER(1)); xx = NUMERIC_POINTER(xout); yy = NUMERIC_POINTER(yout); nn = INTEGER_POINTER(nout); // copy data into output storage ExamplePattern.Return(xx, yy, nn, noutmax); // pack up into output list PROTECT(out = NEW_LIST(3)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, nout); // return UNPROTECT(8); // 4 arguments plus xout, yout, nout, out return(out); } } spatstat/src/geyer.c0000755000176000001440000002363212333041761014224 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" void fexitc(const char *msg); #undef MH_DEBUG /* Conditional intensity function for a Geyer saturation process. */ typedef struct Geyer { /* model parameters */ double gamma; double r; double s; /* transformations of the parameters */ double r2; double loggamma; int hard; /* periodic distance */ double *period; int per; /* auxiliary counts */ int *aux; #ifdef MH_DEBUG int *freshaux; int prevtype; #endif } Geyer; Cdata *geyerinit(state, model, algo) State state; Model model; Algor algo; { int i, j, n1; Geyer *geyer; double r2; double *period; DECLARE_CLOSE_VARS; geyer = (Geyer *) R_alloc(1, sizeof(Geyer)); /* Interpret model parameters*/ geyer->gamma = model.ipar[0]; geyer->r = model.ipar[1]; /* not squared any more */ geyer->s = model.ipar[2]; geyer->r2 = geyer->r * geyer->r; #ifdef MHDEBUG Rprintf("Initialising Geyer gamma=%lf, r=%lf, sat=%lf\n", geyer->gamma, geyer->r, geyer->s); #endif /* is the model numerically equivalent to hard core ? */ geyer->hard = (geyer->gamma < DOUBLE_EPS); geyer->loggamma = (geyer->hard) ? 0 : log(geyer->gamma); /* periodic boundary conditions? */ geyer->period = model.period; geyer->per = (model.period[0] > 0.0); /* allocate storage for auxiliary counts */ geyer->aux = (int *) R_alloc((size_t) state.npmax, sizeof(int)); #ifdef MH_DEBUG geyer->freshaux = (int *) R_alloc((size_t) state.npmax, sizeof(int)); geyer->prevtype = -42; #endif r2 = geyer->r2; /* Initialise auxiliary counts */ for(i = 0; i < state.npmax; i++) geyer->aux[i] = 0; if(geyer->per) { /* periodic */ period = geyer->period; if(state.npts > 1) { n1 = state.npts - 1; for(i = 0; i < n1; i++) { for(j = i+1; j < state.npts; j++) { if(CLOSE_PERIODIC(state.x[i], state.y[i], state.x[j], state.y[j], period, r2)) { geyer->aux[i] += 1; geyer->aux[j] += 1; } } } } } else { /* Euclidean distance */ if(state.npts > 1) { n1 = state.npts - 1; for(i = 0; i < n1; i++) { for(j = i+1; j < state.npts; j++) { if(CLOSE(state.x[i], state.y[i], state.x[j], state.y[j], r2)) { geyer->aux[i] += 1; geyer->aux[j] += 1; } } } } } return((Cdata *) geyer); } double geyercif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int ix, j, npts, tee; double u, v, r2, s; double w, a, b, f, cifval; double *x, *y; int *aux; double *period; Geyer *geyer; DECLARE_CLOSE_VARS; geyer = (Geyer *) cdata; npts = state.npts; if(npts==0) return ((double) 1.0); x = state.x; y = state.y; u = prop.u; v = prop.v; ix = prop.ix; r2 = geyer->r2; s = geyer->s; period = geyer->period; aux = geyer->aux; /* tee = neighbour count at the point in question; w = sum of changes in (saturated) neighbour counts at other points */ tee = w = 0.0; if(prop.itype == BIRTH) { if(geyer->per) { /* periodic distance */ for(j=0; j 1) /* j is not saturated after addition of (u,v) */ w = w + 1; /* addition of (u,v) increases count by 1 */ else if(f > 0) /* j becomes saturated by addition of (u,v) */ w = w + f; } } } else { /* Euclidean distance */ for(j=0; j 1) /* j is not saturated after addition of (u,v) */ w = w + 1; /* addition of (u,v) increases count by 1 */ else if(f > 0) /* j becomes saturated by addition of (u,v) */ w = w + f; } } } } else if(prop.itype == DEATH) { tee = aux[ix]; if(geyer->per) { /* Periodic distance */ for(j=0; j 0) /* j is not saturated */ w = w + 1; /* deletion of 'ix' decreases count by 1 */ else { f = f+1; if(f > 0) { /* j is not saturated after deletion of 'ix' (s must be fractional) */ w = w + f; } } } } } else { /* Euclidean distance */ for(j=0; j 0) /* j was not saturated */ w = w + 1; /* deletion of 'ix' decreases count by 1 */ else { f = f+1; if(f > 0) { /* j is not saturated after deletion of 'ix' (s must be fractional) */ w = w + f; } } } } } } else if(prop.itype == SHIFT) { /* Compute the cif at the new point, not the ratio of new/old */ if(geyer->per) { /* Periodic distance */ for(j=0; j= b) w = w + 1; } } } else { /* Euclidean distance */ for(j=0; j= b) w = w + 1; } } } } w = w + ((tee < s) ? tee : s); if(geyer->hard) { if(tee > 0) cifval = 0.0; else cifval = 1.0; } else cifval = exp(geyer->loggamma*w); return cifval; } void geyerupd(state, prop, cdata) State state; Propo prop; Cdata *cdata; { /* Declare other variables */ int ix, npts, j; int oldclose, newclose; double u, v, xix, yix, r2; double *x, *y; int *aux; double *period; Geyer *geyer; #ifdef MH_DEBUG int *freshaux; int i; int oc, nc; #endif DECLARE_CLOSE_VARS; geyer = (Geyer *) cdata; period = geyer->period; aux = geyer->aux; r2 = geyer->r2; x = state.x; y = state.y; npts = state.npts; #ifdef MH_DEBUG /* ........................ debugging cross-check ................ */ /* recompute 'aux' values afresh */ freshaux = geyer->freshaux; for(i = 0; i < state.npts; i++) freshaux[i] = 0; if(geyer->per) { /* periodic */ for(i = 0; i < state.npts; i++) { for(j = 0; j < state.npts; j++) { if(i == j) continue; if(CLOSE_PERIODIC(state.x[i], state.y[i], state.x[j], state.y[j], period, r2)) freshaux[i] += 1; } } } else { /* Euclidean distance */ for(i = 0; i < state.npts; i++) { for(j = 0; j < state.npts; j++) { if(i == j) continue; if(CLOSE(state.x[i], state.y[i], state.x[j], state.y[j], r2)) freshaux[i] += 1; } } } /* Check agreement with 'aux' */ for(j = 0; j < state.npts; j++) { if(aux[j] != freshaux[j]) { Rprintf("\n\taux[%d] = %d, freshaux[%d] = %d\n", j, aux[j], j, freshaux[j]); Rprintf("\tnpts = %d\n", state.npts); Rprintf("\tperiod = (%lf, %lf)\n", period[0], period[1]); if(geyer->prevtype == BIRTH) error("updaux failed after BIRTH"); if(geyer->prevtype == DEATH) error("updaux failed after DEATH"); if(geyer->prevtype == SHIFT) error("updaux failed after SHIFT"); error("updaux failed at start"); } } /* OK. Record type of this transition */ geyer->prevtype = prop.itype; /* ................ end debug cross-check ................ */ #endif if(prop.itype == BIRTH) { /* Birth */ u = prop.u; v = prop.v; /* initialise auxiliary counter for new point */ aux[npts] = 0; /* update all auxiliary counters */ if(geyer->per) { /* periodic distance */ for(j=0; j < npts; j++) { if(CLOSE_PERIODIC(u,v,x[j],y[j],period,r2)) { aux[j] += 1; aux[npts] += 1; } } } else { /* Euclidean distance */ for(j=0; j < npts; j++) { if(CLOSE(u,v,x[j],y[j],r2)) { aux[j] += 1; aux[npts] += 1; } } } } else if(prop.itype == DEATH) { /* Death */ ix = prop.ix; u = x[ix]; v = y[ix]; /* decrement auxiliary counter for each point */ if(geyer->per) { /* periodic distance */ for(j=0; j= ix) aux[j-1] = aux[j]; } } else { /* Euclidean distance */ for(j=0; j= ix) aux[j-1] = aux[j]; } } } else if(prop.itype == SHIFT) { /* Shift */ u = prop.u; v = prop.v; ix = prop.ix; xix = x[ix]; yix = y[ix]; /* recompute auxiliary counter for point 'ix' */ aux[ix] = 0; /* update auxiliary counters for other points */ if(geyer->per) { for(j=0; j #include #include #include "chunkloop.h" /* localpcf.c $Revision: 1.3 $ $Date: 2013/05/27 02:09:10 $ Assumes point patterns are sorted in increasing order of x coordinate */ #undef WEIGHTED #include "localpcf.h" #define WEIGHTED 1 #include "localpcf.h" spatstat/src/seg2pix.c0000755000176000001440000002223712333041761014472 0ustar ripleyusers#include #include #include #include #include "chunkloop.h" #undef DEBUG /* seg2pix.c Discretise line segment on pixel grid seg2pixI pixel value is indicator = 1 if any line crosses pixel seg2pixL pixel value is total (weighted) length of lines inside pixel (rescale R data so that pixels are integer) pixels numbered 0, ..., nx-1 and 0, ..., ny-1 with boundaries at x=0, x=nx, y=0, y=ny. */ #define V(I,J) out[(I) + (J) * (Ny)] int clamp(k, n0, n1) int k, n0, n1; { int m; m = k; if(m < n0) m = n0; if(m > n1) m = n1; return(m); } void seg2pixI(ns,x0,y0,x1,y1,nx,ny,out) int *ns; /* number of segments */ double *x0,*y0,*x1,*y1; /* coordinates of segment endpoints */ int *nx, *ny; /* dimensions of pixel array (columns, rows) */ int *out; { int Ns, Nx, Ny, i, j, k, m, m0, m1, mmin, mmax, maxchunk; double x0i, x1i, y0i, y1i, dx, dy; double leni; double xleft, yleft, xright, yright, slope; double xstart, ystart, xfinish, yfinish; int mleft, mright, kstart, kfinish, kmin, kmax; Ns = *ns; Nx = *nx; Ny = *ny; for(k = 0; k < Ny - 1; k++) for(j = 0; j < Nx - 1; j++) V(k, j) = 0; OUTERCHUNKLOOP(i, Ns, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Ns, maxchunk, 8196) { x0i = x0[i]; y0i = y0[i]; x1i = x1[i]; y1i = y1[i]; dx = x1i - x0i; dy = y1i - y0i; leni = hypot(dx, dy); #ifdef DEBUG Rprintf("(%lf, %lf) to (%lf, %lf)\n", x0i, y0i, x1i, y1i); #endif if(leni < 0.001) { /* tiny segment */ #ifdef DEBUG Rprintf("tiny\n"); #endif k = clamp((int) floor(x0i), 0, Nx-1); j = clamp((int) floor(y0i), 0, Ny-1); V(j,k) = 1; } else if(floor(x1i) == floor(x0i) && floor(y1i) == floor(y0i)) { /* contained in one cell */ #ifdef DEBUG Rprintf("contained in one cell\n"); #endif k = clamp((int) floor(x0i), 0, Nx-1); j = clamp((int) floor(y0i), 0, Ny-1); V(j,k) = 1; } else if(floor(y1i) == floor(y0i)) { /* horizontal */ #ifdef DEBUG Rprintf("horizontal\n"); #endif j = clamp((int) floor(y1i), 0, Ny-1); m0 = clamp((int) floor(x0i), 0, Nx-1); m1 = clamp((int) floor(x1i), 0, Nx-1); mmin = (m0 < m1) ? m0: m1; mmax = (m0 < m1) ? m1: m0; #ifdef DEBUG Rprintf("row %d: columns [%d, %d]\n", j, mmin, mmax); #endif for(k = mmin; k <= mmax; k++) V(j,k) = 1; } else if(floor(x1i) == floor(x0i)) { /* vertical */ #ifdef DEBUG Rprintf("vertical\n"); #endif k = clamp((int) floor(x1i), 0, Nx-1); m0 = clamp((int) floor(y0i), 0, Ny-1); m1 = clamp((int) floor(y1i), 0, Ny-1); mmin = (m0 < m1) ? m0: m1; mmax = (m0 < m1) ? m1: m0; #ifdef DEBUG Rprintf("column %d: rows [%d, %d]\n", k, mmin, mmax); #endif for(j = mmin; j <= mmax; j++) V(j,k) = 1; } else { /* general case */ #ifdef DEBUG Rprintf("general\n"); #endif if(x1i > x0i) { xleft = x0i; yleft = y0i; xright = x1i; yright = y1i; } else { xleft = x1i; yleft = y1i; xright = x0i; yright = y0i; } slope = (yright - yleft)/(xright - xleft); mleft = clamp((int) floor(xleft), 0, Nx-1); mright = clamp((int) floor(xright), 0, Nx-1); #ifdef DEBUG Rprintf("column range [%d, %d]\n", mleft, mright); #endif /* treat each vertical slice */ for(m = mleft; m <= mright; m++) { if(m == mleft) { xstart = xleft; ystart = yleft; } else { xstart = m; ystart = yleft + slope * (xstart - xleft); } if(m == mright) { xfinish = xright; yfinish = yright; } else { xfinish = m+1; yfinish = yleft + slope * (xfinish - xleft); } kstart = clamp((int) floor(ystart), 0, Ny-1); kfinish = clamp((int) floor(yfinish), 0, Ny-1); kmin = (kstart < kfinish) ? kstart : kfinish; kmax = (kstart < kfinish) ? kfinish : kstart; #ifdef DEBUG Rprintf("column %d: rows [%d, %d]\n", m, kmin, kmax); #endif for(k = kmin; k <= kmax; k++) V(k, m) = 1; } } /* end of if-else */ } } #ifdef DEBUG Rprintf("done\n"); #endif } void seg2pixL(ns,x0,y0,x1,y1,weights,pixwidth,pixheight,nx,ny,out) int *ns; double *x0,*y0,*x1,*y1,*weights; /* segment coordinates and weights */ double *pixwidth, *pixheight; /* original pixel dimensions */ int *nx, *ny; double *out; /* output matrix */ { int Ns, Nx, Ny, i, j, k, m, mmin, mmax, maxchunk; double x0i, x1i, y0i, y1i; double leni; double xleft, yleft, xright, yright, slope, scalesecant; double xlow, xhigh, ylow, yhigh, invslope, scalecosecant; double xstart, ystart, xfinish, yfinish; double xxx0, xxx1, yyy0, yyy1; int mleft, mright, kstart, kfinish, kmin, kmax; double pwidth, pheight, pwidth2, pheight2; double wti; Ns = *ns; Nx = *nx; Ny = *ny; /* one scaled x unit = 'pwidth' original x units one scaled y unit = 'pheight' original y units */ pwidth = *pixwidth; pheight = *pixheight; pwidth2 = pwidth * pwidth; pheight2 = pheight * pheight; /* zero the matrix */ for(k = 0; k < Ny - 1; k++) for(j = 0; j < Nx - 1; j++) V(k, j) = 0; OUTERCHUNKLOOP(i, Ns, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Ns, maxchunk, 8196) { x0i = x0[i]; y0i = y0[i]; x1i = x1[i]; y1i = y1[i]; wti = weights[i]; leni = sqrt(pwidth2 * pow(x1i - x0i, 2) + pheight2 * pow(y1i-y0i, 2)); #ifdef DEBUG Rprintf("(%lf, %lf) to (%lf, %lf), length %lf\n", x0i, y0i, x1i, y1i, leni); #endif if(leni < 0.001) { /* tiny segment */ #ifdef DEBUG Rprintf("tiny\n"); #endif k = clamp((int) floor(x0i), 0, Nx-1); j = clamp((int) floor(y0i), 0, Ny-1); V(j,k) += wti * leni; } else if(floor(x1i) == floor(x0i) && floor(y1i) == floor(y0i)) { /* contained in one cell */ #ifdef DEBUG Rprintf("contained in one cell\n"); #endif k = clamp((int) floor(x0i), 0, Nx-1); j = clamp((int) floor(y0i), 0, Ny-1); V(j,k) += wti * leni; } else if(floor(y1i) == floor(y0i)) { /* horizontal */ #ifdef DEBUG Rprintf("horizontal\n"); #endif j = clamp((int) floor(y1i), 0, Ny-1); if(x1i > x0i) { xleft = x0i; yleft = y0i; xright = x1i; yright = y1i; } else { xleft = x1i; yleft = y1i; xright = x0i; yright = y0i; } mmin = clamp((int) floor(xleft), 0, Nx-1); mmax = clamp((int) floor(xright), 0, Nx-1); slope = (yright - yleft)/(xright - xleft); scalesecant = wti * sqrt(pwidth2 + slope * slope * pheight2); /* For this slope, one scaled x unit means 'pwidth' original x units and slope * pheight original y units i.e. line length sqrt(pwidth^2 + slope^2 * pheight^2) */ for(k = mmin; k <= mmax; k++) { xstart = (k == mmin) ? xleft : k; xfinish = (k == mmax) ? xright : (k+1); V(j,k) += (xfinish - xstart) * scalesecant; } } else if(floor(x1i) == floor(x0i)) { /* vertical */ #ifdef DEBUG Rprintf("vertical\n"); #endif k = clamp((int) floor(x1i), 0, Nx-1); if(y1i > y0i) { xlow = x0i; ylow = y0i; xhigh = x1i; yhigh = y1i; } else { xlow = x1i; ylow = y1i; xhigh = x0i; yhigh = y0i; } mmin = clamp((int) floor(ylow), 0, Ny-1); mmax = clamp((int) floor(yhigh), 0, Ny-1); invslope = (xhigh - xlow)/(yhigh - ylow); scalecosecant = wti * sqrt(pheight2 + invslope * invslope * pwidth2); #ifdef DEBUG Rprintf("i = %d\n", i); Rprintf("inverse slope = %lf\n", invslope); Rprintf("scaled cosecant = %lf\n", scalecosecant); #endif /* For this slope, one scaled y unit means 'pheight' original y units and invslope * pwidth original x units i.e. line length sqrt(pheight^2 + invslope^2 * pwidth^2) */ for(j = mmin; j <= mmax; j++) { ystart = (j == mmin)? ylow : j; yfinish = (j == mmax)? yhigh : (j+1); V(j,k) += (yfinish - ystart) * scalecosecant; } } else { /* general case */ #ifdef DEBUG Rprintf("general\n"); #endif if(x1i > x0i) { xleft = x0i; yleft = y0i; xright = x1i; yright = y1i; } else { xleft = x1i; yleft = y1i; xright = x0i; yright = y0i; } slope = (yright - yleft)/(xright - xleft); mleft = clamp((int) floor(xleft), 0, Nx-1); mright = clamp((int) floor(xright), 0, Nx-1); #ifdef DEBUG Rprintf("column range [%d, %d]\n", mleft, mright); #endif /* treat each vertical slice */ for(m = mleft; m <= mright; m++) { if(m == mleft) { xstart = xleft; ystart = yleft; } else { xstart = m; ystart = yleft + slope * (xstart - xleft); } if(m == mright) { xfinish = xright; yfinish = yright; } else { xfinish = m+1; yfinish = yleft + slope * (xfinish - xleft); } kstart = clamp((int) floor(ystart), 0, Ny-1); kfinish = clamp((int) floor(yfinish), 0, Ny-1); if(ystart < yfinish) { kmin = kstart; kmax = kfinish; ylow = ystart; yhigh = yfinish; } else { kmin = kfinish; kmax = kstart; ylow = yfinish; yhigh = ystart; } #ifdef DEBUG Rprintf("column %d: rows [%d, %d]\n", m, kmin, kmax); #endif for(k = kmin; k <= kmax; k++) { yyy0 = (k == kmin) ? ylow : k; yyy1 = (k == kmax) ? yhigh : (k+1); xxx0 = xstart + (yyy0 - ystart)/slope; xxx1 = xstart + (yyy1 - ystart)/slope; V(k, m) += wti * sqrt(pow(yyy1 - yyy0, 2) * pheight2 + pow(xxx1 - xxx0, 2) * pwidth2); } } } } } #ifdef DEBUG Rprintf("done.\n"); #endif } spatstat/src/lincrossdist.c0000644000176000001440000000456612333041761015633 0ustar ripleyusers#include #include #include "chunkloop.h" /* lincrossdist.c Shortest-path distances between pairs of points in linear network $Revision: 1.3 $ $Date: 2012/10/13 03:45:41 $ lincrossdist */ #define DPATH(I,J) dpath[(I) + Nv * (J)] #define ANSWER(I,J) answer[(I) + Np * (J)] #define EUCLID(X,Y,U,V) sqrt(pow((X)-(U),2)+pow((Y)-(V),2)) void lincrossdist(np, xp, yp, /* data points from which distances are measured */ nq, xq, yq, /* data points to which distances are measured */ nv, xv, yv, /* network vertices */ ns, from, to, /* segments */ dpath, /* shortest path distances between vertices */ psegmap, /* map from data points to segments */ qsegmap, /* map from data points to segments */ /* OUTPUT */ answer /* shortest path distances between points */ ) int *np, *nq, *nv, *ns; int *from, *to, *psegmap, *qsegmap; /* integer vectors (mappings) */ double *xp, *yp, *xq, *yq, *xv, *yv; /* vectors of coordinates */ double *dpath, *answer; /* matrices */ { int Np, Nq, Nv, i, j, maxchunk; int Psegi, Qsegj, nbi1, nbi2, nbj1, nbj2; double xpi, ypi, xqj, yqj; double d, dPiV1, dPiV2, dV1Qj, dV2Qj, d11, d12, d21, d22; Np = *np; Nq = *nq; Nv = *nv; OUTERCHUNKLOOP(i, Np, maxchunk, 1024) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Np, maxchunk, 1024) { xpi = xp[i]; ypi = yp[i]; Psegi = psegmap[i]; nbi1 = from[Psegi]; nbi2 = to[Psegi]; dPiV1 = EUCLID(xpi, ypi, xv[nbi1], yv[nbi1]); dPiV2 = EUCLID(xpi, ypi, xv[nbi2], yv[nbi2]); for(j = 0; j < Nq; j++) { xqj = xq[j]; yqj = yq[j]; Qsegj = qsegmap[j]; if(Psegi == Qsegj) { /* points i and j lie on the same segment; use Euclidean distance */ d = sqrt(pow(xpi - xqj, 2) + pow(ypi - yqj, 2)); } else { /* Shortest path from i to j passes through ends of segments; Calculate shortest of 4 possible paths from i to j */ nbj1 = from[Qsegj]; nbj2 = to[Qsegj]; dV1Qj = EUCLID(xv[nbj1], yv[nbj1], xqj, yqj); dV2Qj = EUCLID(xv[nbj2], yv[nbj2], xqj, yqj); d11 = dPiV1 + DPATH(nbi1,nbj1) + dV1Qj; d12 = dPiV1 + DPATH(nbi1,nbj2) + dV2Qj; d21 = dPiV2 + DPATH(nbi2,nbj1) + dV1Qj; d22 = dPiV2 + DPATH(nbi2,nbj2) + dV2Qj; d = d11; if(d12 < d) d = d12; if(d21 < d) d = d21; if(d22 < d) d = d22; } /* write */ ANSWER(i,j) = d; } } } } spatstat/src/Estrauss.c0000755000176000001440000000306412333041761014717 0ustar ripleyusers#include #include #include "chunkloop.h" /* Estrauss.c $Revision: 1.3 $ $Date: 2012/03/28 05:56:24 $ C implementation of 'eval' for Strauss interaction Calculates number of data points within distance r of each quadrature point (when 'source' = quadrature points, 'target' = data points) Assumes point patterns are sorted in increasing order of x coordinate */ double sqrt(); void Ccrosspaircounts(nnsource, xsource, ysource, nntarget, xtarget, ytarget, rrmax, counts) /* inputs */ int *nnsource, *nntarget; double *xsource, *ysource, *xtarget, *ytarget, *rrmax; /* output */ int *counts; { int nsource, ntarget, maxchunk, j, i, ileft, counted; double xsourcej, ysourcej, rmax, r2max, xleft, dx, dy, dx2, d2; nsource = *nnsource; ntarget = *nntarget; rmax = *rrmax; r2max = rmax * rmax; if(nsource == 0 || ntarget == 0) return; ileft = 0; OUTERCHUNKLOOP(j, nsource, maxchunk, 65536) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nsource, maxchunk, 65536) { counted = 0; xsourcej = xsource[j]; ysourcej = ysource[j]; /* adjust starting point */ xleft = xsourcej - rmax; while((xtarget[ileft] < xleft) && (ileft+1 < ntarget)) ++ileft; /* process from ileft to iright */ for(i=ileft; i < ntarget; i++) { dx = xtarget[i] - xsourcej; dx2 = dx * dx; if(dx2 > r2max) break; dy = ytarget[i] - ysourcej; d2 = dx2 + dy * dy; if(d2 <= r2max) ++counted; } counts[j] = counted; } } } spatstat/src/methas.c0000755000176000001440000002642712333041761014377 0ustar ripleyusers#include #include #include #include "methas.h" #include "chunkloop.h" #include "mhsnoop.h" void fexitc(const char *msg); /* To switch on debugging code, insert the line: #define MH_DEBUG YES */ #ifndef MH_DEBUG #define MH_DEBUG NO #endif /* This is the value of 'ix' when we are proposing a birth. It must be equal to -1 so that NONE+1 = 0. */ #define NONE -1 extern Cifns getcif(char *); SEXP xmethas( SEXP ncif, SEXP cifname, SEXP beta, SEXP ipar, SEXP iparlen, SEXP period, SEXP xprop, SEXP yprop, SEXP mprop, SEXP ntypes, SEXP nrep, SEXP p, SEXP q, SEXP nverb, SEXP nrep0, SEXP x, SEXP y, SEXP marks, SEXP ncond, SEXP fixall, SEXP track, SEXP snoopenv) { char *cifstring; double cvd, cvn, qnodds, anumer, adenom, betavalue; double *iparvector; int verb, marked, mustupdate, itype; int nfree; int irep, ix, j, maxchunk, iverb; int Ncif; int *plength; long Nmore; double *xx, *yy, *xpropose, *ypropose; int *mm, *mpropose, *pp, *aa; SEXP out, xout, yout, mout, pout, aout; int tracking; #ifdef HISTORY_INCLUDES_RATIO SEXP numout, denout; double *nn, *dd; #endif State state; Model model; Algor algo; Propo birthprop, deathprop, shiftprop; History history; Snoop snooper; /* The following variables are used only for a non-hybrid interaction */ Cifns thecif; /* cif structure */ Cdata *thecdata; /* pointer to initialised cif data block */ /* The following variables are used only for a hybrid interaction */ Cifns *cif; /* vector of cif structures */ Cdata **cdata; /* vector of pointers to initialised cif data blocks */ int *needupd; /* vector of logical values */ int k; /* loop index for cif's */ /* =================== Protect R objects from garbage collector ======= */ PROTECT(ncif = AS_INTEGER(ncif)); PROTECT(cifname = AS_CHARACTER(cifname)); PROTECT(beta = AS_NUMERIC(beta)); PROTECT(ipar = AS_NUMERIC(ipar)); PROTECT(iparlen = AS_INTEGER(iparlen)); PROTECT(period = AS_NUMERIC(period)); PROTECT(xprop = AS_NUMERIC(xprop)); PROTECT(yprop = AS_NUMERIC(yprop)); PROTECT(mprop = AS_INTEGER(mprop)); PROTECT(ntypes = AS_INTEGER(ntypes)); PROTECT(nrep = AS_INTEGER(nrep)); PROTECT( p = AS_NUMERIC(p)); PROTECT( q = AS_NUMERIC(q)); PROTECT(nverb = AS_INTEGER(nverb)); PROTECT(nrep0 = AS_INTEGER(nrep0)); PROTECT( x = AS_NUMERIC(x)); PROTECT( y = AS_NUMERIC(y)); PROTECT( marks = AS_INTEGER(marks)); PROTECT(fixall = AS_INTEGER(fixall)); PROTECT(ncond = AS_INTEGER(ncond)); PROTECT(track = AS_INTEGER(track)); /* that's 21 protected objects */ /* =================== Translate arguments from R to C ================ */ /* Ncif is the number of cif's plength[i] is the number of interaction parameters in the i-th cif */ Ncif = *(INTEGER_POINTER(ncif)); plength = INTEGER_POINTER(iparlen); /* copy RMH algorithm parameters */ algo.nrep = *(INTEGER_POINTER(nrep)); algo.nverb = *(INTEGER_POINTER(nverb)); algo.nrep0 = *(INTEGER_POINTER(nrep0)); algo.p = *(NUMERIC_POINTER(p)); algo.q = *(NUMERIC_POINTER(q)); algo.fixall = ((*(INTEGER_POINTER(fixall))) == 1); algo.ncond = *(INTEGER_POINTER(ncond)); /* copy model parameters without interpreting them */ model.beta = NUMERIC_POINTER(beta); model.ipar = iparvector = NUMERIC_POINTER(ipar); model.period = NUMERIC_POINTER(period); model.ntypes = *(INTEGER_POINTER(ntypes)); state.ismarked = marked = (model.ntypes > 1); /* copy initial state */ state.npts = LENGTH(x); state.npmax = 4 * ((state.npts > 256) ? state.npts : 256); state.x = (double *) R_alloc(state.npmax, sizeof(double)); state.y = (double *) R_alloc(state.npmax, sizeof(double)); xx = NUMERIC_POINTER(x); yy = NUMERIC_POINTER(y); if(marked) { state.marks =(int *) R_alloc(state.npmax, sizeof(int)); mm = INTEGER_POINTER(marks); } if(!marked) { for(j = 0; j < state.npts; j++) { state.x[j] = xx[j]; state.y[j] = yy[j]; } } else { for(j = 0; j < state.npts; j++) { state.x[j] = xx[j]; state.y[j] = yy[j]; state.marks[j] = mm[j]; } } #if MH_DEBUG Rprintf("\tnpts=%d\n", state.npts); #endif /* access proposal data */ xpropose = NUMERIC_POINTER(xprop); ypropose = NUMERIC_POINTER(yprop); mpropose = INTEGER_POINTER(mprop); /* we need to initialise 'mpropose' to keep compilers happy. mpropose is only used for marked patterns. Note 'mprop' is always a valid pointer */ /* ================= Allocate space for cifs etc ========== */ if(Ncif > 1) { cif = (Cifns *) R_alloc(Ncif, sizeof(Cifns)); cdata = (Cdata **) R_alloc(Ncif, sizeof(Cdata *)); needupd = (int *) R_alloc(Ncif, sizeof(int)); } else { /* Keep the compiler happy */ cif = (Cifns *) R_alloc(1, sizeof(Cifns)); cdata = (Cdata **) R_alloc(1, sizeof(Cdata *)); needupd = (int *) R_alloc(1, sizeof(int)); } /* ================= Determine process to be simulated ========== */ /* Get the cif's */ if(Ncif == 1) { cifstring = (char *) STRING_VALUE(cifname); thecif = getcif(cifstring); mustupdate = NEED_UPDATE(thecif); if(thecif.marked && !marked) fexitc("cif is for a marked point process, but proposal data are not marked points; bailing out."); /* Keep compiler happy*/ cif[0] = thecif; needupd[0] = mustupdate; } else { mustupdate = NO; for(k = 0; k < Ncif; k++) { cifstring = (char *) CHAR(STRING_ELT(cifname, k)); cif[k] = getcif(cifstring); needupd[k] = NEED_UPDATE(cif[k]); if(needupd[k]) mustupdate = YES; if(cif[k].marked && !marked) fexitc("component cif is for a marked point process, but proposal data are not marked points; bailing out."); } } /* ============= Initialise transition history ========== */ tracking = (*(INTEGER_POINTER(track)) != 0); /* Initialise even if not needed, to placate the compiler */ if(tracking) { history.nmax = algo.nrep; } else { history.nmax = 1; } history.n = 0; history.proptype = (int *) R_alloc(history.nmax, sizeof(int)); history.accepted = (int *) R_alloc(history.nmax, sizeof(int)); #ifdef HISTORY_INCLUDES_RATIO history.numerator = (double *) R_alloc(history.nmax, sizeof(double)); history.denominator = (double *) R_alloc(history.nmax, sizeof(double)); #endif /* ============= Visual debugging ========== */ /* Active if 'snoopenv' is an environment */ #if MH_DEBUG Rprintf("Initialising mhsnoop\n"); #endif initmhsnoop(&snooper, snoopenv); #if MH_DEBUG Rprintf("Initialised\n"); if(snooper.active) Rprintf("Debugger is active.\n"); #endif /* ================= Initialise algorithm ==================== */ /* Interpret the model parameters and initialise auxiliary data */ if(Ncif == 1) { thecdata = (*(thecif.init))(state, model, algo); /* keep compiler happy */ cdata[0] = thecdata; } else { for(k = 0; k < Ncif; k++) { if(k > 0) model.ipar += plength[k-1]; cdata[k] = (*(cif[k].init))(state, model, algo); } } /* Set the fixed elements of the proposal objects */ birthprop.itype = BIRTH; deathprop.itype = DEATH; shiftprop.itype = SHIFT; birthprop.ix = NONE; if(!marked) birthprop.mrk = deathprop.mrk = shiftprop.mrk = NONE; /* Set up some constants */ verb = (algo.nverb !=0); qnodds = (1.0 - algo.q)/algo.q; /* Set value of beta for unmarked process */ /* (Overwritten for marked process, but keeps compiler happy) */ betavalue = model.beta[0]; /* ============= Run Metropolis-Hastings ================== */ /* Initialise random number generator */ GetRNGstate(); /* Here comes the code for the M-H loop. The basic code (in mhloop.h) is #included many times using different options The C preprocessor descends through a chain of files mhv1.h, mhv2.h, ... to enumerate all possible combinations of flags. */ #include "mhv1.h" /* relinquish random number generator */ PutRNGstate(); /* ============= Done ================== */ /* Create space for output, and copy final state */ /* Point coordinates */ PROTECT(xout = NEW_NUMERIC(state.npts)); PROTECT(yout = NEW_NUMERIC(state.npts)); xx = NUMERIC_POINTER(xout); yy = NUMERIC_POINTER(yout); for(j = 0; j < state.npts; j++) { xx[j] = state.x[j]; yy[j] = state.y[j]; } /* Marks */ if(marked) { PROTECT(mout = NEW_INTEGER(state.npts)); mm = INTEGER_POINTER(mout); for(j = 0; j < state.npts; j++) mm[j] = state.marks[j]; } else { /* Keep the compiler happy */ PROTECT(mout = NEW_INTEGER(1)); mm = INTEGER_POINTER(mout); mm[0] = 0; } /* Transition history */ if(tracking) { PROTECT(pout = NEW_INTEGER(algo.nrep)); PROTECT(aout = NEW_INTEGER(algo.nrep)); pp = INTEGER_POINTER(pout); aa = INTEGER_POINTER(aout); for(j = 0; j < algo.nrep; j++) { pp[j] = history.proptype[j]; aa[j] = history.accepted[j]; } #ifdef HISTORY_INCLUDES_RATIO PROTECT(numout = NEW_NUMERIC(algo.nrep)); PROTECT(denout = NEW_NUMERIC(algo.nrep)); nn = NUMERIC_POINTER(numout); dd = NUMERIC_POINTER(denout); for(j = 0; j < algo.nrep; j++) { nn[j] = history.numerator[j]; dd[j] = history.denominator[j]; } #endif } else { /* Keep the compiler happy */ PROTECT(pout = NEW_INTEGER(1)); PROTECT(aout = NEW_INTEGER(1)); pp = INTEGER_POINTER(pout); aa = INTEGER_POINTER(aout); pp[0] = aa[0] = 0; #ifdef HISTORY_INCLUDES_RATIO PROTECT(numout = NEW_NUMERIC(1)); PROTECT(denout = NEW_NUMERIC(1)); nn = NUMERIC_POINTER(numout); dd = NUMERIC_POINTER(denout); nn[0] = dd[0] = 0; #endif } /* Pack up into list object for return */ if(!tracking) { /* no transition history */ if(!marked) { PROTECT(out = NEW_LIST(2)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); } else { PROTECT(out = NEW_LIST(3)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, mout); } } else { /* transition history */ if(!marked) { #ifdef HISTORY_INCLUDES_RATIO PROTECT(out = NEW_LIST(6)); #else PROTECT(out = NEW_LIST(4)); #endif SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, pout); SET_VECTOR_ELT(out, 3, aout); #ifdef HISTORY_INCLUDES_RATIO SET_VECTOR_ELT(out, 4, numout); SET_VECTOR_ELT(out, 5, denout); #endif } else { #ifdef HISTORY_INCLUDES_RATIO PROTECT(out = NEW_LIST(7)); #else PROTECT(out = NEW_LIST(5)); #endif SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, mout); SET_VECTOR_ELT(out, 3, pout); SET_VECTOR_ELT(out, 4, aout); #ifdef HISTORY_INCLUDES_RATIO SET_VECTOR_ELT(out, 5, numout); SET_VECTOR_ELT(out, 6, denout); #endif } } #ifdef HISTORY_INCLUDES_RATIO UNPROTECT(29); /* 21 arguments plus xout, yout, mout, pout, aout, out, numout, denout */ #else UNPROTECT(27); /* 21 arguments plus xout, yout, mout, pout, aout, out */ #endif return(out); } spatstat/src/dwpure.c0000755000176000001440000002320212333041761014410 0ustar ripleyusers/* dwpure.c $Revision: 1.5 $ $Date: 2011/09/20 07:54:53 $ Code by Dominic Schuhmacher */ #include #include #include typedef struct State { int n1, n2; /* vectors of length n1 (rows) and n2 (cols) */ int *rowmass, *colmass; /* mass to be moved from row / to col */ int *rowlab, *collab; /* row and col labels (specify previous node (row for collab, col for rowlab)) */ int *rowflow, *colflow; /* second component of labels (specify flow through current node) */ int *rowsurplus, *colsurplus; /* the surplus in each row/col under the current flow */ int *dualu, *dualv; /* vectors of dual variables (u for rows, v for cols) */ int *rowhelper, *colhelper; /* helping vector to store intermediate results */ /* could be local in initcost at the moment */ /* n by n matrices */ int *d; /* matrix of costs */ int *flowmatrix; /* matrix of flows */ int *arcmatrix; /* matrix of arcs for restriced primal problem (1 if arc, 0 if no arc) should be unsigned char to save memory however need to workout problem with R_alloc first (see below) */ /* n*n vector */ int *collectvals; } State; #define COST(I,J,STATE,NVALUE) ((STATE)->d)[(NVALUE) * (J) + (I)] #define FLOW(I,J,STATE,NVALUE) ((STATE)->flowmatrix)[(NVALUE) * (J) + (I)] #define ARC(I,J,STATE,NVALUE) ((STATE)->arcmatrix)[(NVALUE) * (J) + (I)] #define MIN(A,B) ((A)<(B) ? (A) : (B)) int arraysum(int *a, int n); int arraymin(int *a, int n); void initvalues(State *state); void maxflow(State *state); void updateduals(State *state); void augmentflow(int startcol, State *state); /* ------------ The main function ----------------------------- */ void dwpure(int *d, int *rmass, int *cmass, int *numr, int *numc, int *flowmatrix) { int i,j; /* indices */ int n1,n2; unsigned char feasible = 0; /* boolean for main loop */ State state; /* inputs */ state.n1 = n1 = *numr; state.n2 = n2 = *numc; state.d = d; state.rowmass = rmass; state.colmass = cmass; /* scratch space */ state.rowlab = (int *) R_alloc((long) n1, sizeof(int)); state.collab = (int *) R_alloc((long) n2, sizeof(int)); state.rowflow = (int *) R_alloc((long) n1, sizeof(int)); state.colflow = (int *) R_alloc((long) n2, sizeof(int)); state.rowsurplus = (int *) R_alloc((long) n1, sizeof(int)); state.colsurplus = (int *) R_alloc((long) n2, sizeof(int)); state.dualu = (int *) R_alloc((long) n1, sizeof(int)); state.dualv = (int *) R_alloc((long) n2, sizeof(int)); state.rowhelper = (int *) R_alloc((long) n1, sizeof(int)); state.colhelper = (int *) R_alloc((long) n2, sizeof(int)); state.flowmatrix = (int *) R_alloc((long) (n1 * n2), sizeof(int)); state.arcmatrix = (int *) R_alloc((long) (n1 * n2), sizeof(int)); state.collectvals = (int *) R_alloc((long) (n1 * n2), sizeof(int)); for (i = 0; i < n1; ++i) { for (j = 0; j < n2; ++j) { state.flowmatrix[(n1)*(j) + i] = 0; state.arcmatrix[(n1)*(j) + i] = 0; state.collectvals[(n1)*(j) + i] = 0; } } for (i = 0; i < n1; ++i) { state.rowlab[i] = 0; state.rowflow[i] = 0; state.rowsurplus[i] = 0; state.dualu[i] = 0; state.rowhelper[i] = 0; } for (j = 0; j < n2; ++j) { state.collab[j] = 0; state.colflow[j] = 0; state.colsurplus[j] = 0; state.dualv[j] = 0; state.colhelper[j] = 0; } /* Initialize dual variables, arcmatrix, and surpluses */ initvalues(&state); /* For testing: print out cost matrix for (i = 0; i < n1; ++i) { for (j = 0; j < n2; ++j) { Rprintf("%d ", COST(i, j, &state, n1)); } Rprintf("\n"); } */ /* The main loop */ while(feasible == 0) { maxflow(&state); if (arraysum(state.rowsurplus, n1) > 0) { updateduals(&state); /* also updates arcmatrix */ } else { feasible = 1; } } /* "Return" the final flowmatrix */ for (i = 0; i < n1; i++) { for (j = 0; j < n2; j++) { flowmatrix[n1*j+i] = state.flowmatrix[n1*j+i]; } } } /* ------------ Functions called by dwpure_R ------------------------- */ /* Sum of integer array */ int arraysum(int *a, int n) { int i; int asum = 0; for (i = 0; i < n; i++) asum += a[i]; return(asum); } /* Minimal element of an integer array */ int arraymin(int *a, int n) { int i, amin; if (n < 1) return(-1); amin = a[0]; if (n > 1) for (i = 0; i < n; i++) if (a[i] < amin) amin = a[i]; return(amin); } /* Initialize cost matrix: subtract in each row its minimal entry (from all the entries in the row), then subtract in each column its minimal entry (from all the entries in the column) */ void initvalues(State *state) { int i,j,n1,n2; n1 = state->n1; n2 = state->n2; /* Initial surpluses; can I do this shorter? later on surpluses are updated in flow augmentation step */ for (i = 0; i < n1; i++) state->rowsurplus[i] = state->rowmass[i]; for (j = 0; j < n2; j++) state->colsurplus[j] = state->colmass[j]; for (i = 0; i < n1; i++) { for (j = 0; j < n2; j++) state->colhelper[j] = COST(i, j, state, n1); state->dualu[i] = arraymin(state->colhelper, n2); } for (j = 0; j < n2; j++) { for (i = 0; i < n1; i++) state->rowhelper[i] = COST(i, j, state, n1) - state->dualu[i]; state->dualv[j] = arraymin(state->rowhelper, n1); } for (i = 0; i < n1; i++) { for (j = 0; j < n2; j++) { if (COST(i, j, state, n1) == state->dualu[i] + state->dualv[j]) ARC(i, j, state, n1) = 1; else ARC(i, j, state, n1) = 0; } } } /* Maximize the flow on the (zeros of the) current cost matrix */ void maxflow(State *state) { int breakthrough; /* col. no. in which breakthrough occurs */ unsigned char labelfound = 1; /* 0 if no more labels can be found */ int i,j,n1,n2; n1 = state->n1; n2 = state->n2; while (labelfound == 1) { breakthrough = -1; /* initialize labels */ for (i = 0; i < n1; i++) { if (state->rowsurplus[i] > 0) { state->rowlab[i] = -5; state->rowflow[i] = state->rowsurplus[i]; } else { state->rowlab[i] = -1; /* setting rowflow to zero isn't necessary! */ } } for (j = 0; j < n2; j++) state->collab[j] = -1; /* setting colflow to zero isn't necessary! */ /* -1 means "no index", -5 means "source label" (rows only) */ while (labelfound == 1 && breakthrough == -1) { labelfound = 0; /* label unlabeled column j that permits flow from some labeled row i */ /* ("permits flow" means arcmatrix[i][j] = 1). Do so for every j */ for (i = 0; i < n1; i++) { if (state->rowlab[i] != -1) { for (j = 0; j < n2; j++) { if (ARC(i, j, state, n1) == 1 && state->collab[j] == -1) { state->collab[j] = i; state->colflow[j] = state->rowflow[i]; labelfound = 1; if (state->colsurplus[j] > 0 && breakthrough == -1) breakthrough = j; } } } } /* label unlabeled row i that already sends flow to some labeled col j */ /* ("already sends" means flowmatrix[i][j] > 0). Do so for every i */ for (j = 0; j < n2; j++) { if (state->collab[j] != -1) { for (i = 0; i < n1; i++) { if (FLOW(i, j, state, n1) > 0 && state->rowlab[i] == -1) { state->rowlab[i] = j; state->rowflow[i] = MIN(state->colflow[j],FLOW(i, j, state, n1)); labelfound = 1; } } } } } if (breakthrough != -1) augmentflow(breakthrough, state); } } /* Update the dual variables (called if solution of restricted primal is not feasible for the original problem): determine the minimum over the submatrix given by all labeled rows and unlabeled columns, and subtract it from all labeled rows and add it to all labeled columns. */ void updateduals(State *state) { int i,j,n1,n2,mini; int count = 0; n1 = state->n1; n2 = state->n2; for (i = 0; i < n1; i++) { for (j = 0; j < n2; j++) { if (state->rowlab[i] != -1 && state->collab[j] == -1) { state->collectvals[count] = COST(i, j, state, n1) - state->dualu[i] - state->dualv[j]; count++; } } } mini = arraymin(state->collectvals, count); for (i = 0; i < n1; i++) { if (state->rowlab[i] != -1) state->dualu[i] += mini; } for (j = 0; j < n2; j++){ if (state->collab[j] != -1) state->dualv[j] -= mini; } for (i = 0; i < n1; i++) { for (j = 0; j < n2; j++) { if (COST(i, j, state, n1) == state->dualu[i] + state->dualv[j]) ARC(i, j, state, n1) = 1; else ARC(i, j, state, n1) = 0; } } } /* Augment the flow on the graph given by arcmatrix (by aug) according to the row and column labels starting in column startcol */ /* Adjust the surpluses while we're at it (first row and last col have -aug) */ void augmentflow(int startcol, State *state) { int k,l,aug,n1; /* int i,j,k,l,aug,n1,n2; */ n1 = state->n1; l = startcol; aug = MIN(state->colflow[l], state->colsurplus[l]); state->colsurplus[l] -= aug; k = state->collab[l]; FLOW(k, l, state, n1) += aug; l = state->rowlab[k]; while (l != -5) { FLOW(k, l, state, n1) -= aug; k = state->collab[l]; FLOW(k, l, state, n1) += aug; l = state->rowlab[k]; } state->rowsurplus[k] -= aug; } spatstat/src/distmapbin.c0000755000176000001440000000644412333041761015245 0ustar ripleyusers/* distmapbin.c Distance transform of a discrete binary image (8-connected path metric) $Revision: 1.6 $ $Date: 2011/11/20 03:34:16 $ */ #include #include "raster.h" #include void dist_to_bdry(); void shape_raster(); void distmap_bin(in, dist) Raster *in; /* input: binary image */ Raster *dist; /* output: distance to nearest point */ /* rasters must have been dimensioned by shape_raster() and must all have identical dimensions and margins */ { int j,k; double d, dnew; double xstep, ystep, diagstep, huge; int rmin, rmax, cmin, cmax; /* distances between neighbouring pixels */ xstep = in->xstep; ystep = in->ystep; diagstep = sqrt(xstep * xstep + ystep * ystep); if(xstep < 0) xstep = -xstep; if(ystep < 0) ystep = -ystep; /* effectively infinite distance */ huge = 2.0 * Distance(dist->xmin,dist->ymin,dist->xmax,dist->ymax); /* image boundaries */ rmin = in->rmin; rmax = in->rmax; cmin = in->cmin; cmax = in->cmax; #define DISTANCE(ROW, COL) Entry(*dist, ROW, COL, double) #define MASKTRUE(ROW, COL) (Entry(*in, ROW, COL, int) != 0) #define MASKFALSE(ROW, COL) (Entry(*in, ROW, COL, int) == 0) #define UPDATE(D, ROW, COL, STEP) \ dnew = STEP + DISTANCE(ROW, COL); \ if(D > dnew) D = dnew /* initialise edges to boundary condition */ for(j = rmin-1; j <= rmax+1; j++) { DISTANCE(j, cmin-1) = (MASKTRUE(j, cmin-1)) ? 0.0 : huge; DISTANCE(j, cmax+1) = (MASKTRUE(j, cmax+1)) ? 0.0 : huge; } for(k = cmin-1; k <= cmax+1; k++) { DISTANCE(rmin-1, k) = (MASKTRUE(rmin-1, k)) ? 0.0 : huge; DISTANCE(rmax+1, k) = (MASKTRUE(rmax+1, k)) ? 0.0 : huge; } /* forward pass */ for(j = rmin; j <= rmax; j++) { R_CheckUserInterrupt(); for(k = cmin; k <= cmax; k++) { if(MASKTRUE(j, k)) d = DISTANCE(j, k) = 0.0; else { d = huge; UPDATE(d, j-1, k-1, diagstep); UPDATE(d, j-1, k, ystep); UPDATE(d, j-1, k+1, diagstep); UPDATE(d, j, k-1, xstep); DISTANCE(j,k) = d; } } } /* backward pass */ for(j = rmax; j >= rmin; j--) { R_CheckUserInterrupt(); for(k = cmax; k >= cmin; k--) { if(MASKFALSE(j,k)) { d = DISTANCE(j,k); UPDATE(d, j+1, k+1, diagstep); UPDATE(d, j+1, k, ystep); UPDATE(d, j+1, k-1, diagstep); UPDATE(d, j, k+1, xstep); DISTANCE(j,k) = d; } } } } /* R interface */ void distmapbin(xmin, ymin, xmax, ymax, nr, nc, in, distances, boundary) double *xmin, *ymin, *xmax, *ymax; /* x, y dimensions */ int *nr, *nc; /* raster dimensions EXCLUDING margin of 1 on each side */ int *in; /* input: binary image */ double *distances; /* output: distance to nearest point */ double *boundary; /* output: distance to boundary of rectangle */ /* all images must have identical dimensions including a margin of 1 on each side */ { Raster data, dist, bdist; shape_raster( &data, (void *) in, *xmin,*ymin,*xmax,*ymax, *nr+2, *nc+2, 1, 1); shape_raster( &dist, (void *) distances,*xmin,*ymin,*xmax,*ymax, *nr+2,*nc+2,1,1); shape_raster( &bdist, (void *) boundary, *xmin,*ymin,*xmax,*ymax, *nr+2,*nc+2,1,1); distmap_bin(&data, &dist); dist_to_bdry(&bdist); } spatstat/src/dist2.h0000755000176000001440000000434212333041761014140 0ustar ripleyusers/* dist2.h External declarations for the functions defined in dist2.c and In-line cpp macros for similar purposes $Revision: 1.19 $ $Date: 2014/05/08 02:13:22 $ */ double dist2(double u, double v, double x, double y, double *period); double dist2either(double u, double v, double x, double y, double *period); int dist2thresh(double u, double v, double x, double y, double *period, double r2); int dist2Mthresh(double u, double v, double x, double y, double *period, double r2); /* Efficient macros to test closeness of points */ /* These variables must be declared (note: some files e.g. straush.c use 'RESIDUE' explicitly) */ #define DECLARE_CLOSE_VARS \ register double DX, DY, DXP, DYP, RESIDUE #define DECLARE_CLOSE_D2_VARS \ register double DX, DY, DXP, DYP, DX2 #define CLOSE(U,V,X,Y,R2) \ ((DX = X - U), \ (RESIDUE = R2 - DX * DX), \ ((RESIDUE > 0.0) && \ ((DY = Y - V), \ (RESIDUE = RESIDUE - DY * DY), \ (RESIDUE > 0.0)))) #define CLOSE_D2(U,V,X,Y,R2,D2) \ ((DX = X - U), \ (DX2 = DX * DX), \ (DX2 < R2) && (((DY = Y - V), \ (D2 = DX2 + DY * DY), \ (D2 < R2)))) /* The following calculates X mod P, but it works only if X \in [-P, P] so that X is the difference between two values that lie in an interval of length P */ #define CLOSE_PERIODIC(U,V,X,Y,PERIOD,R2) \ ((DX = X - U), \ (DX = (DX < 0.0) ? -DX : DX), \ (DXP = (PERIOD)[0] - DX), \ (DX = (DX < DXP) ? DX : DXP), \ (RESIDUE = R2 - DX * DX), \ ((RESIDUE > 0.0) && ((DY = Y - V), \ (DY = (DY < 0.0) ? -DY : DY), \ (DYP = (PERIOD)[1] - DY), \ (DY = (DY < DYP) ? DY : DYP), \ (RESIDUE = RESIDUE - DY * DY), \ (RESIDUE > 0.0) ))) #define CLOSE_PERIODIC_D2(U,V,X,Y,PERIOD,R2,D2) \ ((DX = X - U), \ (DX = (DX < 0.0) ? -DX : DX), \ (DXP = (PERIOD)[0] - DX), \ (DX = (DX < DXP) ? DX : DXP), \ (D2 = DX * DX), \ ((D2 < R2) && ((DY = Y - V), \ (DY = (DY < 0.0) ? -DY : DY), \ (DYP = (PERIOD)[1] - DY), \ (DY = (DY < DYP) ? DY : DYP), \ (D2 += DY * DY), \ (D2 < R2) ))) spatstat/src/dgs.c0000755000176000001440000000505612333041761013666 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" #include "constants.h" /* Conditional intensity computation for Diggle-Gates-Stibbard process */ /* Conditional intensity function for a pairwise interaction point process with interaction function as given by e(t) = sin^2(pi*t/2*rho) for t < rho = 1 for t >= rho (See page 767 of Diggle, Gates, and Stibbard, Biometrika vol. 74, 1987, pages 763 -- 770.) */ #define PION2 M_PI_2 /* pi/2 defined in Rmath.h */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Dgs { double rho; double rho2; double pion2rho; double *period; int per; } Dgs; /* initialiser function */ Cdata *dgsinit(state, model, algo) State state; Model model; Algor algo; { Dgs *dgs; /* allocate storage */ dgs = (Dgs *) R_alloc(1, sizeof(Dgs)); /* Interpret model parameters*/ dgs->rho = model.ipar[0]; dgs->period = model.period; /* constants */ dgs->rho2 = pow(dgs->rho, 2); dgs->pion2rho = PION2/dgs->rho; /* periodic boundary conditions? */ dgs->per = (model.period[0] > 0.0); return((Cdata *) dgs); } /* conditional intensity evaluator */ double dgscif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *x, *y; double u, v; double d2, r2, pairprod, cifval; Dgs *dgs; DECLARE_CLOSE_D2_VARS; dgs = (Dgs *) cdata; r2 = dgs->rho2; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; cifval = pairprod = 1.0; if(npts == 0) return(cifval); ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(dgs->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],dgs->period,r2,d2)) pairprod *= sin(dgs->pion2rho * sqrt(d2)); } } if(ixp1 < npts) { for(j=ixp1; jperiod,r2,d2)) pairprod *= sin(dgs->pion2rho * sqrt(d2)); } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u, v, x[j], y[j], r2, d2)) pairprod *= sin(dgs->pion2rho * sqrt(d2)); } } if(ixp1 < npts) { for(j=ixp1; jpion2rho * sqrt(d2)); } } } /* sin to sin^2 */ cifval = pairprod * pairprod; return cifval; } Cifns DgsCifns = { &dgsinit, &dgscif, (updafunptr) NULL, NO}; spatstat/src/distan3.c0000755000176000001440000002436312333041761014460 0ustar ripleyusers/* distan3.c Distances between pairs of 3D points $Revision: 1.3 $ $Date: 2013/11/03 03:34:15 $ D3pairdist Pairwise distances D3pair2dist Pairwise distances squared D3pairPdist Pairwise distances with periodic correction D3pairP2dist Pairwise distances squared, with periodic correction D3crossdist Pairwise distances for two sets of points D3cross2dist Pairwise distances squared, for two sets of points D3crossPdist Pairwise distances for two sets of points, periodic correction matchxyz Find matches between two sets of points */ #include /* #include */ double sqrt(); void D3pairdist(n, x, y, z, squared, d) /* inputs */ int *n; double *x, *y, *z; int *squared; /* output */ double *d; { void D3pair1dist(), D3pair2dist(); if(*squared == 0) { D3pair1dist(n, x, y, z, d); } else { D3pair2dist(n, x, y, z, d); } } void D3pair1dist(n, x, y, z, d) /* inputs */ int *n; double *x, *y, *z; /* output */ double *d; { int i, j, npoints; double *dp; double xi, yi, zi, dx, dy, dz, dist; npoints = *n; /* set d[0,0] = 0 */ *d = 0.0; for (i=1; i < npoints; i++) { xi = x[i]; yi = y[i]; zi = z[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dz = z[j] - zi; dist = sqrt( dx * dx + dy * dy + dz * dz ); /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } /* squared distances */ void D3pair2dist(n, x, y, z, d) /* inputs */ int *n; double *x, *y, *z; /* output */ double *d; { int i, j, npoints; double *dp; double xi, yi, zi, dx, dy, dz, dist; npoints = *n; /* set d[0,0] = 0 */ *d = 0.0; for (i=1; i < npoints; i++) { xi = x[i]; yi = y[i]; zi = z[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dz = z[j] - zi; dist = dx * dx + dy * dy + dz * dz; /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } void D3crossdist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, squared, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *zfrom, *xto, *yto, *zto; int *squared; /* output */ double *d; { void D3cross1dist(), D3cross2dist(); if(*squared == 0) { D3cross1dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, d); } else { D3cross2dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, d); } } void D3cross1dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *zfrom, *xto, *yto, *zto; /* output */ double *d; { int i, j, nf, nt; double *dptr; double xj, yj, zj, dx, dy, dz; nf = *nfrom; nt = *nto; dptr = d; for (j=0; j < nt; j++) { xj = xto[j]; yj = yto[j]; zj = zto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; dz = zj - zfrom[i]; *dptr = sqrt( dx * dx + dy * dy + dz * dz ); } } } /* squared distances */ void D3cross2dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *zfrom, *xto, *yto, *zto; /* output */ double *d; { int i, j, nf, nt; double *dptr; double xj, yj, zj, dx, dy, dz; nf = *nfrom; nt = *nto; dptr = d; for (j=0; j < nt; j++) { xj = xto[j]; yj = yto[j]; zj = zto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; dz = zj - zfrom[i]; *dptr = dx * dx + dy * dy + dz * dz; } } } /* distances with periodic correction */ void D3pairPdist(n, x, y, z, xwidth, yheight, zdepth, squared, d) /* inputs */ int *n; double *x, *y, *z, *xwidth, *yheight, *zdepth; int *squared; /* output */ double *d; { void D3pairP1dist(), D3pairP2dist(); if(*squared == 0) { D3pairP1dist(n, x, y, z, xwidth, yheight, zdepth, d); } else { D3pairP2dist(n, x, y, z, xwidth, yheight, zdepth, d); } } void D3pairP1dist(n, x, y, z, xwidth, yheight, zdepth, d) /* inputs */ int *n; double *x, *y, *z, *xwidth, *yheight, *zdepth; /* output */ double *d; { int i, j, npoints; double *dp; double xi, yi, zi, dx, dy, dz, dx2, dy2, dz2, dx2p, dy2p, dz2p, dist, wide, high, deep; npoints = *n; wide = *xwidth; high = *yheight; deep = *zdepth; /* set d[0,0] = 0 */ *d = 0.0; for (i=1; i < npoints; i++) { xi = x[i]; yi = y[i]; zi = z[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dz = z[j] - zi; dx2p = dx * dx; dy2p = dy * dy; dz2p = dz * dz; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); dz2 = (dz - deep) * (dz - deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); dz2 = (dz + deep) * (dz + deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; dist = sqrt( dx2p + dy2p + dz2p ); /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } /* same function without the sqrt */ void D3pairP2dist(n, x, y, z, xwidth, yheight, zdepth, d) /* inputs */ int *n; double *x, *y, *z, *xwidth, *yheight, *zdepth; /* output */ double *d; { int i, j, npoints; double *dp; double xi, yi, zi, dx, dy, dz, dx2, dy2, dz2, dx2p, dy2p, dz2p, dist, wide, high, deep; npoints = *n; wide = *xwidth; high = *yheight; deep = *zdepth; /* set d[0,0] = 0 */ *d = 0.0; for (i=1; i < npoints; i++) { xi = x[i]; yi = y[i]; zi = z[i]; /* point at the start of column i */ dp = d + i * npoints; /* set diagonal to zero */ dp[i] = 0.0; for (j=0; j < i; j++) { dx = x[j] - xi; dy = y[j] - yi; dz = z[j] - zi; dx2p = dx * dx; dy2p = dy * dy; dz2p = dz * dz; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); dz2 = (dz - deep) * (dz - deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); dz2 = (dz + deep) * (dz + deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; dist = dx2p + dy2p + dz2p; /* upper triangle */ *dp = dist; ++dp; /* lower triangle */ d[ j * npoints + i] = dist; } } } void D3crossPdist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, xwidth, yheight, zdepth, squared, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *zfrom, *xto, *yto, *zto, *xwidth, *yheight, *zdepth; int *squared; /* output */ double *d; { void D3crossP1dist(), D3crossP2dist(); if(*squared == 0) { D3crossP1dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, xwidth, yheight, zdepth, d); } else { D3crossP2dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, xwidth, yheight, zdepth, d); } } void D3crossP1dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, xwidth, yheight, zdepth, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *zfrom, *xto, *yto, *zto, *xwidth, *yheight, *zdepth; /* output */ double *d; { int i, j, nf, nt; double *dptr; double xj, yj, zj, dx, dy, dz, dx2, dy2, dz2, dx2p, dy2p, dz2p, wide, high, deep; nf = *nfrom; nt = *nto; wide = *xwidth; high = *yheight; deep = *zdepth; dptr = d; for (j=0; j < nt; j++) { xj = xto[j]; yj = yto[j]; zj = zto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; dz = zj - zfrom[i]; dx2p = dx * dx; dy2p = dy * dy; dz2p = dz * dz; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); dz2 = (dz - deep) * (dz - deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); dz2 = (dy + deep) * (dz + deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; *dptr = sqrt( dx2p + dy2p + dz2p ); } } } void D3crossP2dist(nfrom, xfrom, yfrom, zfrom, nto, xto, yto, zto, xwidth, yheight, zdepth, d) /* inputs */ int *nto, *nfrom; double *xfrom, *yfrom, *zfrom, *xto, *yto, *zto, *xwidth, *yheight, *zdepth; /* output */ double *d; { int i, j, nf, nt; double *dptr; double xj, yj, zj, dx, dy, dz, dx2, dy2, dz2, dx2p, dy2p, dz2p, wide, high, deep; nf = *nfrom; nt = *nto; wide = *xwidth; high = *yheight; deep = *zdepth; dptr = d; for (j=0; j < nt; j++) { xj = xto[j]; yj = yto[j]; zj = zto[j]; for(i = 0; i < nf; i++, dptr++) { dx = xj - xfrom[i]; dy = yj - yfrom[i]; dz = zj - zfrom[i]; dx2p = dx * dx; dy2p = dy * dy; dz2p = dz * dz; dx2 = (dx - wide) * (dx - wide); dy2 = (dy - high) * (dy - high); dz2 = (dz - deep) * (dz - deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; dx2 = (dx + wide) * (dx + wide); dy2 = (dy + high) * (dy + high); dz2 = (dy + deep) * (dz + deep); if(dx2 < dx2p) dx2p = dx2; if(dy2 < dy2p) dy2p = dy2; if(dz2 < dz2p) dz2p = dz2; *dptr = dx2p + dy2p + dz2p; } } } /* matchxyz Find matches between two lists of points */ void matchxyz(na, xa, ya, za, nb, xb, yb, zb, match) /* inputs */ int *na, *nb; double *xa, *ya, *za, *xb, *yb, *zb; /* output */ int *match; { int i, j, Na, Nb; double xai, yai, zai; Na = *na; Nb = *nb; for (i=1; i < Na; i++) { xai = xa[i]; yai = ya[i]; zai = za[i]; match[i] = 0; for (j=0; j < Nb; j++) if(xai == xb[j] && yai == yb[j] && zai == zb[i]) { match[i] = j; break; } } } spatstat/src/Knone.c0000644000176000001440000000154712333041761014161 0ustar ripleyusers#include #include #include /* Knone.c Efficient computation of uncorrected estimates of K for large datasets KnoneI() Estimates K function, returns integer numerator KnoneD() Estimates K function, returns double precision numerator Kwnone() Estimates Kinhom, returns double precision numerator Functions require (x,y) data to be sorted in ascending order of x and expect r values to be equally spaced and starting at zero $Revision: 1.2 $ $Date: 2013/05/27 02:09:10 $ */ #undef WEIGHTED #define FNAME KnoneI #define OUTTYPE int #include "Knone.h" #undef FNAME #undef OUTTYPE #define FNAME KnoneD #define OUTTYPE double #include "Knone.h" #undef FNAME #undef OUTTYPE #define FNAME Kwnone #define WEIGHTED #define OUTTYPE double #include "Knone.h" spatstat/src/PerfectDGS.h0000644000176000001440000001231512333041761015035 0ustar ripleyusers // ........................... Diggle-Gates-Stibbard process ................ // $Revision: 1.3 $ $Date: 2012/03/10 11:22:50 $ #ifndef PI #define PI 3.14159265358979 #endif class DgsProcess : public PointProcess { public: double beta, rho, rhosquared; DgsProcess(double xmin, double xmax, double ymin, double ymax, double b, double r); ~DgsProcess(){} void NewEvent(double *x, double *y, char *InWindow); void GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP); double Interaction(double dsquared); }; DgsProcess::DgsProcess(double xmin, double xmax, double ymin, double ymax, double b, double r) : PointProcess(xmin, xmax, ymin, ymax){ beta = b; rho = r; rhosquared = rho * rho; InteractionRange = rho; TotalBirthRate = beta*(xmax-xmin)*(ymax-ymin); } double DgsProcess::Interaction(double dsquared) { double rtn, dist, t; rtn = 1; if(dsquared < rhosquared) { dist = sqrt(dsquared); t = sin((PI/2) * dist/rho); rtn = t * t; } return(rtn); } void DgsProcess::NewEvent(double *x, double *y, char *InWindow) { double Xdim, Ydim; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; *x = slumptal()*Xdim+Xmin; *y = slumptal()*Ydim+Ymin; *InWindow = 1; } void DgsProcess::GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP) { int i; double xtemp, ytemp, L, Xdim, Ydim; struct Point *TempPoint; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; L = beta*Xdim*Ydim; *GeneratedPoints = poisson(L); *LivingPoints = *GeneratedPoints; for (i=1; i<=*GeneratedPoints ; i++){ //Rprintf("Generating DgsProcess Poisson 3\n"); //scanf("%f",&f1); xtemp = slumptal()*Xdim+Xmin; ytemp = slumptal()*Ydim+Ymin; // //Rprintf("Generating DgsProcess Poisson 3.2\n"); TempPoint = ALLOCATE(struct Point); // TempPoint->X = xtemp; TempPoint->Y = ytemp; TempPoint->No = i; TempPoint->R = slumptal(); //Rprintf("Generating DgsProcess Poisson 3.6\n"); TempPoint->next = headPoint->next; headPoint->next = TempPoint; *NoP = *NoP + 1; } } // ........................... Interface to R .......................... extern "C" { SEXP PerfectDGS(SEXP beta, SEXP rho, SEXP xrange, SEXP yrange) { // input parameters double Beta, Rho, Xmin, Xmax, Ymin, Ymax; double *Xrange, *Yrange; // internal int xcells, ycells; long int StartTime, EndTime; // output int noutmax; SEXP xout, yout, nout, out; double *xx, *yy; int *nn; // protect arguments from garbage collector PROTECT(beta = AS_NUMERIC(beta)); PROTECT(rho = AS_NUMERIC(rho)); PROTECT(xrange = AS_NUMERIC(xrange)); PROTECT(yrange = AS_NUMERIC(yrange)); // that's 4 protected objects // extract arguments Beta = *(NUMERIC_POINTER(beta)); Rho = *(NUMERIC_POINTER(rho)); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ Rho); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ Rho); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ Rho); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ Rho); if(ycells > 9) ycells = 9; if(ycells < 1) ycells = 1; #ifdef DBGS Rprintf("xcells %d ycells %d\n",xcells,ycells); Rprintf("Initialising\n"); #endif // Initialise Diggle-Gates-Stibbard point process DgsProcess ExampleProcess(Xmin,Xmax,Ymin,Ymax,Beta,Rho); // Initialise point pattern Point2Pattern ExamplePattern(Xmin,Xmax,Ymin,Ymax, xcells, ycells); // parameters: min x, max x, min y, max y, "cells" in x and y direction // used for speeding up neighbour counting, 9 is max here #ifdef DBGS Rprintf("Initialisation complete\n"); #endif // Synchronise random number generator GetRNGstate(); // Initialise perfect sampler Sampler PerfectSampler(&ExampleProcess); // Perform perfect sampling PerfectSampler.Sim(&ExamplePattern, &StartTime, &EndTime); // Synchronise random number generator PutRNGstate(); // Get upper estimate of number of points noutmax = ExamplePattern.UpperCount() + 1; // Allocate space for output PROTECT(xout = NEW_NUMERIC(noutmax)); PROTECT(yout = NEW_NUMERIC(noutmax)); PROTECT(nout = NEW_INTEGER(1)); xx = NUMERIC_POINTER(xout); yy = NUMERIC_POINTER(yout); nn = INTEGER_POINTER(nout); // copy data into output storage ExamplePattern.Return(xx, yy, nn, noutmax); // pack up into output list PROTECT(out = NEW_LIST(3)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, nout); // return UNPROTECT(8); // 4 arguments plus xout, yout, nout, out return(out); } } spatstat/src/sphefrac.c0000755000176000001440000000622612333041761014704 0ustar ripleyusers#include #include #include "geom3.h" /* $Revision: 1.1 $ $Date: 2009/11/04 23:54:15 $ Routine for calculating surface area of sphere intersected with box # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # MODIFIED BY: Adrian Baddeley, Perth 2009, 2013 # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ #ifdef DEBUG #define DBG(X,Y) Rprintf("%s: %f\n", (X), (Y)); #else #define DBG(X,Y) #endif static double pi = 3.141592653589793; /* Factor of 4 * pi * r * r IS ALREADY TAKEN OUT */ double sphesfrac(point, box, r) Point *point; Box *box; double r; { double sum, p[4], q[4]; double a1(), a2(), a3(); int i, j; p[1] = point->x - box->x0; p[2] = point->y - box->y0; p[3] = point->z - box->z0; q[1] = box->x1 - point->x; q[2] = box->y1 - point->y; q[3] = box->z1 - point->z; sum = 0; for(i = 1; i <= 3; i++) { sum += a1(p[i],r) + a1(q[i],r); #ifdef DEBUG Rprintf("i = %d, a1 = %f, a1 = %f\n", i, a1(p[i],r), a1(q[i],r)); #endif } DBG("Past a1", sum) for(i = 1; i < 3; i++) for(j = i+1; j <= 3; j++) { sum -= a2(p[i], p[j], r) + a2(p[i], q[j], r) + a2(q[i], p[j], r) + a2(q[i], q[j], r); #ifdef DEBUG Rprintf("i = %d, j = %d, sum = %f\n", i, j, sum); #endif } DBG("Past a2", sum) sum += a3(p[1], p[2], p[3], r) + a3(p[1], p[2], q[3], r); DBG("sum", sum) sum += a3(p[1], q[2], p[3], r) + a3(p[1], q[2], q[3], r); DBG("sum", sum) sum += a3(q[1], p[2], p[3], r) + a3(q[1], p[2], q[3], r); DBG("sum", sum) sum += a3(q[1], q[2], p[3], r) + a3(q[1], q[2], q[3], r); DBG("Past a3", sum) return(1 - sum); } double a1(t, r) double t, r; { /* This is the function A1 divided by 4 pi r^2 */ if(t >= r) return(0.0); return((1 - t/r) * 0.5); } double a2(t1, t2, r) double t1, t2, r; { double c2(); /* This is A2 divided by 4 pi r^2 because c2 is C divided by pi */ return(c2( t1 / r, t2 / r) / 2.0); } double a3(t1, t2, t3, r) double t1, t2, t3, r; { double c3(); /* This is A3 divided by 4 pi r^2 because c3 is C divided by pi */ return(c3(t1 / r, t2 / r, t3 / r) / 4.0); } double c2(a, b) double a, b; { double z, z2; double c2(); /* This is the function C(a, b, 0) divided by pi - assumes a, b > 0 */ if( ( z2 = 1.0 - a * a - b * b) < 0.0 ) return(0.0); z = sqrt(z2); return((atan2(z, a * b) - a * atan2(z, b) - b * atan2(z, a)) / pi); } double c3(a, b, c) double a, b, c; { double za, zb, zc, sum; /* This is C(a,b,c) divided by pi. Arguments assumed > 0 */ if(a * a + b * b + c * c >= 1.0) return(0.0); za = sqrt(1 - b * b - c * c); zb = sqrt(1 - a * a - c * c); zc = sqrt(1 - a * a - b * b); sum = atan2(zb, a * c) + atan2(za, b * c) + atan2(zc, a * b) - a * atan2(zb, c) + a * atan2(b, zc) - b * atan2(za, c) + b * atan2(a, zc) - c * atan2(zb, a) + c * atan2(b, za); return(sum / pi - 1); } spatstat/src/connect.c0000755000176000001440000000627112333041761014542 0ustar ripleyusers/* connect.c Connected component transforms cocoImage: connected component transform of a discrete binary image (8-connected topology) cocoGraph: connected component labels for a discrete graph specified by a list of edges $Revision: 1.8 $ $Date: 2013/05/27 02:09:10 $ */ #include #include #include #include #include "raster.h" void shape_raster(); #include "yesno.h" /* workhorse function for cocoImage */ void comcommer(im) Raster *im; /* raster must have been dimensioned by shape_raster() */ /* Pixel values assumed to be 0 in background, and distinct nonzero integers in foreground */ { int j,k; int rmin, rmax, cmin, cmax; int label, curlabel, minlabel; int nchanged; /* image boundaries */ rmin = im->rmin; rmax = im->rmax; cmin = im->cmin; cmax = im->cmax; #define ENTRY(ROW, COL) Entry(*im, ROW, COL, int) #define UPDATE(ROW,COL,BEST,NEW) \ NEW = ENTRY(ROW, COL); \ if(NEW != 0 && NEW < BEST) \ BEST = NEW nchanged = 1; while(nchanged >0) { nchanged = 0; R_CheckUserInterrupt(); for(j = rmin; j <= rmax; j++) { for(k = cmin; k <= cmax; k++) { curlabel = ENTRY(j, k); if(curlabel != 0) { minlabel = curlabel; UPDATE(j-1, k-1, minlabel, label); UPDATE(j-1, k, minlabel, label); UPDATE(j-1, k+1, minlabel, label); UPDATE(j, k-1, minlabel, label); UPDATE(j, k, minlabel, label); UPDATE(j, k+1, minlabel, label); UPDATE(j+1, k-1, minlabel, label); UPDATE(j+1, k, minlabel, label); UPDATE(j+1, k+1, minlabel, label); if(minlabel < curlabel) { ENTRY(j, k) = minlabel; nchanged++; } } } } } } void cocoImage(mat, nr, nc) int *mat; /* input: binary image */ int *nr, *nc; /* raster dimensions EXCLUDING margin of 1 on each side */ { Raster im; shape_raster( &im, (void *) mat, (double) 1, (double) 1, (double) *nc, (double) *nr, *nr+2, *nc+2, 1, 1); comcommer(&im); } void cocoGraph(nv, ne, ie, je, label, status) /* inputs */ int *nv; /* number of graph vertices */ int *ne; /* number of edges */ int *ie, *je; /* vectors of indices of ends of each edge */ /* output */ int *label; /* vector of component labels for each vertex */ /* Component label is lowest serial number of any vertex in the connected component */ int *status; /* 0 if OK, 1 if overflow */ { int Nv, Ne, i, j, k, niter, labi, labj, changed; Nv = *nv; Ne = *ne; /* initialise labels */ for(k = 0; k < Nv; k++) label[k] = k; for(niter = 0; niter < Nv; niter++) { R_CheckUserInterrupt(); changed = NO; for(k = 0; k < Ne; k++) { i = ie[k]; j = je[k]; labi = label[i]; labj = label[j]; if(labi < labj) { label[j] = labi; changed = YES; } else if(labj < labi) { label[i] = labj; changed = YES; } } if(!changed) { /* algorithm has converged */ *status = 0; return; } } /* error exit */ *status = 1; return; } spatstat/src/mhv1.h0000644000176000001440000000040112333041761013753 0ustar ripleyusers/* mhv1.h marked or unmarked simulation */ #undef MH_MARKED if(marked) { /* marked process */ #define MH_MARKED YES #include "mhv2.h" #undef MH_MARKED } else { /* unmarked process */ #define MH_MARKED NO #include "mhv2.h" #undef MH_MARKED } spatstat/src/knndist.h0000644000176000001440000000744312333041761014567 0ustar ripleyusers/* knndist.h Code template for C functions supporting knndist and knnwhich THE FOLLOWING CODE ASSUMES THAT y IS SORTED IN ASCENDING ORDER This code is #included multiple times in knndistance.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour Either or both DIST and WHICH may be defined. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2012 Licence: GPL >= 2 $Revision: 1.3 $ $Date: 2013/05/27 02:09:10 $ */ void FNAME(n, kmax, x, y, #ifdef DIST nnd, #endif #ifdef WHICH nnwhich, #endif huge) /* inputs */ int *n, *kmax; double *x, *y, *huge; /* output matrices (npoints * kmax) in ROW MAJOR order */ #ifdef DIST double *nnd; #endif #ifdef WHICH int *nnwhich; #endif { int npoints, maxchunk, nk, nk1, i, k, k1, left, right, unsorted; double d2, d2minK, xi, yi, dx, dy, dy2, hu, hu2, tmp; double *d2min; #ifdef WHICH int *which; int itmp; #endif hu = *huge; hu2 = hu * hu; npoints = *n; nk = *kmax; nk1 = nk - 1; /* create space to store the nearest neighbour distances and indices for the current point */ d2min = (double *) R_alloc((size_t) nk, sizeof(double)); #ifdef WHICH which = (int *) R_alloc((size_t) nk, sizeof(int)); #endif /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints) maxchunk = npoints; for(; i < maxchunk; i++) { #ifdef SPATSTAT_DEBUG Rprintf("\ni=%d\n", i); #endif /* initialise nn distances and indices */ d2minK = hu2; for(k = 0; k < nk; k++) { d2min[k] = hu2; #ifdef WHICH which[k] = -1; #endif } xi = x[i]; yi = y[i]; /* search backward */ for(left = i - 1; left >= 0; --left) { #ifdef SPATSTAT_DEBUG Rprintf("L"); #endif dy = yi - y[left]; dy2 = dy * dy; if(dy2 > d2minK) break; dx = x[left] - xi; d2 = dx * dx + dy2; if (d2 < d2minK) { /* overwrite last entry */ d2min[nk1] = d2; #ifdef WHICH which[nk1] = left; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[nk1]; } } /* search forward */ for(right = i + 1; right < npoints; ++right) { #ifdef SPATSTAT_DEBUG Rprintf("R"); #endif dy = y[right] - yi; dy2 = dy * dy; if(dy2 > d2minK) break; dx = x[right] - xi; d2 = dx * dx + dy2; if (d2 < d2minK) { /* overwrite last entry */ d2min[nk1] = d2; #ifdef WHICH which[nk1] = right; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[nk1]; } } /* search finished for point i */ #ifdef SPATSTAT_DEBUG Rprintf("\n"); #endif /* copy nn distances for point i to output matrix in ROW MAJOR order */ for(k = 0; k < nk; k++) { #ifdef DIST nnd[nk * i + k] = sqrt(d2min[k]); #endif #ifdef WHICH nnwhich[nk * i + k] = which[k] + 1; /* R indexing */ #endif } /* end of i loop */ } } } spatstat/src/mhsnoopdef.h0000644000176000001440000000104112333041761015243 0ustar ripleyusers/* mhsnoopdef.h Define structure 'Snoop' containing visual debugger parameters and state $Revision: 1.2 $ $Date: 2013/05/27 02:09:10 $ */ #ifndef R_INTERNALS_H_ #include #endif typedef struct Snoop { int active; /* true or false */ int nextstop; /* jump to iteration number 'nextstop' */ int nexttype; /* jump to the next proposal of type 'nexttype' */ SEXP env; /* environment for exchanging data with R */ SEXP expr; /* callback expression for visual debugger */ } Snoop; #define NO_TYPE -1 spatstat/src/inxyp.c0000755000176000001440000000301412333041761014250 0ustar ripleyusers/* inxyp.c Point-in-polygon test NB: relative to other versions, 'score' is multiplied by 2 (and is an integer) $Revision: 1.7 $ $Date: 2013/09/18 04:20:13 $ */ #include #include "chunkloop.h" void inxyp(x,y,xp,yp,npts,nedges,score,onbndry) /* inputs */ double *x, *y; /* points to be tested */ int *npts; double *xp, *yp; /* polygon vertices */ int *nedges; /* outputs */ int *score; int *onbndry; { int i, j, Npts, Nedges, Ne1, contrib, maxchunk; double x0, y0, x1, y1, dx, dy, xj, yj, xcrit, ycrit; Npts = *npts; Nedges = *nedges; Ne1 = Nedges - 1; x0 = xp[Ne1]; y0 = yp[Ne1]; OUTERCHUNKLOOP(i, Nedges, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Nedges, maxchunk, 16384) { /* visit edge (x0,y0) -> (x1,y1) */ x1 = xp[i]; y1 = yp[i]; dx = x1 - x0; dy = y1 - y0; for(j = 0; j < Npts; j++) { xj = x[j]; yj = y[j]; xcrit = (xj - x0) * (xj - x1); if(xcrit <= 0) { if(xcrit == 0) { contrib = 1; } else { contrib = 2; } ycrit = yj * dx - xj * dy + x0 * dy - y0 * dx; if(dx < 0) { if(ycrit >= 0) score[j] += contrib; onbndry[j] = onbndry[j] | (ycrit == 0); } else if(dx > 0) { if(ycrit < 0) score[j] -= contrib; onbndry[j] = onbndry[j] | (ycrit == 0); } else { if(xj == x0) ycrit = (yj - y0) * (yj - y1); onbndry[j] = onbndry[j] | (ycrit <= 0); } } } /* next edge */ x0 = x1; y0 = y1; } } } spatstat/src/Ediggatsti.c0000755000176000001440000000325212333041761015171 0ustar ripleyusers#include #include #include #include "chunkloop.h" #include "constants.h" /* Ediggatsti.c $Revision: 1.2 $ $Date: 2012/03/28 05:55:38 $ C implementation of 'eval' for DiggleGatesStibbard interaction Assumes point patterns are sorted in increasing order of x coordinate */ void Ediggatsti(nnsource, xsource, ysource, idsource, nntarget, xtarget, ytarget, idtarget, rrho, values) /* inputs */ int *nnsource, *nntarget; double *xsource, *ysource, *xtarget, *ytarget; int *idsource, *idtarget; double *rrho; /* output */ double *values; { int nsource, ntarget, maxchunk, j, i, ileft, idsourcej; double xsourcej, ysourcej, xleft, dx, dy, dx2, d2; double rho, rho2, coef, product; nsource = *nnsource; ntarget = *nntarget; rho = *rrho; rho2 = rho * rho; coef = M_PI_2/rho; if(nsource == 0 || ntarget == 0) return; ileft = 0; OUTERCHUNKLOOP(j, nsource, maxchunk, 65536) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nsource, maxchunk, 65536) { product = 1; xsourcej = xsource[j]; ysourcej = ysource[j]; idsourcej = idsource[j]; /* adjust starting position */ xleft = xsourcej - rho; while((xtarget[ileft] < xleft) && (ileft+1 < ntarget)) ++ileft; /* process from ileft until dx > rho */ for(i=ileft; i < ntarget; i++) { dx = xtarget[i] - xsourcej; dx2 = dx * dx; if(dx2 > rho2) break; if(idtarget[i] != idsourcej) { dy = ytarget[i] - ysourcej; d2 = dx2 + dy * dy; if(d2 <= rho2) product *= sin(sqrt(d2) * coef); } } values[j] = log(product * product); } } } spatstat/src/lookup.c0000755000176000001440000001170612333041761014421 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" /* Conditional intensity function for a general pairwise interaction process with the pairwise interaction function given by a ``lookup table'', passed through the par argument. */ /* For debugging code, insert the line: #define DEBUG 1 */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Lookup { int nlook; int equisp; double delta; double rmax; double r2max; double *h; /* values of pair interaction */ double *r; /* r values if not equally spaced */ double *r2; /* r^2 values if not equally spaced */ double *period; int per; } Lookup; /* initialiser function */ Cdata *lookupinit(state, model, algo) State state; Model model; Algor algo; { int i, nlook; double ri; Lookup *lookup; lookup = (Lookup *) R_alloc(1, sizeof(Lookup)); /* Interpret model parameters*/ lookup->nlook = nlook = model.ipar[0]; lookup->equisp = (model.ipar[1] > 0); lookup->delta = model.ipar[2]; lookup->rmax = model.ipar[3]; lookup->r2max = pow(lookup->rmax, 2); /* periodic boundary conditions? */ lookup->period = model.period; lookup->per = (model.period[0] > 0.0); /* If the r-values are equispaced only the h vector is included in ``par'' after ``rmax''; the entries of h then consist of h[0] = par[5], h[1] = par[6], ..., h[k-1] = par[4+k], ..., h[nlook-1] = par[4+nlook]. If the r-values are NOT equispaced then the individual r values are needed and these are included as r[0] = par[5+nlook], r[1] = par[6+nlook], ..., r[k-1] = par[4+nlook+k], ..., r[nlook-1] = par[4+2*nlook]. */ lookup->h = (double *) R_alloc((size_t) nlook, sizeof(double)); for(i = 0; i < nlook; i++) lookup->h[i] = model.ipar[4+i]; if(!(lookup->equisp)) { lookup->r = (double *) R_alloc((size_t) nlook, sizeof(double)); lookup->r2 = (double *) R_alloc((size_t) nlook, sizeof(double)); for(i = 0; i < nlook; i++) { ri = lookup->r[i] = model.ipar[4+nlook+i]; lookup->r2[i] = ri * ri; } } #ifdef DEBUG Rprintf("Exiting lookupinit: nlook=%d, equisp=%d\n", nlook, lookup->equisp); #endif return((Cdata *) lookup); } /* conditional intensity evaluator */ double lookupcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, nlook, k, kk, ix, ixp1, j; double *x, *y; double u, v; double r2max, d2, d, delta, cifval, ux, vy; Lookup *lookup; lookup = (Lookup *) cdata; r2max = lookup->r2max; delta = lookup->delta; nlook = lookup->nlook; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; cifval = 1.0; if(npts == 0) return(cifval); ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(lookup->equisp) { /* equispaced r values */ if(lookup->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { d = sqrt(dist2(u,v,x[j],y[j],lookup->period)); k = floor(d/delta); if(k < nlook) { if(k < 0) k = 0; cifval *= lookup->h[k]; } } } if(ixp1 < npts) { for(j=ixp1; jperiod)); k = floor(d/delta); if(k < nlook) { if(k < 0) k = 0; cifval *= lookup->h[k]; } } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { d = hypot(u - x[j], v-y[j]); k = floor(d/delta); if(k < nlook) { if(k < 0) k = 0; cifval *= lookup->h[k]; } } } if(ixp1 < npts) { for(j=ixp1; jh[k]; } } } } } else { /* non-equispaced r values */ if(lookup->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { d2 = dist2(u,v,x[j],y[j],lookup->period); if(d2 < r2max) { for(kk = 0; kk < nlook && lookup->r2[kk] <= d2; kk++) ; k = (kk == 0) ? 0 : kk-1; cifval *= lookup->h[k]; } } } if(ixp1 < npts) { for(j=ixp1; jperiod); if(d2 < r2max) { for(kk = 0; kk < nlook && lookup->r2[kk] <= d2; kk++) ; k = (kk == 0) ? 0 : kk-1; cifval *= lookup->h[k]; } } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { ux = u - x[j]; vy = v - y[j]; d2 = ux * ux + vy * vy; if(d2 < r2max) { for(kk = 0; kk < nlook && lookup->r2[kk] <= d2; kk++) ; k = (kk == 0) ? 0 : kk-1; cifval *= lookup->h[k]; } } } if(ixp1 < npts) { for(j=ixp1; jr2[kk] <= d2; kk++) ; k = (kk == 0) ? 0 : kk-1; cifval *= lookup->h[k]; } } } } } return cifval; } Cifns LookupCifns = { &lookupinit, &lookupcif, (updafunptr) NULL, NO}; spatstat/src/straushm.c0000755000176000001440000001550112333041761014753 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* for debugging code, include #define DEBUG 1 */ /* Conditional intensity computation for Multitype Strauss hardcore process */ /* NOTE: types (marks) are numbered from 0 to ntypes-1 */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct MultiStraussHard { int ntypes; double *gamma; /* gamma[i,j] = gamma[i+ntypes*j] for i,j = 0... ntypes-1 */ double *rad; /* rad[i,j] = rad[j+ntypes*i] for i,j = 0... ntypes-1 */ double *hc; /* hc[i,j] = hc[j+ntypes*i] for i,j = 0... ntypes-1 */ double *rad2; /* squared radii */ double *hc2; /* squared radii */ double *rad2hc2; /* r^2 - h^2 */ double range2; /* square of interaction range */ double *loggamma; /* logs of gamma[i,j] */ double *period; int *hard; /* hard[i,j] = 1 if gamma[i,j] ~~ 0 */ int *kount; /* space for kounting pairs of each type */ int per; } MultiStraussHard; /* initialiser function */ Cdata *straushminit(state, model, algo) State state; Model model; Algor algo; { int i, j, ntypes, n2, hard; double g, r, h, r2, h2, logg, range2; MultiStraussHard *multistrausshard; multistrausshard = (MultiStraussHard *) R_alloc(1, sizeof(MultiStraussHard)); multistrausshard->ntypes = ntypes = model.ntypes; n2 = ntypes * ntypes; #ifdef DEBUG Rprintf("initialising space for %d types\n", ntypes); #endif /* Allocate space for parameters */ multistrausshard->gamma = (double *) R_alloc((size_t) n2, sizeof(double)); multistrausshard->rad = (double *) R_alloc((size_t) n2, sizeof(double)); multistrausshard->hc = (double *) R_alloc((size_t) n2, sizeof(double)); /* Allocate space for transformed parameters */ multistrausshard->rad2 = (double *) R_alloc((size_t) n2, sizeof(double)); multistrausshard->hc2 = (double *) R_alloc((size_t) n2, sizeof(double)); multistrausshard->rad2hc2 = (double *) R_alloc((size_t) n2, sizeof(double)); multistrausshard->loggamma = (double *) R_alloc((size_t) n2, sizeof(double)); multistrausshard->hard = (int *) R_alloc((size_t) n2, sizeof(int)); /* Allocate scratch space for counts of each pair of types */ multistrausshard->kount = (int *) R_alloc((size_t) n2, sizeof(int)); /* Copy and process model parameters*/ /* ipar will contain n^2 values of gamma, then n^2 values of r, then n^2 values of h */ range2 = 0.0; for(i = 0; i < ntypes; i++) { for(j = 0; j < ntypes; j++) { g = model.ipar[ i + j*ntypes]; r = model.ipar[ n2 + i + j*ntypes]; h = model.ipar[2*n2 + i + j*ntypes]; r2 = r * r; h2 = h * h; hard = (g < DOUBLE_EPS); logg = (hard) ? 0 : log(g); MAT(multistrausshard->gamma, i, j, ntypes) = g; MAT(multistrausshard->rad, i, j, ntypes) = r; MAT(multistrausshard->hc, i, j, ntypes) = h; MAT(multistrausshard->rad2, i, j, ntypes) = r2; MAT(multistrausshard->hc2, i, j, ntypes) = h2; MAT(multistrausshard->rad2hc2, i, j, ntypes) = r2-h2; MAT(multistrausshard->hard, i, j, ntypes) = hard; MAT(multistrausshard->loggamma, i, j, ntypes) = logg; if(r2 > range2) range2 = r2; } } multistrausshard->range2 = range2; /* periodic boundary conditions? */ multistrausshard->period = model.period; multistrausshard->per = (model.period[0] > 0.0); #ifdef DEBUG Rprintf("end initialiser\n"); #endif return((Cdata *) multistrausshard); } /* conditional intensity evaluator */ double straushmcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ntypes, kount, ix, ixp1, j, mrk, mrkj, m1, m2; int *marks; double *x, *y; double u, v, lg; double d2, cifval; double range2; double *period; MultiStraussHard *multistrausshard; DECLARE_CLOSE_D2_VARS; multistrausshard = (MultiStraussHard *) cdata; range2 = multistrausshard->range2; period = multistrausshard->period; u = prop.u; v = prop.v; mrk = prop.mrk; ix = prop.ix; x = state.x; y = state.y; marks = state.marks; npts = state.npts; #ifdef DEBUG Rprintf("computing cif: u=%lf, v=%lf, mrk=%d\n", u, v, mrk); #endif cifval = 1.0; if(npts == 0) return(cifval); ntypes = multistrausshard->ntypes; #ifdef DEBUG Rprintf("initialising pair counts\n"); #endif /* initialise pair counts */ for(m1 = 0; m1 < ntypes; m1++) for(m2 = 0; m2 < ntypes; m2++) MAT(multistrausshard->kount, m1, m2, ntypes) = 0; /* compile pair counts */ #ifdef DEBUG Rprintf("compiling pair counts\n"); #endif ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(multistrausshard->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],period,range2,d2)) { mrkj = marks[j]; if(d2 < MAT(multistrausshard->rad2, mrk, mrkj, ntypes)) { if(d2 < MAT(multistrausshard->hc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } MAT(multistrausshard->kount, mrk, mrkj, ntypes)++; } } } } if(ixp1 < npts) { for(j=ixp1; jrad2, mrk, mrkj, ntypes)) { if(d2 < MAT(multistrausshard->hc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } MAT(multistrausshard->kount, mrk, mrkj, ntypes)++; } } } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u, v, x[j], y[j], range2, d2)) { mrkj = marks[j]; if(d2 < MAT(multistrausshard->rad2, mrk, mrkj, ntypes)) { if(d2 < MAT(multistrausshard->hc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } MAT(multistrausshard->kount, mrk, mrkj, ntypes)++; } } } } if(ixp1 < npts) { for(j=ixp1; jrad2, mrk, mrkj, ntypes)) { if(d2 < MAT(multistrausshard->hc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } MAT(multistrausshard->kount, mrk, mrkj, ntypes)++; } } } } } #ifdef DEBUG Rprintf("multiplying cif factors\n"); #endif /* multiply cif value by pair potential */ for(m1 = 0; m1 < ntypes; m1++) { for(m2 = 0; m2 < ntypes; m2++) { kount = MAT(multistrausshard->kount, m1, m2, ntypes); if(MAT(multistrausshard->hard, m1, m2, ntypes)) { if(kount > 0) { cifval = 0.0; return(cifval); } } else { lg = MAT(multistrausshard->loggamma, m1, m2, ntypes); cifval *= exp(lg * kount); } } } #ifdef DEBUG Rprintf("returning positive cif\n"); #endif return cifval; } Cifns MultiStraussHardCifns = { &straushminit, &straushmcif, (updafunptr) NULL, YES}; spatstat/src/idw.c0000755000176000001440000000703412333041761013672 0ustar ripleyusers/* idw.c Inverse-distance weighted smoothing $Revision: 1.8 $ $Date: 2013/05/27 02:09:10 $ */ #include #include #include "chunkloop.h" #define MAT(X,I,J,NROW) (X)[(J) + (NROW) * (I)] /* inverse-distance smoothing from data points onto pixel grid */ void Cidw(x, y, v, n, xstart, xstep, nx, ystart, ystep, ny, power, num, den, rat) double *x, *y, *v; /* data points and values */ int *n; double *xstart, *xstep, *ystart, *ystep; /* pixel grid */ int *nx, *ny; double *power; /* exponent for IDW */ double *num, *den, *rat; /* output arrays - assumed initialised 0 */ { int N, i, Nx, Ny, ix, iy; double xg, yg, x0, dx, y0, dy, pon2, d2, w; N = *n; Nx = *nx; Ny = *ny; x0 = *xstart; y0 = *ystart; dx = *xstep; dy = *ystep; pon2 = (*power)/2.0; if(pon2 == 1.0) { /* slightly faster code when power=2 */ for(ix = 0, xg=x0; ix < Nx; ix++, xg+=dx) { if(ix % 256 == 0) R_CheckUserInterrupt(); for(iy = 0, yg=y0; iy < Ny; iy++, yg+=dy) { /* loop over data points, accumulating numerator and denominator */ for(i = 0; i < N; i++) { d2 = (xg - x[i]) * (xg - x[i]) + (yg - y[i]) * (yg - y[i]); w = 1.0/d2; MAT(num, ix, iy, Ny) += w * v[i]; MAT(den, ix, iy, Ny) += w; } /* compute ratio */ MAT(rat, ix, iy, Ny) = MAT(num, ix, iy, Ny)/MAT(den, ix, iy, Ny); } } } else { /* general case */ for(ix = 0, xg=x0; ix < Nx; ix++, xg+=dx) { if(ix % 256 == 0) R_CheckUserInterrupt(); for(iy = 0, yg=y0; iy < Ny; iy++, yg+=dy) { /* loop over data points, accumulating numerator and denominator */ for(i = 0; i < N; i++) { d2 = (xg - x[i]) * (xg - x[i]) + (yg - y[i]) * (yg - y[i]); w = 1.0/pow(d2, pon2); MAT(num, ix, iy, Ny) += w * v[i]; MAT(den, ix, iy, Ny) += w; } /* compute ratio */ MAT(rat, ix, iy, Ny) = MAT(num, ix, iy, Ny)/MAT(den, ix, iy, Ny); } } } } /* Leave-one-out IDW at data points only */ void idwloo(x, y, v, n, power, num, den, rat) double *x, *y, *v; /* data points and values */ int *n; double *power; /* exponent for IDW */ double *num, *den, *rat; /* output vectors - assumed initialised 0 */ { int N, i, j, maxchunk; double xi, yi, d2, w, pon2; N = *n; pon2 = (*power)/2.0; if(pon2 == 1.0) { /* slightly faster code when power=2 */ OUTERCHUNKLOOP(i, N, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, N, maxchunk, 16384) { xi = x[i]; yi = y[i]; if(i > 0) { for(j = 0; j < i; j++) { d2 = (xi - x[j]) * (xi - x[j]) + (yi - y[j]) * (yi - y[j]); w = 1.0/d2; num[i] += w * v[j]; den[i] += w; } } if(i < N-1) { for(j = i+1; j < N; j++) { d2 = (xi - x[j]) * (xi - x[j]) + (yi - y[j]) * (yi - y[j]); w = 1.0/d2; num[i] += w * v[j]; den[i] += w; } } /* compute ratio */ rat[i] = num[i]/den[i]; } } } else { /* general case */ OUTERCHUNKLOOP(i, N, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, N, maxchunk, 16384) { xi = x[i]; yi = y[i]; if(i > 0) { for(j = 0; j < i; j++) { d2 = (xi - x[j]) * (xi - x[j]) + (yi - y[j]) * (yi - y[j]); w = 1.0/pow(d2, pon2); num[i] += w * v[j]; den[i] += w; } } if(i < N-1) { for(j = i+1; j < N; j++) { d2 = (xi - x[j]) * (xi - x[j]) + (yi - y[j]) * (yi - y[j]); w = 1.0/pow(d2, pon2); num[i] += w * v[j]; den[i] += w; } } /* compute ratio */ rat[i] = num[i]/den[i]; } } } } spatstat/src/knngrid.h0000644000176000001440000001272412333041761014547 0ustar ripleyusers #if (1 == 0) /* knngrid.h Code template for C functions k-nearest neighbours (k > 1) of each grid point THE FOLLOWING CODE ASSUMES THAT POINT PATTERN (xp, yp) IS SORTED IN ASCENDING ORDER OF x COORDINATE This code is #included multiple times in knngrid.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour Either or both DIST and WHICH may be defined. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2013 Licence: GPL >= 2 $Revision: 1.5 $ $Date: 2013/10/22 01:33:12 $ */ #endif #undef PRINTALOT void FNAME(nx, x0, xstep, ny, y0, ystep, /* pixel grid dimensions */ np, xp, yp, /* data points */ kmax, nnd, nnwhich, huge) /* inputs */ int *nx, *ny, *np; double *x0, *xstep, *y0, *ystep, *huge; double *xp, *yp; int *kmax; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { int Nxcol, Nyrow; int i, j, ijpos; int Npoints, Nk, Nk1; int mleft, mright, mwhich, lastmwhich, unsorted, k, k1; double X0, Y0, Xstep, Ystep; double d2, d2minK, xj, yi, dx, dy, dx2, hu, hu2, tmp; double *d2min; #ifdef WHICH int *which; int itmp; #endif Nxcol = *nx; Nyrow = *ny; Npoints = *np; Nk = *kmax; hu = *huge; X0 = *x0; Y0 = *y0; Xstep = *xstep; Ystep = *ystep; Nk1 = Nk - 1; hu2 = hu * hu; if(Npoints == 0) return; lastmwhich = 0; /* create space to store the nearest neighbour distances and indices for the current grid point */ d2min = (double *) R_alloc((size_t) Nk, sizeof(double)); #ifdef WHICH which = (int *) R_alloc((size_t) Nk, sizeof(int)); #endif /* loop over pixels */ for(j = 0, xj = X0; j < Nxcol; j++, xj += Xstep) { R_CheckUserInterrupt(); #ifdef PRINTALOT Rprintf("j=%d, xj=%lf\n", j, xj); #endif for(i = 0, yi = Y0; i < Nyrow; i++, yi += Ystep) { #ifdef PRINTALOT Rprintf("\ti=%d, yi = %lf\n", i, yi); #endif /* initialise nn distances and indices */ d2minK = hu2; for(k = 0; k < Nk; k++) { d2min[k] = hu2; #ifdef WHICH which[k] = -1; #endif } if(lastmwhich < Npoints) { /* search forward from previous nearest neighbour */ for(mright = lastmwhich; mright < Npoints; ++mright) { dx = xp[mright] - xj; dx2 = dx * dx; #ifdef PRINTALOT Rprintf("\t\t%d\n", mright); #endif if(dx2 > d2minK) /* note that dx2 >= d2minK could break too early */ break; dy = yp[mright] - yi; d2 = dy * dy + dx2; if (d2 < d2minK) { #ifdef PRINTALOT Rprintf("\t\t\tNeighbour: d2=%lf\n", d2); #endif /* overwrite last entry in list of neighbours */ d2min[Nk1] = d2; mwhich = mright; #ifdef WHICH which[Nk1] = mright; #endif /* bubble sort */ unsorted = YES; for(k = Nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[Nk1]; #ifdef PRINTALOT Rprintf("\t\t\tUpdated d2minK=%lf\n", d2minK); for(k = 0; k < Nk; k++) Rprintf("\t\t\t\td2min[%d]=%lf\n", k, d2min[k]); #ifdef WHICH for(k = 0; k < Nk; k++) Rprintf("\t\t\t\twhich[%d]=%d\n", k, which[k]); #endif #endif } } /* end forward search */ } if(lastmwhich > 0) { /* search backward from previous nearest neighbour */ for(mleft = lastmwhich - 1; mleft >= 0; --mleft) { dx = xj - xp[mleft]; dx2 = dx * dx; #ifdef PRINTALOT Rprintf("\t\t%d\n", mleft); #endif if(dx2 > d2minK) /* note that dx2 >= d2minK could break too early */ break; dy = yp[mleft] - yi; d2 = dy * dy + dx2; if (d2 < d2minK) { #ifdef PRINTALOT Rprintf("\t\t\tNeighbour: d2=%lf\n", d2); #endif /* overwrite last entry in list of neighbours */ mwhich = mleft; d2min[Nk1] = d2; #ifdef WHICH which[Nk1] = mleft; #endif /* bubble sort */ unsorted = YES; for(k = Nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[Nk1]; #ifdef PRINTALOT Rprintf("\t\t\tUpdated d2minK=%lf\n", d2minK); for(k = 0; k < Nk; k++) Rprintf("\t\t\t\td2min[%d]=%lf\n", k, d2min[k]); #ifdef WHICH for(k = 0; k < Nk; k++) Rprintf("\t\t\t\twhich[%d]=%d\n", k, which[k]); #endif #endif } } /* end backward search */ } /* remember index of most recently-encountered neighbour */ lastmwhich = mwhich; #ifdef PRINTALOT Rprintf("\t\tlastmwhich=%d\n", lastmwhich); #endif /* copy nn distances for grid point (i, j) to output array nnd[ , i, j] */ ijpos = Nk * (i + j * Nyrow); for(k = 0; k < Nk; k++) { #ifdef DIST nnd[ijpos + k] = sqrt(d2min[k]); #endif #ifdef WHICH nnwhich[ijpos + k] = which[k] + 1; /* R indexing */ #endif } /* end of loop over points i */ } } } spatstat/src/strauss.c0000755000176000001440000000474312333041761014617 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Strauss process */ /* Format for storage of parameters and precomputed/auxiliary data */ typedef struct Strauss { double gamma; double r; double loggamma; double r2; double *period; int hard; int per; } Strauss; /* initialiser function */ Cdata *straussinit(state, model, algo) State state; Model model; Algor algo; { /* create storage for model parameters */ Strauss *strauss; strauss = (Strauss *) R_alloc(1, sizeof(Strauss)); /* Interpret model parameters*/ strauss->gamma = model.ipar[0]; strauss->r = model.ipar[1]; /* No longer passed as r^2 */ strauss->r2 = strauss->r * strauss->r; strauss->period = model.period; #ifdef MHDEBUG Rprintf("Initialising Strauss gamma=%lf, r=%lf\n", strauss->gamma, strauss->r); #endif /* is the model numerically equivalent to hard core ? */ strauss->hard = (strauss->gamma < DOUBLE_EPS); strauss->loggamma = (strauss->hard) ? 0 : log(strauss->gamma); /* periodic boundary conditions? */ strauss->per = (model.period[0] > 0.0); return((Cdata *) strauss); } /* conditional intensity evaluator */ double strausscif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, kount, ix, ixp1, j; double *x, *y; double u, v; double r2, cifval; Strauss *strauss; DECLARE_CLOSE_VARS; strauss = (Strauss *) cdata; r2 = strauss->r2; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; if(npts == 0) return((double) 1.0); kount = 0; ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(strauss->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC(u,v,x[j],y[j],strauss->period, r2)) ++kount; } } if(ixp1 < npts) { for(j=ixp1; jperiod, r2)) ++kount; } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE(u,v,x[j],y[j], r2)) ++kount; } } if(ixp1 < npts) { for(j=ixp1; jhard) { if(kount > 0) cifval = 0.0; else cifval = 1.0; } else cifval = exp((strauss->loggamma) * kount); return cifval; } Cifns StraussCifns = { &straussinit, &strausscif, (updafunptr) NULL, NO}; spatstat/src/call3d.c0000755000176000001440000002474712333041761014263 0ustar ripleyusers/* $Revision: 1.5 $ $Date: 2010/10/24 10:57:02 $ R interface Pass data between R and internally-defined data structures # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # MODIFIED BY: Adrian Baddeley, Perth 2009 # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ #include #include "geom3.h" #include "functable.h" #undef DEBUG #ifdef DEBUG #define DEBUGMESSAGE(S) Rprintf(S); #else #define DEBUGMESSAGE(S) #endif void g3one(Point *p, int n, Box *b, Ftable *g); void g3three(Point *p, int n, Box *b, Ftable *g); void g3cen(Point *p, int n, Box *b, H4table *count); void k3trans(Point *p, int n, Box *b, Ftable *k); void k3isot(Point *p, int n, Box *b, Ftable *k); void pcf3trans(Point *p, int n, Box *b, Ftable *pcf, double delta); void pcf3isot(Point *p, int n, Box *b, Ftable *pcf, double delta); void phatminus(Point *p, int n, Box *b, double vside, Itable *count); void phatnaive(Point *p, int n, Box *b, double vside, Itable *count); void p3hat4(Point *p, int n, Box *b, double vside, H4table *count); /* ALLOCATION OF SPACE FOR STRUCTURES/ARRAYS We have defined an alloc() and free() function for each type. However, the free() functions currently do nothing, because we use R_alloc to allocate transient space, which is freed automatically by R. */ Ftable * allocFtable(n) /* allocate function table of size n */ int n; { Ftable *x; x = (Ftable *) R_alloc(1, sizeof(Ftable)); x->n = n; x->f = (double *) R_alloc(n, sizeof(double)); x->num = (double *) R_alloc(n, sizeof(double)); x->denom = (double *) R_alloc(n, sizeof(double)); return(x); } void freeFtable(x) Ftable *x; { } Itable * allocItable(n) int n; { Itable *x; x = (Itable *) R_alloc(1, sizeof(Itable)); x->n = n; x->num = (int *) R_alloc(n, sizeof(int)); x->denom = (int *) R_alloc(n, sizeof(int)); return(x); } void freeItable(x) Itable *x; { } H4table * allocH4table(n) int n; { H4table *x; x = (H4table *) R_alloc(1, sizeof(H4table)); x->n = n; x->obs = (int *) R_alloc(n, sizeof(int)); x->nco = (int *) R_alloc(n, sizeof(int)); x->cen = (int *) R_alloc(n, sizeof(int)); x->ncc = (int *) R_alloc(n, sizeof(int)); return(x); } void freeH4table(x) H4table *x; { } Box * allocBox() /* I know this is ridiculous but it's consistent. */ { Box *b; b = (Box *) R_alloc(1, sizeof(Box)); return(b); } void freeBox(x) Box *x; { } Point * allocParray(n) /* allocate array of n Points */ int n; { Point *p; p = (Point *) R_alloc(n, sizeof(Point)); return(p); } void freeParray(x) Point *x; { } /* CREATE AND INITIALISE DATA STORAGE */ Ftable * MakeFtable(t0, t1, n) double *t0, *t1; int *n; { Ftable *tab; int i, nn; nn = *n; tab = allocFtable(nn); tab->t0 = *t0; tab->t1 = *t1; for(i = 0; i < nn; i++) { tab->f[i] = 0.0; tab->num[i] = 0; tab->denom[i] = 0; } return(tab); } Itable * MakeItable(t0, t1, n) double *t0, *t1; int *n; { Itable *tab; int i, nn; nn = *n; tab = allocItable(nn); tab->t0 = *t0; tab->t1 = *t1; for(i = 0; i < nn; i++) { tab->num[i] = 0; tab->denom[i] = 0; } return(tab); } H4table * MakeH4table(t0, t1, n) double *t0, *t1; int *n; { H4table *tab; int i, nn; nn = *n; tab = allocH4table(nn); tab->t0 = *t0; tab->t1 = *t1; for(i = 0; i < nn; i++) { tab->obs[i] = 0; tab->nco[i] = 0; tab->cen[i] = 0; tab->ncc[i] = 0; } tab->upperobs = 0; tab->uppercen = 0; return(tab); } /* CONVERSION OF DATA TYPES R -> internal including allocation of internal data types as needed */ Point * RtoPointarray(x,y,z,n) double *x, *y, *z; int *n; { int i, nn; Point *p; nn = *n; p = allocParray(nn); for(i = 0; i < nn; i++) { p[i].x = x[i]; p[i].y = y[i]; p[i].z = z[i]; } return(p); } Box * RtoBox(x0, x1, y0, y1, z0, z1) double *x0, *x1, *y0, *y1, *z0, *z1; { Box *b; b = allocBox(); b->x0 = *x0; b->x1 = *x1; b->y0 = *y0; b->y1 = *y1; b->z0 = *z0; b->z1 = *z1; return(b); } /* CONVERSION OF DATA TYPES internal -> R Note: it can generally be assumed that the R arguments are already allocated vectors of correct length, so we do not allocate them. */ void FtabletoR(tab, t0, t1, n, f, num, denom) /* internal */ Ftable *tab; /* R representation */ double *t0, *t1; int *n; double *f, *num, *denom; { int i; *t0 = tab->t0; *t1 = tab->t1; *n = tab->n; for(i = 0; i < tab->n; i++) { f[i] = tab->f[i]; num[i] = tab->num[i]; denom[i] = tab->denom[i]; } freeFtable(tab); } void ItabletoR(tab, t0, t1, m, num, denom) /* internal */ Itable *tab; /* R representation */ double *t0, *t1; int *m; int *num, *denom; { int i; *t0 = tab->t0; *t1 = tab->t1; *m = tab->n; for(i = 0; i < tab->n; i++) { num[i] = tab->num[i]; denom[i] = tab->denom[i]; } freeItable(tab); } void H4tabletoR(tab, t0, t1, m, obs, nco, cen, ncc, upperobs, uppercen) /* internal */ H4table *tab; /* R representation */ double *t0, *t1; int *m; int *obs, *nco, *cen, *ncc; int *upperobs, *uppercen; { int i; *t0 = tab->t0; *t1 = tab->t1; *m = tab->n; *upperobs = tab->upperobs; *uppercen = tab->uppercen; for(i = 0; i < tab->n; i++) { obs[i] = tab->obs[i]; nco[i] = tab->nco[i]; cen[i] = tab->cen[i]; ncc[i] = tab->ncc[i]; } freeH4table(tab); } /* R CALLING INTERFACE These routines are called from R by > .C("routine-name", ....) */ void RcallK3(x,y,z, n, x0, x1, y0, y1, z0, z1, t0, t1, m, f, num, denom, method) double *x, *y, *z; /* points */ int *n; double *x0, *x1, /* box */ *y0, *y1, *z0, *z1; double *t0, *t1; /* Ftable */ int *m; double *f, *num, *denom; int *method; { Point *p; Box *b; Ftable *tab; p = RtoPointarray(x, y, z, n); b = RtoBox(x0, x1, y0, y1, z0, z1); tab = MakeFtable(t0, t1, m); switch((int) *method) { case 0: k3trans(p, (int) *n, b, tab); break; case 1: k3isot(p, (int) *n, b, tab); break; default: Rprintf("Method %d not implemented: defaults to 0\n", *method); k3trans(p, (int) *n, b, tab); break; } FtabletoR(tab, t0, t1, m, f, num, denom); } void RcallG3(x,y,z, n, x0, x1, y0, y1, z0, z1, t0, t1, m, f, num, denom, method) double *x, *y, *z; /* points */ int *n; double *x0, *x1, /* box */ *y0, *y1, *z0, *z1; double *t0, *t1; /* Ftable */ int *m; double *f, *num, *denom; int *method; { Point *p; Box *b; Ftable *tab; p = RtoPointarray(x, y, z, n); b = RtoBox(x0, x1, y0, y1, z0, z1); tab = MakeFtable(t0, t1, m); switch(*method) { case 1: g3one(p, (int) *n, b, tab); break; case 3: g3three(p, (int) *n, b, tab); break; default: Rprintf("Method %d not implemented: defaults to 3\n", *method); g3three(p, (int) *n, b, tab); } FtabletoR(tab, t0, t1, m, f, num, denom); } void RcallG3cen(x,y,z, n, x0, x1, y0, y1, z0, z1, t0, t1, m, obs, nco, cen, ncc, upperobs, uppercen) double *x, *y, *z; /* points */ int *n; double *x0, *x1, /* box */ *y0, *y1, *z0, *z1; double *t0, *t1; int *m; /* H4table */ int *obs, *nco, *cen, *ncc; int *upperobs, *uppercen; { Point *p; Box *b; H4table *count; DEBUGMESSAGE("Inside RcallG3cen\n") p = RtoPointarray(x, y, z, n); b = RtoBox(x0, x1, y0, y1, z0, z1); count = MakeH4table(t0, t1, m); g3cen(p, (int) *n, b, count); H4tabletoR(count, t0, t1, m, obs, nco, cen, ncc, upperobs, uppercen); DEBUGMESSAGE("Leaving RcallG3cen\n") } void RcallF3(x,y,z, n, x0, x1, y0, y1, z0, z1, vside, t0, t1, m, num, denom, method) double *x, *y, *z; /* points */ int *n; double *x0, *x1, /* box */ *y0, *y1, *z0, *z1; double *vside; double *t0, *t1; int *m; /* Itable */ int *num, *denom; int *method; { Point *p; Box *b; Itable *count; DEBUGMESSAGE("Inside Rcall_f3\n") p = RtoPointarray(x, y, z, n); b = RtoBox(x0, x1, y0, y1, z0, z1); count = MakeItable(t0, t1, m); switch((int) *method) { case 0: phatnaive(p, (int) *n, b, *vside, count); break; case 1: phatminus(p, (int) *n, b, *vside, count); break; default: Rprintf("Method %d not recognised: defaults to 1\n", *method); phatminus(p, (int) *n, b, *vside, count); } ItabletoR(count, t0, t1, m, num, denom); DEBUGMESSAGE("Leaving Rcall_f3\n") } void RcallF3cen(x,y,z, n, x0, x1, y0, y1, z0, z1, vside, t0, t1, m, obs, nco, cen, ncc, upperobs, uppercen) double *x, *y, *z; /* points */ int *n; double *x0, *x1, /* box */ *y0, *y1, *z0, *z1; double *vside; double *t0, *t1; int *m; /* H4table */ int *obs, *nco, *cen, *ncc; int *upperobs, *uppercen; { Point *p; Box *b; H4table *count; DEBUGMESSAGE("Inside Rcallf3cen\n") p = RtoPointarray(x, y, z, n); b = RtoBox(x0, x1, y0, y1, z0, z1); count = MakeH4table(t0, t1, m); p3hat4(p, (int) *n, b, *vside, count); H4tabletoR(count, t0, t1, m, obs, nco, cen, ncc, upperobs, uppercen); DEBUGMESSAGE("Leaving Rcallf3cen\n") } void Rcallpcf3(x,y,z, n, x0, x1, y0, y1, z0, z1, t0, t1, m, f, num, denom, method, delta) double *x, *y, *z; /* points */ int *n; double *x0, *x1, /* box */ *y0, *y1, *z0, *z1; double *t0, *t1; /* Ftable */ int *m; double *f, *num, *denom; int *method; double *delta; /* Epanechnikov kernel halfwidth */ { Point *p; Box *b; Ftable *tab; p = RtoPointarray(x, y, z, n); b = RtoBox(x0, x1, y0, y1, z0, z1); tab = MakeFtable(t0, t1, m); switch((int) *method) { case 0: pcf3trans(p, (int) *n, b, tab, (double) *delta); break; case 1: pcf3isot(p, (int) *n, b, tab, (double) *delta); break; default: Rprintf("Method %d not implemented: defaults to 0\n", *method); pcf3trans(p, (int) *n, b, tab, (double) *delta); break; } FtabletoR(tab, t0, t1, m, f, num, denom); } spatstat/src/veegraf.c0000644000176000001440000000677112333041761014532 0ustar ripleyusers/* veegraf.c $Revision: 1.2 $ $Date: 2013/05/21 08:11:27 $ Given the edges of a graph, determine all "Vees" i.e. triples (i, j, k) where i ~ j and i ~ k. */ #include #include #include #include "chunkloop.h" #undef DEBUGVEE SEXP graphVees(SEXP nv, /* number of vertices */ SEXP iedge, /* vectors of indices of ends of each edge */ SEXP jedge) /* all arguments are integer */ /* Edges should NOT be repeated symmetrically. Indices need not be sorted. */ { int Nv, Ne; int *ie, *je; /* edges */ int *it, *jt, *kt; /* vectors of indices of triples */ int Nt, Ntmax; /* number of triples */ int Nj; int *jj; /* scratch storage */ int i, j, k, m, mj, mk, Nmore, maxchunk; /* output */ SEXP iTout, jTout, kTout, out; int *ito, *jto, *kto; /* =================== Protect R objects from garbage collector ======= */ PROTECT(nv = AS_INTEGER(nv)); PROTECT(iedge = AS_INTEGER(iedge)); PROTECT(jedge = AS_INTEGER(jedge)); /* That's 3 protected objects */ /* numbers of vertices and edges */ Nv = *(INTEGER_POINTER(nv)); Ne = LENGTH(iedge); /* input arrays */ ie = INTEGER_POINTER(iedge); je = INTEGER_POINTER(jedge); /* initialise storage (with a guess at max size) */ Ntmax = 3 * Ne; it = (int *) R_alloc(Ntmax, sizeof(int)); jt = (int *) R_alloc(Ntmax, sizeof(int)); kt = (int *) R_alloc(Ntmax, sizeof(int)); Nt = 0; /* initialise scratch storage */ jj = (int *) R_alloc(Ne, sizeof(int)); XOUTERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { R_CheckUserInterrupt(); XINNERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { #ifdef DEBUGVEE Rprintf("i=%d ---------- \n", i); #endif /* Find Vee triples with apex 'i' */ /* First, find all vertices j connected to i */ Nj = 0; for(m = 0; m < Ne; m++) { if(ie[m] == i) { jj[Nj] = je[m]; Nj++; } else if(je[m] == i) { jj[Nj] = ie[m]; Nj++; } } /* save triples (i,j,k) */ #ifdef DEBUGVEE Rprintf("Nj = %d\n", Nj); #endif if(Nj > 1) { #ifdef DEBUGVEE Rprintf("i=%d\njj=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - allocate more space */ Nmore = 2 * Ntmax; #ifdef DEBUGVEE Rprintf("Doubling space from %d to %d\n", Ntmax, Nmore); #endif it = (int *) S_realloc((char *) it, Nmore, Ntmax, sizeof(int)); jt = (int *) S_realloc((char *) jt, Nmore, Ntmax, sizeof(int)); kt = (int *) S_realloc((char *) kt, Nmore, Ntmax, sizeof(int)); Ntmax = Nmore; } it[Nt] = i; jt[Nt] = j; kt[Nt] = k; Nt++; } } } } } /* allocate space for output */ PROTECT(iTout = NEW_INTEGER(Nt)); PROTECT(jTout = NEW_INTEGER(Nt)); PROTECT(kTout = NEW_INTEGER(Nt)); PROTECT(out = NEW_LIST(3)); /* that's 3+4=7 protected objects */ ito = INTEGER_POINTER(iTout); jto = INTEGER_POINTER(jTout); kto = INTEGER_POINTER(kTout); /* copy triplet indices to output vectors */ for(m = 0; m < Nt; m++) { ito[m] = it[m]; jto[m] = jt[m]; kto[m] = kt[m]; } /* insert output vectors in output list */ SET_VECTOR_ELT(out, 0, iTout); SET_VECTOR_ELT(out, 1, jTout); SET_VECTOR_ELT(out, 2, kTout); UNPROTECT(7); return(out); } spatstat/src/raster.h0000755000176000001440000000474712333041761014424 0ustar ripleyusers/* raster.h Definition of raster structures & operations requires (for floor()) $Revision: 1.3 $ $Date: 2004/11/15 19:25:11 $ */ typedef struct Raster{ /* array of data */ char *data; /* coerced to appropriate type */ int nrow; /* dimensions of entire array */ int ncol; int length; int rmin; /* position of valid subrectangle */ int rmax; int cmin; int cmax; /* definition of mapping into continuous space */ double x0; /* position of entry (rmin,cmin) */ double y0; double x1; /* position of entry (rmax,cmax) */ double y1; double xstep; /* x increment for each column step */ double ystep; /* y increment for each row step */ /* xstep = (x1 - x0)/(cmax - cmin) = (x1 - x0)/(number of valid columns - 1) CAN BE POSITIVE OR NEGATIVE */ /* image of valid subrectangle */ double xmin; /* = min{x0,x1} */ double xmax; double ymin; double ymax; } Raster; /* how to clear the data */ #define Clear(ARRAY,TYPE,VALUE) \ { unsigned int i; TYPE *p; \ for(i = 0, p = (TYPE *) (ARRAY).data; i < (ARRAY).length; i++, p++) \ *p = VALUE; } /* how to index a rectangular array stored sequentially in row-major order */ #define Entry(ARRAY,ROW,COL,TYPE) \ ((TYPE *)((ARRAY).data))[COL + (ROW) * ((ARRAY).ncol)] /* test for indices inside subrectangle */ #define Inside(ARRAY,ROW,COL) \ ( (ROW >= (ARRAY).rmin) && (ROW <= (ARRAY).rmax) && \ (COL >= (ARRAY).cmin) && (COL <= (ARRAY).cmax)) /* how to compute the position in R^2 corresponding to a raster entry */ #define Xpos(ARRAY,COL) \ ((ARRAY).x0 + (ARRAY).xstep * (COL - (ARRAY).cmin)) #define Ypos(ARRAY,ROW) \ ((ARRAY).y0 + (ARRAY).ystep * (ROW - (ARRAY).rmin)) #define Distance(X,Y,XX,YY) sqrt((X - XX)* (X - XX) + (Y - YY) * (Y - YY)) #define DistanceTo(X,Y,ARRAY,ROW,COL)\ Distance(X,Y,Xpos(ARRAY,COL),Ypos(ARRAY,ROW)) #define DistanceSquared(X,Y,XX,YY) ((X - XX)* (X - XX) + (Y - YY) * (Y - YY)) #define DistanceToSquared(X,Y,ARRAY,ROW,COL)\ DistanceSquared(X,Y,Xpos(ARRAY,COL),Ypos(ARRAY,ROW)) /* how to map a point (x,y) in R^2 to a raster entry */ /* (x,y) is guaranteed to lie in the rectangle bounded by the images of the entries (r,c), (r+1,c), (r,c+1), (r+1,c+1) where r = RowIndex(..) and c = ColIndex(..). */ #define RowIndex(ARRAY,Y) \ ((ARRAY).rmin + (int) floor(((Y) - (ARRAY).y0)/(ARRAY).ystep)) #define ColIndex(ARRAY,X) \ ((ARRAY).cmin + (int) floor(((X) - (ARRAY).x0)/(ARRAY).xstep)) spatstat/src/hardcore.c0000755000176000001440000000410712333041761014674 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Hard core process */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Hardcore { double h; /* hard core distance */ double h2; double *period; int per; } Hardcore; /* initialiser function */ Cdata *hardcoreinit(state, model, algo) State state; Model model; Algor algo; { Hardcore *hardcore; double h; hardcore = (Hardcore *) R_alloc(1, sizeof(Hardcore)); /* Interpret model parameters*/ hardcore->h = h = model.ipar[0]; hardcore->h2 = h * h; hardcore->period = model.period; /* periodic boundary conditions? */ hardcore->per = (model.period[0] > 0.0); return((Cdata *) hardcore); } /* conditional intensity evaluator */ double hardcorecif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *x, *y; double u, v; double h2, a; Hardcore *hardcore; hardcore = (Hardcore *) cdata; h2 = hardcore->h2; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; if(npts == 0) return((double) 1.0); ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(hardcore->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(dist2thresh(u,v,x[j],y[j],hardcore->period, h2)) return((double) 0.0); } } if(ixp1 < npts) { for(j=ixp1; jperiod, h2)) return((double) 0.0); } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { a = h2 - pow(u - x[j], 2); if(a > 0) { a -= pow(v - y[j], 2); if(a > 0) return((double) 0.0); } } } if(ixp1 < npts) { for(j=ixp1; j 0) { a -= pow(v - y[j], 2); if(a > 0) return((double) 0.0); } } } } return ((double) 1.0); } Cifns HardcoreCifns = { &hardcoreinit, &hardcorecif, (updafunptr) NULL, NO}; spatstat/src/denspt.c0000755000176000001440000002044412333041761014404 0ustar ripleyusers#include #include #include "chunkloop.h" #include "pairloop.h" #include "constants.h" /* denspt.c Calculation of density estimate at data points $Revision: 1.13 $ $Date: 2014/04/02 07:30:05 $ Assumes point pattern is sorted in increasing order of x coordinate *denspt* Density estimate at points *smoopt* Smoothed mark values at points */ #define TWOPI M_2PI double sqrt(), exp(); #define STD_DECLARATIONS \ int n, i, j, maxchunk; \ double xi, yi, rmax, r2max, dx, dy, dx2, d2 #define STD_INITIALISE \ n = *nxy; \ rmax = *rmaxi; \ r2max = rmax * rmax /* ----------------- density estimation -------------------- */ void denspt(nxy, x, y, rmaxi, sig, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *rmaxi; /* maximum distance at which points contribute */ double *sig; /* Gaussian sd */ /* output */ double *result; /* vector of computed density values */ { STD_DECLARATIONS; double resulti, coef; double sigma, twosig2; STD_INITIALISE; sigma = *sig; twosig2 = 2.0 * sigma * sigma; coef = 1.0/(TWOPI * sigma * sigma); if(n == 0) return; PAIRLOOP( { resulti = 0.0; }, { resulti += exp(-d2/twosig2); } , { result[i] = coef * resulti; }) } void wtdenspt(nxy, x, y, rmaxi, sig, weight, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *rmaxi; /* maximum distance */ double *sig; /* Gaussian sd */ double *weight; /* vector of weights */ /* output */ double *result; /* vector of weighted density values */ { STD_DECLARATIONS; double resulti, coef; double sigma, twosig2; STD_INITIALISE; sigma = *sig; twosig2 = 2.0 * sigma * sigma; coef = 1.0/(TWOPI * sigma * sigma); if(n == 0) return; PAIRLOOP( { resulti = 0.0; }, { resulti += weight[j] * exp(-d2/twosig2); }, { result[i] = coef * resulti; } ) } /* ------------- anisotropic versions -------------------- */ void adenspt(nxy, x, y, rmaxi, detsigma, sinv, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *rmaxi; /* maximum distance at which points contribute */ double *detsigma; /* determinant of variance matrix */ double *sinv; /* inverse variance matrix (2x2, flattened) */ /* output */ double *result; /* vector of density values */ { STD_DECLARATIONS; double resulti, coef; double detsig, s11, s12, s21, s22; STD_INITIALISE; detsig = *detsigma; coef = 1.0/(TWOPI * sqrt(detsig)); s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; PAIRLOOP( { resulti = 0.0; }, { resulti += exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); }, { result[i] = coef * resulti; }) } void awtdenspt(nxy, x, y, rmaxi, detsigma, sinv, weight, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *rmaxi; /* maximum distance at which points contribute */ double *detsigma; /* determinant of variance matrix */ double *sinv; /* inverse variance matrix (2x2, flattened) */ double *weight; /* vector of weights */ /* output */ double *result; /* vector of weighted density values */ { STD_DECLARATIONS; double resulti, coef; double detsig, s11, s12, s21, s22; STD_INITIALISE; detsig = *detsigma; coef = 1.0/(TWOPI * sqrt(detsig)); s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n == 0) return; PAIRLOOP( { resulti = 0.0; }, { resulti += weight[j] * \ exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); }, { result[i] = coef * resulti; }) } /* --------------- smoothing --------------------------- */ void smoopt(nxy, x, y, v, self, rmaxi, sig, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *v; /* vector of mark values to be smoothed */ int *self; /* 0 if leave-one-out */ double *rmaxi; /* maximum distance at which points contribute */ double *sig; /* Gaussian sd */ /* output */ double *result; /* vector of computed smoothed values */ { STD_DECLARATIONS; int countself; double sigma, twosig2; double numer, denom, wij; STD_INITIALISE; sigma = *sig; countself = *self; twosig2 = 2.0 * sigma * sigma; if(n == 0) return; PAIRLOOP({ numer = denom = 0.0; }, { \ wij = exp(-d2/twosig2); \ denom += wij; \ numer += wij * v[j]; \ }, { \ if(countself != 0) { \ numer += 1; \ denom += v[i]; \ } \ result[i] = numer/denom; \ }) } void wtsmoopt(nxy, x, y, v, self, rmaxi, sig, weight, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *v; /* vector of mark values to be smoothed */ int *self; /* 0 if leave-one-out */ double *rmaxi; /* maximum distance */ double *sig; /* Gaussian sd */ double *weight; /* vector of weights */ /* output */ double *result; /* vector of computed smoothed values */ { STD_DECLARATIONS; int countself; double sigma, twosig2; double numer, denom, wij; STD_INITIALISE; sigma = *sig; countself = *self; twosig2 = 2.0 * sigma * sigma; if(n == 0) return; PAIRLOOP({ numer = denom = 0.0; }, { \ wij = weight[j] * exp(-d2/twosig2); \ denom += wij; \ numer += wij * v[j]; \ }, { \ if(countself != 0) { \ numer += weight[i]; \ denom += weight[i] * v[i]; \ } \ result[i] = numer/denom; \ }) } /* ------------- anisotropic versions -------------------- */ void asmoopt(nxy, x, y, v, self, rmaxi, sinv, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *v; /* vector of mark values to be smoothed */ int *self; /* 0 if leave-one-out */ double *rmaxi; /* maximum distance at which points contribute */ double *sinv; /* inverse variance matrix (2x2, flattened) */ /* output */ double *result; /* vector of smoothed values */ { STD_DECLARATIONS; int countself; double s11, s12, s21, s22; double numer, denom, wij; STD_INITIALISE; countself = *self; s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n == 0) return; PAIRLOOP({ numer = denom = 0.0; }, { \ wij = exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); \ denom += wij; \ numer += wij * v[j]; \ }, { \ if(countself != 0) { \ numer += 1; \ denom += v[i]; \ } \ result[i] = numer/denom; \ }) } void awtsmoopt(nxy, x, y, v, self, rmaxi, sinv, weight, result) /* inputs */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ double *v; /* vector of mark values to be smoothed */ int *self; /* 0 if leave-one-out */ double *rmaxi; /* maximum distance at which points contribute */ double *sinv; /* inverse variance matrix (2x2, flattened) */ double *weight; /* vector of weights */ /* output */ double *result; /* vector of smoothed values */ { STD_DECLARATIONS; int countself; double s11, s12, s21, s22; double numer, denom, wij; STD_INITIALISE; countself = *self; s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n == 0) return; PAIRLOOP({ numer = denom = 0.0; }, { \ wij = weight[j] * exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); \ denom += wij; \ numer += wij * v[j]; \ }, { \ if(countself != 0) { \ numer += weight[i]; \ denom += weight[i] * v[i]; \ } \ result[i] = numer/denom; \ }) } spatstat/src/exactPdist.c0000755000176000001440000001007612333041761015217 0ustar ripleyusers/* exactPdist.c `Pseudoexact' distance transform of a discrete binary image (the closest counterpart to `exactdist.c') $Revision: 1.12 $ $Date: 2011/05/17 12:27:20 $ */ #include #include "raster.h" void dist_to_bdry(); void shape_raster(); void ps_exact_dt(in, dist, row, col) Raster *in; /* input: binary image */ Raster *dist; /* output: exact distance to nearest point */ Raster *row; /* output: row index of closest point */ Raster *col; /* output: column index of closest point */ /* rasters must have been dimensioned by shape_raster() and must all have identical dimensions and margins */ { int j,k; double d, x, y; int r, c; double dnew; double huge; /* double bdiag; */ /* initialise */ #define UNDEFINED -1 #define Is_Defined(I) (I >= 0) #define Is_Undefined(I) (I < 0) Clear(*row,int,UNDEFINED) Clear(*col,int,UNDEFINED) huge = 2.0 * DistanceSquared(dist->xmin,dist->ymin,dist->xmax,dist->ymax); Clear(*dist,double,huge) /* if input pixel is TRUE, set distance to 0 and make pixel point to itself */ for(j = in->rmin; j <= in->rmax; j++) for(k = in->cmin; k <= in->cmax; k++) if(Entry(*in, j, k, int) != 0) { Entry(*dist, j, k, double) = 0.0; Entry(*row, j, k, int) = j; Entry(*col, j, k, int) = k; } /* how to update the distance values */ #define GETVALUES(ROW,COL) \ x = Xpos(*in, COL); \ y = Ypos(*in, ROW); \ d = Entry(*dist,ROW,COL,double); #define COMPARE(ROW,COL,RR,CC) \ r = Entry(*row,RR,CC,int); \ c = Entry(*col,RR,CC,int); \ if(Is_Defined(r) && Is_Defined(c) \ && Entry(*dist,RR,CC,double) < d) { \ dnew = DistanceSquared(x, y, Xpos(*in,c), Ypos(*in,r)); \ if(dnew < d) { \ Entry(*row,ROW,COL,int) = r; \ Entry(*col,ROW,COL,int) = c; \ Entry(*dist,ROW,COL,double) = dnew; \ d = dnew; \ } \ } /* bound on diagonal step distance squared */ /* bdiag = (in->xstep * in->xstep + in->ystep * in->ystep); */ /* forward pass */ for(j = in->rmin; j <= in->rmax; j++) for(k = in->cmin; k <= in->cmax; k++) { GETVALUES(j, k) COMPARE(j,k, j-1,k-1) COMPARE(j,k, j-1, k) COMPARE(j,k, j-1,k+1) COMPARE(j,k, j, k-1) } /* backward pass */ for(j = in->rmax; j >= in->rmin; j--) for(k = in->cmax; k >= in->cmin; k--) { GETVALUES(j, k) COMPARE(j,k, j+1,k+1) COMPARE(j,k, j+1, k) COMPARE(j,k, j+1,k-1) COMPARE(j,k, j, k+1) } /* take square roots of distances^2 */ for(j = in->rmax; j >= in->rmin; j--) for(k = in->cmax; k >= in->cmin; k--) Entry(*dist,j,k,double) = sqrt(Entry(*dist,j,k,double)); } /* R interface */ void ps_exact_dt_R(xmin, ymin, xmax, ymax, nr, nc, mr, mc, inp, distances, rows, cols, boundary) double *xmin, *ymin, *xmax, *ymax; /* x, y dimensions */ int *nr, *nc; /* raster dimensions EXCLUDING margins */ int *mr, *mc; /* margins */ int *inp; /* input: binary image */ double *distances; /* output: distance to nearest point */ int *rows; /* output: row of nearest point (start= 0) */ int *cols; /* output: column of nearest point (start = 0) */ double *boundary; /* output: distance to boundary of rectangle */ /* all images must have identical dimensions including a margin of 1 on each side */ { Raster data, dist, row, col, bdist; int mrow, mcol, nrow, ncol; mrow = *mr; mcol = *mc; /* full dimensions */ nrow = *nr + 2 * mrow; ncol = *nc + 2 * mcol; shape_raster( &data, (void *) inp, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); shape_raster( &dist, (void *) distances, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); shape_raster( &row, (void *) rows, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); shape_raster( &col, (void *) cols, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); shape_raster( &bdist, (void *) boundary, *xmin,*ymin,*xmax,*ymax, nrow, ncol, mrow, mcol); ps_exact_dt(&data, &dist, &row, &col); dist_to_bdry(&bdist); } spatstat/src/areaint.c0000755000176000001440000001624412333041761014535 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" /* Conditional intensity function for an area-interaction process: cif = eta^(1-B) where B = (uncovered area)/(pi r^2) */ #define NGRID 16 /* To explore serious bug, #define BADBUG */ #undef BADBUG /* Format for storage of parameters and precomputed/auxiliary data */ typedef struct AreaInt { /* model parameters */ double eta; double r; /* transformations of the parameters */ double r2; double range2; double logeta; int hard; /* periodic distance */ double *period; int per; /* grid counting */ double dx; double xgrid0; int *my; int kdisc; /* scratch space for saving list of neighbours */ int *neighbour; } AreaInt; /* initialiser function */ Cdata *areaintInit(state, model, algo) State state; Model model; Algor algo; { double r, dx, dy, x0; int i, my, kdisc; AreaInt *areaint; /* create storage */ areaint = (AreaInt *) R_alloc(1, sizeof(AreaInt)); /* Interpret model parameters*/ areaint->eta = model.ipar[0]; areaint->r = r = model.ipar[1]; #ifdef BADBUG Rprintf("r = %lf\n", r); #endif areaint->r2 = r * r; areaint->range2 = 4 * r * r; /* square of interaction distance */ /* is the model numerically equivalent to hard core ? */ areaint->hard = (areaint->eta == 0.0); areaint->logeta = (areaint->hard) ? log(DOUBLE_XMIN) : log(areaint->eta); #ifdef BADBUG if(areaint->hard) Rprintf("Hard core recognised\n"); #endif /* periodic boundary conditions? */ areaint->period = model.period; areaint->per = (model.period[0] > 0.0); #ifdef BADBUG if(areaint->per) { Rprintf("*** periodic boundary conditions ***\n"); Rprintf("period = %lf, %lf\n", model.period[0], model.period[1]); } #endif /* grid counting */ dx = dy = areaint->dx = (2 * r)/NGRID; #ifdef BADBUG Rprintf("areaint->dx = %lf\n", areaint->dx); #endif areaint->xgrid0 = -r + dx/2; areaint->my = (int *) R_alloc((long) NGRID, sizeof(int)); kdisc = 0; for(i = 0; i < NGRID; i++) { x0 = areaint->xgrid0 + i * dx; my = floor(sqrt(r * r - x0 * x0)/dy); my = (my < 0) ? 0 : my; areaint->my[i] = my; #ifdef BADBUG Rprintf("\tmy[%ld] = %ld\n", i, my); #endif kdisc += 2 * my + 1; } areaint->kdisc = kdisc; #ifdef BADBUG Rprintf("areaint->kdisc = %ld\n", areaint->kdisc); #endif /* allocate space for neighbour indices */ areaint->neighbour = (int *) R_alloc((long) state.npmax, sizeof(int)); return((Cdata *) areaint); } #ifdef BADBUG void fexitc(); #endif /* conditional intensity evaluator */ double areaintCif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *period, *x, *y; double u, v; double r2, dx, dy, a, range2; double xgrid, ygrid, xgrid0, covfrac, cifval; int kount, kdisc, kx, my, ky; int *neighbour; int nn, k; AreaInt *areaint; areaint = (AreaInt *) cdata; r2 = areaint->r2; range2 = areaint->range2; /* square of interaction distance */ dy = dx = areaint->dx; kdisc = areaint->kdisc; /* pointers */ period = areaint->period; neighbour = areaint->neighbour; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; if(npts == 0) return ((double) 1.0); if(!areaint->per) { /* .......... Euclidean distance .................... First identify which data points are neighbours of (u,v) */ nn = 0; ixp1 = ix + 1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(ix > 0) { for(j=0; j < ix; j++) { a = range2 - pow(u - x[j], 2); if(a > 0.) { a -= pow(v - y[j], 2); if(a > 0.) { /* point j is a neighbour of (u,v) */ neighbour[nn] = j; ++nn; } } } } if(ixp1 < npts) { for(j=ixp1; j < npts; j++) { a = range2 - pow(u - x[j], 2); if(a > 0.) { a -= pow(v - y[j], 2); if(a > 0.) { /* point j is a neighbour of (u,v) */ neighbour[nn] = j; ++nn; } } } } if(nn == 0) { /* no neighbours; no interaction */ cifval = 1.0; return cifval; } else if(areaint->hard) { /* neighbours forbidden if it's a hard core process */ cifval = 0.0; return cifval; } else { /* scan a grid of points centred at (u,v) */ kount = 0; xgrid0 = u + areaint->xgrid0; for(kx=0; kxmy[kx]; for(ky=(-my); ky<=my; ky++) { ygrid = v + ky * dy; /* Grid point (xgrid,ygrid) is inside disc of radius r centred at (u,v) Loop through all neighbouring data points to determine whether the grid point is covered by another disc */ if(nn > 0) { for(k=0; k < nn; k++) { j = neighbour[k]; a = r2 - pow(xgrid - x[j], 2); if(a > 0) { a -= pow(ygrid - y[j], 2); if(a > 0) { /* point j covers grid point */ ++kount; break; } } } } /* finished consideration of grid point (xgrid, ygrid) */ } } } } else { /* ............. periodic distance ...................... First identify which data points are neighbours of (u,v) */ nn = 0; ixp1 = ix + 1; if(ix > 0) { for(j=0; j < ix; j++) { if(dist2thresh(u,v,x[j],y[j],period,range2)) { /* point j is a neighbour of (u,v) */ neighbour[nn] = j; ++nn; } } } if(ixp1 < npts) { for(j=ixp1; jhard) { /* neighbours forbidden if it's a hard core process */ cifval = 0.0; return cifval; } else { /* scan a grid of points centred at (u,v) */ kount = 0; xgrid0 = u + areaint->xgrid0; for(kx=0; kxmy[kx]; for(ky=(-my); ky<=my; ky++) { ygrid = v + ky * dy; /* Grid point (xgrid,ygrid) is inside disc of radius r centred at (u,v) Loop through all neighbouring data points to determine whether the grid point is covered by another disc */ for(k=0; k < nn; k++) { j = neighbour[k]; if(dist2Mthresh(xgrid,ygrid,x[j],y[j],period,r2)) { /* point j covers grid point */ ++kount; break; } } /* finished considering grid point (xgrid,ygrid) */ } } } } /* `kdisc' is the number of grid points in the disc `kount' is the number of COVERED grid points in the disc */ /* Hard core case has been handled. */ /* Usual calculation: covered area fraction */ covfrac = ((double) kount)/((double) kdisc); cifval = exp(areaint->logeta * covfrac); #ifdef BADBUG if(!R_FINITE(cifval)) { Rprintf("Non-finite CIF value\n"); Rprintf("kount=%ld, kdisc=%ld, covfrac=%lf, areaint->logeta=%lf\n", kount, kdisc, covfrac, areaint->logeta); Rprintf("u=%lf, v=%lf\n", u, v); fexitc("Non-finite CIF"); } #endif return cifval; } Cifns AreaIntCifns = { &areaintInit, &areaintCif, (updafunptr) NULL, NO}; spatstat/src/Perfect.cc0000755000176000001440000005742112333041761014647 0ustar ripleyusers// Debug switch // #define DBGS #include #include #include #include #include #include #include #include // #include // FILE *out; // File i/o is deprecated in R implementation #ifdef DBGS #define CHECK(PTR,MESSAGE) if(((void *) PTR) == ((void *) NULL)) error(MESSAGE) #define CLAMP(X, LOW, HIGH, XNAME) \ if((X) > (HIGH)) { \ Rprintf("Value of %s exceeds upper limit %d\n", XNAME, HIGH); \ X = HIGH; \ } else if((X) < (LOW)) { \ Rprintf("Value of %s is below %d\n", XNAME, LOW); \ X = LOW; \ } #else #define CHECK(PTR,MESSAGE) #define CLAMP(X, LOW, HIGH, XNAME) \ if((X) > (HIGH)) X = HIGH; else if((X) < (LOW)) X = LOW; #endif // ......................................... // memory allocation // using R_alloc #define ALLOCATE(TYPE) (TYPE *) R_alloc(1, sizeof(TYPE)) #define FREE(PTR) // Alternative using Calloc and Free // #define ALLOCATE(TYPE) (TYPE *) Calloc(1, sizeof(TYPE)) // #define FREE(PTR) Free(PTR) void R_CheckUserInterrupt(void); struct Point{ long int No; float X; float Y; float R; struct Point *next; }; struct Point2{ long int No; float X; float Y; char InLower[2]; double Beta; double TempBeta; struct Point2 *next; }; struct Point3{ char Case; char XCell; char YCell; struct Point3 *next; }; // const float Pi=3.141593; double slumptal(void){ return(runif((double) 0.0, (double) 1.0)); } long int poisson(double lambda){ return((long int)rpois(lambda)); } // ........................... Point patterns .......................... class Point2Pattern { public: long int UpperLiving[2]; long int MaxXCell, MaxYCell, NoP; double XCellDim, YCellDim, Xmin, Xmax, Ymin, Ymax; struct Point2 *headCell[10][10],*dummyCell; char DirX[10], DirY[10]; Point2Pattern(double xmin, double xmax, double ymin, double ymax, long int mxc, long int myc){ long int i,j; UpperLiving[0] = 0; UpperLiving[1] = 0; Xmin = xmin; Xmax = xmax; Ymin = ymin; Ymax = ymax; DirX[1] = 1; DirY[1] = 0; DirX[2] = 1; DirY[2] = -1; DirX[3] = 0; DirY[3] = -1; DirX[4] = -1; DirY[4] = -1; DirX[5] = -1; DirY[5] = 0; DirX[6] = -1; DirY[6] = 1; DirX[7] = 0; DirY[7] = 1; DirX[8] = 1; DirY[8] = 1; NoP = 0; // dummyCell = ALLOCATE(struct Point2); // dummyCell->next = dummyCell; dummyCell->No = 0; MaxXCell = mxc; MaxYCell = myc; if(MaxXCell>9) MaxXCell = 9; if(MaxYCell>9) MaxYCell = 9; for(i=0;i<=MaxXCell;i++){ for(j=0;j<=MaxYCell;j++){ // headCell[i][j] = ALLOCATE(struct Point2); // headCell[i][j]->next=dummyCell; } } XCellDim = (Xmax-Xmin)/((double)(MaxXCell+1)); YCellDim = (Ymax-Ymin)/((double)(MaxYCell+1)); }; ~Point2Pattern(){} void Print(); void Return(double *X, double *Y, int *num, int maxnum); long int Count(); long int UpperCount(); void Empty(); void Clean(); // void DumpToFile(char FileName[100]); // void ReadFromFile(char FileName[100]); }; void Point2Pattern::Print(){ long int i,j,k; k = 0; struct Point2 *TempCell; for(i=0;i<=MaxXCell;i++){ for(j=0;j<=MaxYCell;j++){ //Rprintf("%d %d:\n",i,j); TempCell = headCell[i][j]->next; CHECK(TempCell, "internal error: TempCell is null in Print()"); while(TempCell->next != TempCell){ k++; Rprintf("%f %f %ld %ld %ld=%d %ld=%d UL0 %d UL1 %d %f\n", TempCell->X,TempCell->Y,k, TempCell->No, i,int(TempCell->X/XCellDim), j,int(TempCell->Y/YCellDim), TempCell->InLower[0],TempCell->InLower[1], TempCell->Beta); TempCell = TempCell->next; CHECK(TempCell, "internal error: TempCell is null in Print() loop"); } } } Rprintf("Printed %ld points.\n",k); } void Point2Pattern::Return(double *X, double *Y, int *num, int maxnum){ long int i,j,k; k =0; *num = 0; #ifdef DBGS Rprintf("executing Return()\n"); #endif if(UpperLiving[0]<=maxnum){ struct Point2 *TempCell; for(i=0;i<=MaxXCell;i++){ for(j=0;j<=MaxYCell;j++){ #ifdef DBGS // Rprintf("%d %d:\n",i,j); #endif TempCell = headCell[i][j]->next; CHECK(TempCell, "internal error: TempCell is null in Return()"); while(TempCell->next != TempCell){ X[k] = TempCell->X; Y[k] = TempCell->Y; k++; TempCell = TempCell->next; CHECK(TempCell, "internal error: TempCell is null in Return() loop"); } } } *num = k; } else { *num = -1; } } long int Point2Pattern::Count(){ long int i,j,k; k = 0; struct Point2 *TempCell; for(i=0;i<=MaxXCell;i++){ for(j=0;j<=MaxYCell;j++){ // Rprintf("%d %d:\n",i,j); TempCell = headCell[i][j]->next; CHECK(TempCell, "internal error: TempCell is null in Count()"); while(TempCell->next != TempCell){ k++; TempCell = TempCell->next; CHECK(TempCell, "internal error: TempCell is null in Count() loop"); } } } //Rprintf("Printed %d points.\n",k); return(k); } // a quick (over)estimate of the number of points in the pattern, // for storage allocation long int Point2Pattern::UpperCount(){ return(UpperLiving[0]); } void Point2Pattern::Empty(){ struct Point2 *TempCell, *TempCell2; long int i,j; #ifdef DBGS long int k; k=0; Rprintf("executing Empty()\n"); #endif for(i=0; i<=this->MaxXCell; i++){ for(j=0; j<=this->MaxYCell; j++){ TempCell = headCell[i][j]->next; CHECK(TempCell, "internal error: TempCell is null in Empty()"); while(TempCell!=TempCell->next){ #ifdef DBGS // k++; Rprintf("%d %d %d\n",i,j,k); #endif TempCell2 = TempCell->next; FREE(TempCell); TempCell = TempCell2; CHECK(TempCell, "internal error: TempCell is null in Empty() loop"); } headCell[i][j]->next = dummyCell; } } } void Point2Pattern::Clean(){ struct Point2 *TempCell, *TempCell2; long int i,j; #ifdef DBGS Rprintf("executing Clean()\n"); #endif for(i=0; i<=MaxXCell; i++){ for(j=0; j<=MaxYCell; j++){ TempCell = headCell[i][j]; CHECK(TempCell, "internal error: TempCell is null in Clean()"); TempCell2 = headCell[i][j]->next; CHECK(TempCell2, "internal error: TempCell2 is null in Clean()"); while(TempCell2!=TempCell2->next){ TempCell2->No = 0; if(TempCell2->InLower[0]==0){ TempCell->next = TempCell2->next; FREE(TempCell2); TempCell2 = TempCell->next; CHECK(TempCell2, "internal error: TempCell2 is null in Clean() loop A"); } else{ TempCell2 = TempCell2->next; TempCell = TempCell->next; CHECK(TempCell, "internal error: TempCell is null in Clean() loop B"); CHECK(TempCell2, "internal error: TempCell2 is null in Clean() loop B"); } } } } } //void Point2Pattern::DumpToFile(char FileName[100]){ // FILE *out; // long int i,j; // out = fopen(FileName,"w"); // struct Point2 *TempCell; // for(i=0;i<=MaxXCell;i++){ // for(j=0;j<=MaxYCell;j++){ // //Rprintf("%d %d:\n",i,j); // TempCell = headCell[i][j]->next; // while(TempCell->next != TempCell){ // fprintf(out,"%f\t%f\t%ld\n", // TempCell->X,TempCell->Y,TempCell->No); // TempCell = TempCell->next; // } // } //} //fclose(out); //} //void Point2Pattern::ReadFromFile(char FileName[100]){ // FILE *out; //long int k,XCell,YCell; //float f1,xs,ys; //out = fopen(FileName,"r"); //struct Point2 *TempCell; //k=0; //while(feof(out)==0){ // k++; // fscanf(out,"%f%f\n",&xs,&ys); // //Rprintf("%f %f\n",xs,ys); // // // TempCell = ALLOCATE(struct Point2); // // // TempCell->No = k; // TempCell->X = xs; // TempCell->Y = ys; // TempCell->InLower[0] = 1; // TempCell->InLower[1] = 1; // // f1 = (xs-Xmin)/XCellDim; XCell = int(f1); // if(XCell>MaxXCell) XCell = MaxXCell; // f1 = (ys-Ymin)/YCellDim; YCell = int(f1); // if(YCell>MaxYCell) YCell = MaxYCell; // // TempCell->next = headCell[XCell][YCell]->next; // headCell[XCell][YCell]->next = TempCell; // //} //fclose(out); //Rprintf("%ld points loaded.\n",k); // //} // ........................... Point processes .......................... // ...................... (stationary, pairwise interaction) ............ class PointProcess { public: double Xmin, Xmax, Ymin, Ymax, TotalBirthRate, InteractionRange; PointProcess(double xmin, double xmax, double ymin, double ymax){ Xmin = xmin; Xmax = xmax; Ymin = ymin; Ymax = ymax; } ~PointProcess(){} virtual void NewEvent(double *x, double *y, char *InWindow)=0; virtual void GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP)=0; virtual double Interaction(double dsquared)=0; // virtual void CalcBeta(long int xsidepomm, long int ysidepomm, // double *betapomm){ // Rprintf("Define CalcBeta...\n"); // } // virtual void CheckBeta(long int xsidepomm, long int ysidepomm, // double *betapomm){ //Rprintf("Define CheckBeta...\n"); //} // virtual double lnCondInt(struct Point2 *TempCell, Point2Pattern *p2p) //{ return(0.0);}; // virtual double lnDens(Point2Pattern *p2p); // virtual void Beta(struct Point2 *TempCell){ // TempCell->Beta = 0; // Rprintf("Define Beta...\n");}; }; //double PointProcess::lnDens(Point2Pattern *p2p){ //// double f1; //long int xco,yco,xc,yc,fx,tx,fy,ty,ry,rx; //double dy,dx, lnDens,dst2; //struct Point2 *TempCell, *TempCell2; // //dx = (Xmax-Xmin)/(double(p2p->MaxXCell+1)); //dy = (Ymax-Ymin)/(double(p2p->MaxYCell+1)); //rx = int(InteractionRange/dx+1.0); //ry = int(InteractionRange/dy+1.0); // // //Rprintf("1:%f 2:%f 3:%d 4:%d 5:%f 6:%f\n",dx,dy,rx,ry, // // this->InteractionRange,InteractionRange); // //Rprintf("mx:%d my:%d\n",p2p->MaxXCell,p2p->MaxYCell); // // lnDens = 0; // // //Rprintf("lnDens: %f (0)\n",lnDens); // // for(xc = 0; xc <= p2p->MaxXCell; xc++){ // for(yc = 0; yc <= p2p->MaxYCell; yc++){ // //if(xc==1) Rprintf("%d %d\n",xc,yc); // CHECK(p2p->headCell[xc][yc], // "internal error: p2p->headCell[xc][yc] is null in lnDens()"); // TempCell = p2p->headCell[xc][yc]->next; // CHECK(TempCell, "internal error: TempCell is null in lnDens()"); // while(TempCell != TempCell->next){ // lnDens += log(TempCell->Beta); // //Rprintf("lnDens: %f (1) %d %d %d %d Beta %f\n",lnDens,xc,yc, // // p2p->MaxXCell,p2p->MaxYCell,TempCell->Beta); // //if(lnDens<(-100000)){Rprintf("%f",lnDens); scanf("%f",&f1);} // if(InteractionRange>0){ // if((xc+rx)<=p2p->MaxXCell) tx=xc+rx; else tx = p2p->MaxXCell; // if((yc+ry)<=p2p->MaxYCell) ty=yc+ry; else ty = p2p->MaxYCell; // if((xc-rx)>=0) fx=xc-rx; else fx = 0; // if((yc-ry)>=0) fy=yc-ry; else fy = 0; // for(xco = fx; xco <= tx; xco++){ // for(yco = fy; yco <= ty; yco++){ // //if(xc==1) Rprintf("%d %d %d %d %d %d\n",xco,yco,fx,tx,fy,ty); // CHECK(p2p->headCell[xco][yco], // "internal error: p2p->headCell[xco][yco] is null in lnDens() loop"); // TempCell2 = p2p->headCell[xco][yco]->next; // CHECK(TempCell2, // "internal error: TempCell2 is null in lnDens() loop A"); // while(TempCell2!=TempCell2->next){ // if(TempCell2 != TempCell){ // dst2 = pow(TempCell->X-TempCell2->X,2)+ // pow(TempCell->Y-TempCell2->Y,2); // lnDens += log(Interaction(dst2)); // } // TempCell2 = TempCell2->next; // CHECK(TempCell2, // "internal error: TempCell2 is null in lnDens() loop B"); // } // } // } // //Rprintf("lnDens: %f\n",lnDens); // } // TempCell = TempCell->next; // CHECK(TempCell, // "internal error: TempCell is null in lnDens() at end"); // } // } // } // return(lnDens); // //} // ........................... Sampler .......................... class Sampler{ public: PointProcess *PP; Point2Pattern *P2P; long int GeneratedPoints, LivingPoints, NoP; //long int UpperLiving[2]; Sampler(PointProcess *p){ PP = p;} ~Sampler(){} void Sim(Point2Pattern *p2p, long int *ST, long int *ET); long int BirthDeath(long int TimeStep, struct Point *headLiving, struct Point *headDeleted, struct Point3 *headTransition); // WAS: Sampler::Forward void Forward(long int TS, long int TT, char TX, char TY, struct Point *Proposal, long int *DDD); }; void Sampler::Forward(long int TS, long int TT, char TX, char TY, struct Point *Proposal, long int *DDD){ long int XCell, YCell, DirectionN; double dtmp2,dtmpx,dtmpy, tmpR, TempGamma[2], TempI; struct Point2 *TempCell, *TempCell2; float f1; /* Birth */ if(TT==1){ f1 = (Proposal->X-P2P->Xmin)/P2P->XCellDim; XCell = int(f1); CLAMP(XCell, 0, P2P->MaxXCell, "XCell"); f1 = (Proposal->Y-P2P->Ymin)/P2P->YCellDim; YCell = int(f1); CLAMP(YCell, 0, P2P->MaxYCell, "YCell"); // TempCell = ALLOCATE(struct Point2); // TempCell->No = Proposal->No; TempCell->X = Proposal->X; TempCell->Y = Proposal->Y; tmpR = Proposal->R; TempCell->next = P2P->headCell[XCell][YCell]->next; P2P->headCell[XCell][YCell]->next = TempCell; TempCell->InLower[0]=0; TempCell->InLower[1]=0; TempGamma[0] = 1.0; TempGamma[1] = 1.0; /*same cell*/ TempCell2 = TempCell->next; CHECK(TempCell2, "internal error: TempCell2 is null in Forward() birth case"); while(TempCell2 != TempCell2->next){ dtmpx = TempCell->X - TempCell2->X; dtmpy = TempCell->Y - TempCell2->Y; dtmp2 = dtmpx*dtmpx+dtmpy*dtmpy; TempI = PP->Interaction(dtmp2); if(TempCell2->InLower[0]==1) TempGamma[0] = TempGamma[0]*TempI; if(TempCell2->InLower[1]==1) TempGamma[1] = TempGamma[1]*TempI; TempCell2=TempCell2->next; CHECK(TempCell2, "internal error: TempCell2 is null in Forward() birth case loop"); } /*eight other cells*/ for(DirectionN=1;DirectionN<=8;DirectionN++){ if(((XCell+P2P->DirX[DirectionN])>=0) && ((XCell+P2P->DirX[DirectionN])<=P2P->MaxXCell) && ((YCell+P2P->DirY[DirectionN])>=0) && ((YCell+P2P->DirY[DirectionN])<=P2P->MaxYCell)){ CHECK(P2P->headCell[XCell+P2P->DirX[DirectionN]][YCell+P2P->DirY[DirectionN]], "internal error: HUGE P2P EXPRESSION is null in Forward() birth case loop A"); TempCell2 = P2P->headCell[XCell+P2P->DirX[DirectionN]] [YCell+P2P->DirY[DirectionN]]->next; CHECK(TempCell2, "internal error: TempCell2 is null in Forward() birth case loop B"); while(TempCell2!=TempCell2->next){ dtmpx = TempCell->X - TempCell2->X; dtmpy = TempCell->Y - TempCell2->Y; dtmp2 = dtmpx*dtmpx+dtmpy*dtmpy; TempI = PP->Interaction(dtmp2); if(TempCell2->InLower[0]==1) TempGamma[0] = TempGamma[0]*TempI; if(TempCell2->InLower[1]==1) TempGamma[1] = TempGamma[1]*TempI; TempCell2=TempCell2->next; CHECK(TempCell2, "internal error: TempCell2 is null in Forward() birth case loop C"); } } } if(tmpR <= TempGamma[1] ){ TempCell->InLower[0]=1; P2P->UpperLiving[0] = P2P->UpperLiving[0] +1; } if(tmpR <= TempGamma[0] ){ TempCell->InLower[1]=1; P2P->UpperLiving[1] = P2P->UpperLiving[1] +1; } } /* Death */ if(TT==0){ TempCell=P2P->headCell[(int)TX][(int)TY]; CHECK(TempCell, "internal error: TempCell is null in Forward() death case"); while(TempCell->next->No != *DDD){ TempCell = TempCell->next; CHECK(TempCell, "internal error: TempCell is null in Forward() death case loop"); if(TempCell->next == TempCell) { Rprintf("internal error: unexpected self-reference. Dumping...\n"); P2P->Print(); error("internal error: unexpected self-reference"); break; } }; CHECK(TempCell->next, "internal error: TempCell->next is null in Forward() death case"); if(*DDD!=TempCell->next->No) Rprintf("diagnostic message: multi cell: !!DDD:%ld TempUpper->No:%ld ", *DDD,TempCell->No); if(TempCell->next->InLower[0]==1) P2P->UpperLiving[0] = P2P->UpperLiving[0] -1; if(TempCell->next->InLower[1]==1) P2P->UpperLiving[1] = P2P->UpperLiving[1] -1; TempCell2 = TempCell->next; CHECK(TempCell2, "internal error: TempCell2 is null in Forward() death case B"); TempCell->next = TempCell2->next; FREE(TempCell2); /* Common stuff */ //KillCounter ++; *DDD = *DDD - 1; } } long int Sampler::BirthDeath(long int TimeStep, struct Point *headLiving, struct Point *headDeleted, struct Point3 *headTransition){ long int i,n; float f1,f2,f3,f4; double xtemp,ytemp; char InWindow, Success; struct Point *TempPoint, *TempPoint2; struct Point3 *TempTransition; R_CheckUserInterrupt(); f1 = LivingPoints; f2 = PP->TotalBirthRate; f3 = f2/(f1+f2); f4 = slumptal(); n = 0; Success = 0; //Rprintf("LivingPoints: %d TotalBirthRate %f GeneratedPoints %d\n", // LivingPoints,PP->TotalBirthRate,GeneratedPoints); /* Birth */ while(Success==0){ if(f4NewEvent(&xtemp, &ytemp, &InWindow); //Rprintf("Ping 2 (BD)\n"); if(InWindow==1){ Success = 1; // TempTransition = ALLOCATE(struct Point3); // //Rprintf("Ping 3 (BD)\n"); TempTransition->Case = 0; LivingPoints ++; GeneratedPoints ++; // TempPoint = ALLOCATE(struct Point); // TempPoint->X = xtemp; TempPoint->Y = ytemp; TempPoint->No = GeneratedPoints; TempPoint->R = slumptal(); TempPoint->next = headLiving->next; headLiving->next = TempPoint; NoP ++; f1 = (TempPoint->X-P2P->Xmin)/P2P->XCellDim; TempTransition->XCell = int(f1); f1 = (TempPoint->Y-P2P->Ymin)/P2P->YCellDim; TempTransition->YCell = int(f1); //Rprintf("X %f XCell %d\n",TempPoint->X,TempTransition->XCell); // CLAMP(TempTransition->XCell, 0, P2P->MaxXCell, "TempTransition->XCell"); CLAMP(TempTransition->YCell, 0, P2P->MaxYCell, "TempTransition->YCell"); TempTransition->next = headTransition->next; headTransition->next = TempTransition; } } /* Death */ else{ Success = 1; // TempTransition = ALLOCATE(struct Point3); // TempTransition->Case = 1; f1 = LivingPoints; f2 = f1*slumptal()+1.0; n = int(f2); if(n < 1) n = 1; if(n>LivingPoints){ // Rprintf("diagnostic message: random integer n=%ld > %ld = number of living points\n", n,LivingPoints); n=LivingPoints; } TempPoint2 = TempPoint = headLiving; for(i=1; i<=n; i++){ TempPoint2 = TempPoint; TempPoint = TempPoint->next; } TempPoint2->next = TempPoint->next; TempPoint->next = headDeleted->next; headDeleted->next = TempPoint; LivingPoints --; NoP --; TempTransition->next = headTransition->next; headTransition->next = TempTransition; } } return(n); } void Sampler::Sim(Point2Pattern *p2p, long int *ST, long int *ET) { P2P = p2p; long int StartTime, EndTime, TimeStep, D0Time, D0Living; long int XCell, YCell, DDD, i; float f1; /* Initialising linked listed for backward simulation */ struct Point *headDeleted, *headLiving, *dummyDeleted, *dummyLiving; struct Point *TempPoint; // headLiving = ALLOCATE(struct Point); dummyLiving = ALLOCATE(struct Point); // headLiving->next = dummyLiving; dummyLiving->next = dummyLiving; // headDeleted = ALLOCATE(struct Point); dummyDeleted = ALLOCATE(struct Point); // headDeleted->next = dummyDeleted; dummyDeleted->next = dummyDeleted; struct Point2 *TempCell2; struct Point3 *headTransition, *dummyTransition; // headTransition = ALLOCATE(struct Point3); dummyTransition = ALLOCATE(struct Point3); // headTransition->next = dummyTransition; dummyTransition->next = dummyTransition; PP->GeneratePoisson(headLiving, &GeneratedPoints, &LivingPoints, &NoP); StartTime=1; EndTime=1; TimeStep = 0; D0Time = 0; D0Living = GeneratedPoints; long int tmp, D0; do{ tmp=BirthDeath(TimeStep, headLiving, headDeleted, headTransition); if(tmp>0){ if(tmp>(LivingPoints+1-D0Living)){ D0Living --; } } D0Time++; }while(D0Living>0); tmp=BirthDeath(TimeStep, headLiving, headDeleted, headTransition); StartTime=1; EndTime=D0Time+1; D0 = 0; do{ if(D0==1){ for(TimeStep=StartTime;TimeStep<=EndTime;TimeStep ++){ tmp=BirthDeath(TimeStep, headLiving, headDeleted, headTransition); } } D0 = 1; P2P->Empty(); /* headUpper->next = dummyUpper; dummyUpper->next = dummyUpper; for(XCell=0;XCell<=P2P->MaxXCell;XCell++){ for(YCell=0;YCell<=P2P->MaxYCell;YCell++){ headUpperCell[XCell][YCell]->next=dummyUpper; } } */ P2P->UpperLiving[0] = LivingPoints; P2P->UpperLiving[1] = 0; P2P->NoP = 0; i=0; TempPoint = headLiving->next; CHECK(TempPoint, "internal error: TempPoint is null in Sim()"); while(TempPoint!=TempPoint->next){ i++; // TempCell2 = ALLOCATE(struct Point2); // TempCell2->No = TempPoint->No; TempCell2->X = TempPoint->X; TempCell2->Y = TempPoint->Y; TempCell2->InLower[0] = 1; TempCell2->InLower[1] = 0; f1 = (TempPoint->X-P2P->Xmin)/P2P->XCellDim; XCell = int(floor(f1)); CLAMP(XCell, 0, P2P->MaxXCell, "XCell"); f1 = (TempPoint->Y-P2P->Ymin)/P2P->YCellDim; YCell = int(floor(f1)); CLAMP(YCell, 0, P2P->MaxYCell, "YCell"); TempCell2->next = P2P->headCell[XCell][YCell]->next; P2P->headCell[XCell][YCell]->next = TempCell2; TempPoint = TempPoint->next; CHECK(TempPoint, "internal error: TempPoint is null in Sim() loop"); } //P2P->DumpToFile("temp0.dat"); struct Point3 *TempTransition; struct Point *Proposal; TempTransition = headTransition->next; CHECK(TempTransition, "internal error: TempTransition is null in Sim()"); Proposal = headDeleted->next; DDD = GeneratedPoints; for(TimeStep=EndTime;TimeStep>=1;TimeStep--){ R_CheckUserInterrupt(); Forward(TimeStep,TempTransition->Case, TempTransition->XCell,TempTransition->YCell, Proposal,&DDD); if(TempTransition->Case == 1) Proposal = Proposal ->next; TempTransition = TempTransition->next; CHECK(TempTransition, "internal error: TempTransition is null in Sim() loop"); } /* Doubling strategy used!*/ StartTime = EndTime+1; EndTime=EndTime*2; //P2P->DumpToFile("temp.dat"); }while(P2P->UpperLiving[0]!=P2P->UpperLiving[1]); P2P->Clean(); i=0; struct Point *TempPoint2; TempPoint = headLiving; TempPoint2 = headLiving->next; CHECK(TempPoint2, "internal error: TempPoint2 is null in Sim() position B"); while(TempPoint!=TempPoint->next){ i++; FREE(TempPoint); TempPoint = TempPoint2; TempPoint2 = TempPoint2->next; CHECK(TempPoint2, "internal error: TempPoint2 is null in Sim() loop C"); } FREE(TempPoint); i = 0; TempPoint = headDeleted; TempPoint2 = headDeleted->next; CHECK(TempPoint2, "internal error: TempPoint2 is null in Sim() position D"); while(TempPoint!=TempPoint->next){ i++; FREE(TempPoint); TempPoint = TempPoint2; TempPoint2 = TempPoint2->next; CHECK(TempPoint2, "internal error: TempPoint2 is null in Sim() loop D"); } FREE(TempPoint); //Rprintf("%d ",i); struct Point3 *TempTransition,*TempTransition2; i = 0; TempTransition = headTransition; TempTransition2 = headTransition->next; CHECK(TempTransition2, "internal error: TempTransition2 is null in Sim() position E"); while(TempTransition!=TempTransition->next){ i++; FREE(TempTransition); TempTransition = TempTransition2; TempTransition2 = TempTransition2->next; CHECK(TempTransition2, "internal error: TempTransition2 is null in Sim() loop F"); } FREE(TempTransition); //Rprintf("%d ST: %d ET: %d\n",i,StartTime,EndTime); //scanf("%f",&f1); *ST = StartTime; *ET = EndTime; } #include "PerfectStrauss.h" #include "PerfectStraussHard.h" #include "PerfectHardcore.h" #include "PerfectDiggleGratton.h" #include "PerfectDGS.h" spatstat/src/KrectFunDec.h0000644000176000001440000000563012333041761015246 0ustar ripleyusers/* KrectFunDec.h $Revision: 1.3 $ $Date: 2014/02/09 02:51:15 $ Function declarations for Krect Macros: FNAME function name WEIGHTED #defined for weighted version (Kinhom etc) +++ Copyright (C) Adrian Baddeley 2014 ++++ */ void FNAME(width, height, nxy, x, y, #ifdef WEIGHTED w, #endif nr, rmax, trimedge, doIso, doTrans, doBord, doUnco, iso, trans, bnumer, bdenom, unco) /* input data */ double *width, *height; /* window is (0, width) x (0, height) */ int *nxy; /* number of (x,y) points */ double *x, *y; /* (x,y) coordinates */ #ifdef WEIGHTED double *w; /* weights (e.g. reciprocal intensities) */ #endif /* algorithm parameters */ int *nr; /* number of r values */ double *rmax; /* maximum r value */ double *trimedge; /* maximum edge correction weight */ int *doIso; /* logical: whether to do isotropic correction */ int *doTrans; /* logical: whether to do translation correction */ int *doBord; /* logical: whether to do border correction */ int *doUnco; /* logical: whether to do uncorrected estimator */ /* outputs */ /* These are vectors of length nr if required, otherwise ignored */ double *iso; /* isotropic-corrected estimator */ double *trans; /* translation-corrected estimator */ COUNTTYPE *bnumer; /* numerator of border-corrected estimator */ COUNTTYPE *bdenom; /* denominator of border-corrected estimator */ COUNTTYPE *unco; /* uncorrected estimator */ { int i, j, l, ldist, lbord, M, maxchunk, N, Nr, N1, Nr1; double rstep, Rmax, R2max, wide, high, trim; double xi, yi, bdisti, bx, by, bratio; double dx, dy, dx2, dij, dij2, dratio, edgetrans, edgeiso; double dL, dR, dD, dU, bLU, bLD, bRU, bRD, bUL, bUR, bDL, bDR; double aL, aR, aD, aU, cL, cR, cU, cD, extang; int ncor, corner; COUNTTYPE *numerLowAccum, *numerHighAccum, *denomAccum; COUNTTYPE naccum, daccum; double accum; #ifdef WEIGHTED double wi, wj, wij; #endif #ifdef WEIGHTED #define ZERO 0.0 #define WIJ wij #else #define ZERO 0 #define WIJ 1 #endif N = *nxy; if(N == 0) return; Nr = *nr; Rmax = *rmax; trim = *trimedge; N1 = N - 1; Nr1 = Nr - 1; R2max = Rmax * Rmax; rstep = Rmax/Nr1; wide = *width; high = *height; /* Allocate and initialise scratch space - for border correction, but do it in all cases to keep the compiler happy */ M = (*doBord == 1) ? Nr : 1; numerLowAccum = (COUNTTYPE *) R_alloc(M, sizeof(COUNTTYPE)); numerHighAccum = (COUNTTYPE *) R_alloc(M, sizeof(COUNTTYPE)); denomAccum = (COUNTTYPE *) R_alloc(M, sizeof(COUNTTYPE)); for(l = 0; l < M; l++) numerLowAccum[l] = numerHighAccum[l] = denomAccum[l] = ZERO; #include "KrectV1.h" } #undef ZERO #undef WIJ spatstat/src/Krect.c0000644000176000001440000000373712333041761014162 0ustar ripleyusers/* Krect.c $Revision: 1.3 $ $Date: 2014/02/09 03:02:42 $ +++ Copyright (C) Adrian Baddeley, Julian Gilbey and Rolf Turner 2014 ++++ Fast code for K function in rectangular case. **Assumes point pattern is sorted in increasing order of x coordinate** **Assumes window is (0,wide) x (0, high) ** **Assumes output vectors were initialised to zero** Krect.c defines three interface functions, for weighted, unweighted double, and unweighted integer cases KrectFunDec.h (#included thrice) Function declaration, arguments, storage allocation KrectV1.h split according to whether Isotropic Correction is wanted Macro ISOTROPIC is #defined KrectV2.h split according to whether Translation Correction is wanted Macro TRANSLATION is #defined KrectV3.h split according to whether Border Correction is wanted Macro BORDER is #defined KrectV4.h split according to whether Uncorrected estimate is wanted Macro UNCORRECTED is #defined KrectBody.h Function body, including loops over i and j KrectIncrem.h (#included twice) Code performed when a close pair of points has been found: calculate edge corrections, increment results. */ #include #include #include /* This constant is defined in Rmath.h */ #define TWOPI M_2PI #define ABS(X) (((X) >= 0) ? (X) : (-X)) #define SMALL(X) ((ABS(X) < 1.0e-12) ? 1 : 0) #define MIN(X,Y) (((X) < (Y)) ? (X) : (Y)) #undef FNAME #undef WEIGHTED #undef COUNTTYPE #define FNAME KrectInt #define COUNTTYPE int #include "KrectFunDec.h" #undef FNAME #undef WEIGHTED #undef COUNTTYPE #define FNAME KrectDbl #define COUNTTYPE double #include "KrectFunDec.h" #undef FNAME #undef WEIGHTED #undef COUNTTYPE #define FNAME KrectWtd #define COUNTTYPE double #define WEIGHTED #include "KrectFunDec.h" spatstat/src/KrectIncrem.h0000644000176000001440000000462612333041761015323 0ustar ripleyusers/* KrectIncrem.h Code to increment numerators of K-function $Revision: 1.5 $ $Date: 2014/02/09 03:00:51 $ +++ Copyright (C) Adrian Baddeley, Julian Gilbey and Rolf Turner 2014 ++++ */ #ifdef WEIGHTED wj = w[j]; wij = wi * wj; #endif /* determine index of entry to be incremented */ dij = (double) sqrt(dij2); dratio = dij/rstep; /* smallest integer greater than or equal to dratio */ ldist = (int) ceil(dratio); #ifdef UNCORRECTED /* ............ uncorrected estimate ................. */ #ifdef WEIGHTED unco[ldist] += wij; #else (unco[ldist])++; #endif #endif #ifdef BORDER /* ............ border correction ................. */ /* increment numerator for all r such that dij <= r < bi */ /* increment entries ldist to lbord inclusive */ #ifdef WEIGHTED if(lbord >= ldist) { numerLowAccum[ldist] += wij; numerHighAccum[lbord] += wij; } #else if(lbord >= ldist) { (numerLowAccum[ldist])++; (numerHighAccum[lbord])++; } #endif #endif #ifdef TRANSLATION /* ............ translation correction ................. */ edgetrans = 1.0/((1.0 - ABS(dx)/wide) * (1.0 - ABS(dy)/high)); edgetrans = MIN(edgetrans, trim); #ifdef WEIGHTED trans[ldist] += wij * edgetrans; #else trans[ldist] += edgetrans; #endif #endif #ifdef ISOTROPIC /* ............ isotropic correction ................. */ /* half the angle subtended by the intersection between the circle of radius d[i,j] centred on point i and each edge of the rectangle (prolonged to an infinite line) */ aL = (dL < dij) ? acos(dL/dij) : 0.0; aR = (dR < dij) ? acos(dR/dij) : 0.0; aD = (dD < dij) ? acos(dD/dij) : 0.0; aU = (dU < dij) ? acos(dU/dij) : 0.0; /* apply maxima */ cL = MIN(aL, bLU) + MIN(aL, bLD); cR = MIN(aR, bRU) + MIN(aR, bRD); cU = MIN(aU, bUL) + MIN(aU, bUR); cD = MIN(aD, bDL) + MIN(aD, bDR); /* total exterior angle over 2 pi */ extang = (cL + cR + cU + cD)/TWOPI; /* add pi/2 for corners */ if(corner) extang += 1/4; /* edge correction factor */ edgeiso = 1 / (1 - extang); edgeiso = MIN(edgeiso, trim); #ifdef WEIGHTED iso[ldist] += wij * edgeiso; #else iso[ldist] += edgeiso; #endif #endif spatstat/src/areapair.c0000644000176000001440000000360312333041761014666 0ustar ripleyusers/* areapair.c $Revision: 1.6 $ $Date: 2013/09/18 04:11:42 $ Specialised code for the second order conditional intensity of the area-interaction process */ #include #include #include "yesno.h" /* computes area of b(A, r) \int b(B, r) \setminus \bigcup_i b(X[i], r) */ void delta2area(xa, ya, xb, yb, nother, xother, yother, radius, epsilon, pixcount) double *xa, *ya, *xb, *yb; int *nother; double *xother, *yother; double *radius, *epsilon; int *pixcount; { int Ni, Nj, Nk, i, j, k, count, covered; double xA, yA, xB, yB, r, eps, r2; double xmin, xmax, ymin, ymax, xi, yj; double dxA, dyA; double dxB, dyB; double dx, dy; Nk = *nother; xA = *xa; yA = *ya; xB = *xb; yB = *yb; r = *radius; eps = *epsilon; r2 = r * r; /* find intersection of squares centred on A and B */ if(xA < xB) { xmin = xB - r; xmax = xA + r; } else { xmin = xA - r; xmax = xB + r; } if(xmin > xmax) return; if(yA < yB) { ymin = yB - r; ymax = yA + r; } else { ymin = yA - r; ymax = yB + r; } if(ymin > ymax) return; /* set up grid */ Ni = (int) ceil((xmax - xmin)/eps) + 1; Nj = (int) ceil((ymax - ymin)/eps) + 1; count = 0; for(i = 0, xi = xmin; i < Ni; i++, xi += eps) { dxA = xi - xA; for(j = 0, yj = ymin; j < Nj; j++, yj += eps) { dyA = yj - yA; if(dxA * dxA + dyA * dyA <= r2) { /* grid point belongs to b(A, r) */ dxB = xi - xB; dyB = yj - yB; if(dxB * dxB + dyB * dyB <= r2) { /* grid point belongs to b(A,r) \cap b(B,r) */ covered = NO; /* test whether it is covered by another b(X[k], r) */ for(k = 0; k < Nk; k++) { dx = xi - xother[k]; dy = yj - yother[k]; if(dx * dx + dy * dy <= r2) { covered = YES; break; } } if(!covered) { ++count; } } } } } *pixcount = count; } spatstat/src/yesno.h0000644000176000001440000000011612333041761014240 0ustar ripleyusers/* yesno.h */ #ifndef YES #define YES (0 == 0) #define NO (!YES) #endif spatstat/src/dist2dpath.c0000755000176000001440000000052712333041761015155 0ustar ripleyusers#include #include /* given matrix of edge lengths compute matrix of shortest-path distances Uses dist2dpath.h */ #define FNAME Ddist2dpath #define DTYPE double #define FLOATY #include "dist2dpath.h" #undef FNAME #undef DTYPE #undef FLOATY #define FNAME Idist2dpath #define DTYPE int #include "dist2dpath.h" spatstat/src/distseg.c0000755000176000001440000001166212333041761014553 0ustar ripleyusers/* distseg.c Distances from point pattern to line segment pattern Distance transform of a line segment pattern nndist2segs: minimum distance from point to any line segment prdist2segs: pairwise distances from each point to each line segment $Revision: 1.9 $ $Date: 2012/03/27 05:38:51 $ Author: Adrian Baddeley */ #include #include #include #include #include "chunkloop.h" void nndist2segs(xp, yp, npoints, x0, y0, x1, y1, nsegments, epsilon, dist2, index) /* input */ double *xp, *yp; /* point/pixel coordinates */ int *npoints; double *x0, *y0, *x1, *y1; /* line segment endpoints */ int *nsegments; double *epsilon; /* tolerance for short segments */ /* output */ double *dist2; /* squared distance from pixel to nearest line segment INITIALISED TO LARGE VALUE */ int *index; /* which line segment is closest */ { int i,j, np, nseg, maxchunk; double dx,dy,leng,co,si; /* parameters of segment */ double xdif0,ydif0,xdif1,ydif1,xpr,ypr; /* vectors */ double dsq0,dsq1,dsq,dsqperp; /* squared distances */ double eps; np = *npoints; nseg = *nsegments; eps = *epsilon; OUTERCHUNKLOOP(j, nseg, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nseg, maxchunk, 16384) { dx = x1[j] - x0[j]; dy = y1[j] - y0[j]; leng = hypot(dx, dy); if(leng > eps) { /* normal case */ co = dx/leng; si = dy/leng; for(i = 0; i < np; i++) { /* vectors from pixel to segment endpoints */ xdif0 = xp[i] - x0[j]; ydif0 = yp[i] - y0[j]; xdif1 = xp[i] - x1[j]; ydif1 = yp[i] - y1[j]; /* squared distances to segment endpoints */ dsq0 = xdif0 * xdif0 + ydif0 * ydif0; dsq1 = xdif1 * xdif1 + ydif1 * ydif1; dsq = (dsq0 < dsq1) ? dsq0 : dsq1; /* rotate pixel around 1st endpoint of segment so that line segment lies in x axis */ xpr = xdif0 * co + ydif0 * si; ypr = -xdif0 * si + ydif0 * co; /* perpendicular distance applies only in perpendicular region */ if(xpr >= 0.0 && xpr <= leng) { dsqperp = ypr * ypr; if(dsqperp < dsq) dsq = dsqperp; } if(dist2[i] > dsq) { dist2[i] = dsq; index[i] = j; } } } else { /* short segment - use endpoints only */ for(i = 0; i < np; i++) { /* vectors from pixel to segment endpoints */ xdif0 = xp[i] - x0[j]; ydif0 = yp[i] - y0[j]; xdif1 = xp[i] - x1[j]; ydif1 = yp[i] - y1[j]; /* squared distances to segment endpoints */ dsq0 = xdif0 * xdif0 + ydif0 * ydif0; dsq1 = xdif1 * xdif1 + ydif1 * ydif1; dsq = (dsq0 < dsq1) ? dsq0 : dsq1; if(dist2[i] > dsq) { dist2[i] = dsq; index[i] = j; } } } } } } void prdist2segs(xp, yp, npoints, x0, y0, x1, y1, nsegments, epsilon, dist2) /* input */ double *xp, *yp; /* point/pixel coordinates */ int *npoints; double *x0, *y0, *x1, *y1; /* line segment endpoints */ int *nsegments; double *epsilon; /* tolerance for short segments */ /* output */ double *dist2; /* squared distances from each pixel to each line segment */ { int i,j, np, nseg, maxchunk; double dx,dy,leng,co,si; /* parameters of segment */ double xdif0,ydif0,xdif1,ydif1,xpr,ypr; /* vectors */ double dsq0,dsq1,dsq,dsqperp; /* squared distances */ double eps; np = *npoints; nseg = *nsegments; eps = *epsilon; OUTERCHUNKLOOP(j, nseg, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nseg, maxchunk, 16384) { dx = x1[j] - x0[j]; dy = y1[j] - y0[j]; leng = hypot(dx, dy); if(leng > eps) { /* normal case */ co = dx/leng; si = dy/leng; for(i = 0; i < np; i++) { /* vectors from pixel to segment endpoints */ xdif0 = xp[i] - x0[j]; ydif0 = yp[i] - y0[j]; xdif1 = xp[i] - x1[j]; ydif1 = yp[i] - y1[j]; /* squared distances to segment endpoints */ dsq0 = xdif0 * xdif0 + ydif0 * ydif0; dsq1 = xdif1 * xdif1 + ydif1 * ydif1; dsq = (dsq0 < dsq1) ? dsq0 : dsq1; /* rotate pixel around 1st endpoint of segment so that line segment lies in x axis */ xpr = xdif0 * co + ydif0 * si; ypr = -xdif0 * si + ydif0 * co; /* perpendicular distance applies only in perpendicular region */ if(xpr >= 0.0 && xpr <= leng) { dsqperp = ypr * ypr; if(dsqperp < dsq) dsq = dsqperp; } dist2[i + j * np] = dsq; } } else { /* short segment */ for(i = 0; i < np; i++) { /* vectors from pixel to segment endpoints */ xdif0 = xp[i] - x0[j]; ydif0 = yp[i] - y0[j]; xdif1 = xp[i] - x1[j]; ydif1 = yp[i] - y1[j]; /* squared distances to segment endpoints */ dsq0 = xdif0 * xdif0 + ydif0 * ydif0; dsq1 = xdif1 * xdif1 + ydif1 * ydif1; dsq = (dsq0 < dsq1) ? dsq0 : dsq1; dist2[i + j * np] = dsq; } } } } } spatstat/src/diggra.c0000755000176000001440000000637012333041761014346 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Diggle-Gratton process */ /* Conditional intensity function for a pairwise interaction point process with interaction function as given by e(t) = 0 for t < delta = (t-delta)/(rho-delta)^kappa for delta <= t < rho = 1 for t >= rho (See page 767 of Diggle, Gates, and Stibbard, Biometrika vol. 74, 1987, pages 763 -- 770.) */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Diggra { double kappa; double delta; double rho; double delta2; /* delta^2 */ double rho2; /* rho^2 */ double fac; /* 1/(rho-delta) */ double *period; int per; } Diggra; /* initialiser function */ Cdata *diggrainit(state, model, algo) State state; Model model; Algor algo; { Diggra *diggra; diggra = (Diggra *) R_alloc(1, sizeof(Diggra)); /* Interpret model parameters*/ diggra->kappa = model.ipar[0]; diggra->delta = model.ipar[1]; diggra->rho = model.ipar[2]; diggra->period = model.period; /* constants */ diggra->delta2 = pow(diggra->delta, 2); diggra->rho2 = pow(diggra->rho, 2); diggra->fac = 1/(diggra->rho - diggra->delta); /* periodic boundary conditions? */ diggra->per = (model.period[0] > 0.0); return((Cdata *) diggra); } /* conditional intensity evaluator */ double diggracif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *x, *y; double u, v; double d2, pairprod, cifval; double rho2, delta, delta2, fac; double *period; DECLARE_CLOSE_D2_VARS; Diggra *diggra; diggra = (Diggra *) cdata; period = diggra->period; rho2 = diggra->rho2; delta = diggra->delta; delta2 = diggra->delta2; fac = diggra->fac; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; cifval = pairprod = 1.0; if(npts == 0) return(cifval); ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(diggra->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],period,rho2,d2)) { if(d2 < delta2) { cifval = 0.0; return(cifval); } else { pairprod *= fac * (sqrt(d2)-delta); } } } } if(ixp1 < npts) { for(j=ixp1; j 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u, v, x[j], y[j], rho2, d2)) { if(d2 <= delta2) { cifval = 0.0; return(cifval); } else { pairprod *= fac * (sqrt(d2)-delta); } } } } if(ixp1 < npts) { for(j=ixp1; jkappa); return cifval; } Cifns DiggraCifns = { &diggrainit, &diggracif, (updafunptr) NULL, NO}; spatstat/src/mhv3.h0000644000176000001440000000042712333041761013765 0ustar ripleyusers/* mhv3.h tracking or not */ #undef MH_TRACKING if(tracking) { /* saving transition history */ #define MH_TRACKING YES #include "mhv4.h" #undef MH_TRACKING } else { /* not saving transition history */ #define MH_TRACKING NO #include "mhv4.h" #undef MH_TRACKING } spatstat/src/nngrid.c0000644000176000001440000000356212333041761014367 0ustar ripleyusers/* nngrid.c Nearest Neighbour Distances from a pixel grid to a point pattern Copyright (C) Adrian Baddeley, Jens Oehlschlaegel and Rolf Turner 2000-2013 Licence: GNU Public Licence >= 2 $Revision: 1.4 $ $Date: 2013/11/03 03:41:23 $ Function body definition is #included from nngrid.h THE FOLLOWING FUNCTIONS ASSUME THAT x IS SORTED IN ASCENDING ORDER */ #undef SPATSTAT_DEBUG #include #include #include #include "yesno.h" double sqrt(); /* THE FOLLOWING CODE ASSUMES THAT x IS SORTED IN ASCENDING ORDER */ /* general interface */ void nnGinterface(nx, x0, xstep, ny, y0, ystep, /* pixel grid dimensions */ np, xp, yp, /* data points */ wantdist, wantwhich, /* options */ nnd, nnwhich, huge) /* inputs */ int *nx, *ny, *np; double *x0, *xstep, *y0, *ystep, *huge; double *xp, *yp; /* options */ int *wantdist, *wantwhich; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { void nnGdw(), nnGd(), nnGw(); int di, wh; di = (*wantdist != 0); wh = (*wantwhich != 0); if(di && wh) { nnGdw(nx, x0, xstep, ny, y0, ystep, np, xp, yp, nnd, nnwhich, huge); } else if(di) { nnGd(nx, x0, xstep, ny, y0, ystep, np, xp, yp, nnd, nnwhich, huge); } else if(wh) { nnGw(nx, x0, xstep, ny, y0, ystep, np, xp, yp, nnd, nnwhich, huge); } } #undef FNAME #undef DIST #undef WHICH /* nnGdw returns distances and indices */ #define FNAME nnGdw #define DIST #define WHICH #include "nngrid.h" #undef FNAME #undef DIST #undef WHICH /* nnGd returns distances only */ #define FNAME nnGd #define DIST #include "nngrid.h" #undef FNAME #undef DIST #undef WHICH /* nnGw returns indices only */ #define FNAME nnGw #define WHICH #include "nngrid.h" #undef FNAME #undef DIST #undef WHICH spatstat/src/discarea.c0000755000176000001440000001502412333041761014660 0ustar ripleyusers/* disc.c Area of intersection between disc and polygonal window $Revision: 1.6 $ $Date: 2011/12/03 00:15:52 $ */ #undef DEBUG #include #include #define MIN(A,B) (((A) < (B)) ? (A) : (B)) #define MAX(A,B) (((A) > (B)) ? (A) : (B)) #ifndef PI #define PI 3.1415926535898 #endif void discareapoly(nc, xc, yc, nr, rmat, nseg, x0, y0, x1, y1, eps, out) /* inputs */ int *nc, *nr, *nseg; double *xc, *yc, *rmat; double *x0, *y0, *x1, *y1; double *eps; /* output */ double *out; { int n, m, i, j, k, nradperpt; double radius, radius2, total, contrib; double xx0, xx1, yy0, yy1, xleft, xright, yleft, yright, xcentre, ycentre; double epsilon; double DiscContrib(); n = *nc; nradperpt = *nr; m = *nseg; epsilon = *eps; for(i = 0; i < n; i++) { xcentre = xc[i]; ycentre = yc[i]; #ifdef DEBUG Rprintf("\ni = %d:\n centre = (%lf, %lf)\n", i, xcentre, ycentre); #endif for(j = 0; j < nradperpt; j++) { radius = rmat[ j * n + i]; radius2 = radius * radius; #ifdef DEBUG Rprintf("radius = %lf\n", radius); #endif total = 0.0; for(k=0; k < m; k++) { #ifdef DEBUG Rprintf("k = %d\n", k); #endif xx0 = x0[k]; yy0 = y0[k]; xx1 = x1[k]; yy1 = y1[k]; #ifdef DEBUG Rprintf("(%lf,%lf) to (%lf,%lf)\n", xx0, yy0, xx1, yy1); #endif /* refer to unit disc at origin */ /* arrange so that xleft < xright */ if(radius <= epsilon) contrib = 0.0; else if(xx0 < xx1) { xleft = (xx0 - xcentre)/radius; xright = (xx1 - xcentre)/radius; yleft = (yy0 - ycentre)/radius; yright = (yy1 - ycentre)/radius; contrib = - radius2 * DiscContrib(xleft,yleft,xright,yright,epsilon); } else { xleft = (xx1 - xcentre)/radius; xright = (xx0 - xcentre)/radius; yleft = (yy1 - ycentre)/radius; yright = (yy0 - ycentre)/radius; contrib = radius2 * DiscContrib(xleft,yleft,xright,yright,epsilon); } #ifdef DEBUG Rprintf("contrib = %lf\n contrib/(pi * r^2)=%lf\n", contrib, contrib/(PI * radius2)); #endif total += contrib; } out[ j * n + i] = total; #ifdef DEBUG Rprintf("total = %lf\ntotal/(pi * r^2) = %lf\n", total, total/(PI * radius2)); #endif } } } /* area of intersection of unit disc with halfplane x <= v */ #ifdef DEBUG #define TRIGBIT(V) trigbit(V) double trigbit(v) double v; { double zero, result; zero = 0.0; if(v < -1.0) return(zero); if(v > 1.0) return(PI); result = PI/2 + asin(v) + v * sqrt(1 - v * v); Rprintf("trigbit: v = %lf, asin(v)=%lf, result=%lf\n", v, asin(v), result); return(result); } #else #define TRIGBIT(V) (((V) <= -1.0) ? 0.0 : (((V) >= 1.0) ? PI : \ (PI/2 + asin(V) + (V) * sqrt(1 - (V) * (V))))) #endif /* Find the area of intersection between a disc centre = (0,0), radius = 1 and the trapezium with upper segment (xleft, yleft) to (xright, yright) ASSUMES xleft < xright */ double DiscContrib(xleft, yleft, xright, yright, eps) double xleft, yleft, xright, yright, eps; /* NOTE: unit disc centred at origin */ { double xlo, xhi, zero, slope, intercept, A, B, C, det; double xcut1, xcut2, ycut1, ycut2, xunder1, xunder2, dx, dx2, result; #ifdef DEBUG double increm; Rprintf( "DiscContrib: xleft=%lf, yleft=%lf, xright=%lf, yright=%lf\n", xleft, yleft, xright, yright); #endif zero = 0.0; /* determine relevant range of x coordinates */ xlo = MAX(xleft, (-1.0)); xhi = MIN(xright, 1.0); if(xlo >= xhi - eps) { /* intersection is empty or negligible */ #ifdef DEBUG Rprintf("intersection is empty or negligible\n"); #endif return(zero); } /* find intersection points between the circle and the line containing upper segment */ slope = (yright - yleft)/(xright - xleft); intercept = yleft - slope * xleft; A = 1 + slope * slope; B = 2 * slope * intercept; C = intercept * intercept - 1.0; det = B * B - 4 * A * C; #ifdef DEBUG Rprintf("slope=%lf, intercept=%lf\nA = %lf, B=%lf, C=%lf, det=%lf\n", slope, intercept, A, B, C, det); #endif if(det <= 0.0) { /* no crossing between disc and infinite line */ if(intercept < 0.0) /* segment is below disc; intersection is empty */ return(zero); /* segment is above disc */ result = TRIGBIT(xhi) - TRIGBIT(xlo); return(result); } xcut1 = (- B - sqrt(det))/(2 * A); xcut2 = (- B + sqrt(det))/(2 * A); /* partition [xlo, xhi] into pieces delimited by {xcut1, xcut2} */ if(xcut1 >= xhi || xcut2 <= xlo) { /* segment is outside disc */ if(yleft < 0.0) { #ifdef DEBUG Rprintf("segment is beneath disc\n"); #endif result = zero; } else { #ifdef DEBUG Rprintf("segment is above disc\n"); #endif result = TRIGBIT(xhi) - TRIGBIT(xlo); } return(result); } /* possibly three parts */ #ifdef DEBUG Rprintf("up to three pieces\n"); #endif result = zero; ycut1 = intercept + slope * xcut1; ycut2 = intercept + slope * xcut2; if(xcut1 > xlo) { /* part to left of cut */ #ifdef DEBUG Rprintf("left of cut: [%lf, %lf]\n", xlo, xcut1); if(ycut1 < 0.0) Rprintf("below disc - no intersection\n"); else { increm = TRIGBIT(xcut1) - TRIGBIT(xlo); Rprintf("increment = %lf\n", increm); result += increm; } #else if(ycut1 >= 0.0) result += TRIGBIT(xcut1) - TRIGBIT(xlo); #endif } if(xcut2 < xhi) { /* part to right of cut */ #ifdef DEBUG Rprintf("right of cut: [%lf, %lf]\n", xcut2, xhi); if(ycut2 < 0.0) Rprintf("below disc - no intersection\n"); else { increm = TRIGBIT(xhi) - TRIGBIT(xcut2); Rprintf("increment = %lf\n", increm); result += increm; } #else if(ycut2 >= 0.0) result += TRIGBIT(xhi) - TRIGBIT(xcut2); #endif } /* part underneath cut */ xunder1 = MAX(xlo, xcut1); xunder2 = MIN(xhi, xcut2); dx = xunder2 - xunder1; dx2 = xunder2 * xunder2 - xunder1 * xunder1; #ifdef DEBUG Rprintf("underneath cut: [%lf, %lf]\n", xunder1, xunder2); increm = intercept * dx + slope * dx2/2 + (TRIGBIT(xunder2) - TRIGBIT(xunder1))/2; Rprintf("increment = %lf\n", increm); result += increm; #else result += intercept * dx + slope * dx2/2 + (TRIGBIT(xunder2) - TRIGBIT(xunder1))/2; #endif return(result); } #ifdef DEBUG /* interface to low level function, for debugging only */ void RDCtest(xleft, yleft, xright, yright, eps, value) double *xleft, *yleft, *xright, *yright, *eps, *value; { double DiscContrib(); *value = DiscContrib(*xleft, *yleft, *xright, *yright, *eps); } #endif spatstat/src/chunkloop.h0000644000176000001440000000144412333041761015112 0ustar ripleyusers/* chunkloop.h Divide a loop into chunks Convenient for divide-and-recombine, and reducing calls to R_CheckUserInterrupt, etc. $Revision: 1.2 $ $Date: 2013/05/27 02:09:10 $ */ #define OUTERCHUNKLOOP(IVAR, LOOPLENGTH, ICHUNK, CHUNKSIZE) \ IVAR = 0; \ ICHUNK = 0; \ while(IVAR < LOOPLENGTH) #define INNERCHUNKLOOP(IVAR, LOOPLENGTH, ICHUNK, CHUNKSIZE) \ ICHUNK += CHUNKSIZE; \ if(ICHUNK > LOOPLENGTH) ICHUNK = LOOPLENGTH; \ for(; IVAR < ICHUNK; IVAR++) #define XOUTERCHUNKLOOP(IVAR, ISTART, IEND, ICHUNK, CHUNKSIZE) \ IVAR = ISTART; \ ICHUNK = 0; \ while(IVAR <= IEND) #define XINNERCHUNKLOOP(IVAR, ISTART, IEND, ICHUNK, CHUNKSIZE) \ ICHUNK += CHUNKSIZE; \ if(ICHUNK > IEND) ICHUNK = IEND; \ for(; IVAR <= IEND; IVAR++) #define CHUNKLOOP_H spatstat/src/scan.c0000644000176000001440000000365412333041761014034 0ustar ripleyusers/* scan.c Scan transform $Revision: 1.2 $ $Date: 2012/04/16 12:00:07 $ */ #include #include #include "raster.h" void shape_raster(); void Cscantrans(x, y, npt, R, out) double *x, *y; /* data points */ int npt; double R; /* radius */ Raster *out; /* scan image */ { int i,j,k,l,m; double d2, R2; int rmin, rmax, cmin, cmax, Rrow, Rcol, lmin, lmax, mmin, mmax; /* initialise raster */ Clear(*out,int,0); /* If the list of data points is empty, ... exit now */ if(npt == 0) return; R2 = R * R; cmin = out->cmin; cmax = out->cmax; rmin = out->rmin; rmax = out->rmax; /* disc size in rows/columns */ Rrow = (int) ceil(R/(out->ystep)); Rcol = (int) ceil(R/(out->xstep)); if(Rrow < 1) Rrow = 1; if(Rcol < 1) Rcol = 1; /* run through points */ for(i = 0; i < npt; i++) { j = RowIndex(*out,y[i]); k = ColIndex(*out,x[i]); lmin = j - Rrow; if(lmin < rmin) lmin = rmin; lmax = j + Rrow; if(lmax > rmax) lmax = rmax; mmin = k - Rcol; if(mmin < cmin) mmin = cmin; mmax = k + Rcol; if(mmax > cmax) mmax = cmax; for(l = lmin; l <= lmax; l++) { for(m = mmin; m <= mmax; m++) { d2 = DistanceToSquared(x[i],y[i],*out,l,m); if(d2 <= R2) Entry(*out,l,m,int) += 1; } } } } /* R interface */ void scantrans(x, y, n, xmin, ymin, xmax, ymax, nr, nc, R, counts) double *x, *y; /* input data points */ int *n; double *xmin, *ymin, *xmax, *ymax; /* guaranteed bounding box */ int *nr, *nc; /* desired raster dimensions */ double *R; /* radius */ /* output array */ int *counts; /* number of R-close points */ { Raster out; int nrow, ncol, npoints; double r; nrow = *nr; ncol = *nc; npoints = *n; r = *R; shape_raster( &out, (void *) counts, *xmin,*ymin,*xmax,*ymax, nrow, ncol, 0, 0); Cscantrans(x, y, npoints, r, &out); } spatstat/src/PerfectStrauss.h0000644000176000001440000002146012333041761016065 0ustar ripleyusers // ........................... Strauss process .......................... // $Revision: 1.4 $ $Date: 2014/02/18 10:43:00 $ class StraussProcess : public PointProcess { public: double beta, gamma, R, Rsquared; StraussProcess(double xmin, double xmax, double ymin, double ymax, double b, double g, double Ri); ~StraussProcess(){} void NewEvent(double *x, double *y, char *InWindow); void GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP); double Interaction(double dsquared); // void CalcBeta(long int xsidepomm, long int ysidepomm, // double *betapomm); // void CheckBeta(long int xsidepomm, long int ysidepomm, // double *betapomm); // double lnCondInt(struct Point2 *TempCell, Point2Pattern *p2p); // void Beta(struct Point2 *TempCell); // void CalcBeta(Point2Pattern *p2p); }; StraussProcess::StraussProcess(double xmin, double xmax, double ymin, double ymax, double b, double g, double Ri) : PointProcess(xmin, xmax, ymin, ymax){ beta = b; gamma = g; R = Ri; Rsquared = R * R; InteractionRange = R; TotalBirthRate = beta*(xmax-xmin)*(ymax-ymin); } double StraussProcess::Interaction(double dsquared) { double rtn; rtn = 1; if(dsquared < Rsquared) rtn = gamma; return(rtn); } void StraussProcess::NewEvent(double *x, double *y, char *InWindow) { double Xdim, Ydim; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; *x = slumptal()*Xdim+Xmin; *y = slumptal()*Ydim+Ymin; *InWindow = 1; } void StraussProcess::GeneratePoisson(Point *headPoint, long int *GeneratedPoints, long int *LivingPoints, long int *NoP) { int i; double xtemp, ytemp, L, Xdim, Ydim; struct Point *TempPoint; Xdim = Xmax-Xmin; Ydim = Ymax-Ymin; L = beta*Xdim*Ydim; *GeneratedPoints = poisson(L); *LivingPoints = *GeneratedPoints; for (i=1; i<=*GeneratedPoints ; i++){ //Rprintf("Generating StraussProcess Poisson 3\n"); //scanf("%f",&f1); xtemp = slumptal()*Xdim+Xmin; ytemp = slumptal()*Ydim+Ymin; // //Rprintf("Generating StraussProcess Poisson 3.2\n"); TempPoint = ALLOCATE(struct Point); // TempPoint->X = xtemp; TempPoint->Y = ytemp; TempPoint->No = i; TempPoint->R = slumptal(); //Rprintf("Generating StraussProcess Poisson 3.6\n"); TempPoint->next = headPoint->next; headPoint->next = TempPoint; *NoP = *NoP + 1; } } //void StraussProcess::CalcBeta(long int xsidepomm, long int ysidepomm, // double *betapomm){ // long int i,j,k; // k=0; // // Rprintf("\ndiagnostic message: Strauss CalcBeta... %ld %ld\n",xsidepomm,ysidepomm); // for(i=0; ibeta; // k++; // } // } //} //void StraussProcess::CheckBeta(long int xsidepomm, long int ysidepomm, // double *betapomm){ // long int i,j,k; // // double d1; // k=0; // // Rprintf("\ndiagnostic message: Strauss CalcBeta... %ld %ld\n",xsidepomm,ysidepomm); // for(i=0; i0.001) && (k==0)){ // Rprintf("%f %f %f %ld %ld\n",fabs(*(betapomm + i*ysidepomm + j)- beta), // *(betapomm + i*ysidepomm + j),beta,i,j); // k++; // // scanf("%lf",&d1); // } // } // } //} //double StraussProcess::lnCondInt(struct Point2 *TempCell, // Point2Pattern *p2p){ // double f1; // long int xco,yco,xc,yc,fx,tx,fy,ty,ry,rx,k; // double dy,dx, lnCI,dst2; // struct Point2 *TempCell2; // // f1 = (TempCell->X-p2p->Xmin)/p2p->XCellDim; xc = int(f1); // CLAMP(xc, 0, p2p->MaxXCell, "xc"); // f1 = (TempCell->Y-p2p->Ymin)/p2p->YCellDim; yc = int(f1); // CLAMP(yc, 0, p2p->MaxYCell, "yc"); // // dx = (Xmax-Xmin)/(double(p2p->MaxXCell+1)); // dy = (Ymax-Ymin)/(double(p2p->MaxYCell+1)); // rx = int(this->InteractionRange/dx+1.0); // ry = int(this->InteractionRange/dy+1.0); // // lnCI = log(TempCell->Beta); // // k = 0; // // if((xc+rx)<=p2p->MaxXCell) tx=xc+rx; else tx = p2p->MaxXCell; // if((yc+ry)<=p2p->MaxYCell) ty=yc+ry; else ty = p2p->MaxYCell; // if((xc-rx)>=0) fx=xc-rx; else fx = 0; // if((yc-ry)>=0) fy=yc-ry; else fy = 0; // // //Rprintf("MCI! %d %d %d %d\n",fx,tx,fy,ty); // // for(xco = fx; xco <= tx; xco++){ // for(yco = fy; yco <= ty; yco++){ // CHECK(p2p->headCell[xco][yco], // "internal error: p2p->headCell[xco][yco] is null in lnCondInt()"); // TempCell2 = p2p->headCell[xco][yco]->next; // CHECK(TempCell2, "internal error: TempCell2 is null in lnCondInt()"); // while(TempCell2!=TempCell2->next){ // if(TempCell2 != TempCell){ // k++; // dst2 = pow(TempCell->X-TempCell2->X,2)+ // pow(TempCell->Y-TempCell2->Y,2); // lnCI += log(Interaction(dst2)); // } // TempCell2 = TempCell2->next; // CHECK(TempCell2, // "internal error: TempCell2 is null in lnCondInt() loop"); // } // } // } // return(lnCI); //} //void StraussProcess::Beta(struct Point2 *TempCell){ // TempCell->Beta = beta; //} //void StraussProcess::CalcBeta(Point2Pattern *p2p){ // long int xco,yco; // // double dy,dx; // struct Point2 *TempMother; // // for(xco = 0; xco <= p2p->MaxXCell; xco++){ // for(yco = 0; yco <= p2p->MaxYCell; yco++){ // CHECK(p2p->headCell[xco][yco], // "internal error: p2p->headCell[xco][yco] is null in CalcBeta()"); // TempMother = p2p->headCell[xco][yco]->next; // CHECK(TempMother, "internal error: TempMother is null in CalcBeta()"); // while(TempMother!=TempMother->next){ // TempMother->Beta = this->beta; // TempMother = TempMother->next; // CHECK(TempMother, // "internal error: TempMother is null in CalcBeta() loop"); // } // } // } //} // ........................... Interface to R .......................... extern "C" { SEXP PerfectStrauss(SEXP beta, SEXP gamma, SEXP r, SEXP xrange, SEXP yrange) { // input parameters double Beta, Gamma, R, Xmin, Xmax, Ymin, Ymax; double *Xrange, *Yrange; // internal int xcells, ycells; long int EndTime, StartTime; // output int noutmax; SEXP xout, yout, nout, out; double *xx, *yy; int *nn; SEXP stout, etout; int *ss, *ee; // protect arguments from garbage collector PROTECT(beta = AS_NUMERIC(beta)); PROTECT(gamma = AS_NUMERIC(gamma)); PROTECT(r = AS_NUMERIC(r)); PROTECT(xrange = AS_NUMERIC(xrange)); PROTECT(yrange = AS_NUMERIC(yrange)); // that's 5 protected objects // extract arguments Beta = *(NUMERIC_POINTER(beta)); Gamma = *(NUMERIC_POINTER(gamma)); R = *(NUMERIC_POINTER(r)); Xrange = NUMERIC_POINTER(xrange); Xmin = Xrange[0]; Xmax = Xrange[1]; Yrange = NUMERIC_POINTER(yrange); Ymin = Yrange[0]; Ymax = Yrange[1]; // compute cell array size xcells = (int) floor((Xmax-Xmin)/ R); if(xcells > 9) xcells = 9; if(xcells < 1) xcells = 1; ycells = (int) floor((Ymax-Ymin)/ R); if(ycells > 9) ycells = 9; if(ycells < 1) ycells = 1; #ifdef DBGS Rprintf("xcells %d ycells %d\n",xcells,ycells); Rprintf("Initialising\n"); #endif // Initialise Strauss point process StraussProcess ExampleProcess(Xmin,Xmax,Ymin,Ymax, Beta, Gamma, R); // Initialise point pattern Point2Pattern ExamplePattern(Xmin,Xmax,Ymin,Ymax, xcells, ycells); // parameters: min x, max x, min y, max y, "cells" in x and y direction // used for speeding up neighbour counting, 9 is max here #ifdef DBGS Rprintf("Initialisation complete\n"); #endif // Synchronise random number generator GetRNGstate(); // Initialise perfect sampler Sampler PerfectSampler(&ExampleProcess); // Perform perfect sampling PerfectSampler.Sim(&ExamplePattern, &StartTime, &EndTime); // Synchronise random number generator PutRNGstate(); // Get upper estimate of number of points noutmax = ExamplePattern.UpperCount() + 1; // Allocate space for output PROTECT(xout = NEW_NUMERIC(noutmax)); PROTECT(yout = NEW_NUMERIC(noutmax)); PROTECT(nout = NEW_INTEGER(1)); PROTECT(stout = NEW_INTEGER(1)); PROTECT(etout = NEW_INTEGER(1)); xx = NUMERIC_POINTER(xout); yy = NUMERIC_POINTER(yout); nn = INTEGER_POINTER(nout); ss = INTEGER_POINTER(stout); ee = INTEGER_POINTER(etout); // copy data into output storage ExamplePattern.Return(xx, yy, nn, noutmax); *ss = StartTime; *ee = EndTime; // pack up into output list PROTECT(out = NEW_LIST(5)); SET_VECTOR_ELT(out, 0, xout); SET_VECTOR_ELT(out, 1, yout); SET_VECTOR_ELT(out, 2, nout); SET_VECTOR_ELT(out, 3, stout); SET_VECTOR_ELT(out, 4, etout); // return UNPROTECT(11); // 5 arguments plus xout, yout, nout, stout, etout, out return(out); } } spatstat/src/pcf3.c0000755000176000001440000001201112333041761013731 0ustar ripleyusers#include #include #include #include #include "geom3.h" #include "functable.h" #include "chunkloop.h" #include "constants.h" /* $Revision: 1.7 $ $Date: 2012/03/27 05:01:41 $ pair correlation function of 3D point pattern (Epanechnikov kernel) pcf3trans translation correction pcf3isot isotropic correction */ #define FOURPI (2.0 * M_2PI) void pcf3trans(p, n, b, pcf, delta) Point *p; int n; Box *b; Ftable *pcf; double delta; { register int i, j, l, lmin, lmax, maxchunk; register double dx, dy, dz, dist; register double vx, vy, vz, tval; Point *ip, *jp; double dt, vol, lambda, denom; double coef, twocoef, frac, invweight, kernel; double sphesfrac(), sphevol(); /* compute denominator & initialise numerator*/ vol = (b->x1 - b->x0) * (b->y1 - b->y0) * (b->z1 - b->z0); lambda = ((double) n )/ vol; denom = lambda * lambda; for(l = 0; l < pcf->n; l++) { (pcf->denom)[l] = denom; (pcf->num)[l] = 0.0; } /* spacing of argument in result vector */ dt = (pcf->t1 - pcf->t0)/(pcf->n - 1); /* compute numerator */ OUTERCHUNKLOOP(i, n, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, n, maxchunk, 8196) { ip = p + i; for(j = i + 1; j < n; j++) { /* compute pairwise distance */ jp = p + j; dx = jp->x - ip->x; dy = jp->y - ip->y; dz = jp->z - ip->z; dist = sqrt(dx * dx + dy * dy + dz * dz); lmin = ceil( ((dist - delta) - pcf->t0) / dt ); lmax = floor( ((dist + delta) - pcf->t0) / dt ); if(lmax >= 0 && lmin < pcf->n) { /* kernel centred at 'dist' has nonempty intersection with specified range of t values */ /* compute intersection */ if(lmin < 0) lmin = 0; if(lmax >= pcf->n) lmax = pcf->n - 1; /* compute (inverse) edge correction weight */ vx = b->x1 - b->x0 - (dx > 0 ? dx : -dx); vy = b->y1 - b->y0 - (dy > 0 ? dy : -dy); vz = b->z1 - b->z0 - (dz > 0 ? dz : -dz); invweight = vx * vy * vz * FOURPI * dist * dist; if(invweight > 0.0) { for(l = lmin; l < pcf->n; l++) { tval = pcf->t0 + l * dt; /* unnormalised Epanechnikov kernel with halfwidth delta */ frac = (dist - tval)/delta; kernel = (1 - frac * frac); if(kernel > 0) (pcf->num)[l] += kernel / invweight; } } } } } } /* constant factor in kernel */ coef = 3.0/(4.0 * delta); /* multiplied by 2 because we only visited i < j pairs */ twocoef = 2.0 * coef; /* normalise kernel and compute ratio estimate */ for(l = 0; l < pcf->n; l++) { (pcf->num)[l] *= twocoef; (pcf->f)[l] = ((pcf->denom)[l] > 0.0) ? (pcf->num)[l] / (pcf->denom)[l] : 0.0; } } void pcf3isot(p, n, b, pcf, delta) Point *p; int n; Box *b; Ftable *pcf; double delta; { register int i, j, l, lmin, lmax, maxchunk; register double dx, dy, dz, dist; Point *ip, *jp; double dt, vol, denom, mass, tval; double coef, frac, kernel; double sphesfrac(), sphevol(); Point vertex; Box half; /* compute denominator & initialise numerator*/ vol = (b->x1 - b->x0) * (b->y1 - b->y0) * (b->z1 - b->z0); denom = ((double) (n * n))/vol; for(l = 0; l < pcf->n; l++) { (pcf->denom)[l] = denom; (pcf->num)[l] = 0.0; } /* spacing of argument in result vector */ dt = (pcf->t1 - pcf->t0)/(pcf->n - 1); /* set up for volume correction */ vertex.x = b->x0; vertex.y = b->y0; vertex.z = b->z0; half.x1 = b->x1; half.y1 = b->y1; half.z1 = b->z1; half.x0 = (b->x0 + b->x1)/2.0; half.y0 = (b->y0 + b->y1)/2.0; half.z0 = (b->z0 + b->z1)/2.0; /* compute numerator */ OUTERCHUNKLOOP(i, n, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, n, maxchunk, 8196) { ip = p + i; for(j = i + 1; j < n; j++) { jp = p + j; dx = jp->x - ip->x; dy = jp->y - ip->y; dz = jp->z - ip->z; dist = sqrt(dx * dx + dy * dy + dz * dz); lmin = ceil( ((dist - delta) - pcf->t0) / dt ); lmax = floor( ((dist + delta) - pcf->t0) / dt ); if(lmax >= 0 && lmin < pcf->n) { /* kernel centred at 'dist' has nonempty intersection with specified range of t values */ /* compute intersection */ if(lmin < 0) lmin = 0; if(lmax >= pcf->n) lmax = pcf->n - 1; /* compute edge correction weight */ mass = (1.0 / sphesfrac(ip, b, dist)) + (1.0 / sphesfrac(jp, b, dist)); mass *= 1.0 - 8.0 * sphevol(&vertex, &half, dist) / vol; if(mass > 0.0) { mass /= FOURPI * dist * dist; for(l = lmin; l < pcf->n; l++) { tval = pcf->t0 + l * dt; /* unnormalised Epanechnikov kernel with halfwidth delta */ frac = (dist - tval)/delta; kernel = (1 - frac * frac); if(kernel > 0) (pcf->num)[l] += kernel * mass; } } } } } } /* constant factor in kernel */ coef = 3.0/(4.0 * delta); /* normalise kernel and compute ratio estimate */ for(l = 0; l < pcf->n; l++) { (pcf->num)[l] *= coef; (pcf->f)[l] = ((pcf->denom)[l] > 0.0)? (pcf->num)[l] / (pcf->denom)[l] : 0.0; } } spatstat/src/straush.c0000755000176000001440000000573512333041761014606 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Hard core Strauss process */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct StraussHard { double gamma; double r; /* interaction distance */ double h; /* hard core distance */ double loggamma; double r2; double h2; double r2h2; /* r^2 - h^2 */ double *period; int hard; int per; } StraussHard; /* initialiser function */ Cdata *straushinit(state, model, algo) State state; Model model; Algor algo; { StraussHard *strausshard; strausshard = (StraussHard *) R_alloc(1, sizeof(StraussHard)); /* Interpret model parameters*/ strausshard->gamma = model.ipar[0]; strausshard->r = model.ipar[1]; /* No longer passed as r^2 */ strausshard->h = model.ipar[2]; /* No longer passed as h^2 */ strausshard->r2 = pow(strausshard->r, 2); strausshard->h2 = pow(strausshard->h, 2); strausshard->r2h2 = strausshard->r2 - strausshard->h2; strausshard->period = model.period; /* is the interaction numerically equivalent to hard core ? */ strausshard->hard = (strausshard->gamma < DOUBLE_EPS); strausshard->loggamma = (strausshard->hard) ? 0 : log(strausshard->gamma); /* periodic boundary conditions? */ strausshard->per = (model.period[0] > 0.0); return((Cdata *) strausshard); } /* conditional intensity evaluator */ double straushcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, kount, ix, ixp1, j; double *x, *y; double u, v; double r2, r2h2, cifval; StraussHard *strausshard; double *period; DECLARE_CLOSE_VARS; strausshard = (StraussHard *) cdata; r2 = strausshard->r2; r2h2 = strausshard->r2h2; period = strausshard->period; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; if(npts == 0) return((double) 1.0); kount = 0; ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(strausshard->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC(u,v,x[j],y[j],period,r2)) { /* RESIDUE = r2 - distance^2 */ if(RESIDUE > r2h2) return(0.0); ++kount; } } } if(ixp1 < npts) { for(j=ixp1; j r2h2) return(0.0); ++kount; } } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE(u,v,x[j],y[j],r2)) { if(RESIDUE > r2h2) return(0.0); ++kount; } } } if(ixp1 < npts) { for(j=ixp1; j r2h2) return(0.0); ++kount; } } } } if(strausshard->hard) { if(kount > 0) cifval = 0.0; else cifval = 1.0; } else cifval = exp(strausshard->loggamma*kount); return cifval; } Cifns StraussHardCifns = { &straushinit, &straushcif, (updafunptr) NULL, NO}; spatstat/src/crossloop.h0000644000176000001440000000341112333041761015127 0ustar ripleyusers/* crossloop.h Generic code template for loop for cross-close-pairs operations collecting contributions to point x_i from all points y_j such that ||x_i - y_j|| <= r cpp variables used: INITIAL_I code executed at start of 'i' loop CONTRIBUTE_IJ code executed to compute contribution from j to i COMMIT_I code executed to save total contribution to i C variables used: int i, j, n1, n2, maxchunk, jleft; double x1i, y1i, xleft, dx, dy, d2, rmax, r2max; double *x1, *y1, *x2, *y2; $Revision: 1.2 $ $Date: 2014/04/02 07:59:10 $ */ #ifndef CHUNKLOOP_H #include "chunkloop.h" #endif #define CROSSLOOP(INITIAL_I, CONTRIBUTE_IJ, COMMIT_I) \ OUTERCHUNKLOOP(i, n1, maxchunk, 65536) { \ R_CheckUserInterrupt(); \ INNERCHUNKLOOP(i, n1, maxchunk, 65536) { \ \ x1i = x1[i]; \ y1i = y1[i]; \ \ INITIAL_I; \ \ jleft = 0; \ \ /* \ adjust starting point jleft \ */ \ xleft = x1i - rmax; \ while((x2[jleft] < xleft) && (jleft+1 < n2)) \ ++jleft; \ \ /* \ process from j = jleft until dx > rmax \ */ \ for(j=jleft; j < n2; j++) { \ dx = x2[j] - x1i; \ if(dx > rmax) \ break; \ dy = y2[j] - y1i; \ d2 = dx * dx + dy * dy; \ if(d2 <= r2max) { \ /* add this (i, j) pair to output */ \ CONTRIBUTE_IJ; \ } \ } \ COMMIT_I; \ } \ } spatstat/src/badgey.c0000755000176000001440000003136512333041761014346 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" /* To get debug output, insert the line: #define DEBUG 1 */ void fexitc(const char *msg); /* Conditional intensity function for a multiscale saturation process. parameter vector: ipar[0] = ndisc ipar[1] = gamma[0] ipar[2] = r[0] ipar[3] = s[0] ... */ typedef struct BadGey { /* model parameters */ int ndisc; double *gamma; double *r; double *s; /* transformations of the parameters */ double *r2; double *loggamma; int *hard; /* periodic distance */ double *period; int per; /* auxiliary counts */ int *aux; /* matrix[ndisc, npmax]: neighbour counts in current state */ int *tee; /* vector[ndisc] : neighbour count at point in question */ double *w; /* vector[ndisc] : sum of changes in counts at other points */ } BadGey; Cdata *badgeyinit(state, model, algo) State state; Model model; Algor algo; { int i, j, k, i0, ndisc, nmatrix; double r, g, d2; BadGey *badgey; /* create storage */ badgey = (BadGey *) R_alloc(1, sizeof(BadGey)); badgey->ndisc = ndisc = model.ipar[0]; /* Allocate space for parameter vectors */ badgey->gamma = (double *) R_alloc((size_t) ndisc, sizeof(double)); badgey->r = (double *) R_alloc((size_t) ndisc, sizeof(double)); badgey->s = (double *) R_alloc((size_t) ndisc, sizeof(double)); /* Derived values */ badgey->r2 = (double *) R_alloc((size_t) ndisc, sizeof(double)); badgey->loggamma = (double *) R_alloc((size_t) ndisc, sizeof(double)); badgey->hard = (int *) R_alloc((size_t) ndisc, sizeof(int)); /* copy and transform parameters */ for(i=0; i < ndisc; i++) { i0 = 3*i + 1; g = badgey->gamma[i] = model.ipar[i0]; r = badgey->r[i] = model.ipar[i0 + 1]; badgey->s[i] = model.ipar[i0 + 2]; badgey->r2[i] = r * r; badgey->hard[i] = (g < DOUBLE_EPS); badgey->loggamma[i] = (g < DOUBLE_EPS) ? 0 : log(g); } /* periodic boundary conditions? */ badgey->period = model.period; badgey->per = (model.period[0] > 0.0); /* Allocate scratch space */ badgey->tee = (int *) R_alloc((size_t) ndisc, sizeof(int)); badgey->w = (double *) R_alloc((size_t) ndisc, sizeof(double)); /* Allocate space for auxiliary counts */ nmatrix = ndisc * state.npmax; badgey->aux = (int *) R_alloc((size_t) nmatrix, sizeof(int)); /* Initialise auxiliary counts */ for(i = 0; i < nmatrix; i++) badgey->aux[i] = 0; for(i = 0; i < state.npts; i++) { for(j = 0; j < state.npts; j++) { if(j == i) continue; d2 = dist2either(state.x[i], state.y[i], state.x[j], state.y[j], badgey->period); for(k = 0; k < ndisc; k++) { if(d2 < badgey->r2[k]) MAT(badgey->aux, k, i, ndisc) += 1; } } } #ifdef DEBUG Rprintf("Finished initialiser; ndisc=%d\n", ndisc); #endif return((Cdata *) badgey); } #define AUX(I,J) MAT(aux, I, J, ndisc) double badgeycif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int ix, j, k, npts, ndisc, tk; double u, v, d2; double a, dd2, b, f, r2, s, cifval; double *x, *y; int *tee, *aux; double *w; BadGey *badgey; badgey = (BadGey *) cdata; #ifdef DEBUG Rprintf("Entering badgeycif\n"); #endif npts = state.npts; cifval = 1.0; if(npts==0) return cifval; x = state.x; y = state.y; u = prop.u; v = prop.v; ix = prop.ix; ndisc = badgey->ndisc; tee = badgey->tee; aux = badgey->aux; w = badgey->w; /* For disc k, tee[k] = neighbour count at the point in question; w[k] = sum of changes in (saturated) neighbour counts at other points */ if(prop.itype == BIRTH) { /* compute tee[k] and w[k] from scratch */ for(k = 0; k < ndisc; k++) { tee[k] = 0; w[k] = 0.0; } if(badgey->per) { /* periodic distance */ for(j=0; jperiod); for(k = 0; k < ndisc; k++) { if(d2 < badgey->r2[k]) { tee[k]++; f = badgey->s[k] - AUX(k,j); if(f > 1) /* j is not saturated after addition of (u,v) */ w[k] += 1; /* addition of (u,v) increases count by 1 */ else if(f > 0) /* j becomes saturated by addition of (u,v) */ w[k] += f; } } } } else { /* Euclidean distance */ for(j=0; jr2[k]) { tee[k]++; f = badgey->s[k] - AUX(k,j); if(f > 1) /* j is not saturated after addition of (u,v) */ w[k] += 1; /* addition of (u,v) increases count by 1 */ else if(f > 0) /* j becomes saturated by addition of (u,v) */ w[k] += f; } } } } } else if(prop.itype == DEATH) { /* extract current auxiliary counts for point ix */ /* compute w[k] from scratch */ for(k = 0; k < ndisc; k++) { tee[k] = AUX(k,ix); w[k] = 0.0; } /* compute change in counts for other points */ if(badgey->per) { /* Periodic distance */ for(j=0; jperiod); for(k = 0; k < ndisc; k++) { if(d2 < badgey->r2[k]) { f = badgey->s[k] - AUX(k,j); if(f > 0) /* j is not saturated */ w[k] += 1; /* deletion of 'ix' decreases count by 1 */ else { f += 1; if(f > 0) { /* j is not saturated after deletion of 'ix' (s must be fractional) */ w[k] += f; } } } } } } else { /* Euclidean distance */ for(j=0; jr2[k]) { f = badgey->s[k] - AUX(k,j); if(f > 0) /* j is not saturated */ w[k] += 1; /* deletion of 'ix' decreases count by 1 */ else { f += 1; if(f > 0) { /* j is not saturated after deletion of 'ix' (s must be fractional) */ w[k] += f; } } } } } } } else if(prop.itype == SHIFT) { /* compute auxiliary counts from scratch */ for(k = 0; k < ndisc; k++) { tee[k] = 0; w[k] = 0.0; } /* Compute the cif at the new point, not the ratio of new/old */ if(badgey->per) { /* periodic distance */ for(j=0; jperiod); for(k = 0; k < ndisc; k++) { r2 = badgey->r2[k]; if(d2 < r2) { /* shifted point is a neighbour of point j */ tee[k]++; a = AUX(k,j); s = badgey->s[k]; /* Adjust */ dd2 = dist2(x[ix],y[ix], x[j],y[j],badgey->period); if(dd2 < r2) a -= 1; b = a + 1; /* b is the number of neighbours of point j in new state */ if(a < s && s < b) { w[k] += s - a; /* s is fractional and j is saturated */ } else if(s >= b) w[k] += 1; } } } } else { /* Euclidean distance */ for(j=0; jr2[k]; if(d2 < r2) { /* shifted point is a neighbour of point j */ tee[k]++; a = AUX(k,j); s = badgey->s[k]; /* Adjust */ dd2 = pow(x[ix] - x[j], 2) + pow(y[ix] - y[j], 2); if(dd2 < r2) a -= 1; b = a + 1; /* b is the number of neighbours of point j in new state */ if(a < s && s < b) { w[k] += s - a; /* s is fractional and j is saturated */ } else if(s >= b) w[k] += 1; } } } } } #ifdef DEBUG Rprintf("ndisc=%d\n", ndisc); #endif /* compute total change in saturated count */ for(k = 0; k < ndisc; k++) { s = badgey->s[k]; tk = tee[k]; w[k] += ((tk < s) ? tk : s); #ifdef DEBUG Rprintf("s[%d]=%lf, t[%d]=%d, w[%d]=%lf\n", k, s, k, tk, k, w[k]); #endif } /* evaluate cif */ for(k = 0; k < ndisc; k++) { if(badgey->hard[k]) { if(tee[k] > 0) return(0.0); /* else cifval multiplied by 0^0 = 1 */ } else cifval *= exp(badgey->loggamma[k] * w[k]); } return cifval; } void badgeyupd(state, prop, cdata) State state; Propo prop; Cdata *cdata; { /* Declare other variables */ int ix, npts, ndisc, j, k; double u, v, xix, yix, r2, d2, d2old, d2new; double *x, *y; int *aux; BadGey *badgey; badgey = (BadGey *) cdata; aux = badgey->aux; /* 'state' is current state before transition */ x = state.x; y = state.y; npts = state.npts; ndisc = badgey->ndisc; #ifdef DEBUG Rprintf("start update ---- \n"); for(j=0; j < npts; j++) { for(k=0; k < ndisc; k++) Rprintf("aux[%d,%d]=%d\t", k, j, AUX(k,j)); Rprintf("\n"); } #endif if(prop.itype == BIRTH) { #ifdef DEBUG Rprintf("Update for birth ---- \n"); #endif /* Birth */ u = prop.u; v = prop.v; /* initialise auxiliary counters for new point x[npts], y[npts] */ for(k = 0; k < ndisc; k++) AUX(k, npts) = 0; /* update all auxiliary counters */ if(badgey->per) { /* periodic distance */ for(j=0; j < npts; j++) { d2 = dist2(u,v,x[j],y[j],badgey->period); for(k = 0; k < ndisc; k++) { if(d2 < badgey->r2[k]) { AUX(k, j) += 1; AUX(k, npts) += 1; } } } } else { /* Euclidean distance */ for(j=0; j < npts; j++) { d2 = pow(u - x[j], 2) + pow(v - y[j], 2); for(k = 0; k < ndisc; k++) { if(d2 < badgey->r2[k]) { AUX( k, j) += 1; AUX( k, npts) += 1; } } } } #ifdef DEBUG Rprintf("end update ---- \n"); for(j=0; j <= npts; j++) { for(k=0; k < ndisc; k++) Rprintf("aux[%d,%d]=%d\t", k, j, AUX(k,j)); Rprintf("\n"); } #endif return; } if(prop.itype == DEATH) { /* Death */ ix = prop.ix; u = x[ix]; v = y[ix]; #ifdef DEBUG Rprintf("--- Update for death of point %d = (%lf,%lf) ---- \n", ix, u, v); #endif /* Decrement auxiliary counter for each neighbour of deleted point, and remove entry corresponding to deleted point */ if(badgey->per) { /* periodic distance */ for(j=0; jperiod); for(k = 0; k < ndisc; k++) { if(d2 < badgey->r2[k]) { if(j < ix) AUX(k,j) -= 1; else AUX(k,j-1) = AUX(k,j) - 1; } else if(j >= ix) AUX(k,j-1) = AUX(k,j); } } } else { /* Euclidean distance */ for(j=0; jr2[k]) { #ifdef DEBUG Rprintf("hit for point %d with radius r[%d]\n", j, k); #endif if(j < ix) AUX(k,j) -= 1; else AUX(k,j-1) = AUX(k,j) - 1; } else if(j >= ix) AUX(k,j-1) = AUX(k,j); } } } #ifdef DEBUG Rprintf("end update ---- \n"); for(j=0; j < npts-1; j++) { for(k=0; k < ndisc; k++) Rprintf("aux[%d,%d]=%d\t", k, j, AUX(k,j)); Rprintf("\n"); } #endif return; } if(prop.itype == SHIFT) { #ifdef DEBUG Rprintf("Update for shift ---- \n"); #endif /* Shift */ u = prop.u; v = prop.v; ix = prop.ix; xix = x[ix]; yix = y[ix]; /* recompute all auxiliary counters for point ix */ for(k = 0; k < ndisc; k++) AUX(k,ix) = 0; if(badgey->per) { for(j=0; jperiod); d2old = dist2(xix,yix,x[j],y[j],badgey->period); for(k = 0; k < ndisc; k++) { r2 = badgey->r2[k]; if(d2old >= r2 && d2new >= r2) continue; if(d2new < r2) { /* increment neighbour count for new point */ AUX(k,ix) += 1; if(d2old >= r2) AUX(k,j) += 1; /* point j gains a new neighbour */ } else if(d2old < r2) AUX(k,j) -= 1; /* point j loses a neighbour */ } } } else { /* Euclidean distance */ for(j=0; jr2[k]; if(d2old >= r2 && d2new >= r2) continue; if(d2new < r2) { #ifdef DEBUG Rprintf("shifted point is close to j=%d\n", j); #endif /* increment neighbour count for new point */ AUX(k,ix) += 1; if(d2old >= r2) { #ifdef DEBUG Rprintf("\t(previous position was not)\n"); #endif AUX(k,j) += 1; /* point j gains a new neighbour */ } } else if(d2old < r2) { #ifdef DEBUG Rprintf("previous position was close to j=%d, shifted point is not\n", j); #endif AUX(k,j) -= 1; /* point j loses a neighbour */ } } } } #ifdef DEBUG Rprintf("end update ---- \n"); for(j=0; j < npts; j++) { for(k=0; k < ndisc; k++) Rprintf("aux[%d,%d]=%d\t", k, j, AUX(k,j)); Rprintf("\n"); } #endif return; } fexitc("Unrecognised transition type; bailing out.\n"); } Cifns BadGeyCifns = { &badgeyinit, &badgeycif, &badgeyupd, NO}; spatstat/src/mhsnoop.h0000644000176000001440000000050012333041761014563 0ustar ripleyusers/* Function declarations from mhsnoop.c $Revision: 1.4 $ $Date: 2013/05/27 02:09:10 $ */ #include "mhsnoopdef.h" void initmhsnoop(Snoop *s, SEXP env); void mhsnoop(Snoop *s, int irep, Algor *algo, State *state, Propo *prop, double numer, double denom, int *itype); spatstat/src/sumsymouter.h0000644000176000001440000000343412333041761015525 0ustar ripleyusers/* sumsymouter.h Code template for some functions in linalg.c $Revision: 1.3 $ $Date: 2013/04/18 11:55:24 $ Macros used: FNAME = function name, WEIGHTED = #defined for weighted version */ void FNAME( x, #ifdef WEIGHTED w, #endif p, n, y ) double *x; /* p by n by n array */ #ifdef WEIGHTED double *w; /* n by n matrix (symmetric) */ #endif int *p, *n; double *y; /* output matrix p by p, initialised to zero */ { int N, P; register int i, j, k, m, ijpos, jipos, maxchunk; register double *xij, *xji; #ifdef WEIGHTED register double wij; #endif N = *n; P = *p; OUTERCHUNKLOOP(i, N, maxchunk, 256) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, N, maxchunk, 256) { /* loop over j != i */ if(i > 0) { for(j = 0; j < i; j++) { /* pointers to [i,j] and [j,i] in N*N matrices */ ijpos = i + N * j; jipos = j + N * i; /* pointers to x[, i, j] and x[ , j, i] */ xij = x + ijpos * P; xji = x + jipos * P; /* outer product */ #ifdef WEIGHTED wij = w[ijpos]; #endif for(k = 0; k < P; k++) { for(m = 0; m < P; m++) { #ifdef WEIGHTED y[m + k * P] += wij * xij[m] * xji[k]; #else y[m + k * P] += xij[m] * xji[k]; #endif } } } } if(i + 1 < N) { for(j = i+1; j < N; j++) { /* pointers to [i,j] and [j,i] in N*N matrices */ ijpos = i + N * j; jipos = j + N * i; /* pointers to x[, i, j] and x[ , j, i] */ xij = x + ijpos * P; xji = x + jipos * P; /* outer product */ #ifdef WEIGHTED wij = w[ijpos]; #endif for(k = 0; k < P; k++) { for(m = 0; m < P; m++) { #ifdef WEIGHTED y[m + k * P] += wij * xij[m] * xji[k]; #else y[m + k * P] += xij[m] * xji[k]; #endif } } } } /* end of loop over j */ } } } spatstat/src/sphevol.c0000755000176000001440000000746412333041761014576 0ustar ripleyusers#include #include #include "geom3.h" /* $Revision: 1.2 $ $Date: 2013/05/27 02:09:10 $ Routine for calculating ABSOLUTE volume of intersection between sphere and box Arbitrary positions: point is allowed to be inside or outside box. # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # MODIFIED BY: Adrian Baddeley, Perth 2009 # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ #ifdef DEBUG #define DBG(X,Y) Rprintf("%s: %f\n", (X), (Y)); #else #define DBG(X,Y) #endif #include "yesno.h" #define ABS(X) ((X >= 0.0) ? (X) : -(X)) static double rcubed, spherevol; double sphevol(point, box, r) Point *point; Box *box; double r; { double sum, p[4], q[4]; double v1(), v2(), v3(); int i, j; rcubed = r * r * r; spherevol = (4.0/3.0) * PI * rcubed; p[1] = box->x0 - point->x; p[2] = box->y0 - point->y; p[3] = box->z0 - point->z; q[1] = box->x1 - point->x; q[2] = box->y1 - point->y; q[3] = box->z1 - point->z; sum = 0; for(i = 1; i <= 3; i++) { sum += v1(p[i], -1, r) + v1(q[i], 1, r); #ifdef DEBUG Rprintf("i = %d, v1 = %f, v1 = %f\n", i, v1(p[i], -1, r), v1(q[i], 1, r)); #endif } DBG("Past v1", sum) for(i = 1; i < 3; i++) for(j = i+1; j <= 3; j++) { sum -= v2(p[i], -1, p[j], -1, r) + v2(p[i], -1, q[j], 1, r) + v2(q[i], 1, p[j], -1, r) + v2(q[i], 1, q[j], 1, r); #ifdef DEBUG Rprintf("i = %d, j = %d, sum = %f\n", i, j, sum); #endif } DBG("Past v2", sum) sum += v3(p[1], -1, p[2], -1, p[3], -1, r) + v3(p[1], -1, p[2], -1, q[3], 1, r); DBG("sum", sum) sum += v3(p[1], -1, q[2], 1, p[3], -1, r) + v3(p[1], -1, q[2], 1, q[3], 1, r); DBG("sum", sum) sum += v3(q[1], 1, p[2], -1, p[3], -1, r) + v3(q[1], 1, p[2], -1, q[3], 1, r); DBG("sum", sum) sum += v3(q[1], 1, q[2], 1, p[3], -1, r) + v3(q[1], 1, q[2], 1, q[3], 1, r); DBG("Past v3", sum) DBG("sphere volume", spherevol) return(spherevol - sum); } double v1(a,s,r) double a, r; int s; { double value; double u(); short sign; value = 4.0 * rcubed * u(ABS(a)/r, 0.0, 0.0); sign = (a >= 0.0) ? 1 : -1; if(sign == s) return(value); else return(spherevol - value); } double v2(a, sa, b, sb, r) double a, b, r; int sa, sb; { short sign; double u(); sign = (b >= 0.0) ? 1 : -1; if(sign != sb ) return(v1(a, sa, r) - v2(a, sa, ABS(b), 1, r)); b = ABS(b); sb = 1; sign = (a >= 0.0) ? 1 : -1; if(sign != sa) return(v1(b, sb, r) - v2(ABS(a), 1, b, sb, r)); a = ABS(a); return(2.0 * rcubed * u(a/r, b/r, 0.0)); } double v3(a, sa, b, sb, c, sc, r) double a, b, c, r; int sa, sb, sc; { short sign; double u(); sign = (c >= 0.0) ? 1 : -1; if(sign != sc) return(v2(a,sa,b,sb,r) - v3(a,sa,b,sb, ABS(c), 1, r)); c = ABS(c); sc = 1; sign = (b >= 0.0) ? 1 : -1; if(sign != sb) return(v2(a,sa,c,sc,r) - v3(a,sa,ABS(b),1,c,sc,r)); b = ABS(b); sb = 1; sign = (a >= 0.0) ? 1 : -1; if(sign != sa) return(v2(b,sb, c, sc, r) - v3(ABS(a),1, b, sb, c, sc, r)); a = ABS(a); return(rcubed * u(a/r, b/r, c/r)); } double u(a, b, c) double a, b, c; { double w(); if(a * a + b * b + c * c >= 1.0) return(0.0); return( (PI/12.0) * (2.0 - 3.0 * (a + b + c) + (a * a * a + b * b * b + c * c * c)) + w(a,b) + w(b,c) + w(a,c) - a * b * c ); } double w(x,y) double x,y; /* Arguments assumed >= 0 */ { double z; z = sqrt(1 - x * x - y * y); return( (x / 2.0 - x * x * x / 6.0) * atan2(y, z) + (y / 2.0 - y * y * y / 6.0) * atan2(x, z) - ( atan2(x * y , z) - x * y * z )/3.0 ); } spatstat/src/densptcross.c0000644000176000001440000002226312333041761015454 0ustar ripleyusers#include #include #include "chunkloop.h" #include "crossloop.h" #include "constants.h" /* densptcross.c $Revision: 1.2 $ $Date: 2014/04/02 10:27:43 $ Assumes point patterns are sorted in increasing order of x coordinate *crdenspt Density estimate at points *crsmoopt Smoothed mark values at points */ #define TWOPI M_2PI double sqrt(), exp(); #define STD_DECLARATIONS \ int i, j, n1, n2, maxchunk, jleft; \ double x1i, y1i, xleft, dx, dy, d2, rmax, r2max; \ double *x1, *y1, *x2, *y2; #define STD_INITIALISE \ n1 = *nquery; \ x1 = xq; y1 = yq; \ n2 = *ndata; \ x2 = xd; y2 = yd; \ rmax = *rmaxi; \ r2max = rmax * rmax /* ----------------- density estimation -------------------- */ void crdenspt(nquery, xq, yq, ndata, xd, yd, rmaxi, sig, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *rmaxi; /* maximum distance at which points contribute */ double *sig; /* Gaussian sd */ /* output */ double *result; /* vector of computed density values */ { STD_DECLARATIONS; double resulti, coef; double sigma, twosig2; STD_INITIALISE; sigma = *sig; twosig2 = 2.0 * sigma * sigma; coef = 1.0/(TWOPI * sigma * sigma); if(n1 == 0 || n2 == 0) return; CROSSLOOP( { resulti = 0.0; }, { resulti += exp(-d2/twosig2); } , { result[i] = coef * resulti; }) } void wtcrdenspt(nquery, xq, yq, ndata, xd, yd, wd, rmaxi, sig, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *wd; /* weights of data points */ double *rmaxi; /* maximum distance at which points contribute */ double *sig; /* Gaussian sd */ /* output */ double *result; /* vector of computed density values */ { STD_DECLARATIONS; double resulti, coef; double sigma, twosig2; STD_INITIALISE; sigma = *sig; twosig2 = 2.0 * sigma * sigma; coef = 1.0/(TWOPI * sigma * sigma); if(n1 == 0 || n2 == 0) return; CROSSLOOP( { resulti = 0.0; }, { resulti += wd[j] * exp(-d2/twosig2); }, { result[i] = coef * resulti; } ) } /* ------------- anisotropic versions -------------------- */ void acrdenspt(nquery, xq, yq, ndata, xd, yd, rmaxi, detsigma, sinv, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *rmaxi; /* maximum distance at which points contribute */ double *detsigma; /* determinant of variance matrix */ double *sinv; /* inverse variance matrix (2x2, flattened) */ /* output */ double *result; /* vector of computed density values */ { STD_DECLARATIONS; double resulti, coef; double detsig, s11, s12, s21, s22; STD_INITIALISE; detsig = *detsigma; coef = 1.0/(TWOPI * sqrt(detsig)); s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n1 == 0 || n2 == 0) return; CROSSLOOP( { resulti = 0.0; }, { resulti += exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); }, { result[i] = coef * resulti; }) } void awtcrdenspt(nquery, xq, yq, ndata, xd, yd, wd, rmaxi, detsigma, sinv, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *wd; /* weights of data points */ double *rmaxi; /* maximum distance at which points contribute */ double *detsigma; /* determinant of variance matrix */ double *sinv; /* inverse variance matrix (2x2, flattened) */ /* output */ double *result; /* vector of weighted density values */ { STD_DECLARATIONS; double resulti, coef; double detsig, s11, s12, s21, s22; STD_INITIALISE; detsig = *detsigma; coef = 1.0/(TWOPI * sqrt(detsig)); s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n1 == 0 || n2 == 0) return; CROSSLOOP( { resulti = 0.0; }, { resulti += wd[j] * \ exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); }, { result[i] = coef * resulti; }) } /* --------------- smoothing --------------------------- */ void crsmoopt(nquery, xq, yq, ndata, xd, yd, vd, rmaxi, sig, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *vd; /* mark values at data points */ double *rmaxi; /* maximum distance at which points contribute */ double *sig; /* Gaussian sd */ /* output */ double *result; /* vector of computed smoothed values */ { STD_DECLARATIONS; double sigma, twosig2; double numer, denom, wij; STD_INITIALISE; sigma = *sig; twosig2 = 2.0 * sigma * sigma; if(n1 == 0 || n2 == 0) return; CROSSLOOP({ numer = denom = 0.0; }, { \ wij = exp(-d2/twosig2); \ denom += wij; \ numer += wij * vd[j]; \ }, { \ result[i] = numer/denom; \ }) } void wtcrsmoopt(nquery, xq, yq, ndata, xd, yd, vd, wd, rmaxi, sig, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *vd; /* mark values at data points */ double *wd; /* weights of data points */ double *rmaxi; /* maximum distance */ double *sig; /* Gaussian sd */ /* output */ double *result; /* vector of computed smoothed values */ { STD_DECLARATIONS; double sigma, twosig2; double numer, denom, wij; STD_INITIALISE; sigma = *sig; twosig2 = 2.0 * sigma * sigma; if(n1 == 0 || n2 == 0) return; CROSSLOOP({ numer = denom = 0.0; }, { \ wij = wd[j] * exp(-d2/twosig2); \ denom += wij; \ numer += wij * vd[j]; \ }, { \ result[i] = numer/denom; \ }) } /* ------------- anisotropic versions -------------------- */ void acrsmoopt(nquery, xq, yq, ndata, xd, yd, vd, rmaxi, sinv, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *vd; /* mark values at data points */ double *rmaxi; /* maximum distance at which points contribute */ double *sinv; /* inverse variance matrix (2x2, flattened) */ /* output */ double *result; /* vector of smoothed values */ { STD_DECLARATIONS; double s11, s12, s21, s22; double numer, denom, wij; STD_INITIALISE; s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n1 == 0 || n2 == 0) return; CROSSLOOP({ numer = denom = 0.0; }, { \ wij = exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); \ denom += wij; \ numer += wij * vd[j]; \ }, { \ result[i] = numer/denom; \ }) } void awtcrsmoopt(nquery, xq, yq, ndata, xd, yd, vd, wd, rmaxi, sinv, result) /* inputs */ int *nquery; /* number of locations to be interrogated */ double *xq, *yq; /* (x,y) coordinates to be interrogated */ int *ndata; /* number of data points */ double *xd, *yd; /* (x,y) coordinates of data */ double *vd; /* mark values at data points */ double *wd; /* weights of data points */ double *rmaxi; /* maximum distance at which points contribute */ double *sinv; /* inverse variance matrix (2x2, flattened) */ /* output */ double *result; /* vector of smoothed values */ { STD_DECLARATIONS; double s11, s12, s21, s22; double numer, denom, wij; STD_INITIALISE; s11 = sinv[0]; s12 = sinv[1]; s21 = sinv[2]; s22 = sinv[3]; if(n1 == 0 || n2 == 0) return; CROSSLOOP({ numer = denom = 0.0; }, { \ wij = wd[j] * exp(-(dx * (dx * s11 + dy * s12) \ + dy * (dx * s21 + dy * s22))/2.0); \ denom += wij; \ numer += wij * vd[j]; \ }, { \ result[i] = numer/denom; \ }) } spatstat/src/loccums.h0000644000176000001440000000400112333041761014545 0ustar ripleyusers/* loccums.h C template for loccum.c data-to-data functions $Revision: 1.5 $ $Date: 2013/09/18 04:28:45 $ macros: FNAME function name NULVAL initial value (empty sum = 0, empty product = 1) INC(A,B) increment operation A += B or A *= B */ void FNAME(n, x, y, v, nr, rmax, ans) /* inputs */ int *n, *nr; double *x, *y, *v; double *rmax; /* output */ double *ans; /* matrix of column vectors of functions for each point */ { int N, Nr, Nans; double Rmax; int i, j, k, kmin, maxchunk, columnstart; double Rmax2, rstep, xi, yi; double dx, dy, dx2, d2, d, contrib; N = *n; Nr = *nr; Rmax = *rmax; if(N == 0) return; rstep = Rmax/(Nr-1); Rmax2 = Rmax * Rmax; Nans = Nr * N; /* initialise products to 1 */ OUTERCHUNKLOOP(k, Nans, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(k, Nans, maxchunk, 8196) { ans[k] = NULVAL; } } OUTERCHUNKLOOP(i, N, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, N, maxchunk, 8196) { xi = x[i]; yi = y[i]; columnstart = Nr * i; /* start position for f_i(.) in 'ans' */ /* process backward until |dx| > Rmax */ if(i > 0) { for(j=i-1; j >= 0; j--) { dx = x[j] - xi; dx2 = dx * dx; if(dx2 > Rmax2) break; dy = y[j] - yi; d2 = dx2 + dy * dy; if(d2 <= Rmax2) { d = sqrt(d2); kmin = (int) ceil(d/rstep); if(kmin < Nr) { contrib = v[j]; for(k = kmin; k < Nr; k++) INC(ans[columnstart + k] , contrib); } } } } /* process forward until |dx| > Rmax */ if(i < N - 1) { for(j=i+1; j < N; j++) { dx = x[j] - xi; dx2 = dx * dx; if(dx2 > Rmax2) break; dy = y[j] - yi; d2 = dx2 + dy * dy; if(d2 <= Rmax2) { d = sqrt(d2); kmin = (int) ceil(d/rstep); if(kmin < Nr) { contrib = v[j]; for(k = kmin; k < Nr; k++) INC(ans[columnstart + k] , contrib); } } } } } } } spatstat/src/methas.h0000755000176000001440000000673012333041761014377 0ustar ripleyusers/* Definitions of types and data structures for Metropolis-Hastings State Current state of point pattern Model Model parameters passed from R Cdata (pointer to) model parameters and precomputed data in C Algor Algorithm parameters (p, q, nrep etc) Propo Proposal in Metropolis-Hastings algorithm History Transition history of MH algorithm Cifns Set of functions for computing the conditional intensity for a point process model. This consists of three functions init(State, Model, Algor) .... initialises auxiliary data eval(State, Propo) ........... evaluates cif update(State,Propo) .......... updates auxiliary data */ /* Current state of point pattern */ typedef struct State { double *x; /* vectors of Cartesian coordinates */ double *y; int *marks; /* vector of mark values */ int npts; /* current number of points */ int npmax; /* storage limit */ int ismarked; /* whether the pattern is marked */ } State; /* Parameters of model passed from R */ typedef struct Model { double *beta; /* vector of activity parameters */ double *ipar; /* vector of interaction parameters */ double *period; /* width & height of rectangle, if torus */ int ntypes; /* number of possible marks */ } Model; /* A pointer to Cdata is a pointer to C storage for parameters of model */ typedef void Cdata; /* RMH Algorithm parameters */ typedef struct Algor { double p; /* probability of proposing shift */ double q; /* conditional probability of proposing death */ int fixall; /* if TRUE, only shifts of location are feasible */ int ncond; /* For conditional simulation, the first 'ncond' points are fixed */ int nrep; /* number of iterations */ int nverb; /* print report every 'nverb' iterations */ int nrep0; /* number of iterations already performed in previous blocks - for reporting purposes */ } Algor; /* Metropolis-Hastings proposal */ typedef struct Propo { double u; /* location of point of interest */ double v; int mrk; /* mark of point of interest */ int ix; /* index of point of interest, if already in pattern */ int itype; /* transition type */ } Propo; /* transition codes 'itype' */ #define REJECT 0 #define BIRTH 1 #define DEATH 2 #define SHIFT 3 #define HISTORY_INCLUDES_RATIO /* Record of transition history */ typedef struct History { int nmax; /* length of vectors */ int n; /* number of events recorded */ int *proptype; /* vector: proposal type */ int *accepted; /* vector: 0 for reject, 1 for accept */ #ifdef HISTORY_INCLUDES_RATIO double *numerator; /* vectors: Hastings ratio numerator & denominator */ double *denominator; #endif } History; /* conditional intensity functions */ typedef Cdata * (*initfunptr)(State state, Model model, Algor algo); typedef double (*evalfunptr)(Propo prop, State state, Cdata *cdata); typedef void (*updafunptr)(State state, Propo prop, Cdata *cdata); typedef struct Cifns { initfunptr init; evalfunptr eval; updafunptr update; int marked; } Cifns; #define NEED_UPDATE(X) ((X).update != (updafunptr) NULL) #define NULL_CIFNS { (initfunptr) NULL, (evalfunptr) NULL, (updafunptr) NULL, NO} /* miscellaneous macros */ #include "yesno.h" # define MAT(X,I,J,M) (X[(I)+(J)*(M)]) spatstat/src/KrectBody.h0000644000176000001440000001042512333041761014775 0ustar ripleyusers /* KrectBody.h +++ Copyright (C) Adrian Baddeley, Julian Gilbey and Rolf Turner 2014 ++++ Main function body for 'Krect' Included multiple times with different values of the macros: (#define or #undef) WEIGHTED ISOTROPIC TRANSLATION BORDER UNCORRECTED **Assumes point pattern is sorted in increasing order of x coordinate** **Assumes window is (0,wide) x (0, high) ** **Assumes output vectors were initialised to zero** Variables are declared in 'KrectFunDec.c' This algorithm is optimal (amongst the choices in spatstat) when the window is a rectangle *and* at least one of the ISOTROPIC, TRANSLATION corrections is needed. There are faster algorithms for the border correction on its own. $Revision: 1.3 $ $Date: 2014/02/09 03:01:27 $ */ /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < N) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > N) maxchunk = N; /* ............. LOOP OVER i ................. */ for(; i < maxchunk; i++) { xi = x[i]; yi = y[i]; #ifdef WEIGHTED wi = w[i]; #endif #ifdef BORDER /* For border correction */ /* compute distance to border */ bx = MIN(xi, (wide - xi)); by = MIN(yi, (high - yi)); bdisti = MIN(bx, by); /* denominator will ultimately be incremented for all r < b[i] */ bratio = bdisti/rstep; /* lbord is the largest integer STRICTLY less than bratio */ lbord = (int) ceil(bratio) - 1; lbord = (lbord <= Nr1) ? lbord : Nr1; /* increment entry corresponding to r = b[i] */ #ifdef WEIGHTED if(lbord >= 0) denomAccum[lbord] += wi; #else if(lbord >= 0) (denomAccum[lbord])++; #endif #endif #ifdef ISOTROPIC /* For isotropic correction */ /* perpendicular distance from point i to each edge of rectangle L = left, R = right, D = down, U = up */ dL = xi; dR = wide - xi; dD = yi; dU = high - yi; /* test for corner of the rectangle */ ncor = SMALL(dL) + SMALL(dR) + SMALL(dD) + SMALL(dU); corner = (ncor >= 2); /* angle between - perpendicular to edge of rectangle and - line from point to corner of rectangle */ bLU = atan2(dU, dL); bLD = atan2(dD, dL); bRU = atan2(dU, dR); bRD = atan2(dD, dR); bUL = atan2(dL, dU); bUR = atan2(dR, dU); bDL = atan2(dL, dD); bDR = atan2(dR, dD); #endif /* ............. LOOP OVER j ................. */ /* scan through points (x[j],y[j]) */ /* scan backward from i-1 until |x[j]-x[i]| > Rmax */ if(i > 0) { for(j=i-1; j >= 0; j--) { /* squared interpoint distance */ dx = xi - x[j]; dx2 = dx * dx; if(dx2 >= R2max) break; dy = y[j] - yi; dij2 = dx2 + dy * dy; if(dij2 < R2max) { #include "KrectIncrem.h" } } } /* scan forward from i+1 until x[j]-x[i] > Rmax */ if(i < N1) { for(j=i+1; j < N; j++) { /* squared interpoint distance */ dx = x[j] - xi; dx2 = dx * dx; if(dx2 >= R2max) break; dy = y[j] - yi; dij2 = dx2 + dy * dy; if(dij2 < R2max) { #include "KrectIncrem.h" } } } } } /* .................. END OF LOOPS ................................ */ /* ............. compute cumulative functions ..................... */ #ifdef UNCORRECTED naccum = ZERO; for(l = 0; l < Nr; l++) { unco[l] += naccum; naccum = unco[l]; } #endif #ifdef ISOTROPIC accum = 0.0; for(l = 0; l < Nr; l++) { iso[l] += accum; accum = iso[l]; } #endif #ifdef TRANSLATION accum = 0.0; for(l = 0; l < Nr; l++) { trans[l] += accum; accum = trans[l]; } #endif #ifdef BORDER /* Now use the accumulated values to compute the numerator and denominator. The value of denomAccum[l] should be added to denom[k] for all k <= l. numerHighAccum[l] should be added to numer[k] for all k <=l numerLowAccum[l] should then be subtracted from numer[k] for k <= l. */ for(l=Nr1, naccum=daccum=ZERO; l>=0; l--) { daccum += denomAccum[l]; bdenom[l] = daccum; naccum += numerHighAccum[l]; bnumer[l] = naccum; naccum -= numerLowAccum[l]; } #endif spatstat/src/dist2dpath.h0000644000176000001440000000765512333041761015170 0ustar ripleyusers/* Function body for dist2dpath.c Macros used: FNAME function name DTYPE declaration for distance values ('double' or 'int') FLOATY (DTYPE == 'double') $Revision: 1.3 $ $Date: 2013/05/27 02:09:10 $ */ #undef DEBUG #define MATRIX(X,I,J) (X)[(J) + n * (I)] #define D(I,J) MATRIX(d, I, J) #define DPATH(I,J) MATRIX(dpath, I, J) #define ADJ(I,J) (MATRIX(adj, I, J) != 0) #define INFIN -1 #define FINITE(X) ((X) >= 0) void FNAME(nv, d, adj, dpath, tol, niter, status) int *nv; /* number of vertices */ DTYPE *d; /* matrix of edge lengths */ int *adj; /* 0/1 edge matrix of graph */ DTYPE *tol; /* tolerance threshold (ignored in integer case) */ DTYPE *dpath; /* output - shortest path distance matrix */ int *niter, *status; /* status = 0 for convergence */ { int i, j, k, n, iter, maxiter, changed; DTYPE dij, dik, dkj, dikj; #ifdef FLOATY DTYPE eps, diff, maxdiff; #endif int totaledges, starti, nneighi, increm, pos; int *start, *nneigh, *indx; n = *nv; #ifdef FLOATY eps = *tol; #endif /* initialise and count edges */ *status = -1; totaledges = 0; for(i = 0; i < n; i++) { for(j = 0; j < n; j++) { DPATH(i, j) = (i == j) ? 0 : ((ADJ(i,j)) ? D(i, j) : INFIN); if((i != j) && ADJ(i,j)) ++totaledges; } } maxiter = 2 + ((totaledges > n) ? totaledges : n); /* store indices j for each edge (i,j) */ indx = (int *) R_alloc(totaledges, sizeof(int)); nneigh = (int *) R_alloc(n, sizeof(int)); start = (int *) R_alloc(n, sizeof(int)); pos = 0; for(i = 0; i < n; i++) { nneigh[i] = 0; start[i] = pos; #ifdef DEBUG Rprintf("Neighbours of %d:\n", i); #endif for(j = 0; j < n; j++) { if((i != j) && ADJ(i,j) && FINITE(D(i,j))) { #ifdef DEBUG Rprintf("\t%d\n", j); #endif ++(nneigh[i]); if(pos > totaledges) error("internal error: pos exceeded storage"); indx[pos] = j; ++pos; } } } /* run */ for(iter = 0; iter < maxiter; iter++) { changed = 0; #ifdef FLOATY maxdiff = 0; #endif #ifdef DEBUG Rprintf("--------- iteration %d ---------------\n", iter); #endif for(i = 0; i < n; i++) { R_CheckUserInterrupt(); nneighi = nneigh[i]; if(nneighi > 0) { /* run through neighbours k of i */ starti = start[i]; for(increm = 0, pos=starti; increm < nneighi; ++increm, ++pos) { k = indx[pos]; dik = DPATH(i,k); #ifdef DEBUG #ifdef FLOATY Rprintf("i=%d k=%d dik=%lf\n", i, k, dik); #else Rprintf("i=%d k=%d dik=%d\n", i, k, dik); #endif #endif /* now run through all other vertices j */ for(j = 0; j < n; j++) { if(j != i && j != k) { dij = DPATH(i,j); dkj = DPATH(k,j); if(FINITE(dkj)) { dikj = dik + dkj; #ifdef DEBUG #ifdef FLOATY Rprintf("considering %d -> (%d) -> %d,\t dij=%lf, dikj=%lf\n", i, k, j, dij, dikj); #else Rprintf("considering %d -> (%d) -> %d,\t dij=%d, dikj=%d\n", i, k, j, dij, dikj); #endif #endif if(!FINITE(dij) || dikj < dij) { #ifdef DEBUG #ifdef FLOATY Rprintf("updating i=%d j=%d via k=%d from %lf to %lf\n", i, j, k, dij, dikj); #else Rprintf("updating i=%d j=%d via k=%d from %d to %d\n", i, j, k, dij, dikj); #endif #endif DPATH(i,j) = DPATH(j,i) = dikj; changed = 1; #ifdef FLOATY diff = (FINITE(dij)) ? dij - dikj : dikj; if(diff > maxdiff) maxdiff = diff; #endif } } } } } } } if(changed == 0) { /* algorithm converged */ #ifdef DEBUG Rprintf("Algorithm converged\n"); #endif *status = 0; break; #ifdef FLOATY } else if(FINITE(maxdiff) && maxdiff < eps) { /* tolerance reached */ #ifdef DEBUG Rprintf("Algorithm terminated with maxdiff=%lf\n", maxdiff); #endif *status = 1; break; #endif } } #ifdef DEBUG Rprintf("Returning after %d iterations on %d vertices\n", iter, n); #endif *niter = iter; } #undef DEBUG #undef MATRIX #undef D #undef DPATH #undef ADJ #undef INFIN #undef FINITE spatstat/src/pairloop.h0000644000176000001440000000327512333041761014741 0ustar ripleyusers/* pairloop.h Generic code template for loop collecting contributions to point x_i from all points x_j such that ||x_i - x_j|| <= r cpp variables used: INITIAL_I code executed at start of 'i' loop CONTRIBUTE_IJ code executed to compute contribution from j to i COMMIT_I code executed to save total contribution to i C variables used: int i, j, n, maxchunk; double xi, yi, dx, dy, dx2, d2, r2max; double *x, *y; $Revision: 1.3 $ $Date: 2013/05/27 02:09:10 $ */ #ifndef CHUNKLOOP_H #include "chunkloop.h" #endif #define PAIRLOOP(INITIAL_I, CONTRIBUTE_IJ, COMMIT_I) \ OUTERCHUNKLOOP(i, n, maxchunk, 65536) { \ R_CheckUserInterrupt(); \ INNERCHUNKLOOP(i, n, maxchunk, 65536) { \ \ xi = x[i]; \ yi = y[i]; \ \ INITIAL_I; \ \ if(i > 0) { \ for(j=i-1; j > 0; j--) { \ dx = x[j] - xi; \ dx2 = dx * dx; \ if(dx2 > r2max) \ break; \ dy = y[j] - yi; \ d2 = dx2 + dy * dy; \ if(d2 <= r2max) { \ CONTRIBUTE_IJ; \ } \ } \ } \ \ if(i+1 < n) { \ for(j=i+1; j < n; j++) { \ dx = x[j] - xi; \ dx2 = dx * dx; \ if(dx2 > r2max) \ break; \ dy = y[j] - yi; \ d2 = dx2 + dy * dy; \ if(d2 <= r2max) { \ CONTRIBUTE_IJ; \ } \ } \ } \ COMMIT_I; \ } \ } spatstat/src/quasirandom.c0000644000176000001440000000105612333041761015425 0ustar ripleyusers/* quasirandom.c Quasi-random sequence generators Copyright (C) Adrian Baddeley 2014 GNU Public Licence version 2 | 3 $Revision: 1.1 $ $Date: 2014/03/17 03:31:59 $ */ #include void Corput(base, n, result) int *base, *n; double *result; { int b, N, i, j; register double f, f0, z; N = *n; b = *base; f0 = 1.0/((double) b); for(i = 0; i < N; i++) { j = i+1; z = 0; f = f0; while(j > 0) { z = z + f * (j % b); j = j/b; f = f / ((double) b); } result[i] = z; } } spatstat/src/nnMDdist.c0000755000176000001440000004360112333041761014627 0ustar ripleyusers/* nnMDdist.c Nearest Neighbour Distances in m dimensions $Revision: 1.8 $ $Date: 2013/05/27 02:09:10 $ Argument x is an m * n matrix with columns corresponding to points and rows corresponding to coordinates. Spatial dimension m must be > 1 THE FOLLOWING FUNCTIONS ASSUME THAT THE ROWS OF x ARE SORTED IN ASCENDING ORDER OF THE FIRST COLUMN nndMD Nearest neighbour distances nnwMD Nearest neighbours and their distances nnXwMD Nearest neighbour from one list to another nnXxMD Nearest neighbour from one list to another, with overlaps knndMD k-th nearest neighbour distances knnwMD k-th nearest neighbours and their distances */ #undef SPATSTAT_DEBUG #include #include #include #include "chunkloop.h" #include "yesno.h" double sqrt(); void nndMD(n, m, x, nnd, huge) /* inputs */ int *n, *m; double *x, *huge; /* output */ double *nnd; { int npoints, mdimen, i, j, left, right, leftpos, rightpos, maxchunk; double d2, d2min, hu, hu2, xi0, dx0, dxj; double *xi; npoints = *n; mdimen = *m; xi = (double *) R_alloc((size_t) mdimen, sizeof(double)); /* dx = (double *) R_alloc((size_t) mdimen, sizeof(double)); */ hu = *huge; hu2 = hu * hu; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { #ifdef SPATSTAT_DEBUG Rprintf("\ni=%d\n", i); #endif d2min = hu2; for(j = 0; j < mdimen; j++) xi[j] = x[i * mdimen + j]; xi0 = xi[0]; #ifdef SPATSTAT_DEBUG Rprintf("\n ("); for(j = 0; j < mdimen; j++) Rprintf("%lf, ", x[i * mdimen + j]); Rprintf(")\n"); #endif /* search backward */ if(i > 0) { for(left = i - 1; left >= 0; --left) { #ifdef SPATSTAT_DEBUG Rprintf("L=%d, d2min=%lf\n", left, d2min); #endif dx0 = xi0 - x[left * mdimen]; d2 = dx0 * dx0; if(d2 > d2min) break; leftpos = left * mdimen; for(j = 1; j < mdimen && d2 < d2min; j++) { dxj = xi[j] - x[leftpos + j]; d2 += dxj * dxj; } if (d2 < d2min) { d2min = d2; #ifdef SPATSTAT_DEBUG Rprintf("\tupdating d2min=%lf\n", d2min); #endif } } } /* search forward */ if(i < npoints - 1) { for(right = i + 1; right < npoints; ++right) { #ifdef SPATSTAT_DEBUG Rprintf("R=%d, d2min=%lf\n", right, d2min); #endif dx0 = x[right * mdimen] - xi0; d2 = dx0 * dx0; if(d2 > d2min) break; rightpos = right * mdimen; for(j = 1; j < mdimen && d2 < d2min; j++) { dxj = xi[j] - x[rightpos + j]; d2 += dxj * dxj; } if (d2 < d2min) { d2min = d2; #ifdef SPATSTAT_DEBUG Rprintf("\tupdating d2min=%lf\n", d2min); #endif } } } #ifdef SPATSTAT_DEBUG Rprintf("\n"); #endif nnd[i] = sqrt(d2min); } } } /* nnwMD: same as nndMD, but also returns id of nearest neighbour */ void nnwMD(n, m, x, nnd, nnwhich, huge) /* inputs */ int *n, *m; double *x, *huge; /* output */ double *nnd; int *nnwhich; { int npoints, mdimen, i, j, left, right, leftpos, rightpos, which, maxchunk; double d2, d2min, hu, hu2, xi0, dx0, dxj; double *xi; npoints = *n; mdimen = *m; xi = (double *) R_alloc((size_t) mdimen, sizeof(double)); /* dx = (double *) R_alloc((size_t) mdimen, sizeof(double)); */ hu = *huge; hu2 = hu * hu; OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { #ifdef SPATSTAT_DEBUG Rprintf("\ni=%d\n", i); #endif d2min = hu2; which = -1; for(j = 0; j < mdimen; j++) xi[j] = x[i * mdimen + j]; xi0 = xi[0]; /* search backward */ if(i > 0) { for(left = i - 1; left >= 0; --left) { #ifdef SPATSTAT_DEBUG Rprintf("L"); #endif dx0 = xi0 - x[left * mdimen]; d2 = dx0 * dx0; if(d2 > d2min) break; leftpos = left * mdimen; for(j = 1; j < mdimen && d2 < d2min; j++) { dxj = xi[j] - x[leftpos + j]; d2 += dxj * dxj; } if (d2 < d2min) { d2min = d2; which = left; } } } /* search forward */ if(i < npoints - 1) { for(right = i + 1; right < npoints; ++right) { #ifdef SPATSTAT_DEBUG Rprintf("R"); #endif dx0 = x[right * mdimen] - xi0; d2 = dx0 * dx0; if(d2 > d2min) break; rightpos = right * mdimen; for(j = 1; j < mdimen && d2 < d2min; j++) { dxj = xi[j] - x[rightpos + j]; d2 += dxj * dxj; } if (d2 < d2min) { d2min = d2; which = right; } } } #ifdef SPATSTAT_DEBUG Rprintf("\n"); #endif nnd[i] = sqrt(d2min); /* convert index to R convention */ nnwhich[i] = which + 1; } } } /* nnXwMD: for TWO point patterns X and Y, find the nearest neighbour (from each point of X to the nearest point of Y) returning both the distance and the identifier Requires both patterns to be sorted in order of increasing z coord */ void nnXwMD(m, n1, x1, n2, x2, nnd, nnwhich, huge) /* inputs */ int *m, *n1, *n2; double *x1, *x2, *huge; /* outputs */ double *nnd; int *nnwhich; { int mdimen, npoints1, npoints2, i, ell, jleft, jright, jwhich, lastjwhich; double d2, d2min, x1i0, dx0, dxell, hu, hu2; double *x1i; int maxchunk; hu = *huge; hu2 = hu * hu; npoints1 = *n1; npoints2 = *n2; mdimen = *m; if(npoints1 == 0 || npoints2 == 0) return; x1i = (double *) R_alloc((size_t) mdimen, sizeof(double)); /* dx = (double *) R_alloc((size_t) mdimen, sizeof(double)); */ lastjwhich = 0; OUTERCHUNKLOOP(i, npoints1, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints1, maxchunk, 16384) { d2min = hu2; jwhich = -1; for(ell = 0; ell < mdimen; ell++) x1i[ell] = x1[i * mdimen + ell]; x1i0 = x1i[0]; /* search backward from previous nearest neighbour */ if(lastjwhich > 0) { for(jleft = lastjwhich - 1; jleft >= 0; --jleft) { dx0 = x1i0 - x2[jleft * mdimen]; d2 = dx0 * dx0; if(d2 > d2min) break; for(ell = 1; ell < mdimen && d2 < d2min; ell++) { dxell = x1i[ell] - x2[jleft * mdimen + ell]; d2 += dxell * dxell; } if (d2 < d2min) { d2min = d2; jwhich = jleft; } } } /* search forward from previous nearest neighbour */ if(lastjwhich < npoints2) { for(jright = lastjwhich; jright < npoints2; ++jright) { dx0 = x2[jright * mdimen] - x1i0; d2 = dx0 * dx0; if(d2 > d2min) break; for(ell = 1; ell < mdimen && d2 < d2min; ell++) { dxell = x1i[ell] - x2[jright * mdimen + ell]; d2 += dxell * dxell; } if (d2 < d2min) { d2min = d2; jwhich = jright; } } } nnd[i] = sqrt(d2min); nnwhich[i] = jwhich; lastjwhich = jwhich; } } } /* nnXxMD: similar to nnXwMD but allows X and Y to include common points (which are not to be counted as neighbours) Code numbers id1, id2 are attached to the patterns X and Y respectively, such that x1[i], y1[i] and x2[j], y2[j] are the same point iff id1[i] = id2[j]. Requires both patterns to be sorted in order of increasing y coord */ void nnXxMD(m, n1, x1, id1, n2, x2, id2, nnd, nnwhich, huge) /* inputs */ int *m, *n1, *n2; double *x1, *x2, *huge; int *id1, *id2; /* outputs */ double *nnd; int *nnwhich; { int mdimen, npoints1, npoints2, i, ell, jleft, jright, jwhich, lastjwhich, id1i; double d2, d2min, x1i0, dx0, dxell, hu, hu2; double *x1i; int maxchunk; hu = *huge; hu2 = hu * hu; npoints1 = *n1; npoints2 = *n2; mdimen = *m; if(npoints1 == 0 || npoints2 == 0) return; x1i = (double *) R_alloc((size_t) mdimen, sizeof(double)); /* dx = (double *) R_alloc((size_t) mdimen, sizeof(double)); */ lastjwhich = 0; OUTERCHUNKLOOP(i, npoints1, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints1, maxchunk, 16384) { d2min = hu2; jwhich = -1; id1i = id1[i]; for(ell = 0; ell < mdimen; ell++) x1i[ell] = x1[i * mdimen + ell]; x1i0 = x1i[0]; /* search backward from previous nearest neighbour */ if(lastjwhich > 0) { for(jleft = lastjwhich - 1; jleft >= 0; --jleft) { dx0 = x1i0 - x2[jleft * mdimen]; d2 = dx0 * dx0; if(d2 > d2min) break; /* do not compare identical points */ if(id2[jleft] != id1i) { for(ell = 1; ell < mdimen && d2 < d2min; ell++) { dxell = x1i[ell] - x2[jleft * mdimen + ell]; d2 += dxell * dxell; } if (d2 < d2min) { d2min = d2; jwhich = jleft; } } } } /* search forward from previous nearest neighbour */ if(lastjwhich < npoints2) { for(jright = lastjwhich; jright < npoints2; ++jright) { dx0 = x2[jright * mdimen] - x1i0; d2 = dx0 * dx0; if(d2 > d2min) break; /* do not compare identical points */ if(id2[jright] != id1i) { for(ell = 1; ell < mdimen && d2 < d2min; ell++) { dxell = x1i[ell] - x2[jright * mdimen + ell]; d2 += dxell * dxell; } if (d2 < d2min) { d2min = d2; jwhich = jright; } } } } nnd[i] = sqrt(d2min); nnwhich[i] = jwhich; lastjwhich = jwhich; } } } /* knndMD nearest neighbours 1:kmax */ void knndMD(n, m, kmax, x, nnd, huge) /* inputs */ int *n, *m, *kmax; double *x, *huge; /* output matrix (kmax * npoints) */ double *nnd; { int npoints, mdimen, nk, nk1, i, j, k, k1, left, right, unsorted, maxchunk; double d2, d2minK, xi0, dx0, dxj, hu, hu2, tmp; double *d2min, *xi; hu = *huge; hu2 = hu * hu; npoints = *n; mdimen = *m; nk = *kmax; nk1 = nk - 1; /* create space to store the squared k-th nearest neighbour distances for the current point */ d2min = (double *) R_alloc((size_t) nk, sizeof(double)); /* scratch space */ xi = (double *) R_alloc((size_t) mdimen, sizeof(double)); /* loop over points */ OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { #ifdef SPATSTAT_DEBUG Rprintf("\ni=%d\n", i); #endif /* initialise nn distances */ d2minK = hu2; for(k = 0; k < nk; k++) d2min[k] = hu2; for(j = 0; j < mdimen; j++) xi[j] = x[i* mdimen + j]; xi0 = xi[0]; #ifdef SPATSTAT_DEBUG Rprintf("\n ("); for(j = 0; j < mdimen; j++) Rprintf("%lf, ", xi[j]); Rprintf(")\n"); #endif /* search backward */ for(left = i - 1; left >= 0; --left) { dx0 = xi0 - x[left * mdimen]; d2 = dx0 * dx0; if(d2 > d2minK) break; #ifdef SPATSTAT_DEBUG Rprintf("L=%d\n", left); Rprintf("\t 0 "); #endif for(j = 1; j < mdimen && d2 < d2minK; j++) { #ifdef SPATSTAT_DEBUG Rprintf("%d ", j); #endif dxj = xi[j] - x[left * mdimen + j]; d2 += dxj * dxj; } #ifdef SPATSTAT_DEBUG Rprintf("\n\t d2=%lf\n", d2); #endif if (d2 < d2minK) { /* overwrite last entry */ #ifdef SPATSTAT_DEBUG Rprintf("\td2=%lf overwrites d2min[%d] = %lf\n", d2, nk1, d2min[nk1]); #endif d2min[nk1] = d2; /* bubble sort */ #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] before bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); #endif unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; } else { unsorted = NO; } } #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] after bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); #endif /* adjust maximum distance */ d2minK = d2min[nk1]; } } /* search forward */ for(right = i + 1; right < npoints; ++right) { #ifdef SPATSTAT_DEBUG Rprintf("R=%d\n", right); Rprintf("\t 0 "); #endif dx0 = x[right * mdimen] - xi0; d2 = dx0 * dx0; if(d2 > d2minK) break; for(j = 1; j < mdimen && d2 < d2minK; j++) { #ifdef SPATSTAT_DEBUG Rprintf("%d ", j); #endif dxj = xi[j] - x[right * mdimen + j]; d2 += dxj * dxj; } #ifdef SPATSTAT_DEBUG Rprintf("\n\t d2=%lf\n", d2); #endif if (d2 < d2minK) { #ifdef SPATSTAT_DEBUG Rprintf("\td2=%lf overwrites d2min[%d] = %lf\n", d2, nk1, d2min[nk1]); #endif /* overwrite last entry */ d2min[nk1] = d2; /* bubble sort */ #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] before bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); #endif unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; } else { unsorted = NO; } } #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] after bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); #endif /* adjust maximum distance */ d2minK = d2min[nk1]; } } #ifdef SPATSTAT_DEBUG Rprintf("\n"); #endif /* copy nn distances for point i to output matrix in ROW MAJOR order */ for(k = 0; k < nk; k++) { nnd[nk * i + k] = sqrt(d2min[k]); } } } } /* knnwMD nearest neighbours 1:kmax returns distances and indices */ void knnwMD(n, m, kmax, x, nnd, nnwhich, huge) /* inputs */ int *n, *m, *kmax; double *x, *huge; /* output matrix (kmax * npoints) */ double *nnd; int *nnwhich; { int npoints, mdimen, nk, nk1, i, j, k, k1, left, right, unsorted, itmp; double d2, d2minK, xi0, dx0, dxj, hu, hu2, tmp; double *d2min, *xi; int *which; int maxchunk; hu = *huge; hu2 = hu * hu; npoints = *n; mdimen = *m; nk = *kmax; nk1 = nk - 1; /* create space to store the nearest neighbour distances and indices for the current point */ d2min = (double *) R_alloc((size_t) nk, sizeof(double)); which = (int *) R_alloc((size_t) nk, sizeof(int)); /* scratch space */ xi = (double *) R_alloc((size_t) mdimen, sizeof(double)); /* loop over points */ OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { #ifdef SPATSTAT_DEBUG Rprintf("\ni=%d\n", i); #endif /* initialise nn distances */ d2minK = hu2; for(k = 0; k < nk; k++) { d2min[k] = hu2; which[k] = -1; } for(j = 0; j < mdimen; j++) xi[j] = x[i* mdimen + j]; xi0 = xi[0]; #ifdef SPATSTAT_DEBUG Rprintf("\n ("); for(j = 0; j < mdimen; j++) Rprintf("%lf, ", x[i * mdimen + j]); Rprintf(")\n"); #endif /* search backward */ for(left = i - 1; left >= 0; --left) { #ifdef SPATSTAT_DEBUG Rprintf("L=%d, d2minK=%lf\n", left, d2minK); Rprintf("\t 0 "); #endif dx0 = xi0 - x[left * mdimen]; d2 = dx0 * dx0; if(d2 > d2minK) break; for(j = 1; j < mdimen && d2 < d2minK; j++) { #ifdef SPATSTAT_DEBUG Rprintf("%d ", j); #endif dxj = xi[j] - x[left * mdimen + j]; d2 += dxj * dxj; } if (d2 < d2minK) { #ifdef SPATSTAT_DEBUG Rprintf("\td2=%lf overwrites d2min[%d] = %lf\n", d2, nk1, d2min[nk1]); #endif /* overwrite last entry */ d2min[nk1] = d2; which[nk1] = left; /* bubble sort */ #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] before bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); Rprintf("\twhich[] before bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%d, ", which[k]); Rprintf("\n"); #endif unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; } else { unsorted = NO; } } #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] after bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); Rprintf("\twhich[] after bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%d, ", which[k]); Rprintf("\n"); #endif /* adjust maximum distance */ d2minK = d2min[nk1]; } } /* search forward */ for(right = i + 1; right < npoints; ++right) { #ifdef SPATSTAT_DEBUG Rprintf("R=%d, d2minK=%lf\n", right, d2minK); Rprintf("\t 0 "); #endif dx0 = x[right * mdimen] - xi0; d2 = dx0 * dx0; if(d2 > d2minK) break; for(j = 1; j < mdimen && d2 < d2minK; j++) { #ifdef SPATSTAT_DEBUG Rprintf("%d ", j); #endif dxj = xi[j] - x[right * mdimen + j]; d2 += dxj * dxj; } if (d2 < d2minK) { #ifdef SPATSTAT_DEBUG Rprintf("\td2=%lf overwrites d2min[%d] = %lf\n", d2, nk1, d2min[nk1]); #endif /* overwrite last entry */ d2min[nk1] = d2; which[nk1] = right; /* bubble sort */ #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] before bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); Rprintf("\twhich[] before bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%d, ", which[k]); Rprintf("\n"); #endif unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; } else { unsorted = NO; } } #ifdef SPATSTAT_DEBUG Rprintf("\td2min[] after bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%lf, ", d2min[k]); Rprintf("\n"); Rprintf("\twhich[] after bubble sort:"); for(k = 0; k < nk; k++) Rprintf("%d, ", which[k]); Rprintf("\n"); #endif /* adjust maximum distance */ d2minK = d2min[nk1]; } } #ifdef SPATSTAT_DEBUG Rprintf("\n"); #endif /* copy nn distances for point i to output matrix in ROW MAJOR order */ for(k = 0; k < nk; k++) { nnd[nk * i + k] = sqrt(d2min[k]); /* convert index back to R convention */ nnwhich[nk * i + k] = which[k] + 1; } } } } spatstat/src/knn3Ddist.h0000644000176000001440000000730112333041761014747 0ustar ripleyusers/* knn3Ddist.h Code template for k-nearest-neighbour algorithms for 3D point patterns Input is a single point pattern - supports 'nndist' and 'nnwhich' This code is #included multiple times in nn3Ddist.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour Either or both DIST and WHICH may be defined. THE FOLLOWING CODE ASSUMES THAT THE POINT PATTERN IS SORTED IN ASCENDING ORDER OF THE z COORDINATE $Revision: 1.3 $ $Date: 2013/06/29 02:38:19 $ */ void FNAME(n, kmax, x, y, z, nnd, nnwhich, huge) /* inputs */ int *n, *kmax; double *x, *y, *z, *huge; /* output matrices (npoints * kmax) in ROW MAJOR order */ double *nnd; int *nnwhich; { int npoints, nk, nk1, i, j, k, k1, unsorted, maxchunk; double d2, d2minK, xi, yi, zi, dx, dy, dz, dz2, hu, hu2, tmp; double *d2min; #ifdef WHICH int *which; int itmp; #endif hu = *huge; hu2 = hu * hu; npoints = *n; nk = *kmax; nk1 = nk - 1; /* create space to store the nearest neighbour distances and indices for the current point */ d2min = (double *) R_alloc((size_t) nk, sizeof(double)); #ifdef WHICH which = (int *) R_alloc((size_t) nk, sizeof(int)); #endif /* loop over points */ OUTERCHUNKLOOP(i, npoints, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, npoints, maxchunk, 16384) { #ifdef SPATSTAT_DEBUG Rprintf("\ni=%d\n", i); #endif /* initialise nn distances and indices */ d2minK = hu2; for(k = 0; k < nk; k++) { d2min[k] = hu2; #ifdef WHICH which[k] = -1; #endif } xi = x[i]; yi = y[i]; zi = z[i]; /* search backward */ if(i > 0) { for(j = i - 1; j >= 0; --j) { #ifdef SPATSTAT_DEBUG Rprintf("L"); #endif dz = z[j] - zi; dz2 = dz * dz; if(dz2 > d2minK) break; dx = x[j] - xi; dy = y[j] - yi; d2 = dx * dx + dy * dy + dz2; if (d2 < d2minK) { /* overwrite last entry */ d2min[nk1] = d2; #ifdef WHICH which[nk1] = j; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[nk1]; } } } /* search forward */ if(i + 1 < npoints) { for(j = i + 1; j < npoints; ++j) { #ifdef SPATSTAT_DEBUG Rprintf("R"); #endif dz = z[j] - zi; dz2 = dz * dz; if(dz2 > d2minK) break; dx = x[j] - xi; dy = y[j] - yi; d2 = dx * dx + dy * dy + dz2; if (d2 < d2minK) { /* overwrite last entry */ d2min[nk1] = d2; #ifdef WHICH which[nk1] = j; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[nk1]; } } } #ifdef SPATSTAT_DEBUG Rprintf("\n"); #endif /* calculate nn distances for point i and copy to output matrix in ROW MAJOR order */ for(k = 0; k < nk; k++) { #ifdef DIST nnd[nk * i + k] = sqrt(d2min[k]); #endif #ifdef WHICH /* convert from C to R indexing */ nnwhich[nk * i + k] = which[k] + 1; #endif } } } } spatstat/src/fexitc.c0000755000176000001440000000045512333041761014371 0ustar ripleyusers# include # include # include void fexitc(const char *msg) { size_t nc = strlen(msg); char buf[256]; if(nc > 255) { warning("invalid character length in fexitc"); nc = 255; } strncpy(buf, msg, nc); buf[nc] = '\0'; error(buf); } spatstat/src/nngrid.h0000644000176000001440000000572612333041761014400 0ustar ripleyusers #if (1 == 0) /* nngrid.h Code template for C functions nearest neighbour of each grid point THE FOLLOWING CODE ASSUMES THAT POINT PATTERN (xp, yp) IS SORTED IN ASCENDING ORDER OF x COORDINATE This code is #included multiple times in nngrid.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour Either or both DIST and WHICH may be defined. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2013 Licence: GPL >= 2 $Revision: 1.4 $ $Date: 2014/02/18 08:43:29 $ */ #endif void FNAME(nx, x0, xstep, ny, y0, ystep, /* pixel grid dimensions */ np, xp, yp, /* data points */ nnd, nnwhich, huge) /* inputs */ int *nx, *ny, *np; double *x0, *xstep, *y0, *ystep, *huge; double *xp, *yp; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { int Nxcol, Nyrow, Npoints; int i, j, ijpos; int mleft, mright, mwhich, lastmwhich; double X0, Y0, Xstep, Ystep; double d2, d2min, xj, yi, dx, dy, dx2, hu, hu2; Nxcol = *nx; Nyrow = *ny; Npoints = *np; hu = *huge; X0 = *x0; Y0 = *y0; Xstep = *xstep; Ystep = *ystep; hu2 = hu * hu; if(Npoints == 0) return; lastmwhich = 0; /* loop over pixels */ for(j = 0, xj = X0; j < Nxcol; j++, xj += Xstep) { R_CheckUserInterrupt(); for(i = 0, yi = Y0; i < Nyrow; i++, yi += Ystep) { /* reset nn distance and index */ d2min = hu2; mwhich = -1; if(lastmwhich < Npoints) { /* search forward from previous nearest neighbour */ for(mright = lastmwhich; mright < Npoints; ++mright) { dx = xp[mright] - xj; dx2 = dx * dx; if(dx2 > d2min) /* note that dx2 >= d2min could break too early */ break; dy = yp[mright] - yi; d2 = dy * dy + dx2; if (d2 < d2min) { /* save as nearest neighbour */ d2min = d2; mwhich = mright; } } /* end forward search */ } if(lastmwhich > 0) { /* search backward from previous nearest neighbour */ for(mleft = lastmwhich - 1; mleft >= 0; --mleft) { dx = xj - xp[mleft]; dx2 = dx * dx; if(dx2 > d2min) /* note that dx2 >= d2min could break too early */ break; dy = yp[mleft] - yi; d2 = dy * dy + dx2; if (d2 < d2min) { /* save as nearest neighbour */ d2min = d2; mwhich = mleft; } } /* end backward search */ } /* remember index of most recently-encountered neighbour */ lastmwhich = mwhich; /* copy nn distance for grid point (i, j) to output array nnd[i, j] */ ijpos = i + j * Nyrow; #ifdef DIST nnd[ijpos] = sqrt(d2min); #endif #ifdef WHICH nnwhich[ijpos] = mwhich + 1; /* R indexing */ #endif /* end of loop over grid points (i, j) */ } } } spatstat/src/KrectV4.h0000644000176000001440000000027512333041761014373 0ustar ripleyusers/* KrectV5.h with or without uncorrected estimator */ if((*doUnco) == 1) { #define UNCORRECTED #include "KrectBody.h" } else { #undef UNCORRECTED #include "KrectBody.h" } spatstat/src/KrectV2.h0000644000176000001440000000027312333041761014367 0ustar ripleyusers/* KrectV3.h with or without translation correction */ if((*doTrans) == 1) { #define TRANSLATION #include "KrectV3.h" } else { #undef TRANSLATION #include "KrectV3.h" } spatstat/src/Efiksel.c0000755000176000001440000000305112333041761014464 0ustar ripleyusers#include #include #include "chunkloop.h" /* Efiksel.c $Revision: 1.3 $ $Date: 2012/03/28 05:55:29 $ C implementation of 'eval' for Fiksel interaction (non-hardcore part) Assumes point patterns are sorted in increasing order of x coordinate */ double sqrt(), exp(); void Efiksel(nnsource, xsource, ysource, nntarget, xtarget, ytarget, rrmax, kkappa, values) /* inputs */ int *nnsource, *nntarget; double *xsource, *ysource, *xtarget, *ytarget, *rrmax, *kkappa; /* output */ double *values; { int nsource, ntarget, maxchunk, j, i, ileft; double xsourcej, ysourcej, xleft, dx, dy, dx2, d2; double rmax, r2max, kappa, total; nsource = *nnsource; ntarget = *nntarget; rmax = *rrmax; kappa = *kkappa; r2max = rmax * rmax; if(nsource == 0 || ntarget == 0) return; ileft = 0; OUTERCHUNKLOOP(j, nsource, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nsource, maxchunk, 16384) { total = 0; xsourcej = xsource[j]; ysourcej = ysource[j]; /* adjust starting point */ xleft = xsourcej - rmax; while((xtarget[ileft] < xleft) && (ileft+1 < ntarget)) ++ileft; /* process from ileft until dx > rmax */ for(i=ileft; i < ntarget; i++) { /* squared interpoint distance */ dx = xtarget[i] - xsourcej; dx2 = dx * dx; if(dx2 > r2max) break; dy = ytarget[i] - ysourcej; d2 = dx2 + dy * dy; if(d2 <= r2max) total += exp(- kappa * sqrt(d2)); } values[j] = total; } } } spatstat/src/loccum.c0000644000176000001440000000252512333041761014366 0ustar ripleyusers#include #include #include #include "chunkloop.h" /* loccum.c $Revision: 1.1 $ $Date: 2013/05/27 02:09:10 $ Compute local cumulative sums or products of weights locsum: f_i(t) = \sum_{j: j \neq i, ||x_j - x_i|| \le t} v(x_j) for a data point pattern {x_i} locxsum: f_u(t) = \sum_{||x_i - u|| \le t} v(x_i) for a grid of points {u} and a data point pattern {x_i} locprod: f_i(t) = \prod_{j: j \neq i, ||x_j - x_i|| \le t} v(x_j) for a data point pattern {x_i} locxprod: f_u(t) = \prod_{||x_i - u|| \le t} v(x_i) for a grid of points {u} and a data point pattern {x_i} Assumes point patterns are sorted in increasing order of x coordinate Uses C code template files : loccums.h, loccumx.h */ /* data-to-data */ #undef FNAME #undef NULVAL #undef INC #define FNAME locsum #define NULVAL 0.0 #define INC(A,B) A += B #include "loccums.h" #undef FNAME #undef NULVAL #undef INC #define FNAME locprod #define NULVAL 1.0 #define INC(A,B) A *= B #include "loccums.h" /* test-grid-to-data */ #undef FNAME #undef NULVAL #undef INC #define FNAME locxsum #define NULVAL 0.0 #define INC(A,B) A += B #include "loccumx.h" #undef FNAME #undef NULVAL #undef INC #define FNAME locxprod #define NULVAL 1.0 #define INC(A,B) A *= B #include "loccumx.h" spatstat/src/k3.c0000755000176000001440000000716312333041761013427 0ustar ripleyusers#include #include #include "geom3.h" #include "functable.h" /* $Revision: 1.1 $ $Date: 2009/11/04 23:54:15 $ K function of 3D point pattern k3trans translation correction k3isot isotropic correction # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # MODIFIED BY: Adrian Baddeley, Perth 2009. # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ void k3trans(p, n, b, k) Point *p; int n; Box *b; Ftable *k; { register int i, j, l, lmin; register double dx, dy, dz, dist; register double vx, vy, vz; Point *ip, *jp; double dt, vol, lambda, denom, term; double sphesfrac(), sphevol(); /* compute denominator & initialise numerator*/ vol = (b->x1 - b->x0) * (b->y1 - b->y0) * (b->z1 - b->z0); lambda = ((double) n )/ vol; denom = lambda * lambda; for(l = 0; l < k->n; l++) { (k->denom)[l] = denom; (k->num)[l] = 0.0; } /* spacing of argument in result vector k */ dt = (k->t1 - k->t0)/(k->n - 1); /* compute numerator */ for( i = 0; i < n; i++) { ip = p + i; for(j = i + 1; j < n; j++) { jp = p + j; dx = jp->x - ip->x; dy = jp->y - ip->y; dz = jp->z - ip->z; dist = sqrt(dx * dx + dy * dy + dz * dz); lmin = ceil( (dist - k->t0) / dt ); if(lmin < 0) lmin = 0; vx = b->x1 - b->x0 - (dx > 0 ? dx : -dx); vy = b->y1 - b->y0 - (dy > 0 ? dy : -dy); vz = b->z1 - b->z0 - (dz > 0 ? dz : -dz); if(vx >= 0.0 && vy >= 0.0 && vz >= 0.0) { term = 2.0 /(vx * vy * vz); /* 2 because they're ordered pairs */ for(l = lmin; l < k->n; l++) (k->num)[l] += term; } } } /* compute ratio */ for(l = 0; l < k->n; l++) (k->f)[l] = ((k->denom)[l] > 0.0)? (k->num)[l] / (k->denom)[l] : 0.0; } void k3isot(p, n, b, k) Point *p; int n; Box *b; Ftable *k; { register int i, j, l, lmin; register double dx, dy, dz, dist; Point *ip, *jp; double dt, vol, denom, term; double sphesfrac(), sphevol(); Point vertex; Box half; /* compute denominator & initialise numerator*/ vol = (b->x1 - b->x0) * (b->y1 - b->y0) * (b->z1 - b->z0); denom = ((double) (n * n))/vol; for(l = 0; l < k->n; l++) { (k->denom)[l] = denom; (k->num)[l] = 0.0; } /* spacing of argument in result vector k */ dt = (k->t1 - k->t0)/(k->n - 1); /* set up for volume correction */ vertex.x = b->x0; vertex.y = b->y0; vertex.z = b->z0; half.x1 = b->x1; half.y1 = b->y1; half.z1 = b->z1; half.x0 = (b->x0 + b->x1)/2.0; half.y0 = (b->y0 + b->y1)/2.0; half.z0 = (b->z0 + b->z1)/2.0; /* compute numerator */ for( i = 0; i < n; i++) { ip = p + i; for(j = i + 1; j < n; j++) { jp = p + j; dx = jp->x - ip->x; dy = jp->y - ip->y; dz = jp->z - ip->z; dist = sqrt(dx * dx + dy * dy + dz * dz); lmin = ceil( (dist - k->t0) / dt ); if(lmin < 0) lmin = 0; term = (1.0 / sphesfrac(ip, b, dist)) + (1.0 / sphesfrac(jp, b, dist)); term *= 1.0 - 8.0 * sphevol(&vertex, &half, dist) / vol; for(l = lmin; l < k->n; l++) (k->num)[l] += term; } } /* compute ratio */ for(l = 0; l < k->n; l++) (k->f)[l] = ((k->denom)[l] > 0.0)? (k->num)[l] / (k->denom)[l] : 0.0; } spatstat/src/g3.c0000755000176000001440000001266612333041761013427 0ustar ripleyusers#include #include #include "geom3.h" #include "functable.h" /* $Revision: 1.3 $ $Date: 2012/05/22 07:17:31 $ G function (nearest neighbour distribution) of 3D point pattern Let b = distance from point p[i] to boundary of box d = distance from p[i] to nearest p[j] method = 1 naive ratio estimator (Ripley 1981) numerator(r) = count(i: b >= r, d <= r) denominator(r) = count(i: b >= r) method = 2 minus sampling estimator numerator(r) = count(i: b >= r, d <= r) denominator(r) = lambda * volume(x: b >= r) where lambda = (no of points)/volume(box) method = 3 Hanisch's G3 numerator(r) = count(i: b >= d, d <= r) denominator(r) = count(i: b >= d) method = 4 Hanisch's G4 numerator(r) = count(i: b >= d, d <= r) denominator(r) = fudge * volume(x: b >= r) fudge = numerator(R)/denominator(R) R = sup{r : denominator(r) > 0 } # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # MODIFIED BY: Adrian Baddeley, Perth 2009, 2012. # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ #define MIN(X,Y) (((X) > (Y)) ? (Y) : (X)) double * nndist3(p, n, b) /* compute nearest neighbour distance for each p[i] */ Point *p; int n; Box *b; { register int i, j; register double dx, dy, dz, dist2, nearest2, huge2; Point *ip, *jp; double *nnd; nnd = (double *) R_alloc(n, sizeof(double)); dx = b->x1 - b->x0; dy = b->y1 - b->y0; dz = b->z1 - b->z0; huge2 = 2.0 * (dx * dx + dy * dy + dz * dz); /* scan each point and find closest */ for( i = 0; i < n; i++) { ip = p + i; nearest2 = huge2; for(j = 0; j < n; j++) if(j != i) { jp = p + j; dx = jp->x - ip->x; dy = jp->y - ip->y; dz = jp->z - ip->z; dist2 = dx * dx + dy * dy + dz * dz; if(dist2 < nearest2) nearest2 = dist2; } nnd[i] = sqrt(nearest2); } return(nnd); } double * border3(p, n, b) /* compute distances to border */ Point *p; int n; Box *b; { register int i; register double bord; register Point *ip; double *bored; bored = (double *) R_alloc(n, sizeof(double)); for( i = 0; i < n; i++) { ip = p + i; bord = MIN(ip->x - b->x0, b->x1 - ip->x); bord = MIN(bord, ip->y - b->y0); bord = MIN(bord, b->y1 - ip->y); bord = MIN(bord, ip->z - b->z0); bord = MIN(bord, b->z1 - ip->z); bored[i] = bord; } return(bored); } void g3one(p, n, b, g) Point *p; int n; Box *b; Ftable *g; { register int i, l, lbord, lnnd; double dt; double *bord, *nnd; bord = border3(p, n, b); nnd = nndist3(p, n, b); /* initialise */ for(l = 0; l < g->n; l++) (g->num)[l] = (g->denom)[l] = 0.0; /* spacing of argument in result vector g */ dt = (g->t1 - g->t0)/(g->n - 1); for(i = 0; i < n; i++) { lbord = floor( (bord[i] - g->t0) / dt ); if(lbord >= g->n) lbord = g->n - 1; for(l = 0; l <= lbord; l++) (g->denom)[l] += 1.0; lnnd = ceil( (nnd[i] - g->t0) / dt ); if(lnnd < 0) lnnd = 0; for(l = lnnd; l <= lbord; l++) (g->num)[l] += 1.0; } /* compute ratio */ for(l = 0; l < g->n; l++) (g->f)[l] = ((g->denom)[l] > 0)? (g->num)[l] / (g->denom)[l] : 1.0; } void g3three(p, n, b, g) Point *p; int n; Box *b; Ftable *g; { register int i, l, lmin; double dt; int denom; double *bord, *nnd; bord = border3(p, n, b); nnd = nndist3(p, n, b); /* initialise */ denom = 0; for(l = 0; l < g->n; l++) (g->num)[l] = 0.0; /* spacing of argument in result vector g */ dt = (g->t1 - g->t0)/(g->n - 1); for(i = 0; i < n; i++) { if(nnd[i] <= bord[i]) { ++denom; lmin = ceil( (nnd[i] - g->t0) / dt ); if(lmin < 0) lmin = 0; for(l = lmin; l < g->n; l++) (g->num)[l] += 1.0; } } /* compute ratio */ for(l = 0; l < g->n; l++) { (g->denom)[l] = denom; (g->f)[l] = (denom > 0)? (g->num)[l] / (double) denom : 1.0; } } void g3cen(p, n, b, count) Point *p; int n; Box *b; H4table *count; { register int i, lcen, lobs; register double dt, cens, obsv; double *bord, *nnd; bord = border3(p, n, b); nnd = nndist3(p, n, b); /* spacing of histogram cells */ dt = (count->t1 - count->t0)/(count->n - 1); /* 'count' is assumed to have been initialised */ for(i = 0; i < n; i++) { obsv = nnd[i]; cens = bord[i]; lobs = ceil( (obsv - count->t0) / dt ); lcen = floor( (cens - count->t0) / dt ); if(obsv <= cens) { /* observation is uncensored; increment all four histograms */ if(lobs >= count->n) ++(count->upperobs); else if(lobs >= 0) { (count->obs)[lobs]++; (count->nco)[lobs]++; } if(lcen >= count->n) ++(count->uppercen); else if(lcen >= 0) { (count->cen)[lcen]++; (count->ncc)[lcen]++; } } else { /* observation is censored; increment only two histograms */ lobs = MIN(lobs, lcen); if(lobs >= count->n) ++(count->upperobs); else if(lobs >= 0) (count->obs)[lobs]++; if(lcen >= count->n) ++(count->uppercen); else if(lcen >= 0) (count->cen)[lcen]++; } } } spatstat/src/trigraf.c0000755000176000001440000005575612333041761014563 0ustar ripleyusers/* trigraf.c Form list of all triangles in a planar graph, given list of edges $Revision: 1.13 $ $Date: 2012/04/06 09:26:50 $ Form list of all triangles in a planar graph, given list of edges Note: vertex indices ie, je are indices in R. They are handled without converting to C convention, because we only need to test equality and ordering. (*except in 'trioxgraph'*) Called by .C: ------------- trigraf() Generic C implementation with fixed storage limit usable with Delaunay triangulation trigrafS() Faster version when input data are sorted (again with predetermined storage limit) suited for handling Delaunay triangulation Called by .Call: --------------- trigraph() Version with dynamic storage allocation triograph() Faster version assuming 'iedge' is sorted in increasing order trioxgraph() Even faster version for use with quadrature schemes Diameters: ----------- triDgraph() Also computes diameters of triangles */ #include #include #include #include "chunkloop.h" #undef DEBUGTRI void trigraf(nv, ne, ie, je, ntmax, nt, it, jt, kt, status) /* inputs */ int *nv; /* number of graph vertices */ int *ne; /* number of edges */ int *ie, *je; /* vectors of indices of ends of each edge */ int *ntmax; /* length of storage space for triangles */ /* output */ int *nt; /* number of triangles (<= *ntmax) */ int *it, *jt, *kt; /* vectors of indices of vertices of triangles */ int *status; /* 0 if OK, 1 if overflow */ { int Nv, Ne, Ntmax; int Nt, Nj, m, i, j, k, mj, mk, maxchunk; int *jj; Nv = *nv; Ne = *ne; Ntmax = *ntmax; /* initialise scratch storage */ jj = (int *) R_alloc(Ne, sizeof(int)); Nt = 0; /* vertex index i ranges from 1 to Nv */ XOUTERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { R_CheckUserInterrupt(); XINNERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { /* Find triangles involving vertex 'i' in which 'i' is the lowest-numbered vertex */ /* First, find vertices j > i connected to i */ Nj = 0; for(m = 0; m < Ne; m++) { if(ie[m] == i) { j = je[m]; if(j > i) { jj[Nj] = j; Nj++; } } else if(je[m] == i) { j = ie[m]; if(j > i) { jj[Nj] = j; Nj++; } } } /* Determine which pairs of vertices j, k are joined by an edge; save triangles (i,j,k) */ if(Nj > 1) { /* Sort jj in ascending order */ for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(k < j) { /* swap */ jj[mk] = j; jj[mj] = k; j = k; } } } for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(j != k) { /* Run through edges to determine whether j, k are neighbours */ for(m = 0; m < Ne; m++) { if((ie[m] == j && je[m] == k) || (ie[m] == k && je[m] == j)) { /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - exit */ *status = 1; return; } it[Nt] = i; jt[Nt] = j; kt[Nt] = k; Nt++; } } } } } } } } *nt = Nt; *status = 0; } /* faster version of trigraf() assuming that ie[m] < je[m] ie[] is in ascending order je[] is in ascending order within ie[], that is, je[ie[]=i] is in ascending order for each fixed i */ void trigrafS(nv, ne, ie, je, ntmax, nt, it, jt, kt, status) /* inputs */ int *nv; /* number of graph vertices */ int *ne; /* number of edges */ int *ie, *je; /* vectors of indices of ends of each edge */ int *ntmax; /* length of storage space for triangles */ /* output */ int *nt; /* number of triangles */ int *it, *jt, *kt; /* vectors of indices of vertices of triangles */ int *status; /* 0 if OK, 1 if overflow */ { int Ne, Nt, Ntmax; int m, i, j, k, mj, mk; int firstedge, lastedge; Ne = *ne; Ntmax = *ntmax; /* nv is not used, but retained for harmony with trigraf */ /* Avoid compiler warnings */ Nt = *nv; /* initialise output */ Nt = 0; lastedge = -1; while(lastedge + 1 < Ne) { if(lastedge % 256 == 0) R_CheckUserInterrupt(); /* Consider next vertex i. The edges (i,j) with i < j appear contiguously in the edge list. */ firstedge = lastedge + 1; i = ie[firstedge]; for(m= firstedge+1; m < Ne && ie[m] == i; m++) ; lastedge = m-1; /* Consider each pair j, k of neighbours of i, where i < j < k. Scan entire edge list to determine whether j, k are joined by an edge. If so, save triangle (i,j,k) */ if(lastedge > firstedge) { for(mj = firstedge; mj < lastedge; mj++) { j = je[mj]; for(mk = firstedge+1; mk <= lastedge; mk++) { k = je[mk]; /* Run through edges to determine whether j, k are neighbours */ for(m = 0; m < Ne && ie[m] < j; m++) ; while(m < Ne && ie[m] == j) { if(je[m] == k) { /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - exit */ *status = 1; return; } it[Nt] = i; jt[Nt] = j; kt[Nt] = k; Nt++; } m++; } } } } } *nt = Nt; *status = 0; } /* ------------------- callable by .Call ------------------------- */ SEXP trigraph(SEXP nv, /* number of vertices */ SEXP iedge, /* vectors of indices of ends of each edge */ SEXP jedge) /* all arguments are integer */ { int Nv, Ne; int *ie, *je; /* edges */ int *it, *jt, *kt; /* vectors of indices of vertices of triangles */ int Nt, Ntmax; /* number of triangles */ int Nj; int *jj; /* scratch storage */ int i, j, k, m, mj, mk, Nmore, maxchunk; /* output */ SEXP iTout, jTout, kTout, out; int *ito, *jto, *kto; /* =================== Protect R objects from garbage collector ======= */ PROTECT(nv = AS_INTEGER(nv)); PROTECT(iedge = AS_INTEGER(iedge)); PROTECT(jedge = AS_INTEGER(jedge)); /* That's 3 protected objects */ /* numbers of vertices and edges */ Nv = *(INTEGER_POINTER(nv)); Ne = LENGTH(iedge); /* input arrays */ ie = INTEGER_POINTER(iedge); je = INTEGER_POINTER(jedge); /* initialise storage (with a guess at max size) */ Ntmax = 3 * Ne; it = (int *) R_alloc(Ntmax, sizeof(int)); jt = (int *) R_alloc(Ntmax, sizeof(int)); kt = (int *) R_alloc(Ntmax, sizeof(int)); Nt = 0; /* initialise scratch storage */ jj = (int *) R_alloc(Ne, sizeof(int)); XOUTERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { R_CheckUserInterrupt(); XINNERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { #ifdef DEBUGTRI Rprintf("i=%d ---------- \n", i); #endif /* Find triangles involving vertex 'i' in which 'i' is the lowest-numbered vertex */ /* First, find vertices j > i connected to i */ Nj = 0; for(m = 0; m < Ne; m++) { if(ie[m] == i) { j = je[m]; if(j > i) { jj[Nj] = j; Nj++; } } else if(je[m] == i) { j = ie[m]; if(j > i) { jj[Nj] = j; Nj++; } } } /* Determine which pairs of vertices j, k are joined by an edge; save triangles (i,j,k) */ #ifdef DEBUGTRI Rprintf("Nj = %d\n", Nj); #endif if(Nj > 1) { #ifdef DEBUGTRI Rprintf("i=%d\njj=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif /* Sort jj in ascending order */ for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(k < j) { /* swap */ jj[mk] = j; jj[mj] = k; j = k; } } } #ifdef DEBUGTRI Rprintf("sorted=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(j != k) { /* Run through edges to determine whether j, k are neighbours */ for(m = 0; m < Ne; m++) { if((ie[m] == j && je[m] == k) || (ie[m] == k && je[m] == j)) { /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - allocate more space */ Nmore = 2 * Ntmax; #ifdef DEBUGTRI Rprintf("Doubling space from %d to %d\n", Ntmax, Nmore); #endif it = (int *) S_realloc((char *) it, Nmore, Ntmax, sizeof(int)); jt = (int *) S_realloc((char *) jt, Nmore, Ntmax, sizeof(int)); kt = (int *) S_realloc((char *) kt, Nmore, Ntmax, sizeof(int)); Ntmax = Nmore; } /* output indices in R convention */ it[Nt] = i; jt[Nt] = j; kt[Nt] = k; Nt++; } } } } } } } } /* allocate space for output */ PROTECT(iTout = NEW_INTEGER(Nt)); PROTECT(jTout = NEW_INTEGER(Nt)); PROTECT(kTout = NEW_INTEGER(Nt)); PROTECT(out = NEW_LIST(3)); /* that's 3+4=7 protected objects */ ito = INTEGER_POINTER(iTout); jto = INTEGER_POINTER(jTout); kto = INTEGER_POINTER(kTout); /* copy triangle indices to output vectors */ for(m = 0; m < Nt; m++) { ito[m] = it[m]; jto[m] = jt[m]; kto[m] = kt[m]; } /* insert output vectors in output list */ SET_VECTOR_ELT(out, 0, iTout); SET_VECTOR_ELT(out, 1, jTout); SET_VECTOR_ELT(out, 2, kTout); UNPROTECT(7); return(out); } /* faster version assuming iedge is in increasing order */ SEXP triograph(SEXP nv, /* number of vertices */ SEXP iedge, /* vectors of indices of ends of each edge */ SEXP jedge) /* all arguments are integer */ { int Nv, Ne; int *ie, *je; /* edges */ int *it, *jt, *kt; /* vectors of indices of vertices of triangles */ int Nt, Ntmax; /* number of triangles */ int Nj; int *jj; /* scratch storage */ int i, j, k, m, mj, mk, maxjk, Nmore, maxchunk; /* output */ SEXP iTout, jTout, kTout, out; int *ito, *jto, *kto; /* =================== Protect R objects from garbage collector ======= */ PROTECT(nv = AS_INTEGER(nv)); PROTECT(iedge = AS_INTEGER(iedge)); PROTECT(jedge = AS_INTEGER(jedge)); /* That's 3 protected objects */ /* numbers of vertices and edges */ Nv = *(INTEGER_POINTER(nv)); Ne = LENGTH(iedge); /* input arrays */ ie = INTEGER_POINTER(iedge); je = INTEGER_POINTER(jedge); /* initialise storage (with a guess at max size) */ Ntmax = 3 * Ne; it = (int *) R_alloc(Ntmax, sizeof(int)); jt = (int *) R_alloc(Ntmax, sizeof(int)); kt = (int *) R_alloc(Ntmax, sizeof(int)); Nt = 0; /* initialise scratch storage */ jj = (int *) R_alloc(Ne, sizeof(int)); XOUTERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { R_CheckUserInterrupt(); XINNERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { #ifdef DEBUGTRI Rprintf("i=%d ---------- \n", i); #endif /* Find triangles involving vertex 'i' in which 'i' is the lowest-numbered vertex */ /* First, find vertices j > i connected to i */ Nj = 0; for(m = 0; m < Ne; m++) { if(ie[m] == i) { j = je[m]; if(j > i) { jj[Nj] = j; Nj++; } } else if(je[m] == i) { j = ie[m]; if(j > i) { jj[Nj] = j; Nj++; } } } /* Determine which pairs of vertices j, k are joined by an edge; save triangles (i,j,k) */ #ifdef DEBUGTRI Rprintf("Nj = %d\n", Nj); #endif if(Nj > 1) { #ifdef DEBUGTRI Rprintf("i=%d\njj=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif /* Sort jj in ascending order */ for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(k < j) { /* swap */ jj[mk] = j; jj[mj] = k; j = k; } } } #ifdef DEBUGTRI Rprintf("sorted=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(j != k) { /* Run through edges to determine whether j, k are neighbours */ maxjk = (j > k) ? j : k; for(m = 0; m < Ne; m++) { if(ie[m] > maxjk) break; /* since iedge is in increasing order, the test below will always be FALSE when ie[m] > max(j,k) */ if((ie[m] == j && je[m] == k) || (ie[m] == k && je[m] == j)) { /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - allocate more space */ Nmore = 2 * Ntmax; #ifdef DEBUGTRI Rprintf("Doubling space from %d to %d\n", Ntmax, Nmore); #endif it = (int *) S_realloc((char *) it, Nmore, Ntmax, sizeof(int)); jt = (int *) S_realloc((char *) jt, Nmore, Ntmax, sizeof(int)); kt = (int *) S_realloc((char *) kt, Nmore, Ntmax, sizeof(int)); Ntmax = Nmore; } it[Nt] = i; jt[Nt] = j; kt[Nt] = k; Nt++; } } } } } } } } /* allocate space for output */ PROTECT(iTout = NEW_INTEGER(Nt)); PROTECT(jTout = NEW_INTEGER(Nt)); PROTECT(kTout = NEW_INTEGER(Nt)); PROTECT(out = NEW_LIST(3)); /* that's 3+4=7 protected objects */ ito = INTEGER_POINTER(iTout); jto = INTEGER_POINTER(jTout); kto = INTEGER_POINTER(kTout); /* copy triangle indices to output vectors */ for(m = 0; m < Nt; m++) { ito[m] = it[m]; jto[m] = jt[m]; kto[m] = kt[m]; } /* insert output vectors in output list */ SET_VECTOR_ELT(out, 0, iTout); SET_VECTOR_ELT(out, 1, jTout); SET_VECTOR_ELT(out, 2, kTout); UNPROTECT(7); return(out); } /* Even faster version using information about dummy vertices. Dummy-to-dummy edges are forbidden. For generic purposes use 'friendly' for 'isdata' Edge between j and k is possible iff friendly[j] || friendly[k]. Edges with friendly = FALSE cannot be connected to one another. */ SEXP trioxgraph(SEXP nv, /* number of vertices */ SEXP iedge, /* vectors of indices of ends of each edge */ SEXP jedge, SEXP friendly) /* indicator vector, length nv */ { /* input */ int Nv, Ne; int *ie, *je; /* edges */ int *friend; /* indicator */ /* output */ int *it, *jt, *kt; /* vectors of indices of vertices of triangles */ int Nt, Ntmax; /* number of triangles */ /* scratch storage */ int Nj; int *jj; int i, j, k, m, mj, mk, maxjk, Nmore, maxchunk; /* output to R */ SEXP iTout, jTout, kTout, out; int *ito, *jto, *kto; /* =================== Protect R objects from garbage collector ======= */ PROTECT(nv = AS_INTEGER(nv)); PROTECT(iedge = AS_INTEGER(iedge)); PROTECT(jedge = AS_INTEGER(jedge)); PROTECT(friendly = AS_INTEGER(friendly)); /* That's 4 protected objects */ /* numbers of vertices and edges */ Nv = *(INTEGER_POINTER(nv)); Ne = LENGTH(iedge); /* input arrays */ ie = INTEGER_POINTER(iedge); je = INTEGER_POINTER(jedge); friend = INTEGER_POINTER(friendly); /* initialise storage (with a guess at max size) */ Ntmax = 3 * Ne; it = (int *) R_alloc(Ntmax, sizeof(int)); jt = (int *) R_alloc(Ntmax, sizeof(int)); kt = (int *) R_alloc(Ntmax, sizeof(int)); Nt = 0; /* initialise scratch storage */ jj = (int *) R_alloc(Ne, sizeof(int)); /* convert to C indexing convention */ for(m = 0; m < Ne; m++) { ie[m] -= 1; je[m] -= 1; } OUTERCHUNKLOOP(i, Nv, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(i, Nv, maxchunk, 8196) { #ifdef DEBUGTRI Rprintf("i=%d ---------- \n", i); #endif /* Find triangles involving vertex 'i' in which 'i' is the lowest-numbered vertex */ /* First, find vertices j > i connected to i */ Nj = 0; for(m = 0; m < Ne; m++) { if(ie[m] == i) { j = je[m]; if(j > i) { jj[Nj] = j; Nj++; } } else if(je[m] == i) { j = ie[m]; if(j > i) { jj[Nj] = j; Nj++; } } } /* Determine which pairs of vertices j, k are joined by an edge; save triangles (i,j,k) */ #ifdef DEBUGTRI Rprintf("Nj = %d\n", Nj); #endif if(Nj > 1) { #ifdef DEBUGTRI Rprintf("i=%d\njj=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif /* Sort jj in ascending order */ for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(k < j) { /* swap */ jj[mk] = j; jj[mj] = k; j = k; } } } #ifdef DEBUGTRI Rprintf("sorted=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(j != k && (friend[j] || friend[k])) { /* Run through edges to determine whether j, k are neighbours */ maxjk = (j > k) ? j : k; for(m = 0; m < Ne; m++) { if(ie[m] > maxjk) break; /* since iedge is in increasing order, the test below will always be FALSE when ie[m] > max(j,k) */ if((ie[m] == j && je[m] == k) || (ie[m] == k && je[m] == j)) { /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - allocate more space */ Nmore = 2 * Ntmax; #ifdef DEBUGTRI Rprintf("Doubling space from %d to %d\n", Ntmax, Nmore); #endif it = (int *) S_realloc((char *) it, Nmore, Ntmax, sizeof(int)); jt = (int *) S_realloc((char *) jt, Nmore, Ntmax, sizeof(int)); kt = (int *) S_realloc((char *) kt, Nmore, Ntmax, sizeof(int)); Ntmax = Nmore; } /* convert back to R indexing */ it[Nt] = i + 1; jt[Nt] = j + 1; kt[Nt] = k + 1; Nt++; } } } } } } } } /* allocate space for output */ PROTECT(iTout = NEW_INTEGER(Nt)); PROTECT(jTout = NEW_INTEGER(Nt)); PROTECT(kTout = NEW_INTEGER(Nt)); PROTECT(out = NEW_LIST(3)); /* that's 4+4=8 protected objects */ ito = INTEGER_POINTER(iTout); jto = INTEGER_POINTER(jTout); kto = INTEGER_POINTER(kTout); /* copy triangle indices to output vectors */ for(m = 0; m < Nt; m++) { ito[m] = it[m]; jto[m] = jt[m]; kto[m] = kt[m]; } /* insert output vectors in output list */ SET_VECTOR_ELT(out, 0, iTout); SET_VECTOR_ELT(out, 1, jTout); SET_VECTOR_ELT(out, 2, kTout); UNPROTECT(8); return(out); } /* also calculates diameter (max edge length) of triangle */ SEXP triDgraph(SEXP nv, /* number of vertices */ SEXP iedge, /* vectors of indices of ends of each edge */ SEXP jedge, SEXP edgelength) /* edge lengths */ { int Nv, Ne; int *ie, *je; /* edges */ double *edgelen; int *it, *jt, *kt; /* vectors of indices of vertices of triangles */ double *dt; /* diameters (max edge lengths) of triangles */ int Nt, Ntmax; /* number of triangles */ /* scratch storage */ int Nj; int *jj; double *dd; int i, j, k, m, mj, mk, Nmore, maxchunk; double dij, dik, djk, diam; /* output */ SEXP iTout, jTout, kTout, dTout, out; int *ito, *jto, *kto; double *dto; /* =================== Protect R objects from garbage collector ======= */ PROTECT(nv = AS_INTEGER(nv)); PROTECT(iedge = AS_INTEGER(iedge)); PROTECT(jedge = AS_INTEGER(jedge)); PROTECT(edgelength = AS_NUMERIC(edgelength)); /* That's 4 protected objects */ /* numbers of vertices and edges */ Nv = *(INTEGER_POINTER(nv)); Ne = LENGTH(iedge); /* input arrays */ ie = INTEGER_POINTER(iedge); je = INTEGER_POINTER(jedge); edgelen = NUMERIC_POINTER(edgelength); /* initialise storage (with a guess at max size) */ Ntmax = 3 * Ne; it = (int *) R_alloc(Ntmax, sizeof(int)); jt = (int *) R_alloc(Ntmax, sizeof(int)); kt = (int *) R_alloc(Ntmax, sizeof(int)); dt = (double *) R_alloc(Ntmax, sizeof(double)); Nt = 0; /* initialise scratch storage */ jj = (int *) R_alloc(Ne, sizeof(int)); dd = (double *) R_alloc(Ne, sizeof(double)); XOUTERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { R_CheckUserInterrupt(); XINNERCHUNKLOOP(i, 1, Nv, maxchunk, 8196) { #ifdef DEBUGTRI Rprintf("i=%d ---------- \n", i); #endif /* Find triangles involving vertex 'i' in which 'i' is the lowest-numbered vertex */ /* First, find vertices j > i connected to i */ Nj = 0; for(m = 0; m < Ne; m++) { if(ie[m] == i) { j = je[m]; if(j > i) { jj[Nj] = j; dd[Nj] = edgelen[m]; Nj++; } } else if(je[m] == i) { j = ie[m]; if(j > i) { jj[Nj] = j; dd[Nj] = edgelen[m]; Nj++; } } } /* Determine which pairs of vertices j, k are joined by an edge; save triangles (i,j,k) */ #ifdef DEBUGTRI Rprintf("Nj = %d\n", Nj); #endif if(Nj > 1) { #ifdef DEBUGTRI Rprintf("i=%d\njj=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif /* Sort jj in ascending order */ for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; if(k < j) { /* swap */ jj[mk] = j; jj[mj] = k; dik = dd[mj]; dd[mj] = dd[mk]; dd[mk] = dik; j = k; } } } #ifdef DEBUGTRI Rprintf("sorted=\n", i); for(mj = 0; mj < Nj; mj++) Rprintf("%d ", jj[mj]); Rprintf("\n\n"); #endif for(mj = 0; mj < Nj-1; mj++) { j = jj[mj]; dij = dd[mj]; for(mk = mj+1; mk < Nj; mk++) { k = jj[mk]; dik = dd[mk]; if(j != k) { /* Run through edges to determine whether j, k are neighbours */ for(m = 0; m < Ne; m++) { if((ie[m] == j && je[m] == k) || (ie[m] == k && je[m] == j)) { /* triangle (i, j, k) */ /* determine triangle diameter */ diam = (dij > dik) ? dij : dik; djk = edgelen[m]; if(djk > diam) diam = djk; /* add (i, j, k) to list of triangles */ if(Nt >= Ntmax) { /* overflow - allocate more space */ Nmore = 2 * Ntmax; #ifdef DEBUGTRI Rprintf("Doubling space from %d to %d\n", Ntmax, Nmore); #endif it = (int *) S_realloc((char *) it, Nmore, Ntmax, sizeof(int)); jt = (int *) S_realloc((char *) jt, Nmore, Ntmax, sizeof(int)); kt = (int *) S_realloc((char *) kt, Nmore, Ntmax, sizeof(int)); dt = (double *) S_realloc((char *) dt, Nmore, Ntmax, sizeof(double)); Ntmax = Nmore; } it[Nt] = i; jt[Nt] = j; kt[Nt] = k; dt[Nt] = diam; Nt++; } } } } } } } } /* allocate space for output */ PROTECT(iTout = NEW_INTEGER(Nt)); PROTECT(jTout = NEW_INTEGER(Nt)); PROTECT(kTout = NEW_INTEGER(Nt)); PROTECT(dTout = NEW_NUMERIC(Nt)); PROTECT(out = NEW_LIST(4)); /* that's 4+5=9 protected objects */ ito = INTEGER_POINTER(iTout); jto = INTEGER_POINTER(jTout); kto = INTEGER_POINTER(kTout); dto = NUMERIC_POINTER(dTout); /* copy triangle indices to output vectors */ for(m = 0; m < Nt; m++) { ito[m] = it[m]; jto[m] = jt[m]; kto[m] = kt[m]; dto[m] = dt[m]; } /* insert output vectors in output list */ SET_VECTOR_ELT(out, 0, iTout); SET_VECTOR_ELT(out, 1, jTout); SET_VECTOR_ELT(out, 2, kTout); SET_VECTOR_ELT(out, 3, dTout); UNPROTECT(9); return(out); } spatstat/src/poly2im.c0000755000176000001440000002025312333041761014500 0ustar ripleyusers/* poly2im.c Conversion from (x,y) polygon to pixel image poly2imI pixel value = 1{pixel centre is inside polygon} poly2imA pixel value = area of intersection between pixel and polygon $Revision: 1.8 $ $Date: 2013/09/18 04:50:36 $ */ #undef DEBUG #include #include #include #include "chunkloop.h" #define OUT(I,J) out[I + (J) * Ny] void poly2imI(xp, yp, np, nx, ny, out) double *xp, *yp; /* polygon vertices, anticlockwise, CLOSED */ int *np; int *nx, *ny; /* INTEGER raster points from (0,0) to (nx-1, ny-1) */ int *out; /* output matrix [ny, nx], byrow=FALSE, initialised to 0 */ { int Np, Nx, Ny, Np1, maxchunk, mstart, mend; int j, k, m; double x0, y0, x1, y1, xleft, xright, yleft, yright; double dx, dy, y, slope, intercept; int jleft, jright, imax; int sign; Np = *np; Nx = *nx; Ny = *ny; /* Nxy = Nx * Ny; */ Np1 = Np - 1; /* run through polygon edges */ OUTERCHUNKLOOP(k, Np1, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(k, Np1, maxchunk, 8196) { x0 = xp[k]; y0 = yp[k]; x1 = xp[k+1]; y1 = yp[k+1]; if(x0 < x1) { xleft = x0; xright = x1; yleft = y0; yright = y1; sign = -1; } else { xleft = x1; xright = x0; yleft = y1; yright = y0; sign = +1; } /* determine relevant columns of pixels */ jleft = (int) ceil(xleft); jright = (int) floor(xright); if(jleft < Nx && jright >= 0 && jleft <= jright) { if(jleft < 0) { jleft = 0; } if(jright >= Nx) {jright = Nx - 1; } /* equation of edge */ dx = xright - xleft; dy = yright - yleft; slope = dy/dx; intercept = yleft - slope * xleft; /* visit relevant columns */ for(j = jleft; j <= jright; j++) { y = slope * ((double) j) + intercept; imax = (int) floor(y); if(imax >= Ny) imax = Ny-1; if(imax >= 0) { /* increment entries below edge in this column: out[i + j * Ny] += sign for 0 <= i <= imax */ mstart = j * Ny; mend = mstart + imax; for(m = mstart; m <= mend; m++) { out[m] += sign; } } } } } } } #define BELOW -1 #define INSIDE 0 #define ABOVE 1 void poly2imA(ncol, nrow, xpoly, ypoly, npoly, out, status) int *ncol, *nrow; /* pixels are unit squares from (0,0) to (ncol,nrow) */ double *xpoly, *ypoly; /* vectors of coordinates of polygon vertices */ int *npoly; double *out; /* double array [nrow, ncol] of pixel areas, byrow=TRUE, initialised to 0 */ int *status; { double *xp, *yp; int nx, ny, np, np1, maxchunk; int i, j, k; double xcur, ycur, xnext, ynext, xleft, yleft, xright, yright; int sgn, jmin, jmax, imin, imax; double x0, y0, x1, y1, slope, yhi, ylo, area, xcut, xcutA, xcutB; int klo, khi; nx = *ncol; ny = *nrow; xp = xpoly; yp = ypoly; np = *npoly; *status = 0; /* initialise output array */ for(i = 0; i < ny; i++) for(j = 0; j < nx; j++) out[j + ny * i] = 0; /* ............ loop over polygon edges ...................*/ np1 = np - 1; OUTERCHUNKLOOP(k, np1, maxchunk, 8196) { R_CheckUserInterrupt(); INNERCHUNKLOOP(k, np1, maxchunk, 8196) { xcur = xp[k]; ycur = yp[k]; xnext = xp[k+1]; ynext = yp[k+1]; #ifdef DEBUG Rprintf("\nEdge %d from (%lf, %lf) to (%lf, %lf) .........\n", k, xcur, ycur, xnext, ynext); #endif if(xcur != xnext) { /* vertical edges are ignored */ if(xcur < xnext) { #ifdef DEBUG Rprintf("negative sign\n"); #endif sgn = -1; xleft = xcur; yleft = ycur; xright = xnext; yright = ynext; } else { #ifdef DEBUG Rprintf("positive sign\n"); #endif sgn = 1; xleft = xnext; yleft = ynext; xright = xcur; yright = ycur; } /* we have now ensured xleft < xright */ slope = (yright - yleft)/(xright - xleft); /* Find relevant columns of pixels */ jmin = floor(xleft); jmin = (jmin < 0) ? 0 : jmin; jmax = ceil(xright); jmax = (jmax > nx - 1) ? nx - 1 : jmax; /* Find relevant rows of pixels */ imin = floor((yleft < yright) ? yleft : yright); imin = (imin < 0) ? 0 : imin; imax = ceil((yleft < yright) ? yright : yleft); imax = (imax > ny - 1) ? ny - 1 : imax; #ifdef DEBUG Rprintf( "imin=%d, imax=%d, jmin=%d, jmax=%d\n", imin, imax, jmin, jmax); #endif /* ........... loop over columns of pixels ..............*/ for(j = jmin; j <= jmax; j++) { #ifdef DEBUG Rprintf( "\t j=%d:\n", j); #endif /* Intersect trapezium with column of pixels */ if(xleft <= j+1 && xright >= j) { if(xleft >= j) { /* retain left corner */ #ifdef DEBUG Rprintf( "\tretain left corner\n"); #endif x0 = xleft; y0 = yleft; } else { /* trim left corner */ #ifdef DEBUG Rprintf( "\ttrim left corner\n"); #endif x0 = (double) j; y0 = yleft + slope * (x0 - xleft); } if(xright <= j+1) { /* retain right corner */ #ifdef DEBUG Rprintf( "\tretain right corner\n"); #endif x1 = xright; y1 = yright; } else { /* trim right corner */ #ifdef DEBUG Rprintf( "\ttrim right corner\n"); #endif x1 = (double) (j+1); y1 = yright + slope * (x1 - xright); } /* save min and max y */ if(y0 < y1) { #ifdef DEBUG Rprintf( "slope %lf > 0\n", slope); #endif ylo = y0; yhi = y1; } else { #ifdef DEBUG Rprintf( "slope %lf <= 0\n", slope); #endif ylo = y1; yhi = y0; } /* ............ loop over pixels within column ......... */ /* first part */ if(imin > 0) { for(i = 0; i < imin; i++) { #ifdef DEBUG Rprintf( "\ti=%d:\n", i); #endif /* The trimmed pixel [x0, x1] * [i, i+1] lies below the polygon edge. */ area = (x1 - x0); #ifdef DEBUG Rprintf( "\tIncrementing area by %lf\n", sgn * area); #endif out[j + ny * i] += sgn * area; } } /* second part */ for(i = imin; i <= imax; i++) { #ifdef DEBUG Rprintf( "\ti=%d:\n", i); #endif /* Compute area of intersection between trapezium and trimmed pixel [x0, x1] x [i, i+1] */ klo = (ylo <= i) ? BELOW : (ylo >= (i+1))? ABOVE: INSIDE; khi = (yhi <= i) ? BELOW : (yhi >= (i+1))? ABOVE: INSIDE; if(klo == ABOVE) { /* trapezium covers pixel */ #ifdef DEBUG Rprintf( "\t\ttrapezium covers pixel\n"); #endif area = (x1-x0); } else if(khi == BELOW) { #ifdef DEBUG Rprintf( "\t\tpixel avoids trapezium\n"); #endif /* pixel avoids trapezium */ area = 0.0; } else if(klo == INSIDE && khi == INSIDE) { /* polygon edge is inside pixel */ #ifdef DEBUG Rprintf( "\t\t polygon edge is inside pixel\n"); #endif area = (x1-x0) * ((ylo + yhi)/2.0 - i); } else if(klo == INSIDE && khi == ABOVE) { /* polygon edge crosses upper edge of pixel */ #ifdef DEBUG Rprintf( "\t\t polygon edge crosses upper edge of pixel\n"); #endif xcut = x0 + ((i+1) - y0)/slope; if(slope > 0) area = (xcut - x0) * ((y0 + (i+1))/2 - i) + (x1 - xcut); else area = (x1 - xcut) * ((y1 + (i+1))/2 - i) + (xcut - x0); } else if(klo == BELOW && khi == INSIDE) { /* polygon edge crosses lower edge of pixel */ #ifdef DEBUG Rprintf( "\t\t polygon edge crosses lower edge of pixel\n"); #endif xcut = x0 + (i - y0)/slope; if(slope > 0) area = (x1 - xcut) * ((y1 + i)/2 - i); else area = (xcut - x0) * ((y0 + i)/2 - i); } else if(klo == BELOW && khi == ABOVE) { /* polygon edge crosses upper and lower edges of pixel */ #ifdef DEBUG Rprintf( "\t\t polygon edge crosses upper and lower edges of pixel\n"); #endif xcutA = x0 + (i - y0)/slope; xcutB = x0 + ((i+1) - y0)/slope; if(slope > 0) area = (xcutB - xcutA)/2 + (x1 - xcutB); else area = (xcutB - x0) + (xcutA - xcutB)/2; } else { /* control should not pass to here */ *status = 1; return; } /* add contribution to area of pixel */ #ifdef DEBUG Rprintf( "\tIncrementing area by %lf\n", sgn * area); #endif out[j + ny * i] += sgn * area; } /* ............ end of loop over pixels within column ......... */ } } /* ........ end of loop over columns of pixels ...............*/ } } } /* ......... end of loop over polygon edges ...................*/ } spatstat/src/fiksel.c0000755000176000001440000000574412333041761014372 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Fiksel process */ /* Conditional intensity function for a pairwise interaction point process with interaction function e(t) = 0 for t < h = exp(a * exp(- kappa * t)) for h <= t < r = 1 for t >= r */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Fiksel { double r; double h; double kappa; double a; double h2; /* h^2 */ double r2; /* r^2 */ double *period; int per; } Fiksel; /* initialiser function */ Cdata *fikselinit(state, model, algo) State state; Model model; Algor algo; { Fiksel *fiksel; fiksel = (Fiksel *) R_alloc(1, sizeof(Fiksel)); /* Interpret model parameters*/ fiksel->r = model.ipar[0]; fiksel->h = model.ipar[1]; fiksel->kappa = model.ipar[2]; fiksel->a = model.ipar[3]; fiksel->period = model.period; /* constants */ fiksel->h2 = pow(fiksel->h, 2); fiksel->r2 = pow(fiksel->r, 2); /* periodic boundary conditions? */ fiksel->per = (model.period[0] > 0.0); return((Cdata *) fiksel); } /* conditional intensity evaluator */ double fikselcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *x, *y; double u, v; double d2, pairpotsum, cifval; double kappa, r2, h2; double *period; Fiksel *fiksel; DECLARE_CLOSE_D2_VARS; fiksel = (Fiksel *) cdata; period = fiksel->period; kappa = fiksel->kappa; r2 = fiksel->r2; h2 = fiksel->h2; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; cifval = 1.0; if(npts == 0) return(cifval); pairpotsum = 0; ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(fiksel->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],period,r2,d2)) { if(d2 < h2) { cifval = 0.0; return(cifval); } else { pairpotsum += exp(-kappa * sqrt(d2)); } } } } if(ixp1 < npts) { for(j=ixp1; j 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u,v,x[j],y[j],r2,d2)) { if(d2 < h2) { cifval = 0.0; return(cifval); } else { pairpotsum += exp(-kappa * sqrt(d2)); } } } } if(ixp1 < npts) { for(j=ixp1; ja * pairpotsum); return cifval; } Cifns FikselCifns = { &fikselinit, &fikselcif, (updafunptr) NULL, NO}; spatstat/src/mhv4.h0000644000176000001440000000040512333041761013762 0ustar ripleyusers/* mhv4.h visual debugger or not */ #undef MH_SNOOP if(snooper.active) { /* visual debugger */ #define MH_SNOOP YES #include "mhloop.h" #undef MH_SNOOP } else { /* no visual debugger */ #define MH_SNOOP NO #include "mhloop.h" #undef MH_SNOOP } spatstat/src/knn3DdistX.h0000644000176000001440000001226412333041761015103 0ustar ripleyusers #if (1 == 0) /* knn3DdistX.h Code template for C functions supporting nncross for k-nearest neighbours (k > 1) for 3D point patterns THE FOLLOWING CODE ASSUMES THAT LISTS ARE SORTED IN ASCENDING ORDER OF z COORDINATE This code is #included multiple times in nn3Ddist.c Variables used: FNAME function name DIST #defined if function returns distance to nearest neighbour WHICH #defined if function returns id of nearest neighbour EXCLUDE #defined if exclusion mechanism is used Either or both DIST and WHICH may be defined. When EXCLUDE is defined, code numbers id1, id2 are attached to the patterns X and Y respectively, such that X[i] and Y[j] are the same point iff id1[i] = id2[j]. Copyright (C) Adrian Baddeley, Jens Oehlschlagel and Rolf Turner 2000-2013 Licence: GPL >= 2 $Revision: 1.1 $ $Date: 2013/06/29 03:04:47 $ */ #endif void FNAME(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, kmax, nnd, nnwhich, huge) /* inputs */ int *n1, *n2; double *x1, *y1, *z1, *x2, *y2, *z2, *huge; int *id1, *id2; int *kmax; /* output matrices (npoints * kmax) in ROW MAJOR order */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { int npoints1, npoints2, nk, nk1; int maxchunk, i, jleft, jright, jwhich, lastjwhich, unsorted, k, k1; double d2, d2minK, x1i, y1i, z1i, dx, dy, dz, dz2, hu, hu2, tmp; double *d2min; #ifdef WHICH int *which; int itmp; #endif #ifdef EXCLUDE int id1i; #endif npoints1 = *n1; npoints2 = *n2; nk = *kmax; nk1 = nk - 1; hu = *huge; hu2 = hu * hu; if(npoints1 == 0 || npoints2 == 0) return; lastjwhich = 0; /* create space to store the nearest neighbour distances and indices for the current point */ d2min = (double *) R_alloc((size_t) nk, sizeof(double)); #ifdef WHICH which = (int *) R_alloc((size_t) nk, sizeof(int)); #endif /* loop in chunks of 2^16 */ i = 0; maxchunk = 0; while(i < npoints1) { R_CheckUserInterrupt(); maxchunk += 65536; if(maxchunk > npoints1) maxchunk = npoints1; for(; i < maxchunk; i++) { /* initialise nn distances and indices */ d2minK = hu2; jwhich = -1; for(k = 0; k < nk; k++) { d2min[k] = hu2; #ifdef WHICH which[k] = -1; #endif } x1i = x1[i]; y1i = y1[i]; z1i = z1[i]; #ifdef EXCLUDE id1i = id1[i]; #endif if(lastjwhich < npoints2) { /* search forward from previous nearest neighbour */ for(jright = lastjwhich; jright < npoints2; ++jright) { dz = z2[jright] - z1i; dz2 = dz * dz; if(dz2 > d2minK) /* note that dz2 >= d2minK could break too early */ break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[jright] != id1i) { #endif dy = y2[jright] - y1i; d2 = dy * dy + dz2; if(d2 < d2minK) { dx = x2[jright] - x1i; d2 = dx * dx + d2; if (d2 < d2minK) { /* overwrite last entry in list of neighbours */ d2min[nk1] = d2; jwhich = jright; #ifdef WHICH which[nk1] = jright; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[nk1]; } } #ifdef EXCLUDE } #endif } /* end forward search */ } if(lastjwhich > 0) { /* search backward from previous nearest neighbour */ for(jleft = lastjwhich - 1; jleft >= 0; --jleft) { dz = z1i - z2[jleft]; dz2 = dz * dz; if(dz2 > d2minK) /* note that dz2 >= d2minK could break too early */ break; #ifdef EXCLUDE /* do not compare identical points */ if(id2[jleft] != id1i) { #endif dy = y2[jleft] - y1i; d2 = dy * dy + dz2; if(d2 < d2minK) { dx = x2[jleft] - x1i; d2 = dx * dx + d2; if (d2 < d2minK) { /* overwrite last entry in list of neighbours */ d2min[nk1] = d2; jwhich = jleft; #ifdef WHICH which[nk1] = jleft; #endif /* bubble sort */ unsorted = YES; for(k = nk1; unsorted && k > 0; k--) { k1 = k - 1; if(d2min[k] < d2min[k1]) { /* swap entries */ tmp = d2min[k1]; d2min[k1] = d2min[k]; d2min[k] = tmp; #ifdef WHICH itmp = which[k1]; which[k1] = which[k]; which[k] = itmp; #endif } else { unsorted = NO; } } /* adjust maximum distance */ d2minK = d2min[nk1]; } } #ifdef EXCLUDE } #endif } /* end backward search */ } /* copy nn distances for point i to output matrix in ROW MAJOR order */ for(k = 0; k < nk; k++) { #ifdef DIST nnd[nk * i + k] = sqrt(d2min[k]); #endif #ifdef WHICH nnwhich[nk * i + k] = which[k] + 1; /* R indexing */ #endif } /* save index of last neighbour encountered */ lastjwhich = jwhich; /* end of loop over points i */ } } } spatstat/src/knngrid.c0000644000176000001440000000400612333041761014534 0ustar ripleyusers/* knngrid.c K-th Nearest Neighbour Distances from a pixel grid to a point pattern Copyright (C) Adrian Baddeley, Jens Oehlschlaegel and Rolf Turner 2000-2013 Licence: GNU Public Licence >= 2 $Revision: 1.6 $ $Date: 2013/11/03 05:06:28 $ Function body definition is #included from knngrid.h THE FOLLOWING FUNCTIONS ASSUME THAT x IS SORTED IN ASCENDING ORDER */ #undef SPATSTAT_DEBUG #include #include #include #include "yesno.h" double sqrt(); /* THE FOLLOWING CODE ASSUMES THAT x IS SORTED IN ASCENDING ORDER */ /* general interface */ void knnGinterface(nx, x0, xstep, ny, y0, ystep, /* pixel grid dimensions */ np, xp, yp, /* data points */ kmax, wantdist, wantwhich, nnd, nnwhich, huge) /* inputs */ int *nx, *ny, *np; double *x0, *xstep, *y0, *ystep, *huge; double *xp, *yp; int *kmax; /* options */ int *wantdist, *wantwhich; /* outputs */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { void knnGdw(), knnGd(), knnGw(); int di, wh; di = (*wantdist != 0); wh = (*wantwhich != 0); if(di && wh) { knnGdw(nx, x0, xstep, ny, y0, ystep, np, xp, yp, kmax, nnd, nnwhich, huge); } else if(di) { knnGd(nx, x0, xstep, ny, y0, ystep, np, xp, yp, kmax, nnd, nnwhich, huge); } else if(wh) { knnGw(nx, x0, xstep, ny, y0, ystep, np, xp, yp, kmax, nnd, nnwhich, huge); } } #undef FNAME #undef DIST #undef WHICH /* knnGdw nearest neighbours 1:kmax returns distances and indices */ #define FNAME knnGdw #define DIST #define WHICH #include "knngrid.h" #undef FNAME #undef DIST #undef WHICH /* knnGd nearest neighbours 1:kmax returns distances only */ #define FNAME knnGd #define DIST #include "knngrid.h" #undef FNAME #undef DIST #undef WHICH /* knnGw nearest neighbours 1:kmax returns indices only */ #define FNAME knnGw #define WHICH #include "knngrid.h" #undef FNAME #undef DIST #undef WHICH spatstat/src/linnncross.c0000644000176000001440000000121212333041761015264 0ustar ripleyusers#include /* linnncross.c Shortest-path distances between nearest neighbours in linear network One pattern to another pattern $Revision: 1.1 $ $Date: 2013/10/21 02:01:29 $ linndcross linndxcross */ #define DPATH(I,J) dpath[(I) + Nv * (J)] #define ANSWER(I,J) answer[(I) + Np * (J)] #define EUCLID(X,Y,U,V) sqrt(pow((X)-(U),2)+pow((Y)-(V),2)) /* definition of linndcross */ #define FNAME linndcross #undef EXCLU #define WHICH #include "linnncross.h" #undef FNAME #undef EXCLU #undef WHICH /* definition of linndxcross */ #define FNAME linndxcross #define EXCLU #define WHICH #include "linnncross.h" spatstat/src/mhv2.h0000644000176000001440000000041212333041761013756 0ustar ripleyusers/* mhv2.h single interaction or hybrid */ #undef MH_SINGLE if(Ncif == 1) { /* single interaction */ #define MH_SINGLE YES #include "mhv3.h" #undef MH_SINGLE } else { /* hybrid interaction */ #define MH_SINGLE NO #include "mhv3.h" #undef MH_SINGLE } spatstat/src/sftcr.c0000755000176000001440000000436612333041761014235 0ustar ripleyusers#include #include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Soft Core process */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Softcore { double sigma; double kappa; double nook; /* -1/kappa */ double stok; /* sigma^(2/kappa) */ double *period; int per; } Softcore; /* initialiser function */ Cdata *sftcrinit(state, model, algo) State state; Model model; Algor algo; { Softcore *softcore; softcore = (Softcore *) R_alloc(1, sizeof(Softcore)); /* Interpret model parameters*/ softcore->sigma = model.ipar[0]; softcore->kappa = model.ipar[1]; softcore->period = model.period; /* constants */ softcore->nook = -1/softcore->kappa; softcore->stok = pow(softcore->sigma, 2/softcore->kappa); /* periodic boundary conditions? */ softcore->per = (model.period[0] > 0.0); return((Cdata *) softcore); } /* conditional intensity evaluator */ double sftcrcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *x, *y; double u, v; double d2, pairsum, cifval, nook, stok; Softcore *softcore; softcore = (Softcore *) cdata; nook = softcore->nook; stok = softcore->stok; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; cifval = 1.0; if(npts == 0) return(cifval); pairsum = 0; ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(softcore->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { d2 = dist2(u,v,x[j],y[j],softcore->period); pairsum += pow(d2, nook); } } if(ixp1 < npts) { for(j=ixp1; jperiod); pairsum += pow(d2, nook); } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { d2 = pow(u - x[j],2) + pow(v-y[j],2); pairsum += pow(d2, nook); } } if(ixp1 < npts) { for(j=ixp1; j #include #include #include "geom3.h" #include "functable.h" #ifdef DEBUG #define DEBUGMESSAGE(S) Rprintf(S); #else #define DEBUGMESSAGE(S) #endif /* $Revision: 1.3 $ $Date: 2011/11/20 04:19:10 $ 3D distance transform # ///////////////////////////////////////////// # AUTHOR: Adrian Baddeley, CWI, Amsterdam, 1991. # # MODIFIED BY: Adrian Baddeley, Perth 2009 # # This software is distributed free # under the conditions that # (1) it shall not be incorporated # in software that is subsequently sold # (2) the authorship of the software shall # be acknowledged in any publication that # uses results generated by the software # (3) this notice shall remain in place # in each file. # ////////////////////////////////////////////// */ /* step lengths in distance transform */ #define STEP1 41 #define STEP2 58 #define STEP3 71 /* (41,58,71)/41 is a good rational approximation to (1, sqrt(2), sqrt(3)) */ #define MIN(X,Y) (((X) < (Y)) ? (X) : (Y)) #define MAX(X,Y) (((X) > (Y)) ? (X) : (Y)) typedef struct IntImage { int *data; int Mx, My, Mz; /* dimensions */ int length; } IntImage; typedef struct BinaryImage { unsigned char *data; int Mx, My, Mz; /* dimensions */ int length; } BinaryImage; #define VALUE(I,X,Y,Z) \ ((I).data)[ (Z) * ((I).Mx) * ((I).My) + (Y) * ((I).Mx) + (X) ] void allocBinImage(b, ok) BinaryImage *b; int *ok; { b->length = b->Mx * b->My * b->Mz; b->data = (unsigned char *) R_alloc(b->length, sizeof(unsigned char)); if(b->data == 0) { Rprintf("Can't allocate memory for %d binary voxels\n", b->length); *ok = 0; } *ok = 1; } void allocIntImage(v, ok) IntImage *v; int *ok; { v->length = v->Mx * v->My * v->Mz; v->data = (int *) R_alloc(v->length, sizeof(int)); if(v->data == 0) { Rprintf("Can't allocate memory for %d integer voxels\n", v->length); *ok = 0; } *ok = 1; } void freeBinImage(b) BinaryImage *b; { } void freeIntImage(v) IntImage *v; { } void cts2bin(p, n, box, vside, b, ok) /* convert a list of points inside a box into a 3D binary image */ Point *p; int n; Box *box; double vside; /* side of a (cubic) voxel */ BinaryImage *b; int *ok; { int i, lx, ly, lz; unsigned char *cp; b->Mx = (int) ceil((box->x1 - box->x0)/vside) + 1; b->My = (int) ceil((box->y1 - box->y0)/vside) + 1; b->Mz = (int) ceil((box->z1 - box->z0)/vside) + 1; allocBinImage(b, ok); if(! (*ok)) return; for(i = b->length, cp = b->data; i ; i--, cp++) *cp = 1; for(i=0;ix0)/vside)-1; ly = (int) ceil((p[i].y - box->y0)/vside)-1; lz = (int) ceil((p[i].z - box->z0)/vside)-1; if( lx >= 0 && lx < b->Mx && ly >= 0 && ly < b->My && lz >= 0 && lz < b->Mz ) VALUE((*b),lx,ly,lz) = 0; } } void distrans3(b, v, ok) /* Distance transform in 3D */ BinaryImage *b; /* input */ IntImage *v; /* output */ int *ok; { register int x, y, z; int infinity, q; /* allocate v same size as b */ v->Mx = b->Mx; v->My = b->My; v->Mz = b->Mz; allocIntImage(v, ok); if(! (*ok)) return; /* compute largest possible distance */ infinity = (int) ceil( ((double) STEP3) * sqrt( ((double) b->Mx) * b->Mx + ((double) b->My) * b->My + ((double) b->Mz) * b->Mz)); /* Forward pass: Top to Bottom; Back to Front; Left to Right. */ for(z=0;zMz;z++) { R_CheckUserInterrupt(); for(y=0;yMy;y++) { for(x=0;xMx;x++) { if(VALUE((*b),x,y,z) == 0) VALUE((*v),x,y,z) = 0; else { q = infinity; #define INTERVAL(W, DW, MW) \ ((DW == 0) || (DW == -1 && W > 0) || (DW == 1 && W < MW - 1)) #define BOX(X,Y,Z,DX,DY,DZ) \ (INTERVAL(X,DX,v->Mx) && INTERVAL(Y,DY,v->My) && INTERVAL(Z,DZ,v->Mz)) #define TEST(DX,DY,DZ,DV) \ if(BOX(x,y,z,DX,DY,DZ) && q > VALUE((*v),x+DX,y+DY,z+DZ) + DV) \ q = VALUE((*v),x+DX,y+DY,z+DZ) + DV /* same row */ TEST(-1, 0, 0, STEP1); /* same plane */ TEST(-1,-1, 0, STEP2); TEST( 0,-1, 0, STEP1); TEST( 1,-1, 0, STEP2); /* previous plane */ TEST( 1, 1,-1, STEP3); TEST( 0, 1,-1, STEP2); TEST(-1, 1,-1, STEP3); TEST( 1, 0,-1, STEP2); TEST( 0, 0,-1, STEP1); TEST(-1, 0,-1, STEP2); TEST( 1,-1,-1, STEP3); TEST( 0,-1,-1, STEP2); TEST(-1,-1,-1, STEP3); VALUE((*v),x,y,z) = q; } } } } /* Backward pass: Bottom to Top; Front to Back; Right to Left. */ for(z = b->Mz - 1; z >= 0; z--) { R_CheckUserInterrupt(); for(y = b->My - 1; y >= 0; y--) { for(x = b->Mx - 1; x >= 0; x--) { if((q = VALUE((*v),x,y,z)) != 0) { /* same row */ TEST(1, 0, 0, STEP1); /* same plane */ TEST(-1, 1, 0, STEP2); TEST( 0, 1, 0, STEP1); TEST( 1, 1, 0, STEP2); /* plane below */ TEST( 1, 1, 1, STEP3); TEST( 0, 1, 1, STEP2); TEST(-1, 1, 1, STEP3); TEST( 1, 0, 1, STEP2); TEST( 0, 0, 1, STEP1); TEST(-1, 0, 1, STEP2); TEST( 1,-1, 1, STEP3); TEST( 0,-1, 1, STEP2); TEST(-1,-1, 1, STEP3); VALUE((*v),x,y,z) = q; } } } } } void hist3d(v, vside, count) /* compute histogram of all values in *v using count->n histogram cells ranging from count->t0 to count->t1 and put results in count->num */ IntImage *v; double vside; Itable *count; { register int i, j, k; register int *ip; register double scale, width; /* relationship between distance transform units and physical units */ scale = vside/STEP1; width = (count->t1 - count->t0)/(count->n - 1); for(i = 0; i < count->n ; i++) { (count->num)[i] = 0; (count->denom)[i] = v->length; } for(i = v->length, ip = v->data; i; i--, ip++) { k = (int) ceil((*ip * scale - count->t0)/width); k = MAX(k, 0); for(j = k; j < count->n; j++) (count->num)[j]++; } } void hist3dminus(v, vside, count) /* minus sampling */ IntImage *v; double vside; Itable *count; { register int x, y, z, val, border, bx, by, bz, byz, j, kbord, kval; register double scale, width; DEBUGMESSAGE("inside hist3dminus\n") scale = vside/STEP1; width = (count->t1 - count->t0)/(count->n - 1); /* table is assumed to have been initialised in MakeItable */ for(z = 0; z < v->Mz; z++) { bz = MIN(z + 1, v->Mz - z); for(y = 0; y < v->My; y++) { by = MIN(y + 1, v->My - y); byz = MIN(by, bz); for(x = 0; x < v->Mx; x++) { bx = MIN(x + 1, v->My - x); border = MIN(bx, byz); kbord = (int) floor((vside * border - count->t0)/width); kbord = MIN(kbord, count->n - 1); /* denominator counts all voxels with distance to boundary >= r */ if(kbord >= 0) for(j = 0; j <= kbord; j++) (count->denom)[j]++; val = VALUE((*v), x, y, z); kval = (int) ceil((val * scale - count->t0)/width); kval = MAX(kval, 0); #ifdef DEBUG /* Rprintf("border=%lf\tkbord=%d\tval=%lf\tkval=%d\n", vside * border, kbord, scale * val, kval); */ #endif /* numerator counts all voxels with distance to boundary >= r and distance to nearest point <= r */ if(kval <= kbord) for(j = kval; j <= kbord; j++) (count->num)[j]++; } } } DEBUGMESSAGE("leaving hist3dminus\n") } void hist3dCen(v, vside, count) /* four censoring-related histograms */ IntImage *v; double vside; H4table *count; { register int x, y, z, val, border, bx, by, bz, byz, kbord, kval; register double scale, width, realborder, realval; DEBUGMESSAGE("inside hist3dCen\n") scale = vside/STEP1; width = (count->t1 - count->t0)/(count->n - 1); /* table is assumed to have been initialised in MakeH4table */ for(z = 0; z < v->Mz; z++) { bz = MIN(z + 1, v->Mz - z); for(y = 0; y < v->My; y++) { by = MIN(y + 1, v->My - y); byz = MIN(by, bz); for(x = 0; x < v->Mx; x++) { bx = MIN(x + 1, v->My - x); border = MIN(bx, byz); realborder = vside * border; kbord = (int) floor((realborder - count->t0)/width); val = VALUE((*v), x, y, z); realval = scale * val; kval = (int) ceil((realval - count->t0)/width); kval = MIN(kval, count->n - 1); #ifdef DEBUG Rprintf("border=%lf\tkbord=%d\tval=%lf\tkval=%d\n", realborder, kbord, realval, kval); #endif if(realval <= realborder) { /* observation is uncensored; increment all four histograms */ if(kval >= count->n) ++(count->upperobs); else if(kval >= 0) { (count->obs)[kval]++; (count->nco)[kval]++; } if(kbord >= count->n) ++(count->uppercen); else if(kbord >= 0) { (count->cen)[kbord]++; (count->ncc)[kbord]++; } } else { /* observation is censored; increment only two histograms */ kval = MIN(kval, kbord); if(kval >= count->n) ++(count->upperobs); else if(kval >= 0) (count->obs)[kval]++; if(kbord >= count->n) ++(count->uppercen); else if(kbord >= 0) (count->cen)[kbord]++; } } } } DEBUGMESSAGE("leaving hist3dCen\n") } /* CALLING ROUTINES */ void phatminus(p, n, box, vside, count) Point *p; int n; Box *box; double vside; Itable *count; { BinaryImage b; IntImage v; int ok; DEBUGMESSAGE("in phatminus\ncalling cts2bin...") cts2bin(p, n, box, vside, &b, &ok); DEBUGMESSAGE("out of cts2bin\ninto distrans3...") if(ok) distrans3(&b, &v, &ok); if(ok) { freeBinImage(&b); DEBUGMESSAGE("out of distrans3\ninto hist3dminus...") hist3dminus(&v, vside, count); DEBUGMESSAGE("out of hist3dminus\n") freeIntImage(&v); } } void phatnaive(p, n, box, vside, count) Point *p; int n; Box *box; double vside; Itable *count; { BinaryImage b; IntImage v; int ok; DEBUGMESSAGE("in phatnaive\ncalling cts2bin...") cts2bin(p, n, box, vside, &b, &ok); DEBUGMESSAGE("out of cts2bin\n into distrans3...") if(ok) distrans3(&b, &v, &ok); if(ok) { freeBinImage(&b); DEBUGMESSAGE("out of distrans3\ninto hist3d..."); hist3d(&v, vside, count); DEBUGMESSAGE("out of hist3d\n") freeIntImage(&v); } } void p3hat4(p, n, box, vside, count) Point *p; int n; Box *box; double vside; H4table *count; { BinaryImage b; IntImage v; int ok; DEBUGMESSAGE("in phatminus\ncalling cts2bin...") cts2bin(p, n, box, vside, &b, &ok); DEBUGMESSAGE("out of cts2bin\ninto distrans3...") if(ok) distrans3(&b, &v, &ok); if(ok) { freeBinImage(&b); DEBUGMESSAGE("out of distrans3\ninto hist3dminus...") hist3dCen(&v, vside, count); DEBUGMESSAGE("out of hist3dminus\n") freeIntImage(&v); } } spatstat/src/digber.c0000644000176000001440000000222312333041761014333 0ustar ripleyusers/* digber.c Diggle-Berman function J used in bandwidth selection J(r) = \int_0^(2r) phi(t, r) dK(t) where K is the K-function and phi(t, r) = 2 r^2 * (acos(y) - y sqrt(1 - y^2)) where y = t/(2r). $Revision: 1.7 $ $Date: 2013/08/24 11:13:43 $ */ #include double sqrt(), acos(); /* r is the vector of distance values, starting from 0, with length nr, equally spaced. dK = diff(K) is the vector of increments of the K-function, with length ndK = nr-1. values of J are computed only up to max(r)/2 nrmax = floor(nr/2). */ void digberJ(r, dK, nr, nrmax, ndK, J) /* inputs */ int *nr, *nrmax, *ndK; double *r, *dK; /* output */ double *J; { int i, j, Ni, NdK; double ri, twori, tj, y, phiy, integral; Ni = *nrmax; NdK = *ndK; J[0] = 0.0; for(i = 1; i < Ni; i++) { ri = r[i]; twori = 2 * ri; integral = 0.0; for(j = 0; j < NdK; j++) { tj = r[j]; y = tj/twori; if(y >= 1.0) break; phiy = acos(y) - y * sqrt(1 - y * y); integral += phiy * dK[j]; } J[i] = 2 * ri * ri * integral; } } spatstat/src/lennard.c0000755000176000001440000000712112333041761014527 0ustar ripleyusers#include #include #include #include #include "methas.h" #include "dist2.h" /* Conditional intensity computation for Lennard-Jones process */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct Lennard { double sigma; double epsilon; double sigma2; /* sigma^2 */ double foureps; /* 4 * epsilon */ double d2min; /* minimum value of d^2 which yields nonzero intensity */ double d2max; /* maximum value of d^2 which has nontrivial contribution */ double *period; int per; } Lennard; /* MAXEXP is intended to be the largest x such that exp(-x) != 0 although the exact value is not needed */ #define MAXEXP (-log(DOUBLE_XMIN)) #define MINEXP (log(1.001)) /* initialiser function */ Cdata *lennardinit(state, model, algo) State state; Model model; Algor algo; { Lennard *lennard; double sigma2, foureps, minfrac, maxfrac; lennard = (Lennard *) R_alloc(1, sizeof(Lennard)); /* Interpret model parameters*/ lennard->sigma = model.ipar[0]; lennard->epsilon = model.ipar[1]; lennard->period = model.period; /* constants */ lennard->sigma2 = sigma2 = pow(lennard->sigma, 2); lennard->foureps = foureps = 4 * lennard->epsilon; /* thresholds where the interaction becomes trivial */ minfrac = pow(foureps/MAXEXP, (double) 1.0/6.0); if(minfrac > 0.5) minfrac = 0.5; maxfrac = pow(foureps/MINEXP, (double) 1.0/3.0); if(maxfrac < 2.0) maxfrac = 2.0; lennard->d2min = sigma2 * minfrac; lennard->d2max = sigma2 * maxfrac; /* periodic boundary conditions? */ lennard->per = (model.period[0] > 0.0); return((Cdata *) lennard); } /* conditional intensity evaluator */ double lennardcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ix, ixp1, j; double *x, *y; double u, v; double d2, ratio6, pairsum, cifval; double sigma2, d2max, d2min; double *period; Lennard *lennard; DECLARE_CLOSE_D2_VARS; lennard = (Lennard *) cdata; sigma2 = lennard->sigma2; d2max = lennard->d2max; d2min = lennard->d2min; period = lennard->period; u = prop.u; v = prop.v; ix = prop.ix; x = state.x; y = state.y; npts = state.npts; cifval = 1.0; if(npts == 0) return(cifval); pairsum = 0; ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(lennard->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],period,d2max,d2)) { if(d2 < d2min) { cifval = 0.0; return cifval; } ratio6 = pow(sigma2/d2, 3); pairsum += ratio6 * (1.0 - ratio6); } } } if(ixp1 < npts) { for(j=ixp1; j 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u, v, x[j], y[j], d2max, d2)) { if(d2 < lennard->d2min) { cifval = 0.0; return cifval; } ratio6 = pow(sigma2/d2, 3); pairsum += ratio6 * (1.0 - ratio6); } } } if(ixp1 < npts) { for(j=ixp1; jd2min) { cifval = 0.0; return cifval; } ratio6 = pow(sigma2/d2, 3); pairsum += ratio6 * (1.0 - ratio6); } } } } cifval *= exp(lennard->foureps * pairsum); return cifval; } Cifns LennardCifns = { &lennardinit, &lennardcif, (updafunptr) NULL, NO}; spatstat/src/Egeyer.c0000755000176000001440000000434512333041761014331 0ustar ripleyusers#include #include #include "chunkloop.h" /* Egeyer.c $Revision: 1.5 $ $Date: 2013/04/18 06:10:06 $ Part of C implementation of 'eval' for Geyer interaction Calculates change in saturated count (xquad, yquad): quadscheme (xdata, ydata): data tdata: unsaturated pair counts for data pattern quadtodata[j] = i if quad[j] == data[i] (indices start from ZERO) Assumes point patterns are sorted in increasing order of x coordinate */ double sqrt(); void Egeyer(nnquad, xquad, yquad, quadtodata, nndata, xdata, ydata, tdata, rrmax, ssat, result) /* inputs */ int *nnquad, *nndata, *quadtodata, *tdata; double *xquad, *yquad, *xdata, *ydata, *rrmax, *ssat; /* output */ double *result; { int nquad, ndata, maxchunk, j, i, ileft, dataindex, isdata; double xquadj, yquadj, rmax, sat, r2max, xleft, dx, dy, dx2, d2; double tbefore, tafter, satbefore, satafter, delta, totalchange; nquad = *nnquad; ndata = *nndata; rmax = *rrmax; sat = *ssat; r2max = rmax * rmax; if(nquad == 0 || ndata == 0) return; ileft = 0; OUTERCHUNKLOOP(j, nquad, maxchunk, 65536) { R_CheckUserInterrupt(); INNERCHUNKLOOP(j, nquad, maxchunk, 65536) { totalchange = 0.0; xquadj = xquad[j]; yquadj = yquad[j]; dataindex = quadtodata[j]; isdata = (dataindex >= 0); /* adjust starting point */ xleft = xquadj - rmax; while((xdata[ileft] < xleft) && (ileft+1 < ndata)) ++ileft; /* process until dx > rmax */ for(i=ileft; i < ndata; i++) { dx = xdata[i] - xquadj; dx2 = dx * dx; if(dx2 > r2max) break; if(i != dataindex) { dy = ydata[i] - yquadj; d2 = dx2 + dy * dy; if(d2 <= r2max) { /* effect of adding dummy point j or negative effect of removing data point */ tbefore = tdata[i]; tafter = tbefore + ((isdata) ? -1 : 1); /* effect on saturated values */ satbefore = (double) ((tbefore < sat)? tbefore : sat); satafter = (double) ((tafter < sat)? tafter : sat); /* sum changes over all i */ delta = satafter - satbefore; totalchange += ((isdata) ? -delta : delta); } } } result[j] = totalchange; } } } spatstat/src/multihard.c0000755000176000001440000000735712333041761015110 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* for debugging code, include #define DEBUG 1 */ /* Conditional intensity computation for Multitype Hardcore process */ /* NOTE: types (marks) are numbered from 0 to ntypes-1 */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct MultiHard { int ntypes; double *hc; /* hc[i,j] = hc[j+ntypes*i] for i,j = 0... ntypes-1 */ double *hc2; /* squared radii */ double range2; /* square of interaction range */ double *period; int per; } MultiHard; /* initialiser function */ Cdata *multihardinit(state, model, algo) State state; Model model; Algor algo; { int i, j, ntypes, n2; double h, h2, range2; MultiHard *multihard; multihard = (MultiHard *) R_alloc(1, sizeof(MultiHard)); multihard->ntypes = ntypes = model.ntypes; n2 = ntypes * ntypes; #ifdef DEBUG Rprintf("initialising space for %d types\n", ntypes); #endif /* Allocate space for parameters */ multihard->hc = (double *) R_alloc((size_t) n2, sizeof(double)); /* Allocate space for transformed parameters */ multihard->hc2 = (double *) R_alloc((size_t) n2, sizeof(double)); /* Copy and process model parameters*/ range2 = 0.0; for(i = 0; i < ntypes; i++) { for(j = 0; j < ntypes; j++) { h = model.ipar[i + j*ntypes]; h2 = h * h; MAT(multihard->hc, i, j, ntypes) = h; MAT(multihard->hc2, i, j, ntypes) = h2; if(range2 > h2) range2 = h2; } } multihard->range2 = range2; /* periodic boundary conditions? */ multihard->period = model.period; multihard->per = (model.period[0] > 0.0); #ifdef DEBUG Rprintf("end initialiser\n"); #endif return((Cdata *) multihard); } /* conditional intensity evaluator */ double multihardcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ntypes, ix, ixp1, j, mrk, mrkj; int *marks; double *x, *y; double u, v; double d2, range2, cifval; double *period; MultiHard *multihard; DECLARE_CLOSE_D2_VARS; multihard = (MultiHard *) cdata; range2 = multihard->range2; period = multihard->period; u = prop.u; v = prop.v; mrk = prop.mrk; ix = prop.ix; x = state.x; y = state.y; marks = state.marks; npts = state.npts; #ifdef DEBUG Rprintf("computing cif: u=%lf, v=%lf, mrk=%d\n", u, v, mrk); #endif cifval = 1.0; if(npts == 0) return(cifval); ntypes = multihard->ntypes; #ifdef DEBUG Rprintf("scanning data\n"); #endif ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(multihard->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],period,range2,d2)) { mrkj = marks[j]; if(d2 < MAT(multihard->hc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } } } } if(ixp1 < npts) { for(j=ixp1; jhc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } } } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u, v, x[j], y[j], range2, d2)) { mrkj = marks[j]; if(d2 < MAT(multihard->hc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } } } } if(ixp1 < npts) { for(j=ixp1; jhc2, mrk, mrkj, ntypes)) { cifval = 0.0; return(cifval); } } } } } #ifdef DEBUG Rprintf("returning positive cif\n"); #endif return cifval; } Cifns MultiHardCifns = { &multihardinit, &multihardcif, (updafunptr) NULL, YES}; spatstat/src/nn3Ddist.c0000755000176000001440000001733312333041761014600 0ustar ripleyusers/* nn3Ddist.c Nearest Neighbour Distances in 3D $Revision: 1.11 $ $Date: 2013/11/03 03:42:48 $ THE FOLLOWING FUNCTIONS ASSUME THAT z IS SORTED IN ASCENDING ORDER nnd3D Nearest neighbour distances nnw3D Nearest neighbours (id) nndw3D Nearest neighbours (id) and distances nnXdw3D Nearest neighbour from one list to another nnXEdw3D Nearest neighbour from one list to another, with overlaps knnd3D k-th nearest neighbour distances knnw3D k-th nearest neighbours (id) knndw3D k-th nearest neighbours (id) and distances */ #undef SPATSTAT_DEBUG #include #include #include #include "chunkloop.h" #include "yesno.h" double sqrt(); /* .......... Single point pattern ...............................*/ #undef FNAME #undef DIST #undef WHICH /* nnd3D: returns nn distance */ #define FNAME nnd3D #define DIST #include "nn3Ddist.h" #undef FNAME #undef DIST #undef WHICH /* nnw3D: returns id of nearest neighbour */ #define FNAME nnw3D #define WHICH #include "nn3Ddist.h" #undef FNAME #undef DIST #undef WHICH /* nndw3D: returns nn distance .and. id of nearest neighbour */ #define FNAME nndw3D #define DIST #define WHICH #include "nn3Ddist.h" #undef FNAME #undef DIST #undef WHICH /* .......... Two point patterns ...............................*/ /* common interface */ void nnX3Dinterface(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, exclude, wantdist, wantwhich, nnd, nnwhich, huge) /* inputs */ int *n1, *n2, *id1, *id2; double *x1, *y1, *z1, *x2, *y2, *z2, *huge; /* options */ int *exclude, *wantdist, *wantwhich; /* outputs */ double *nnd; int *nnwhich; { void nnXdw3D(), nnXd3D(), nnXw3D(); void nnXEdw3D(), nnXEd3D(), nnXEw3D(); int ex, di, wh; ex = (*exclude != 0); di = (*wantdist != 0); wh = (*wantwhich != 0); if(!ex) { if(di && wh) { nnXdw3D(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge); } else if(di) { nnXd3D(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge); } else if(wh) { nnXw3D(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge); } } else { if(di && wh) { nnXEdw3D(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge); } else if(di) { nnXEd3D(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge); } else if(wh) { nnXEw3D(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, nnd, nnwhich, huge); } } } /* nnXdw3D: for TWO point patterns X and Y, find the nearest neighbour (from each point of X to the nearest point of Y) returning both the distance and the identifier Requires both patterns to be sorted in order of increasing z coord */ #define FNAME nnXdw3D #define DIST #define WHICH #undef EXCLUDE #include "nn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* nnXd3D: returns distance only */ #define FNAME nnXd3D #define DIST #undef EXCLUDE #include "nn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* nnXw3D: returns identifier only */ #define FNAME nnXw3D #define WHICH #undef EXCLUDE #include "nn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* .......... Two point patterns with exclusion ........................*/ /* nnXEdw3D: similar to nnXdw3D but allows X and Y to include common points (which are not to be counted as neighbours) Code numbers id1, id2 are attached to the patterns X and Y respectively, such that x1[i], y1[i] and x2[j], y2[j] are the same point iff id1[i] = id2[j]. Requires both patterns to be sorted in order of increasing z coord */ #define FNAME nnXEdw3D #define DIST #define WHICH #define EXCLUDE #include "nn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* nnXEd3D: returns distances only */ #define FNAME nnXEd3D #define DIST #define EXCLUDE #include "nn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* nnXEw3D: returns identifiers only */ #define FNAME nnXEw3D #define WHICH #define EXCLUDE #include "nn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* .......... Single point pattern ...............................*/ /* .......... k-th nearest neighbours ...............................*/ /* knnd3D nearest neighbour distances 1:kmax */ #define FNAME knnd3D #define DIST #include "knn3Ddist.h" #undef FNAME #undef DIST #undef WHICH /* knnw3D nearest neighbour indices 1:kmax */ #define FNAME knnw3D #define WHICH #include "knn3Ddist.h" #undef FNAME #undef DIST #undef WHICH /* knndw3D nearest neighbours 1:kmax returns distances and indices */ #define FNAME knndw3D #define DIST #define WHICH #include "knn3Ddist.h" #undef FNAME #undef DIST #undef WHICH /* .......... Two point patterns ...............................*/ /* .......... k-th nearest neighbours ...............................*/ /* general interface */ void knnX3Dinterface(n1, x1, y1, z1, id1, n2, x2, y2, z2, id2, kmax, exclude, wantdist, wantwhich, nnd, nnwhich, huge) /* inputs */ int *n1, *n2; double *x1, *y1, *z1, *x2, *y2, *z2, *huge; int *id1, *id2; int *kmax; /* options */ int *exclude, *wantdist, *wantwhich; /* output matrices (npoints * kmax) in ROW MAJOR order */ double *nnd; int *nnwhich; /* some inputs + outputs are not used in all functions */ { void knnXdw3D(), knnXd3D(), knnXw3D(); void knnXEdw3D(), knnXEd3D(), knnXEw3D(); int ex, di, wh; ex = (*exclude != 0); di = (*wantdist != 0); wh = (*wantwhich != 0); if(!ex) { if(di && wh) { knnXdw3D(n1,x1,y1,z1,id1,n2,x2,y2,z2,id2,kmax,nnd,nnwhich,huge); } else if(di) { knnXd3D(n1,x1,y1,z1,id1,n2,x2,y2,z2,id2,kmax,nnd,nnwhich,huge); } else if(wh) { knnXw3D(n1,x1,y1,z1,id1,n2,x2,y2,z2,id2,kmax,nnd,nnwhich,huge); } } else { if(di && wh) { knnXEdw3D(n1,x1,y1,z1,id1,n2,x2,y2,z2,id2,kmax,nnd,nnwhich,huge); } else if(di) { knnXEd3D(n1,x1,y1,z1,id1,n2,x2,y2,z2,id2,kmax,nnd,nnwhich,huge); } else if(wh) { knnXEw3D(n1,x1,y1,z1,id1,n2,x2,y2,z2,id2,kmax,nnd,nnwhich,huge); } } } #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* knnXdw3D nearest neighbours 1:kmax between two point patterns returns distances and indices */ #define FNAME knnXdw3D #define DIST #define WHICH #include "knn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* knnXd3D nearest neighbours 1:kmax between two point patterns returns distances */ #define FNAME knnXd3D #define DIST #include "knn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* knnXw3D nearest neighbours 1:kmax between two point patterns returns indices */ #define FNAME knnXw3D #define WHICH #include "knn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* .......... Two point patterns with exclusion ..........................*/ /* .......... k-th nearest neighbours ...............................*/ /* knnXEdw3D nearest neighbours 1:kmax between two point patterns with exclusion returns distances and indices */ #define FNAME knnXEdw3D #define DIST #define WHICH #define EXCLUDE #include "knn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* knnXEd3D nearest neighbours 1:kmax between two point patterns with exclusion returns distances */ #define FNAME knnXEd3D #define DIST #define EXCLUDE #include "knn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE /* knnXEw3D nearest neighbours 1:kmax between two point patterns with exclusion returns indices */ #define FNAME knnXEw3D #define WHICH #define EXCLUDE #include "knn3DdistX.h" #undef FNAME #undef DIST #undef WHICH #undef EXCLUDE spatstat/src/areadiff.c0000755000176000001440000001533312333041761014651 0ustar ripleyusers/* areadiff.c Area difference function $Revision: 1.14 $ $Date: 2013/09/18 04:09:24 $ A(x,r) = area of disc b(0,r) not covered by discs b(x_i,r) for x_i in x Area estimated by point-counting on a fine grid For use in area-interaction model and related calculations */ #undef DEBUG #include #include #include #include "chunkloop.h" #include "constants.h" /* Original version areadiff() 1 point u No trimming of discs */ void areadiff(rad,x,y,nn,ngrid,answer) /* inputs */ double *rad; /* radius */ double *x, *y; /* coordinate vectors for point pattern */ int *nn; /* length of vectors x and y */ int *ngrid; /* dimensions of point-counting grid */ /* output */ double *answer; /* computed area */ { double dx, dy, xg, yg, r, r2, a2, b2, xdif, ydif; int i, j, k, m, n, count, covered; r = *rad; r2 = r * r; n = *nn; m = *ngrid; dx = dy = 2 * r / (m-1); count = 0; /* run through grid points */ for(i = 0, xg = -r; i < m; i++, xg += dx) { a2 = r2 - xg *xg; for(j = 0, yg = -r; j < m; j++, yg += dy) { /* test for inside disc */ if(yg * yg < a2) { #ifdef DEBUG Rprintf("\n\n (xg,yg) = (%lf, %lf)\n", xg, yg); #endif /* run through data points seeking one close to (xy, yg) */ covered = 0; if(n > 0) { for(k = 0; k < n; k++) { #ifdef DEBUG Rprintf("(x[%d],y[%d]) = (%lf,%lf)\n", k, k, x[k], y[k]); #endif xdif = x[k] - xg; b2 = r2 - xdif * xdif; if(b2 > 0) { ydif = y[k] - yg; if(b2 - ydif * ydif > 0) { #ifdef DEBUG Rprintf("(x[%d], y[%d]) = (%lf, %lf) covers!\n", k, k, x[k], y[k]); #endif covered = 1; break; } } } } if(covered == 0) { ++count; #ifdef DEBUG Rprintf("Not covered; incrementing count\n"); #endif } } } } #ifdef DEBUG Rprintf("Count = %d\n", count); #endif /* calculate area */ *answer = ((double) count) * dx * dy; } /* similar function, handles multiple values of 'r' */ void areadifs(rad,nrads,x,y,nxy,ngrid,answer) /* inputs */ double *rad; /* vector of radii */ int *nrads; /* length of 'rads' */ double *x, *y; /* coordinate vectors for point pattern */ int *nxy; /* length of vectors x and y */ int *ngrid; /* dimensions of point-counting grid */ /* output */ double *answer; /* computed areas (vector of length 'nrads') */ { double dx, dy, xg, yg, r, r2, a2, b2, xdif, ydif; int i, j, k, l, m, n, nr, m0, count, covered, maxchunk; n = *nxy; nr = *nrads; m = *ngrid; /* run through radii in chunks of 2^14 */ OUTERCHUNKLOOP(l, nr, maxchunk, 16384) { R_CheckUserInterrupt(); INNERCHUNKLOOP(l, nr, maxchunk, 16384) { r = rad[l]; if(r == 0.0) { answer[l] = 0.0; } else if(n == 0) { answer[l] = M_PI * r * r; } else { r2 = r * r; dx = dy = 2 * r / (m-1); count = 0; /* run through grid points in disc of radius r */ for(i = 0, xg = -r; i < m; i++, xg += dx) { a2 = r2 - xg * xg; m0 = (a2 > 0.0) ? floor(sqrt(a2)/dy) : 0; for(j = -m0, yg = -m0 * dy; j <= m0; j++, yg += dy) { #ifdef DEBUG Rprintf("\n\n (xg,yg) = (%lf, %lf)\n", xg, yg); #endif /* run through data points seeking one close to (xy, yg) */ covered = 0; for(k = 0; k < n; k++) { #ifdef DEBUG Rprintf("(x[%d],y[%d]) = (%lf,%lf)\n", k, k, x[k], y[k]); #endif xdif = x[k] - xg; b2 = r2 - xdif * xdif; if(b2 > 0) { ydif = y[k] - yg; if(b2 - ydif * ydif > 0) { #ifdef DEBUG Rprintf("(x[%d], y[%d]) = (%lf, %lf) covers!\n", k, k, x[k], y[k]); #endif covered = 1; break; } } } /* end of loop through data points */ if(covered == 0) { ++count; #ifdef DEBUG Rprintf("Not covered; incrementing count\n"); #endif } } } /* end of loop over grid points */ #ifdef DEBUG Rprintf("Count = %d\n", count); #endif /* calculate area for this value of r*/ answer[l] = ((double) count) * dx * dy; } /* end of if(r==0).. else {...} */ } } } /* Modified version multiple test points u discs constrained inside a rectangle */ void areaBdif(rad,nrads,x,y,nxy,ngrid,x0,y0,x1,y1,answer) /* inputs */ double *rad; /* vector of radii */ int *nrads; /* length of 'rads' */ double *x, *y; /* coordinate vectors for point pattern */ int *nxy; /* length of vectors x and y */ int *ngrid; /* dimensions of point-counting grid */ double *x0,*y0,*x1,*y1; /* constraint rectangle */ /* output */ double *answer; /* computed areas (vector of length 'nrads') */ { double dx, dy, xg, yg, r, r2, a, a2, b2, xdif, ydif; double xleft, xright, ylow, yhigh; double xmin, ymin, xmax, ymax; int i, j, k, l, m, n, nr, ileft, iright, mlow, mhigh, count, covered; n = *nxy; nr = *nrads; m = *ngrid; xmin = *x0; ymin = *y0; xmax = *x1; ymax = *y1; /* run through radii */ for(l = 0; l < nr; l++) { r = rad[l]; if(r == 0.0) { answer[l] = 0.0; } else if (n == 0) { answer[l]= M_PI * r * r; } else { r2 = r * r; dx = dy = 2 * r / (m-1); count = 0; /* run through grid points in disc intersected with box */ xleft = (xmin > -r) ? xmin : -r; xright = (xmax < r) ? xmax : r; ileft = ceil(xleft/dx); iright = floor(xright/dx); if(ileft <= iright) { for(i = ileft, xg = ileft * dx; i <= iright; i++, xg += dx) { a2 = r2 - xg * xg; a = (a2 > 0) ? sqrt(a2): 0.0; yhigh = (ymax < a) ? ymax: a; ylow = (ymin > -a) ? ymin: -a; mhigh = floor(yhigh/dy); mlow = ceil(ylow/dy); if(mlow <= mhigh) { for(j = mlow, yg = mlow * dy; j <= mhigh; j++, yg += dy) { #ifdef DEBUG Rprintf("\n\n (xg,yg) = (%lf, %lf)\n", xg, yg); #endif /* run through data points seeking one close to (xy, yg) */ covered = 0; for(k = 0; k < n; k++) { #ifdef DEBUG Rprintf("(x[%d],y[%d]) = (%lf,%lf)\n", k, k, x[k], y[k]); #endif xdif = x[k] - xg; b2 = r2 - xdif * xdif; if(b2 > 0) { ydif = y[k] - yg; if(b2 - ydif * ydif > 0) { #ifdef DEBUG Rprintf("(x[%d], y[%d]) = (%lf, %lf) covers!\n", k, k, x[k], y[k]); #endif covered = 1; break; } } } /* end of loop over data points */ if(covered == 0) { ++count; #ifdef DEBUG Rprintf("Not covered; incrementing count\n"); #endif } } } } } /* end of loop over grid points */ #ifdef DEBUG Rprintf("Count = %d\n", count); #endif /* calculate area for this value of r*/ answer[l] = ((double) count) * dx * dy; } /* end of if(r==0).. else {...} */ } /* end of loop over r values */ } spatstat/src/getcif.c0000755000176000001440000000323012333041761014342 0ustar ripleyusers#include #include "methas.h" void fexitc(const char *msg); extern Cifns AreaIntCifns, BadGeyCifns, DgsCifns, DiggraCifns, FikselCifns, GeyerCifns, HardcoreCifns, LennardCifns, LookupCifns, SoftcoreCifns, StraussCifns, StraussHardCifns, MultiStraussCifns, MultiStraussHardCifns, MultiHardCifns, TripletsCifns; Cifns NullCifns = NULL_CIFNS; typedef struct CifPair { char *name; Cifns *p; } CifPair; CifPair CifTable[] = { {"areaint", &AreaIntCifns}, {"badgey", &BadGeyCifns}, {"dgs", &DgsCifns}, {"diggra", &DiggraCifns}, {"geyer", &GeyerCifns}, {"fiksel", &FikselCifns}, {"hardcore", &HardcoreCifns}, {"lookup", &LookupCifns}, {"lennard", &LennardCifns}, {"multihard", &MultiHardCifns}, {"sftcr", &SoftcoreCifns}, {"strauss", &StraussCifns}, {"straush", &StraussHardCifns}, {"straussm", &MultiStraussCifns}, {"straushm", &MultiStraussHardCifns}, {"triplets", &TripletsCifns}, {(char *) NULL, (Cifns *) NULL} }; Cifns getcif(cifname) char *cifname; { int i; CifPair cp; for(i = 0; CifTable[i].name; i++) { cp = CifTable[i]; if(strcmp(cifname, cp.name) == 0) return(*(cp.p)); } fexitc("Unrecognised cif name; bailing out.\n"); /* control never passes to here, but compilers don't know that */ return(NullCifns); } /* R interface function, to check directly whether cif is recognised */ void knownCif(cifname, answer) char** cifname; int* answer; { int i; CifPair cp; for(i = 0; CifTable[i].name; i++) { cp = CifTable[i]; if(strcmp(*cifname, cp.name) == 0) { *answer = 1; return; } } *answer = 0; return; } spatstat/src/straussm.c0000755000176000001440000001300312333041761014761 0ustar ripleyusers#include #include #include "methas.h" #include "dist2.h" /* for debugging code, include #define DEBUG 1 */ /* Conditional intensity computation for Multitype Strauss process */ /* NOTE: types (marks) are numbered from 0 to ntypes-1 */ /* Storage of parameters and precomputed/auxiliary data */ typedef struct MultiStrauss { int ntypes; double *gamma; /* gamma[i,j] = gamma[i+ntypes*j] for i,j = 0... ntypes-1 */ double *rad; /* rad[i,j] = rad[j+ntypes*i] for i,j = 0... ntypes-1 */ double *rad2; /* squared radii */ double range2; /* square of interaction range */ double *loggamma; /* logs of gamma[i,j] */ double *period; int *hard; /* hard[i,j] = 1 if gamma[i,j] ~~ 0 */ int *kount; /* space for kounting pairs of each type */ int per; } MultiStrauss; /* initialiser function */ Cdata *straussminit(state, model, algo) State state; Model model; Algor algo; { int i, j, ntypes, n2, hard; double g, r, r2, logg, range2; MultiStrauss *multistrauss; multistrauss = (MultiStrauss *) R_alloc(1, sizeof(MultiStrauss)); multistrauss->ntypes = ntypes = model.ntypes; n2 = ntypes * ntypes; #ifdef DEBUG Rprintf("initialising space for %d types\n", ntypes); #endif /* Allocate space for parameters */ multistrauss->gamma = (double *) R_alloc((size_t) n2, sizeof(double)); multistrauss->rad = (double *) R_alloc((size_t) n2, sizeof(double)); /* Allocate space for transformed parameters */ multistrauss->rad2 = (double *) R_alloc((size_t) n2, sizeof(double)); multistrauss->loggamma = (double *) R_alloc((size_t) n2, sizeof(double)); multistrauss->hard = (int *) R_alloc((size_t) n2, sizeof(int)); /* Allocate scratch space for counts of each pair of types */ multistrauss->kount = (int *) R_alloc((size_t) n2, sizeof(int)); /* Copy and process model parameters*/ /* ipar will contain n^2 gamma values followed by n^2 values of r */ range2 = 0.0; for(i = 0; i < ntypes; i++) { for(j = 0; j < ntypes; j++) { g = model.ipar[i + j*ntypes]; r = model.ipar[n2 + i + j*ntypes]; r2 = r * r; hard = (g < DOUBLE_EPS); logg = (hard) ? 0 : log(g); MAT(multistrauss->gamma, i, j, ntypes) = g; MAT(multistrauss->rad, i, j, ntypes) = r; MAT(multistrauss->hard, i, j, ntypes) = hard; MAT(multistrauss->loggamma, i, j, ntypes) = logg; MAT(multistrauss->rad2, i, j, ntypes) = r2; if(r2 > range2) range2 = r2; } } multistrauss->range2 = range2; /* periodic boundary conditions? */ multistrauss->period = model.period; multistrauss->per = (model.period[0] > 0.0); #ifdef DEBUG Rprintf("end initialiser\n"); #endif return((Cdata *) multistrauss); } /* conditional intensity evaluator */ double straussmcif(prop, state, cdata) Propo prop; State state; Cdata *cdata; { int npts, ntypes, kount, ix, ixp1, j, mrk, mrkj, m1, m2; int *marks; double *x, *y; double u, v, lg; double d2, cifval; double range2; double *period; MultiStrauss *multistrauss; DECLARE_CLOSE_D2_VARS; multistrauss = (MultiStrauss *) cdata; range2 = multistrauss->range2; period = multistrauss->period; u = prop.u; v = prop.v; mrk = prop.mrk; ix = prop.ix; x = state.x; y = state.y; marks = state.marks; npts = state.npts; #ifdef DEBUG Rprintf("computing cif: u=%lf, v=%lf, mrk=%d\n", u, v, mrk); #endif cifval = 1.0; if(npts == 0) return(cifval); ntypes = multistrauss->ntypes; #ifdef DEBUG Rprintf("initialising pair counts\n"); #endif /* initialise pair counts */ for(m1 = 0; m1 < ntypes; m1++) for(m2 = 0; m2 < ntypes; m2++) MAT(multistrauss->kount, m1, m2, ntypes) = 0; /* compile pair counts */ #ifdef DEBUG Rprintf("compiling pair counts\n"); #endif ixp1 = ix+1; /* If ix = NONE = -1, then ixp1 = 0 is correct */ if(multistrauss->per) { /* periodic distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_PERIODIC_D2(u,v,x[j],y[j],period,range2,d2)) { mrkj = marks[j]; if(d2 < MAT(multistrauss->rad2, mrk, mrkj, ntypes)) MAT(multistrauss->kount, mrk, mrkj, ntypes)++; } } } if(ixp1 < npts) { for(j=ixp1; jrad2, mrk, mrkj, ntypes)) MAT(multistrauss->kount, mrk, mrkj, ntypes)++; } } } } else { /* Euclidean distance */ if(ix > 0) { for(j=0; j < ix; j++) { if(CLOSE_D2(u, v, x[j], y[j], range2, d2)) { mrkj = marks[j]; if(d2 < MAT(multistrauss->rad2, mrk, mrkj, ntypes)) MAT(multistrauss->kount, mrk, mrkj, ntypes)++; } } } if(ixp1 < npts) { for(j=ixp1; jrad2, mrk, mrkj, ntypes)) MAT(multistrauss->kount, mrk, mrkj, ntypes)++; } } } } #ifdef DEBUG Rprintf("multiplying cif factors\n"); #endif /* multiply cif value by pair potential */ for(m1 = 0; m1 < ntypes; m1++) { for(m2 = 0; m2 < ntypes; m2++) { kount = MAT(multistrauss->kount, m1, m2, ntypes); if(MAT(multistrauss->hard, m1, m2, ntypes)) { if(kount > 0) { cifval = 0.0; return(cifval); } } else { lg = MAT(multistrauss->loggamma, m1, m2, ntypes); cifval *= exp(lg * kount); } } } #ifdef DEBUG Rprintf("returning positive cif\n"); #endif return cifval; } Cifns MultiStraussCifns = { &straussminit, &straussmcif, (updafunptr) NULL, YES}; spatstat/NAMESPACE0000644000176000001440000020257212333041502013365 0ustar ripleyusers# spatstat NAMESPACE file import(stats,graphics,grDevices,utils) import(polyclip) importFrom(deldir,deldir,duplicatedxy,tile.list) importFrom(abind,abind) importFrom(tensor,tensor) importFrom(mgcv,gam,gam.control,anova.gam,formula.gam,predict.gam,print.gam,summary.gam,vcov.gam,s) # Do not edit the following. # It is generated automatically. # .................................................. # Automatically-generated list of entry points # .................................................. useDynLib(spatstat, "acrdenspt", "acrsmoopt", "adenspt", "areaBdif", "areadifs", "asmoopt", "awtcrdenspt", "awtcrsmoopt", "awtdenspt", "awtsmoopt", "Cbiform", "Cclosepaircounts", "Ccountends", "Ccrossdist", "Ccrosspaircounts", "CcrossPdist", "Cidw", "Cmatchxy", "cocoGraph", "cocoImage", "Corput", "Cpairdist", "CpairPdist", "Cquadform", "crdenspt", "crosscount", "crsmoopt", "Csumouter", "Csumsymouter", "Cwsumouter", "Cwsumsymouter", "Cxypolyselfint", "Cxysegint", "CxysegXint", "D3crossdist", "D3crossPdist", "D3pairdist", "D3pairPdist", "Ddist2dpath", "delta2area", "denspt", "digberJ", "dinfty_R", "discareapoly", "distmapbin", "dppll", "drevcumsum", "dwpure", "Ediggatsti", "Ediggra", "Efiksel", "Egeyer", "exact_dt_R", "Fclosepairs", "Fcrosspairs", "graphVees", "Idist2dpath", "idwloo", "inxyp", "irevcumsum", "KborderD", "KborderI", "knnd3D", "knndMD", "knndsort", "knnGinterface", "knnsort", "knnw3D", "knnwMD", "knnX3Dinterface", "knnXinterface", "KnoneD", "KnoneI", "knownCif", "KrectDbl", "KrectInt", "KrectWtd", "Kwborder", "Kwnone", "lincrossdist", "linndcross", "linndxcross", "linnndist", "linnnwhich", "linpairdist", "locpcfx", "locprod", "locWpcfx", "locxprod", "maxnnd2", "minnnd2", "nnd3D", "nndist2segs", "nndistsort", "nndMD", "nnGinterface", "nnw3D", "nnwhichsort", "nnwMD", "nnX3Dinterface", "nnXinterface", "paircount", "PerfectDGS", "PerfectDiggleGratton", "PerfectHardcore", "PerfectStrauss", "PerfectStraussHard", "poly2imA", "poly2imI", "prdist2segs", "ps_exact_dt_R", "RcallF3", "RcallF3cen", "RcallG3", "RcallG3cen", "RcallK3", "Rcallpcf3", "ripleybox", "ripleypoly", "scantrans", "seg2pixI", "seg2pixL", "smoopt", "triDgraph", "trigraf", "trigrafS", "triograph", "trioxgraph", "VcloseIJpairs", "Vclosepairs", "Vclosethresh", "VcrossIJpairs", "Vcrosspairs", "wtcrdenspt", "wtcrsmoopt", "wtdenspt", "wtsmoopt", "xmethas", "xypsi", "xysegint", "xysegXint", "xysi", "xysiANY", "xysxi") # .................................................. # Automatically-generated list of documented objects # .................................................. export("acedist.noshow") export("acedist.show") export("active.interactions") export("adaptive.density") export("add.texture") export("addvar") export("adjustthinrange") export("affine") export("affine.im") export("affine.layered") export("affine.linnet") export("affine.lpp") export("affine.owin") export("affine.ppp") export("affine.psp") export("affine.tess") export("affinexy") export("affinexypolygon") export("allstats") export("alltypes") export("angles.psp") export("anova.lppm") export("anova.mppm") export("anova.ppm") export("anova.slrm") export("anycrossing.psp") export("append.psp") export("apply23sum") export("applynbd") export("applytolayers") export("areadelta2") export("areaGain") export("areaGain.diri") export("areaGain.grid") export("AreaInter") export("areaLoss") export("areaLoss.diri") export("areaLoss.grid") export("area.owin") export("area.xypolygon") export("as2vector") export("as.array.im") export("as.box3") export("as.breakpts") export("as.character.units") export("as.data.frame.bw.optim") export("as.data.frame.fv") export("as.data.frame.hyperframe") export("as.data.frame.im") export("as.data.frame.ppp") export("as.data.frame.ppx") export("as.data.frame.psp") export("as.function.fv") export("as.function.linfun") export("as.function.rhohat") export("as.fv") export("as.fv.bw.optim") export("as.fv.data.frame") export("as.fv.fasp") export("as.fv.fv") export("as.fv.kppm") export("as.fv.matrix") export("as.fv.minconfit") export("as.hyperframe") export("as.hyperframe.data.frame") export("as.hyperframe.default") export("as.hyperframe.hyperframe") export("as.hyperframe.listof") export("as.hyperframe.ppx") export("as.im") export("as.im.default") export("as.im.distfun") export("as.im.function") export("as.im.im") export("as.im.leverage.ppm") export("as.im.linim") export("as.im.matrix") export("as.im.nnfun") export("as.im.owin") export("as.im.ppp") export("as.im.scan.test") export("as.im.Smoothfun") export("as.im.tess") export("as.interact") export("as.interact.fii") export("as.interact.interact") export("as.interact.ppm") export("as.linim") export("as.linim.default") export("as.linim.linfun") export("as.linim.linim") export("as.linnet") export("as.linnet.linfun") export("as.linnet.linnet") export("as.linnet.lpp") export("as.linnet.lppm") export("as.list.hyperframe") export("as.listof") export("as.lpp") export("as.mask") export("as.mask.psp") export("as.matrix.im") export("as.matrix.owin") export("as.matrix.ppx") export("as.owin") export("as.owin.boxx") export("as.owin.data.frame") export("as.owin.default") export("as.owin.distfun") export("as.owin.funxy") export("as.owin.im") export("as.owin.influence.ppm") export("as.owin.kppm") export("as.owin.layered") export("as.owin.leverage.ppm") export("as.owin.linfun") export("as.owin.linnet") export("as.owin.lpp") export("as.owin.lppm") export("as.owin.msr") export("as.owin.nnfun") export("as.owin.owin") export("as.owin.ppm") export("as.owin.ppp") export("as.owin.psp") export("as.owin.quad") export("as.owin.quadratcount") export("as.owin.quadrattest") export("as.owin.rmhmodel") export("as.owin.tess") export("as.polygonal") export("as.ppm") export("as.ppm.kppm") export("as.ppm.ppm") export("as.ppm.profilepl") export("as.ppp") export("as.ppp.data.frame") export("as.ppp.default") export("as.ppp.influence.ppm") export("as.ppp.lpp") export("as.ppp.matrix") export("as.ppp.ppp") export("as.ppp.psp") export("as.ppp.quad") export("as.psp") export("as.psp.data.frame") export("as.psp.default") export("as.psp.linnet") export("as.psp.lpp") export("as.psp.matrix") export("as.psp.owin") export("as.psp.psp") export("as.rectangle") export("assemble.plot.objects") export("as.tess") export("as.tess.im") export("as.tess.list") export("as.tess.owin") export("as.tess.quadratcount") export("as.tess.quadrattest") export("as.tess.tess") export("as.units") export("AsymmDistance.psp") export("augment.msr") export("BadGey") export("badprobability") export("bbEngine") export("bdist.pixels") export("bdist.points") export("bdist.tiles") export("bdrylength.xypolygon") export("bdry.mask") export("beachcolourmap") export("beachcolours") export("beginner") export("bermantest") export("bermantestEngine") export("bermantest.lpp") export("bermantest.lppm") export("bermantest.ppm") export("bermantest.ppp") export("bilinearform") export("bind.fv") export("bind.ratfv") export("blankcoefnames") export("blockdiagarray") export("blockdiagmatrix") export("blur") export("border") export("bounding.box") export("boundingbox") export("bounding.box3") export("boundingbox.default") export("boundingbox.im") export("boundingbox.owin") export("boundingbox.ppp") export("bounding.box.xy") export("box3") export("boxx") export("break.holes") export("breakpts") export("breakpts.from.r") export("bt.frame") export("bw.diggle") export("bw.frac") export("bw.optim") export("bw.ppl") export("bw.relrisk") export("bw.scott") export("bw.smoothppp") export("bw.stoyan") export("by.im") export("by.ppp") export("cannot.update") export("cartesian") export("cat.factor") export("cauchy.estK") export("cauchy.estpcf") export("cbind.fv") export("cbind.hyperframe") export("cellmiddles") export("censtimeCDFest") export("centroid.owin") export("change.default.expand") export("check.1.integer") export("check.1.real") export("checkfields") export("check.finite") export("check.hist.lengths") export("check.named.list") export("check.named.thing") export("check.named.vector") export("check.nmatrix") export("check.nvector") export("check.range") export("check.testfun") export("chop.tess") export("circumradius") export("clarkevans") export("clarkevansCalc") export("clarkevans.test") export("clear.simplepanel") export("clf.test") export("clickbox") export("clickjoin") export("clickpoly") export("clickppp") export("clip.infline") export("clippoly.psp") export("clip.psp") export("cliprect.psp") export("closepaircounts") export("closepairs") export("closethresh") export("closing") export("closing.owin") export("closing.ppp") export("closing.psp") export("clusterset") export("cobble.xy") export("codetime") export("coef.fii") export("coef.kppm") export("coef.lppm") export("coef.mppm") export("coef.ppm") export("coef.slrm") export("coef.summary.fii") export("coef.summary.ppm") export("coerce.marks.numeric") export("col2hex") export("collapse.fv") export("colourmap") export("commasep") export("commonGrid") export("compareFit") export("compatible") export("compatible.fasp") export("compatible.fv") export("compatible.im") export("compatible.rat") export("compatible.units") export("compileCDF") export("compileK") export("compilepcf") export("complaining") export("complementarycolour") export("complement.owin") export("concatxy") export("Concom") export("conform.imagelist") export("conform.ratfv") export("connected") export("connected.im") export("connected.owin") export("connected.ppp") export("conspire") export("contour.funxy") export("contour.im") export("contour.listof") export("contour.objsurf") export("convexhull") export("convexhull.xy") export("convolve.im") export("coords<-") export("coords") export("coords<-.ppp") export("coords.ppp") export("coords<-.ppx") export("coords.ppx") export("corners") export("countends") export("countingweights") export("crossdist") export("crossdist.default") export("crossdist.lpp") export("crossdist.pp3") export("crossdist.ppp") export("crossdist.ppx") export("crossdist.psp") export("crossing.psp") export("crosspaircounts") export("crosspairquad") export("crosspairs") export("cut.im") export("cut.ppp") export("damaged.ppm") export("datagen.rpoisppOnLines") export("datagen.runifpointOnLines") export("datagen.runifpoisppOnLines") export("data.mppm") export("data.ppm") export("dclf.progress") export("dclf.test") export("default.clipwindow") export("default.dummy") export("default.expand") export("default.ntile") export("default.n.tiling") export("default.rmhcontrol") export("delaunay") export("delaunay.distance") export("deltametric") export("deltasuffstat") export("densitycrossEngine") export("densitypointsEngine") export("density.ppp") export("density.psp") export("density.splitppp") export("deriv.fv") export("dfbetas.ppm") export("dflt.redraw") export("diagnose.ppm") export("diagnose.ppm.engine") export("diameter") export("diameter.box3") export("diameter.boxx") export("diameter.linnet") export("diameter.owin") export("DiggleGatesStibbard") export("DiggleGratton") export("digital.volume") export("dilated.areas") export("dilate.owin") export("dilation") export("dilation.owin") export("dilation.ppp") export("dilation.psp") export("dim.fasp") export("dim.hyperframe") export("dim.im") export("dim.msr") export("dimnames<-.fasp") export("dimnames.fasp") export("dimnames.msr") export("dirichlet") export("dirichlet.weights") export("disc") export("discpartarea") export("discretise") export("dist2dpath") export("distcdf") export("distfun") export("distfun.lpp") export("distfun.owin") export("distfun.ppp") export("distfun.psp") export("distmap") export("distmap.owin") export("distmap.ppp") export("distmap.psp") export("distpl") export("distppl") export("distppll") export("distppllmin") export("distributecbind") export("divisors") export("dknn") export("dmixpois") export("do.as.im") export("do.call.matched") export("do.call.plotfun") export("do.istat") export("doMultiStraussHard") export("dotexpr.to.call") export("dropifsingle") export("dummify") export("dummy.ppm") export("duplicated.ppp") export("duplicated.ppx") export("edge.Ripley") export("edges") export("edges2triangles") export("edges2vees") export("edge.Trans") export("eem") export("effectfun") export("ellipse") export("Emark") export("empty.space") export("emptywindow") export("endpoints.psp") export("ensure2vector") export("ensure3Darray") export("envelope") export("envelopeEngine") export("envelope.envelope") export("envelope.kppm") export("envelope.lpp") export("envelope.lppm") export("envelope.matrix") export("envelope.pp3") export("envelope.ppm") export("envelope.ppp") export("envelopeProgressData") export("envelopeTest") export("equalpairs") export("equalpairs.quad") export("equalsfun.quad") export("equals.quad") export("eratosthenes") export("eroded.areas") export("eroded.volumes") export("eroded.volumes.box3") export("eroded.volumes.boxx") export("erodemask") export("erode.owin") export("erosion") export("erosion.owin") export("erosion.ppp") export("erosion.psp") export("evalCovar") export("evalCovariate") export("evalCovar.lppm") export("evalCovar.ppm") export("eval.fasp") export("eval.fv") export("eval.hyper") export("eval.im") export("evalInteraction") export("evalInterEngine") export("eval.linim") export("evalPairPotential") export("even.breaks.owin") export("evenly.spaced") export("ewcdf") export("exactdt") export("exactMPLEstrauss") export("exactPdt") export("expand.owin") export("expand.polynom") export("expandwinPerfect") export("explain.ifnot") export("extractAIC.lppm") export("extractAIC.ppm") export("extractAIC.slrm") export("extractAtomicQtests") export("f3Cengine") export("f3engine") export("F3est") export("fakecallstring") export("fakemaintitle") export("[.fasp") export("fasp") export("fave.order") export("Fest") export("fii") export("Fiksel") export("fillNA") export("findbestlegendpos") export("findcbind") export("findCovariate") export("Finhom") export("firstfactor") export("fitin") export("fitin.ppm") export("fitted.kppm") export("fitted.mppm") export("fitted.ppm") export("fitted.slrm") export("flatfname") export("flipxy") export("flipxy.im") export("flipxy.layered") export("flipxy.owin") export("flipxypolygon") export("flipxy.ppp") export("flipxy.psp") export("forbid.logi") export("forbidNA") export("FormatFaspFormulae") export("formula<-") export("formula<-.fv") export("formula.fv") export("formula.kppm") export("formula.lppm") export("formula.ppm") export("formula.slrm") export("fryplot") export("frypoints") export("funxy") export("[.fv") export("fv") export("fvlabelmap") export("fvlabels<-") export("fvlabels") export("fvlegend") export("fvnames<-") export("fvnames") export("g3Cengine") export("g3engine") export("G3est") export("gauss.hermite") export("Gcom") export("Gcross") export("Gdot") export("Gest") export("getdataname") export("getdataobjects") export("getfields") export("getglmdata") export("getglmfit") export("getglmsubset") export("getlambda.lpp") export("getlastshift") export("getppmdatasubset") export("getppmOriginalCovariates") export("getSpatstatVariable") export("getSumFun") export("Geyer") export("geyercounts") export("geyerdelta2") export("Gfox") export("Ginhom") export("GLMpredict") export("Gmulti") export("good.correction.K") export("good.names") export("greatest.common.divisor") export("Gres") export("grid1index") export("gridcenters") export("gridcentres") export("gridindex") export("gridweights") export("grow.mask") export("grow.rectangle") export("grow.simplepanel") export("gsubdot") export("Halton") export("Hammersley") export("handle.r.b.args") export("handle.rshift.args") export("Hardcore") export("harmonic") export("harmonise.im") export("harmonize.im") export("has.offset") export("has.offset.term") export("HermiteCoefs") export("Hest") export("hexgrid") export("hextess") export("hist.im") export("ho.engine") export("hsvim") export("hsvNA") export("Hybrid") export("hybrid.family") export("[<-.hyperframe") export("[.hyperframe") export("$<-.hyperframe") export("$.hyperframe") export("hyperframe") export("identical.formulae") export("IdenticalRows") export("identify.ppp") export("identify.psp") export("idorempty") export("idw") export("Iest") export("ifelse0NA") export("ifelse1NA") export("ifelseAB") export("ifelseAX") export("ifelseNegPos") export("ifelseXB") export("ifelseXY") export("illegal.iformula") export("[<-.im") export("[.im") export("im") export("image.im") export("image.listof") export("image.objsurf") export("im.apply") export("imcov") export("implemented.for.K") export("impliedcoefficients") export("impliedpresence") export("incircle") export("infline") export("[.influence.ppm") export("influence.ppm") export("inforder.family") export("inpoint") export("inside.owin") export("inside.range") export("inside.triangle") export("inside.xypolygon") export("instantiate.interact") export("integral.im") export("integral.msr") export("intensity") export("intensity.lpp") export("intensity.ppm") export("intensity.ppp") export("intensity.quadratcount") export("intensity.splitppp") export("interp.colourmap") export("interp.im") export("intersect.owin") export("intersect.ranges") export("intersect.tess") export("intX.owin") export("intX.xypolygon") export("intY.owin") export("intY.xypolygon") export("invoke.symbolmap") export("iplot") export("iplot.default") export("iplot.layered") export("iplot.ppp") export("ippm") export("is.atomicQtest") export("is.cadlag") export("is.convex") export("is.data") export("is.empty") export("is.empty.default") export("is.empty.owin") export("is.empty.ppp") export("is.empty.psp") export("is.expandable") export("is.expandable.ppm") export("is.expandable.rmhmodel") export("is.fv") export("is.hole.xypolygon") export("is.hybrid") export("is.hybrid.interact") export("is.hybrid.ppm") export("is.hyperframe") export("is.im") export("is.infline") export("is.interact") export("is.kppm") export("is.lpp") export("is.lppm") export("is.marked") export("is.marked.default") export("is.marked.lppm") export("is.marked.ppm") export("is.marked.ppp") export("is.marked.psp") export("is.marked.quad") export("is.mask") export("is.mppm") export("is.multitype") export("is.multitype.default") export("is.multitype.lppm") export("is.multitype.ppm") export("is.multitype.ppp") export("is.multitype.quad") export("is.owin") export("is.parseable") export("is.poisson") export("is.poisson.interact") export("is.poisson.kppm") export("is.poisson.lppm") export("is.poisson.mppm") export("is.poisson.ppm") export("is.poisson.rmhmodel") export("is.poisson.slrm") export("is.polygonal") export("is.pp3") export("is.ppm") export("is.ppp") export("is.ppx") export("is.prime") export("is.psp") export("is.rectangle") export("is.slrm") export("is.stationary") export("is.stationary.kppm") export("is.stationary.lppm") export("is.stationary.ppm") export("is.stationary.rmhmodel") export("is.stationary.slrm") export("is.subset.owin") export("istat") export("is.tess") export("Jcross") export("Jdot") export("Jest") export("Jfox") export("Jinhom") export("Jmulti") export("k3engine") export("K3est") export("kaplan.meier") export("Kborder.engine") export("Kcom") export("Kcross") export("Kcross.inhom") export("Kdot") export("Kdot.inhom") export("Kest") export("Kest.fft") export("killinteraction") export("Kinhom") export("Kmeasure") export("Kmodel") export("Kmodel.kppm") export("Kmodel.ppm") export("km.rs") export("km.rs.opt") export("Kmulti") export("Kmulti.inhom") export("Knone.engine") export("Kount") export("Kpcf.kppm") export("kppm") export("kppmComLik") export("kppm.formula") export("kppmMinCon") export("kppm.ppp") export("kppm.quad") export("Krect.engine") export("Kres") export("Kscaled") export("ksmooth.ppp") export("kstest") export("kstest.lpp") export("kstest.lppm") export("kstest.mppm") export("ks.test.ppm") export("kstest.ppm") export("kstest.ppp") export("kstest.slrm") export("Kwtsum") export("labels.kppm") export("labels.ppm") export("labels.slrm") export("LambertW") export("latest.news") export("[.layered") export("layered") export("layerplotargs<-") export("layerplotargs") export("layout.boxes") export("Lcross") export("Lcross.inhom") export("Ldot") export("Ldot.inhom") export("least.common.multiple") export("lengths.psp") export("LennardJones") export("Lest") export("levelset") export("levels<-.im") export("levels.im") export("leverage") export("[.leverage.ppm") export("leverage.ppm") export("lgcp.estK") export("lgcp.estpcf") export("lhs.of.formula") export("lineardisc") export("linearK") export("linearKcross") export("linearKcross.inhom") export("linearKdot") export("linearKdot.inhom") export("linearKengine") export("linearKinhom") export("linearKmulti") export("linearKmultiEngine") export("linearKmulti.inhom") export("linearmarkconnect") export("linearmarkequal") export("linearpcf") export("linearpcfcross") export("linearpcfcross.inhom") export("linearpcfdot") export("linearpcfdot.inhom") export("linearpcfengine") export("linearpcfinhom") export("linearpcfmulti") export("linearPCFmultiEngine") export("linearpcfmulti.inhom") export("linequad") export("linfun") export("Linhom") export("linim") export("[.linnet") export("linnet") export("[<-.listof") export("listof") export("local2lpp") export("localK") export("localKengine") export("localKinhom") export("localL") export("localLinhom") export("localpcf") export("localpcfengine") export("localpcfinhom") export("[.localpcfmatrix") export("localpcfmatrix") export("logi.dummy") export("logi.engine") export("logLik.lppm") export("logLik.mppm") export("logLik.ppm") export("logLik.slrm") export("lohboot") export("lookup.im") export("[.lpp") export("lpp") export("lppm") export("lppm.formula") export("lppm.lpp") export("Lscaled") export("lty2char") export("lurking") export("lut") export("mad.progress") export("mad.test") export("majorminorversion") export("make.even.breaks") export("makefvlabel") export("make.parseable") export("makeunits") export("markappend") export("markappendop") export("markcbind") export("markconnect") export("markcorr") export("markcorrint") export("markformat") export("markformat.default") export("markformat.ppp") export("markformat.ppx") export("markformat.psp") export("markmean") export("markreplicateop") export("marks<-") export("marks") export("mark.scale.default") export("marks.default") export("marks<-.lpp") export("markspace.integral") export("marks<-.ppp") export("marks.ppp") export("marks<-.ppx") export("marks.ppx") export("marks<-.psp") export("marks.psp") export("marks.quad") export("markstat") export("marksubset") export("marksubsetop") export("marktable") export("markvar") export("markvario") export("mask2df") export("matchingdist") export("matclust.estK") export("matclust.estpcf") export("matcolall") export("matcolany") export("matcolsum") export("matrixsample") export("matrowall") export("matrowany") export("matrowsum") export("maxflow") export("max.im") export("maxnndist") export("mctest.progress") export("mean.im") export("meanlistfv") export("meanX.owin") export("meanY.owin") export("median.im") export("midpoints.psp") export("mincontrast") export("min.im") export("minnndist") export("miplot") export("model.covariates") export("model.depends") export("model.frame.kppm") export("model.frame.lppm") export("model.frame.ppm") export("model.images") export("model.images.kppm") export("model.images.lppm") export("model.images.ppm") export("model.images.slrm") export("model.is.additive") export("model.matrix.kppm") export("model.matrix.lppm") export("model.matrix.ppm") export("model.matrix.slrm") export("model.se.image") export("mpl") export("mpl.engine") export("mpl.get.covariates") export("mpl.prepare") export("mppm") export("[.msr") export("msr") export("MultiHard") export("MultiPair.checkmatrix") export("multiplicity") export("multiplicity.data.frame") export("multiplicity.default") export("multiplicityNumeric") export("multiplicity.ppp") export("multiplicity.ppx") export("multiply.only.finite.entries") export("MultiStrauss") export("MultiStraussHard") export("na.handle.im") export("names<-.hyperframe") export("names.hyperframe") export("nearest.neighbour") export("nearest.pixel") export("nearest.raster.point") export("nearestsegment") export("nearest.valid.pixel") export("newstyle.coeff.handling") export("niceround") export("nnclean") export("nncleanEngine") export("nnclean.pp3") export("nnclean.ppp") export("nncorr") export("nncross") export("nncross.default") export("nncross.lpp") export("nncross.pp3") export("nncross.ppp") export("nndcumfun") export("nndensity") export("nndensity.ppp") export("nndist") export("nndist.default") export("nndist.lpp") export("nndist.pp3") export("nndist.ppp") export("nndist.ppx") export("nndist.psp") export("nnfun") export("nnfun.lpp") export("nnfun.ppp") export("nnfun.psp") export("nnmap") export("nnmark") export("nnmean") export("nnvario") export("nnwhich") export("nnwhich.default") export("nnwhich.lpp") export("nnwhich.pp3") export("nnwhich.ppp") export("nnwhich.ppx") export("nobjects") export("nobjects.ppp") export("nobjects.ppx") export("nobjects.psp") export("nobs.lppm") export("nobs.ppm") export("no.trend.ppm") export("npfun") export("npoints") export("npoints.pp3") export("npoints.ppp") export("npoints.ppx") export("n.quad") export("nsegments") export("nsegments.psp") export("numalign") export("numeric.columns") export("nzpaste") export("objsurf") export("objsurfEngine") export("objsurf.kppm") export("objsurf.minconfit") export("offsetsinformula") export("onecolumn") export("opening") export("opening.owin") export("opening.ppp") export("opening.psp") export("optimStatus") export("Ord") export("ord.family") export("ordinal") export("OrdThresh") export("outdated.interact") export("overlap.owin") export("overlap.trapezium") export("overlap.xypolygon") export("oversize.quad") export("[.owin") export("owin") export("owinpoly2mask") export("owinpolycheck") export("pairdist") export("pairdist.default") export("pairdist.lpp") export("pairdist.pp3") export("pairdist.ppp") export("pairdist.ppx") export("pairdist.psp") export("PairPiece") export("pairsat.family") export("pairs.im") export("pairs.listof") export("Pairwise") export("pairwise.family") export("paletteindex") export("paletteindex") export("param.quad") export("parbreak") export("paren") export("parres") export("partialModelMatrix") export("passthrough") export("paste.expr") export("pcf") export("pcf3engine") export("pcf3est") export("pcfcross") export("pcfcross.inhom") export("pcfdot") export("pcfdot.inhom") export("pcf.fasp") export("pcf.fv") export("pcfinhom") export("pcfmodel") export("pcfmodel.kppm") export("pcfmodel.ppm") export("pcfmulti") export("pcfmulti.inhom") export("pcf.ppp") export("perimeter") export("periodify") export("periodify.owin") export("periodify.ppp") export("periodify.psp") export("persp.funxy") export("persp.im") export("persp.objsurf") export("pickoption") export("pixelcentres") export("pixellate") export("pixellate.owin") export("pixellate.ppp") export("pixellate.psp") export("pixelquad") export("pknn") export("plan.legend.layout") export("plot.addvar") export("plot.barplotdata") export("plot.bermantest") export("plot.bw.frac") export("plot.bw.optim") export("plot.colourmap") export("plot.diagppm") export("plot.envelope") export("ploterodeimage") export("ploterodewin") export("plot.fasp") export("plot.fii") export("plot.funxy") export("plot.fv") export("plot.hyperframe") export("plot.im") export("plot.infline") export("plot.influence.ppm") export("plot.kppm") export("plot.kstest") export("plot.layered") export("plot.leverage.ppm") export("plot.linfun") export("plot.linim") export("plot.linnet") export("plot.listof") export("plot.localpcfmatrix") export("plot.lpp") export("plot.lppm") export("plot.minconfit") export("plot.mppm") export("plot.msr") export("plot.objsurf") export("plot.owin") export("plot.parres") export("plot.plotpairsim") export("plot.plotppm") export("plot.pp3") export("plot.ppm") export("plot.ppp") export("plot.pppmatching") export("plot.ppx") export("plot.profilepl") export("plot.psp") export("plot.qqppm") export("plot.quad") export("plot.quadratcount") export("plot.quadrattest") export("plot.rho2hat") export("plot.rhohat") export("plot.scan.test") export("plot.slrm") export("plot.splitppp") export("plot.symbolmap") export("plot.tess") export("plot.texturemap") export("pmixpois") export("pointgrid") export("pointsOnLines") export("Poisson") export("polynom") export("pool") export("pool.envelope") export("pool.fasp") export("pool.quadrattest") export("pool.rat") export("[.pp3") export("pp3") export("ppllengine") export("ppm") export("ppmCovariates") export("ppm.default") export("ppmDerivatives") export("ppm.formula") export("ppmInfluence") export("ppm.ppp") export("ppm.quad") export("[<-.ppp") export("[.ppp") export("ppp") export("pppdist") export("pppdist.mat") export("pppdist.prohorov") export("pppmatching") export("ppsubset") export("[.ppx") export("ppx") export("prange") export("predict.kppm") export("predict.lppm") export("predict.mppm") export("predict.ppm") export("predict.rhohat") export("predict.slrm") export("prefixfv") export("prettydiscrete") export("prettyinside") export("primefactors") export("primesbelow") export("print.addvar") export("print.autoexec") export("print.box3") export("print.boxx") export("print.bt.frame") export("print.bw.frac") export("print.bw.optim") export("print.colourmap") export("print.diagppm") export("print.distfun") export("print.envelope") export("print.ewcdf") export("print.fasp") export("print.fii") export("print.funxy") export("print.fv") export("print.fvfun") export("print.hyperframe") export("print.im") export("print.infline") export("print.influence.ppm") export("print.interact") export("print.isf") export("print.kppm") export("print.layered") export("print.leverage.ppm") export("print.linfun") export("print.linim") export("print.linnet") export("print.localpcfmatrix") export("print.lpp") export("print.lppm") export("print.lut") export("print.minconfit") export("print.mppm") export("print.msr") export("print.nnfun") export("print.objsurf") export("print.owin") export("print.parres") export("print.plotpairsim") export("print.plotppm") export("print.pp3") export("print.ppm") export("print.ppp") export("print.pppmatching") export("print.ppx") export("print.profilepl") export("print.psp") export("print.qqppm") export("print.quad") export("print.quadrattest") export("print.rat") export("print.rho2hat") export("print.rhohat") export("print.rmhcontrol") export("print.rmhexpand") export("print.rmhInfoList") export("print.rmhmodel") export("print.rmhstart") export("print.simplepanel") export("print.slrm") export("print.Smoothfun") export("print.splitppp") export("print.splitppx") export("printStatus") export("print.summary.fii") export("print.summary.hyperframe") export("print.summary.im") export("print.summary.listof") export("print.summary.logiquad") export("print.summary.lpp") export("print.summary.lut") export("print.summary.mppm") export("print.summary.owin") export("print.summary.pp3") export("print.summary.ppm") export("print.summary.ppp") export("print.summary.psp") export("print.summary.quad") export("print.summary.rmhexpand") export("print.summary.splitppp") export("print.summary.splitppx") export("print.summary.units") export("print.symbolmap") export("print.tess") export("print.texturemap") export("print.timed") export("print.units") export("profilepl") export("progressreport") export("project2segment") export("project.ppm") export("prolongseq") export("[.psp") export("psp") export("psst") export("psstA") export("psstG") export("putlastshift") export("putSpatstatVariable") export("qknn") export("qmixpois") export("qqplot.ppm") export("[.quad") export("quad") export("quadform") export("quad.mppm") export("quad.ppm") export("quadratcount") export("quadratcount.ppp") export("quadratcount.splitppp") export("quadratresample") export("quadrats") export("quadrat.test") export("quadrat.testEngine") export("quadrat.test.mppm") export("quadrat.test.ppm") export("quadrat.test.ppp") export("quadrat.test.quadratcount") export("quadrat.test.splitppp") export("quadscheme") export("quadscheme.logi") export("quadscheme.replicated") export("quadscheme.spatial") export("quantile.ewcdf") export("quantile.im") export("RandomFieldsSafe") export("range.im") export("rastersample") export("raster.x") export("rasterx.im") export("raster.xy") export("rasterxy.im") export("raster.y") export("rastery.im") export("rat") export("ratfv") export("rbind.hyperframe") export("rCauchy") export("rcell") export("rcellnumber") export("rDGS") export("rDiggleGratton") export("reach") export("reach.fii") export("reach.interact") export("reach.ppm") export("reach.rmhmodel") export("rebadge.as.crossfun") export("rebadge.as.dotfun") export("rebadge.fv") export("rebound") export("rebound.im") export("rebound.owin") export("rebound.ppp") export("rebound.psp") export("recognise.spatstat.type") export("reconcile.fv") export("rectquadrat.breaks") export("rectquadrat.countEngine") export("redraw.simplepanel") export("reduced.sample") export("reduceformula") export("reflect") export("reflect.default") export("reflect.im") export("reflect.layered") export("reflect.tess") export("reincarnate.interact") export("reload.or.compute") export("relrisk") export("repair.image.xycoords") export("repair.old.factor.image") export("rescale") export("rescale.im") export("rescale.layered") export("rescale.linnet") export("rescale.lpp") export("rescale.owin") export("rescale.ppp") export("rescale.psp") export("rescale.units") export("rescue.rectangle") export("reset.spatstat.options") export("resid1panel") export("resid1plot") export("resid4plot") export("residuals.mppm") export("residuals.ppm") export("resolve.1.default") export("resolve.2D.kernel") export("resolve.defaults") export("resolveEinfo") export("resolve.vargamma.shape") export("restrict.mask") export("revcumsum") export("reverse.xypolygon") export("rGaussPoisson") export("rgb2hex") export("rgbim") export("rgbNA") export("rHardcore") export("rho2hat") export("rhohat") export("rhohatCalc") export("rhohatEngine") export("rhohat.lpp") export("rhohat.lppm") export("rhohat.ppm") export("rhohat.ppp") export("rhohat.quad") export("rhs.of.formula") export("ripras") export("rjitter") export("rknn") export("rlabel") export("rLGCP") export("rlinegrid") export("rMatClust") export("rMaternI") export("rMaternII") export("rmax.rule") export("rmh") export("rmhcontrol") export("rmhcontrol.default") export("rmhcontrol.list") export("rmhcontrol.rmhcontrol") export("rmh.default") export("rmhEngine") export("rmhexpand") export("RmhExpandRule") export("rmhmodel") export("rmhmodel.default") export("rmhmodel.list") export("rmhmodel.ppm") export("rmhmodel.rmhmodel") export("rmh.ppm") export("rmhResolveControl") export("rmhResolveExpansion") export("rmhResolveTypes") export("rmhsnoop") export("rmhSnoopEnv") export("rmhstart") export("rmhstart.default") export("rmhstart.list") export("rmhstart.rmhstart") export("rmixpois") export("rMosaicField") export("rMosaicSet") export("rmpoint") export("rmpoint.I.allim") export("rmpoispp") export("rNeymanScott") export("rotate") export("rotate.im") export("rotate.layered") export("rotate.linnet") export("rotate.lpp") export("rotate.owin") export("rotate.ppp") export("rotate.psp") export("rotate.tess") export("rotxy") export("rotxypolygon") export("rounding") export("rounding.default") export("rounding.pp3") export("rounding.ppp") export("rounding.ppx") export("round.pp3") export("round.ppp") export("round.ppx") export("row.names<-.hyperframe") export("row.names.hyperframe") export("rpoint") export("rpoint.multi") export("rpoisline") export("rpoislinetess") export("rpoislpp") export("rpoispp") export("rpoispp3") export("rpoisppOnLines") export("rpoisppx") export("rPoissonCluster") export("rQuasi") export("rshift") export("rshift.ppp") export("rshift.psp") export("rshift.splitppp") export("rSSI") export("rstrat") export("rStrauss") export("rStraussHard") export("rsyst") export("rthin") export("rThomas") export("rtoro") export("runifdisc") export("runiflpp") export("runifpoint") export("runifpoint3") export("runifpointOnLines") export("runifpointx") export("runifpoispp") export("runifpoisppOnLines") export("runifrect") export("run.simplepanel") export("rVarGamma") export("safelookup") export("samecolour") export("samefunction") export("SatPiece") export("Saturated") export("scalardilate") export("scalardilate.default") export("scalardilate.im") export("scalardilate.layered") export("scalardilate.linnet") export("scalardilate.lpp") export("scalardilate.owin") export("scalardilate.ppp") export("scalardilate.psp") export("scalardilate.tess") export("scaletointerval") export("scaletointerval.default") export("scaletointerval.im") export("scanBinomLRTS") export("scanLRTS") export("scanmeasure") export("scanmeasure.im") export("scanmeasure.ppp") export("scanPoisLRTS") export("scanpp") export("scan.test") export("second.moment.calc") export("second.moment.engine") export("selfcrossing.psp") export("sensiblevarname") export("sessionLibs") export("setcov") export("setmarks") export("setminus.owin") export("sewpcf") export("sewsmod") export("sharpen") export("sharpen.ppp") export("shift") export("shift.im") export("shift.influence.ppm") export("shift.layered") export("shift.leverage.ppm") export("shift.linnet") export("shift.lpp") export("shift.msr") export("shift.owin") export("shift.ppp") export("shift.psp") export("shift.quadratcount") export("shift.quadrattest") export("shift.tess") export("shiftxy") export("shiftxypolygon") export("short.deparse") export("shortside") export("shortside.box3") export("shortside.boxx") export("shortside.owin") export("sidelengths") export("sidelengths.box3") export("sidelengths.boxx") export("sidelengths.owin") export("signalStatus") export("simplepanel") export("simplify.owin") export("simplify.xypolygon") export("simulate.kppm") export("simulate.ppm") export("simulate.slrm") export("simulrecipe") export("singlestring") export("slrAssemblePixelData") export("slrm") export("slr.prepare") export("Smooth") export("smoothcrossEngine") export("Smoothfun") export("Smoothfun.ppp") export("smooth.fv") export("Smooth.fv") export("Smooth.im") export("smooth.msr") export("Smooth.msr") export("smoothpointsEngine") export("smooth.ppp") export("Smooth.ppp") export("Softcore") export("solutionset") export("sort.im") export("spatialCDFframe") export("spatialCDFtest") export("spatstatClusterModelInfo") export("spatstat.options") export("spatstat.rawdata.location") export("spatstatRmhInfo") export("sp.foundclass") export("sp.foundclasses") export("sphere.volume") export("splat") export("splitHybridInteraction") export("split<-.hyperframe") export("split.hyperframe") export("split.im") export("[<-.splitppp") export("[.splitppp") export("split<-.ppp") export("split.ppp") export("[<-.splitppx") export("[.splitppx") export("split.ppx") export("spokes") export("square") export("stieltjes") export("store.versionstring.spatstat") export("stratrand") export("Strauss") export("strausscounts") export("StraussHard") export("str.hyperframe") export("strsplitretain") export("subfits") export("subfits.new") export("subfits.old") export("substringcount") export("suffloc") export("suffstat") export("suffstat.generic") export("suffstat.poisson") export("sum.im") export("summarise.trend") export("summary.envelope") export("summary.fii") export("summary.hyperframe") export("summary.im") export("summary.linnet") export("summary.listof") export("summary.logiquad") export("summary.lpp") export("summary.lut") export("summary.mppm") export("summary.owin") export("summary.pp3") export("summary.ppm") export("summary.ppp") export("summary.pppmatching") export("summary.ppx") export("summary.profilepl") export("summary.psp") export("summary.quad") export("summary.rmhexpand") export("summary.splitppp") export("summary.splitppx") export("summary.units") export("sumouter") export("sumsymouter") export("superimpose") export("superimpose.default") export("superimposeMarks") export("superimpose.ppp") export("superimpose.psp") export("superimposePSP") export("symbolmap") export("symbolmaptype") export("sympoly") export("termsinformula") export("terms.kppm") export("terms.lppm") export("terms.ppm") export("terms.slrm") export("[<-.tess") export("[.tess") export("tess") export("test.crossing.psp") export("test.selfcrossing.psp") export("texturemap") export("textureplot") export("thomas.estK") export("thomas.estpcf") export("tile.areas") export("tilecentroids") export("tileindex") export("tilenames<-") export("tilenames") export("tiles") export("timed") export("to.grey") export("transect.im") export("trap.extra.arguments") export("trianglediameters") export("trim.mask") export("trim.rectangle") export("triplet.family") export("Triplets") export("truncline") export("Tstat") export("tweak.colourmap") export("tweak.fv.entry") export("union.owin") export("union.quad") export("unique.ppp") export("unique.ppx") export("unitname<-") export("unitname") export("unitname<-.box3") export("unitname.box3") export("unitname<-.boxx") export("unitname.boxx") export("unitname<-.default") export("unitname.default") export("unitname<-.im") export("unitname.im") export("unitname<-.kppm") export("unitname.kppm") export("unitname<-.linnet") export("unitname.linnet") export("unitname<-.lpp") export("unitname.lpp") export("unitname<-.minconfit") export("unitname.minconfit") export("unitname<-.owin") export("unitname.owin") export("unitname<-.pp3") export("unitname.pp3") export("unitname<-.ppm") export("unitname.ppm") export("unitname<-.ppp") export("unitname.ppp") export("unitname<-.ppx") export("unitname.ppx") export("unitname<-.psp") export("unitname.psp") export("unitname<-.quad") export("unitname.quad") export("unitname<-.slrm") export("unitname.slrm") export("unit.square") export("unmark") export("unmark.lpp") export("unmark.ppp") export("unmark.ppx") export("unmark.psp") export("unmark.splitppp") export("unnormdensity") export("unparen") export("update.interact") export("update.kppm") export("update.lppm") export("update.ppm") export("update.rmhcontrol") export("update.rmhstart") export("update.slrm") export("update.symbolmap") export("uptrimat") export("validate.mask") export("validate.quad") export("validposint") export("valid.ppm") export("validradius") export("vanilla.fv") export("varblock") export("vargamma.estK") export("vargamma.estpcf") export("variablesinformula") export("vcov.kppm") export("vcov.lppm") export("vcov.mppm") export("vcov.ppm") export("vcov.slrm") export("vdCorput") export("verbalogic") export("verifyclass") export("verify.xypolygon") export("versionstring.interact") export("versionstring.ppm") export("versionstring.spatstat") export("vertices") export("Vmark") export("volume") export("volume.box3") export("volume.boxx") export("volume.owin") export("warn.ignored.args") export("warn.once") export("waxlyrical") export("weighted.var") export("which.max.im") export("whist") export("will.expand") export("windows.mppm") export("with.fv") export("with.hyperframe") export("with.msr") export("w.quad") export("X2testEngine") export("x.quad") export("xtfrm.im") export("xy.grid") export("xypolygon2psp") export("xypolyselfint") export("y.quad") export("zapsmall.im") # ....... Special cases ........... export("%mapp%") export("%mark%") export("%mrep%") export("%msub%") export("%orifnull%") # ....... End of special cases ... # ......................................... # Automatically generated list of S3 methods # ......................................... S3method("affine", "im") S3method("affine", "layered") S3method("affine", "linnet") S3method("affine", "lpp") S3method("affine", "owin") S3method("affine", "ppp") S3method("affine", "psp") S3method("affine", "tess") S3method("anova", "lppm") S3method("anova", "mppm") S3method("anova", "ppm") S3method("anova", "slrm") S3method("as.array", "im") S3method("as.character", "units") S3method("as.data.frame", "bw.optim") S3method("as.data.frame", "fv") S3method("as.data.frame", "hyperframe") S3method("as.data.frame", "im") S3method("as.data.frame", "ppp") S3method("as.data.frame", "ppx") S3method("as.data.frame", "psp") S3method("as.function", "fv") S3method("as.function", "linfun") S3method("as.function", "rhohat") S3method("as.fv", "bw.optim") S3method("as.fv", "data.frame") S3method("as.fv", "fasp") S3method("as.fv", "fv") S3method("as.fv", "kppm") S3method("as.fv", "matrix") S3method("as.fv", "minconfit") S3method("as.hyperframe", "data.frame") S3method("as.hyperframe", "default") S3method("as.hyperframe", "hyperframe") S3method("as.hyperframe", "listof") S3method("as.hyperframe", "ppx") S3method("as.im", "default") S3method("as.im", "distfun") S3method("as.im", "function") S3method("as.im", "im") S3method("as.im", "leverage.ppm") S3method("as.im", "linim") S3method("as.im", "matrix") S3method("as.im", "nnfun") S3method("as.im", "owin") S3method("as.im", "ppp") S3method("as.im", "scan.test") S3method("as.im", "Smoothfun") S3method("as.im", "tess") S3method("as.interact", "fii") S3method("as.interact", "interact") S3method("as.interact", "ppm") S3method("as.linim", "default") S3method("as.linim", "linfun") S3method("as.linim", "linim") S3method("as.linnet", "linfun") S3method("as.linnet", "linnet") S3method("as.linnet", "lpp") S3method("as.linnet", "lppm") S3method("as.list", "hyperframe") S3method("as.matrix", "im") S3method("as.matrix", "owin") S3method("as.matrix", "ppx") S3method("as.owin", "boxx") S3method("as.owin", "data.frame") S3method("as.owin", "default") S3method("as.owin", "distfun") S3method("as.owin", "funxy") S3method("as.owin", "im") S3method("as.owin", "influence.ppm") S3method("as.owin", "kppm") S3method("as.owin", "layered") S3method("as.owin", "leverage.ppm") S3method("as.owin", "linfun") S3method("as.owin", "linnet") S3method("as.owin", "lpp") S3method("as.owin", "lppm") S3method("as.owin", "msr") S3method("as.owin", "nnfun") S3method("as.owin", "owin") S3method("as.owin", "ppm") S3method("as.owin", "ppp") S3method("as.owin", "psp") S3method("as.owin", "quad") S3method("as.owin", "quadratcount") S3method("as.owin", "quadrattest") S3method("as.owin", "rmhmodel") S3method("as.owin", "tess") S3method("as.ppm", "kppm") S3method("as.ppm", "ppm") S3method("as.ppm", "profilepl") S3method("as.ppp", "data.frame") S3method("as.ppp", "default") S3method("as.ppp", "influence.ppm") S3method("as.ppp", "lpp") S3method("as.ppp", "matrix") S3method("as.ppp", "ppp") S3method("as.ppp", "psp") S3method("as.ppp", "quad") S3method("as.psp", "data.frame") S3method("as.psp", "default") S3method("as.psp", "linnet") S3method("as.psp", "lpp") S3method("as.psp", "matrix") S3method("as.psp", "owin") S3method("as.psp", "psp") S3method("as.tess", "im") S3method("as.tess", "list") S3method("as.tess", "owin") S3method("as.tess", "quadratcount") S3method("as.tess", "quadrattest") S3method("as.tess", "tess") S3method("bermantest", "lpp") S3method("bermantest", "lppm") S3method("bermantest", "ppm") S3method("bermantest", "ppp") S3method("boundingbox", "default") S3method("boundingbox", "im") S3method("boundingbox", "owin") S3method("boundingbox", "ppp") S3method("by", "im") S3method("by", "ppp") S3method("cbind", "fv") S3method("cbind", "hyperframe") S3method("closing", "owin") S3method("closing", "ppp") S3method("closing", "psp") S3method("coef", "fii") S3method("coef", "kppm") S3method("coef", "lppm") S3method("coef", "mppm") S3method("coef", "ppm") S3method("coef", "slrm") S3method("coef", "summary.fii") S3method("coef", "summary.ppm") S3method("compatible", "fasp") S3method("compatible", "fv") S3method("compatible", "im") S3method("compatible", "units") S3method("connected", "im") S3method("connected", "owin") S3method("connected", "ppp") S3method("contour", "funxy") S3method("contour", "im") S3method("contour", "listof") S3method("contour", "objsurf") S3method("coords", "ppp") S3method("coords", "ppx") S3method("crossdist", "default") S3method("crossdist", "lpp") S3method("crossdist", "pp3") S3method("crossdist", "ppp") S3method("crossdist", "ppx") S3method("crossdist", "psp") S3method("cut", "im") S3method("cut", "ppp") S3method("density", "ppp") S3method("density", "psp") S3method("density", "splitppp") S3method("deriv", "fv") S3method("dfbetas", "ppm") S3method("diameter", "box3") S3method("diameter", "boxx") S3method("diameter", "linnet") S3method("diameter", "owin") S3method("dilation", "owin") S3method("dilation", "ppp") S3method("dilation", "psp") S3method("dim", "fasp") S3method("dim", "hyperframe") S3method("dim", "im") S3method("dim", "msr") S3method("dimnames", "fasp") S3method("dimnames", "msr") S3method("distfun", "lpp") S3method("distfun", "owin") S3method("distfun", "ppp") S3method("distfun", "psp") S3method("distmap", "owin") S3method("distmap", "ppp") S3method("distmap", "psp") S3method("duplicated", "ppp") S3method("duplicated", "ppx") S3method("envelope", "envelope") S3method("envelope", "kppm") S3method("envelope", "lpp") S3method("envelope", "lppm") S3method("envelope", "matrix") S3method("envelope", "pp3") S3method("envelope", "ppm") S3method("envelope", "ppp") S3method("eroded.volumes", "box3") S3method("eroded.volumes", "boxx") S3method("erosion", "owin") S3method("erosion", "ppp") S3method("erosion", "psp") S3method("evalCovar", "lppm") S3method("evalCovar", "ppm") S3method("extractAIC", "lppm") S3method("extractAIC", "ppm") S3method("extractAIC", "slrm") S3method("[", "fasp") S3method("fitin", "ppm") S3method("fitted", "kppm") S3method("fitted", "mppm") S3method("fitted", "ppm") S3method("fitted", "slrm") S3method("flipxy", "im") S3method("flipxy", "layered") S3method("flipxy", "owin") S3method("flipxy", "ppp") S3method("flipxy", "psp") S3method("formula", "fv") S3method("formula", "kppm") S3method("formula", "lppm") S3method("formula", "ppm") S3method("formula", "slrm") S3method("[", "fv") S3method("hist", "im") S3method("[", "hyperframe") S3method("$", "hyperframe") S3method("identify", "ppp") S3method("identify", "psp") S3method("[", "im") S3method("image", "listof") S3method("image", "objsurf") S3method("[", "influence.ppm") S3method("influence", "ppm") S3method("intensity", "lpp") S3method("intensity", "ppm") S3method("intensity", "ppp") S3method("intensity", "quadratcount") S3method("intensity", "splitppp") S3method("iplot", "default") S3method("iplot", "layered") S3method("iplot", "ppp") S3method("is.empty", "default") S3method("is.empty", "owin") S3method("is.empty", "ppp") S3method("is.empty", "psp") S3method("is.hybrid", "interact") S3method("is.hybrid", "ppm") S3method("is.marked", "default") S3method("is.marked", "ppm") S3method("is.marked", "ppp") S3method("is.marked", "psp") S3method("is.marked", "quad") S3method("is.multitype", "default") S3method("is.multitype", "ppm") S3method("is.multitype", "ppp") S3method("is.multitype", "quad") S3method("is.poisson", "interact") S3method("is.poisson", "kppm") S3method("is.poisson", "lppm") S3method("is.poisson", "mppm") S3method("is.poisson", "ppm") S3method("is.poisson", "rmhmodel") S3method("is.poisson", "slrm") S3method("is.stationary", "kppm") S3method("is.stationary", "lppm") S3method("is.stationary", "ppm") S3method("is.stationary", "rmhmodel") S3method("is.stationary", "slrm") S3method("Kmodel", "kppm") S3method("Kmodel", "ppm") S3method("kppm", "formula") S3method("kppm", "ppp") S3method("kppm", "quad") S3method("kstest", "lpp") S3method("kstest", "lppm") S3method("kstest", "mppm") S3method("kstest", "ppm") S3method("kstest", "ppp") S3method("kstest", "slrm") S3method("labels", "kppm") S3method("labels", "ppm") S3method("labels", "slrm") S3method("[", "layered") S3method("levels", "im") S3method("[", "leverage.ppm") S3method("leverage", "ppm") S3method("[", "linnet") S3method("[", "localpcfmatrix") S3method("logLik", "lppm") S3method("logLik", "mppm") S3method("logLik", "ppm") S3method("logLik", "slrm") S3method("[", "lpp") S3method("lppm", "formula") S3method("lppm", "lpp") S3method("marks", "default") S3method("marks", "ppp") S3method("marks", "ppx") S3method("marks", "psp") S3method("marks", "quad") S3method("max", "im") S3method("mean", "im") S3method("median", "im") S3method("min", "im") S3method("model.frame", "kppm") S3method("model.frame", "lppm") S3method("model.frame", "ppm") S3method("model.images", "kppm") S3method("model.images", "lppm") S3method("model.images", "ppm") S3method("model.images", "slrm") S3method("model.matrix", "kppm") S3method("model.matrix", "lppm") S3method("model.matrix", "ppm") S3method("model.matrix", "slrm") S3method("[", "msr") S3method("multiplicity", "data.frame") S3method("multiplicity", "default") S3method("multiplicity", "ppp") S3method("multiplicity", "ppx") S3method("names", "hyperframe") S3method("nnclean", "pp3") S3method("nnclean", "ppp") S3method("nncross", "default") S3method("nncross", "lpp") S3method("nncross", "pp3") S3method("nncross", "ppp") S3method("nndensity", "ppp") S3method("nndist", "default") S3method("nndist", "lpp") S3method("nndist", "pp3") S3method("nndist", "ppp") S3method("nndist", "ppx") S3method("nndist", "psp") S3method("nnfun", "lpp") S3method("nnfun", "ppp") S3method("nnfun", "psp") S3method("nnwhich", "default") S3method("nnwhich", "lpp") S3method("nnwhich", "pp3") S3method("nnwhich", "ppp") S3method("nnwhich", "ppx") S3method("nobs", "lppm") S3method("nobs", "ppm") S3method("npoints", "pp3") S3method("npoints", "ppp") S3method("npoints", "ppx") S3method("nsegments", "psp") S3method("objsurf", "kppm") S3method("objsurf", "minconfit") S3method("opening", "owin") S3method("opening", "ppp") S3method("opening", "psp") S3method("[", "owin") S3method("pairdist", "default") S3method("pairdist", "lpp") S3method("pairdist", "pp3") S3method("pairdist", "ppp") S3method("pairdist", "ppx") S3method("pairdist", "psp") S3method("pairs", "im") S3method("pairs", "listof") S3method("pcf", "fasp") S3method("pcf", "fv") S3method("pcfmodel", "kppm") S3method("pcfmodel", "ppm") S3method("pcf", "ppp") S3method("periodify", "owin") S3method("periodify", "ppp") S3method("periodify", "psp") S3method("persp", "funxy") S3method("persp", "im") S3method("persp", "objsurf") S3method("pixellate", "owin") S3method("pixellate", "ppp") S3method("pixellate", "psp") S3method("plot", "addvar") S3method("plot", "barplotdata") S3method("plot", "bermantest") S3method("plot", "bw.frac") S3method("plot", "bw.optim") S3method("plot", "colourmap") S3method("plot", "diagppm") S3method("plot", "envelope") S3method("plot", "fasp") S3method("plot", "fii") S3method("plot", "funxy") S3method("plot", "fv") S3method("plot", "hyperframe") S3method("plot", "im") S3method("plot", "infline") S3method("plot", "influence.ppm") S3method("plot", "kppm") S3method("plot", "kstest") S3method("plot", "layered") S3method("plot", "leverage.ppm") S3method("plot", "linfun") S3method("plot", "linim") S3method("plot", "linnet") S3method("plot", "listof") S3method("plot", "localpcfmatrix") S3method("plot", "lpp") S3method("plot", "lppm") S3method("plot", "minconfit") S3method("plot", "mppm") S3method("plot", "msr") S3method("plot", "objsurf") S3method("plot", "owin") S3method("plot", "parres") S3method("plot", "plotpairsim") S3method("plot", "plotppm") S3method("plot", "pp3") S3method("plot", "ppm") S3method("plot", "ppp") S3method("plot", "pppmatching") S3method("plot", "ppx") S3method("plot", "profilepl") S3method("plot", "psp") S3method("plot", "qqppm") S3method("plot", "quad") S3method("plot", "quadratcount") S3method("plot", "quadrattest") S3method("plot", "rho2hat") S3method("plot", "rhohat") S3method("plot", "scan.test") S3method("plot", "slrm") S3method("plot", "splitppp") S3method("plot", "symbolmap") S3method("plot", "tess") S3method("plot", "texturemap") S3method("pool", "envelope") S3method("pool", "fasp") S3method("pool", "quadrattest") S3method("pool", "rat") S3method("[", "pp3") S3method("ppm", "default") S3method("ppm", "formula") S3method("ppm", "ppp") S3method("ppm", "quad") S3method("[", "ppp") S3method("[", "ppx") S3method("predict", "kppm") S3method("predict", "lppm") S3method("predict", "mppm") S3method("predict", "ppm") S3method("predict", "rhohat") S3method("predict", "slrm") S3method("print", "addvar") S3method("print", "autoexec") S3method("print", "box3") S3method("print", "boxx") S3method("print", "bt.frame") S3method("print", "bw.frac") S3method("print", "bw.optim") S3method("print", "colourmap") S3method("print", "diagppm") S3method("print", "distfun") S3method("print", "envelope") S3method("print", "ewcdf") S3method("print", "fasp") S3method("print", "fii") S3method("print", "funxy") S3method("print", "fv") S3method("print", "fvfun") S3method("print", "hyperframe") S3method("print", "im") S3method("print", "infline") S3method("print", "influence.ppm") S3method("print", "interact") S3method("print", "isf") S3method("print", "kppm") S3method("print", "layered") S3method("print", "leverage.ppm") S3method("print", "linfun") S3method("print", "linim") S3method("print", "linnet") S3method("print", "localpcfmatrix") S3method("print", "lpp") S3method("print", "lppm") S3method("print", "lut") S3method("print", "minconfit") S3method("print", "mppm") S3method("print", "msr") S3method("print", "nnfun") S3method("print", "objsurf") S3method("print", "owin") S3method("print", "parres") S3method("print", "plotpairsim") S3method("print", "plotppm") S3method("print", "pp3") S3method("print", "ppm") S3method("print", "ppp") S3method("print", "pppmatching") S3method("print", "ppx") S3method("print", "profilepl") S3method("print", "psp") S3method("print", "qqppm") S3method("print", "quad") S3method("print", "quadrattest") S3method("print", "rat") S3method("print", "rho2hat") S3method("print", "rhohat") S3method("print", "rmhcontrol") S3method("print", "rmhexpand") S3method("print", "rmhInfoList") S3method("print", "rmhmodel") S3method("print", "rmhstart") S3method("print", "simplepanel") S3method("print", "slrm") S3method("print", "Smoothfun") S3method("print", "splitppp") S3method("print", "splitppx") S3method("print", "summary.fii") S3method("print", "summary.hyperframe") S3method("print", "summary.im") S3method("print", "summary.listof") S3method("print", "summary.logiquad") S3method("print", "summary.lpp") S3method("print", "summary.lut") S3method("print", "summary.mppm") S3method("print", "summary.owin") S3method("print", "summary.pp3") S3method("print", "summary.ppm") S3method("print", "summary.ppp") S3method("print", "summary.psp") S3method("print", "summary.quad") S3method("print", "summary.rmhexpand") S3method("print", "summary.splitppp") S3method("print", "summary.splitppx") S3method("print", "summary.units") S3method("print", "symbolmap") S3method("print", "tess") S3method("print", "texturemap") S3method("print", "timed") S3method("print", "units") S3method("[", "psp") S3method("[", "quad") S3method("quadratcount", "ppp") S3method("quadratcount", "splitppp") S3method("quadrat.test", "mppm") S3method("quadrat.test", "ppm") S3method("quadrat.test", "ppp") S3method("quadrat.test", "quadratcount") S3method("quadrat.test", "splitppp") S3method("quantile", "ewcdf") S3method("quantile", "im") S3method("range", "im") S3method("rbind", "hyperframe") S3method("reach", "fii") S3method("reach", "interact") S3method("reach", "ppm") S3method("reach", "rmhmodel") S3method("reflect", "default") S3method("reflect", "im") S3method("reflect", "layered") S3method("reflect", "tess") S3method("rescale", "im") S3method("rescale", "layered") S3method("rescale", "linnet") S3method("rescale", "lpp") S3method("rescale", "owin") S3method("rescale", "ppp") S3method("rescale", "psp") S3method("rescale", "units") S3method("residuals", "mppm") S3method("residuals", "ppm") S3method("rhohat", "lpp") S3method("rhohat", "lppm") S3method("rhohat", "ppm") S3method("rhohat", "ppp") S3method("rhohat", "quad") S3method("rmhcontrol", "default") S3method("rmh", "default") S3method("rmhmodel", "default") S3method("rmhmodel", "list") S3method("rmhmodel", "ppm") S3method("rmh", "ppm") S3method("rmhstart", "default") S3method("rotate", "im") S3method("rotate", "layered") S3method("rotate", "linnet") S3method("rotate", "lpp") S3method("rotate", "owin") S3method("rotate", "ppp") S3method("rotate", "psp") S3method("rotate", "tess") S3method("rounding", "default") S3method("rounding", "pp3") S3method("rounding", "ppp") S3method("rounding", "ppx") S3method("round", "pp3") S3method("round", "ppp") S3method("round", "ppx") S3method("row.names", "hyperframe") S3method("rshift", "ppp") S3method("rshift", "psp") S3method("rshift", "splitppp") S3method("scalardilate", "default") S3method("scalardilate", "im") S3method("scalardilate", "layered") S3method("scalardilate", "linnet") S3method("scalardilate", "lpp") S3method("scalardilate", "owin") S3method("scalardilate", "ppp") S3method("scalardilate", "psp") S3method("scalardilate", "tess") S3method("scaletointerval", "default") S3method("scaletointerval", "im") S3method("sharpen", "ppp") S3method("shift", "im") S3method("shift", "influence.ppm") S3method("shift", "layered") S3method("shift", "leverage.ppm") S3method("shift", "linnet") S3method("shift", "lpp") S3method("shift", "msr") S3method("shift", "owin") S3method("shift", "ppp") S3method("shift", "psp") S3method("shift", "quadratcount") S3method("shift", "quadrattest") S3method("shift", "tess") S3method("shortside", "box3") S3method("shortside", "boxx") S3method("shortside", "owin") S3method("sidelengths", "box3") S3method("sidelengths", "boxx") S3method("sidelengths", "owin") S3method("simulate", "kppm") S3method("simulate", "ppm") S3method("simulate", "slrm") S3method("Smoothfun", "ppp") S3method("Smooth", "fv") S3method("Smooth", "im") S3method("Smooth", "msr") S3method("Smooth", "ppp") S3method("sort", "im") S3method("split", "hyperframe") S3method("split", "im") S3method("[", "splitppp") S3method("split", "ppp") S3method("split", "ppx") S3method("[", "splitppx") S3method("str", "hyperframe") S3method("sum", "im") S3method("summary", "envelope") S3method("summary", "fii") S3method("summary", "hyperframe") S3method("summary", "im") S3method("summary", "linnet") S3method("summary", "listof") S3method("summary", "logiquad") S3method("summary", "lpp") S3method("summary", "lut") S3method("summary", "mppm") S3method("summary", "owin") S3method("summary", "pp3") S3method("summary", "ppm") S3method("summary", "ppp") S3method("summary", "pppmatching") S3method("summary", "ppx") S3method("summary", "profilepl") S3method("summary", "psp") S3method("summary", "quad") S3method("summary", "rmhexpand") S3method("summary", "splitppp") S3method("summary", "splitppx") S3method("summary", "units") S3method("superimpose", "default") S3method("superimpose", "ppp") S3method("superimpose", "psp") S3method("terms", "kppm") S3method("terms", "lppm") S3method("terms", "ppm") S3method("terms", "slrm") S3method("[", "tess") S3method("unique", "ppp") S3method("unique", "ppx") S3method("unitname", "box3") S3method("unitname", "boxx") S3method("unitname", "im") S3method("unitname", "kppm") S3method("unitname", "linnet") S3method("unitname", "lpp") S3method("unitname", "minconfit") S3method("unitname", "owin") S3method("unitname", "pp3") S3method("unitname", "ppm") S3method("unitname", "ppp") S3method("unitname", "ppx") S3method("unitname", "psp") S3method("unitname", "quad") S3method("unitname", "slrm") S3method("unmark", "lpp") S3method("unmark", "ppp") S3method("unmark", "ppx") S3method("unmark", "psp") S3method("unmark", "splitppp") S3method("update", "interact") S3method("update", "kppm") S3method("update", "lppm") S3method("update", "ppm") S3method("update", "rmhcontrol") S3method("update", "rmhstart") S3method("update", "slrm") S3method("update", "symbolmap") S3method("vcov", "kppm") S3method("vcov", "lppm") S3method("vcov", "mppm") S3method("vcov", "ppm") S3method("vcov", "slrm") S3method("volume", "box3") S3method("volume", "boxx") S3method("volume", "owin") S3method("with", "fv") S3method("with", "hyperframe") S3method("with", "msr") S3method("xtfrm", "im") # ......................................... # Assignment methods # ......................................... S3method("coords<-", "ppp") S3method("coords<-", "ppx") S3method("dimnames<-", "fasp") S3method("formula<-", "fv") S3method("[<-", "hyperframe") S3method("$<-", "hyperframe") S3method("[<-", "im") S3method("levels<-", "im") S3method("[<-", "listof") S3method("marks<-", "lpp") S3method("marks<-", "ppp") S3method("marks<-", "ppx") S3method("marks<-", "psp") S3method("names<-", "hyperframe") S3method("[<-", "ppp") S3method("row.names<-", "hyperframe") S3method("split<-", "hyperframe") S3method("[<-", "splitppp") S3method("split<-", "ppp") S3method("[<-", "splitppx") S3method("[<-", "tess") S3method("unitname<-", "box3") S3method("unitname<-", "boxx") S3method("unitname<-", "im") S3method("unitname<-", "kppm") S3method("unitname<-", "linnet") S3method("unitname<-", "lpp") S3method("unitname<-", "minconfit") S3method("unitname<-", "owin") S3method("unitname<-", "pp3") S3method("unitname<-", "ppm") S3method("unitname<-", "ppp") S3method("unitname<-", "ppx") S3method("unitname<-", "psp") S3method("unitname<-", "quad") S3method("unitname<-", "slrm") # ......................................... # End of methods # ......................................... spatstat/demo/0000755000176000001440000000000012324667727013111 5ustar ripleyusersspatstat/demo/sumfun.R0000644000176000001440000000672412324667727014562 0ustar ripleyusers## demonstration of all summary functions opa <- par(mfrow=c(1,1)) ## Ripley's K-function plot(swedishpines) plot(Kest(swedishpines)) ## Besag's transformation plot(Lest(swedishpines)) ## pair correlation function plot(pcf(swedishpines)) par(mfrow=c(2,3)) ## Showing the utility of the K-function plot(cells) plot(nztrees) plot(redwood) plot(Kest(cells)) plot(Kest(nztrees)) plot(Kest(redwood)) ## Showing the utility of the pair correlation function plot(cells) plot(nztrees) plot(redwood) plot(pcf(cells)) plot(pcf(nztrees)) plot(pcf(redwood)) ## par(mfrow=c(1,1)) ## Analogues for inhomogeneous patterns ## Reweighted K-function plot(japanesepines) fit <- ppm(japanesepines, ~polynom(x,y,2)) plot(predict(fit)) plot(Kinhom(japanesepines, fit)) plot(pcfinhom(japanesepines, fit)) plot(Linhom(japanesepines)) ## Rescaled K-function plot(unmark(bronzefilter)) plot(Kscaled(bronzefilter)) fit <- ppm(unmark(bronzefilter), ~x) plot(predict(fit)) plot(unmark(bronzefilter), add=TRUE) plot(Kscaled(bronzefilter, fit)) plot(Lscaled(bronzefilter, fit)) ## Local indicators of spatial association plot(localL(swedishpines)) plot(localK(swedishpines)) ## par(mfrow=c(2,3)) ## Empty space function F plot(cells) plot(nztrees) plot(redwood) plot(Fest(cells)) plot(Fest(nztrees)) plot(Fest(redwood)) ## Nearest neighbour distance function G plot(cells) plot(nztrees) plot(redwood) plot(Gest(cells)) plot(Gest(nztrees)) plot(Gest(redwood)) ## J-function plot(cells) plot(nztrees) plot(redwood) plot(Jest(cells)) plot(Jest(nztrees)) plot(Jest(redwood)) par(mfrow=c(1,1)) ## versions for inhomogeneous patterns plot(Finhom(japanesepines)) plot(Ginhom(japanesepines)) plot(Jinhom(japanesepines)) ## Display F,G,J,K plot(allstats(swedishpines)) ## Multitype patterns plot(amacrine) plot(Kcross(amacrine)) plot(Kdot(amacrine)) I <- (marks(amacrine) == "on") J <- (marks(amacrine) == "off") plot(Kmulti(amacrine, I, J)) plot(alltypes(amacrine, "K")) plot(Lcross(amacrine)) plot(Ldot(amacrine)) plot(pcfcross(amacrine)) plot(pcfdot(amacrine)) plot(pcfmulti(amacrine, I, J)) plot(Gcross(amacrine)) plot(Gdot(amacrine)) plot(Gmulti(amacrine, I, J)) plot(alltypes(amacrine, "G")) plot(Jcross(amacrine)) plot(Jdot(amacrine)) plot(Jmulti(amacrine,I,J)) plot(alltypes(amacrine, "J")) plot(alltypes(amacrine, "F")) plot(Iest(amacrine)) plot(markconnect(amacrine)) ## Multitype, inhomogeneous plot(Kcross.inhom(amacrine)) plot(Kdot.inhom(amacrine)) plot(Kmulti.inhom(amacrine, I, J)) plot(Lcross.inhom(amacrine)) plot(Ldot.inhom(amacrine)) plot(pcfcross.inhom(amacrine)) plot(pcfdot.inhom(amacrine)) plot(pcfmulti.inhom(amacrine, I, J)) ## Numerical marks plot(markcorr(longleaf)) plot(markvario(longleaf)) plot(Emark(longleaf)) plot(Vmark(longleaf)) ## Linear networks plot(chicago) plot(linearK(chicago)) plot(linearKcross(chicago)) plot(linearKdot(chicago)) plot(linearpcf(chicago)) plot(linearpcfcross(chicago)) plot(linearpcfdot(chicago)) lam <- rep(intensity(unmark(chicago)), npoints(chicago)) A <- split(chicago)$assault B <- split(chicago)$burglary lamA <- rep(intensity(A), npoints(A)) lamB <- rep(intensity(B), npoints(B)) plot(linearKinhom(chicago, lam)) plot(linearKcross.inhom(chicago, "assault", "burglary", lamA, lamB)) plot(linearKdot.inhom(chicago, "assault", lamA, lam)) plot(linearpcfinhom(chicago, lam)) plot(linearpcfcross.inhom(chicago, "assault", "burglary", lamA, lamB)) plot(linearpcfdot.inhom(chicago, "assault", lamA, lam)) plot(linearmarkconnect(chicago)) plot(linearmarkequal(chicago)) rm(I,J,fit) par(opa) spatstat/demo/diagnose.R0000755000176000001440000001230012333041502014775 0ustar ripleyusersif(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && (.Device %in% c("X11", "GTK", "windows", "Macintosh"))) par(mfrow=c(1,1)) oldoptions <- options(warn = -1) # ####################################################### # X <- rpoispp(function(x,y) { 1000 * exp(- 4 * x)}, 1000) plot(X, main="Inhomogeneous Poisson pattern") fit.hom <- ppm(X ~1, Poisson()) fit.inhom <- ppm(X ~x, Poisson()) diagnose.ppm(fit.inhom, which="marks", type="Pearson", main=c("Mark plot", "Circles for positive residual mass", "Colour for negative residual density")) par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="marks", main=c("Wrong model", "(homogeneous Poisson)", "raw residuals")) diagnose.ppm(fit.inhom, which="marks", main=c("Right model", "(inhomogeneous Poisson)", "raw residuals")) par(mfrow=c(1,1)) diagnose.ppm(fit.inhom, which="smooth", main="Smoothed residual field") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="smooth", main=c("Wrong model", "(homogeneous Poisson)", "Smoothed residual field")) diagnose.ppm(fit.inhom, which="smooth", main=c("Right model", "(inhomogeneous Poisson)", "Smoothed residual field")) par(mfrow=c(1,1)) diagnose.ppm(fit.inhom, which="x") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, which="x", main=c("Wrong model", "(homogeneous Poisson)", "lurking variable plot for x")) diagnose.ppm(fit.inhom, which="x", main=c("Right model", "(inhomogeneous Poisson)", "lurking variable plot for x")) par(mfrow=c(1,1)) diagnose.ppm(fit.hom, type="Pearson",main="standard diagnostic plots") par(mfrow=c(1,2)) diagnose.ppm(fit.hom, main=c("Wrong model", "(homogeneous Poisson)")) diagnose.ppm(fit.inhom, main=c("Right model", "(inhomogeneous Poisson)")) par(mfrow=c(1,1)) # ####################################################### # LEVERAGE/INFLUENCE plot(leverage(fit.inhom)) plot(influence(fit.inhom)) plot(dfbetas(fit.inhom)) # ####################################################### # COMPENSATORS ## Takes a long time... CF <- compareFit(listof(hom=fit.hom, inhom=fit.inhom), Kcom, same="iso", different="icom") plot(CF, main="model compensators", legend=FALSE) legend("topleft", legend=c("empirical K function", "compensator of CSR", "compensator of inhomogeneous Poisson"), lty=1:3, col=1:3) # ####################################################### # Q - Q PLOTS # qqplot.ppm(fit.hom, 40) #conclusion: homogeneous Poisson model is not correct title(main="Q-Q plot of smoothed residuals") qqplot.ppm(fit.inhom, 40) # TAKES A WHILE... title(main=c("Right model", "(inhomogeneous Poisson)", "Q-Q plot of smoothed residuals")) # conclusion: fitted inhomogeneous Poisson model looks OK # ####################################################### # plot(cells) fitPoisson <- ppm(cells ~1, Poisson()) diagnose.ppm(fitPoisson, main=c("CSR fitted to cells data", "Raw residuals", "No suggestion of departure from CSR")) diagnose.ppm(fitPoisson, type="pearson", main=c("CSR fitted to cells data", "Pearson residuals", "No suggestion of departure from CSR")) # These diagnostic plots do NOT show evidence of departure from uniform Poisson plot(Kcom(fitPoisson), cbind(iso, icom) ~ r) plot(Gcom(fitPoisson), cbind(han, hcom) ~ r) # K compensator DOES show strong evidence of departure from uniform Poisson qqplot.ppm(fitPoisson, 40) title(main=c("CSR fitted to cells data", "Q-Q plot of smoothed raw residuals", "Strong suggestion of departure from CSR")) # Q-Q plot DOES show strong evidence of departure from uniform Poisson. # fitStrauss <- ppm(cells ~1, Strauss(r=0.1)) diagnose.ppm(fitStrauss, main=c("Strauss model fitted to cells data", "Raw residuals")) diagnose.ppm(fitStrauss, type="pearson", main=c("Strauss model fitted to cells data", "Pearson residuals")) plot(Kcom(fitStrauss), cbind(iso, icom) ~ r) plot(Gcom(fitStrauss), cbind(han, hcom) ~ r) # next line takes a LOOONG time ... qqplot.ppm(fitStrauss, 40, type="pearson") title(main=c("Strauss model fitted to cells data", "Q-Q plot of smoothed Pearson residuals", "Suggests adequate fit")) # Conclusion: Strauss model seems OK # ####################################################### # plot(nztrees) fit <- ppm(nztrees ~1, Poisson()) diagnose.ppm(fit, type="pearson") title(main=c("CSR fitted to NZ trees", "Pearson residuals")) diagnose.ppm(fit, type="pearson", cumulative=FALSE) title(main=c("CSR fitted to NZ trees", "Pearson residuals (non-cumulative)")) lurking(fit, expression(x), type="pearson", cumulative=FALSE, splineargs=list(spar=0.3)) # Sharp peak at right is suspicious qqplot.ppm(fit, 40, type="pearson") title(main=c("CSR fitted to NZ trees", "Q-Q plot of smoothed Pearson residuals")) # Slight suggestion of departure from Poisson at top right of pattern. par(oldpar) options(oldoptions) spatstat/demo/data.R0000755000176000001440000000570212326604361014137 0ustar ripleyusersif(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && dev.interactive(orNone=TRUE)) oldoptions <- options(warn=-1) plot(amacrine) plot(anemones, markscale=1) ants.extra$plotit() plot(bei.extra$elev, main="Beilschmiedia") plot(bei, add=TRUE, pch=16, cex=0.3) plot(betacells) plot(bramblecanes, cols=1:3) plot(split(bramblecanes)) plot(bronzefilter,markscale=2) plot(cells) plot(chicago, main="Chicago Street Crimes", col="grey", cols=c("red", "blue", "black", "blue", "red", "blue", "blue"), chars=c(16,2,22,17,24,15,6), leg.side="left", show.window=FALSE) chorley.extra$plotit() plot(clmfires, which.marks="cause", cols=2:5, cex=0.25, main="Castilla-La Mancha forest fires") plot(clmfires.extra$clmcov200, main="Covariates for forest fires") plot(copper$Points, main="Copper") plot(copper$Lines, add=TRUE) plot(demohyper, quote({ plot(Image, main=""); plot(Points, add=TRUE) }), parargs=list(mar=rep(1,4))) plot(demopat) plot(finpines, main="Finnish pines") wildM1 <- with(flu, virustype == "wt" & stain == "M2-M1") plot(flu[wildM1, 1, drop=TRUE], main=c("flu data", "wild type virus, M2-M1 stain"), chars=c(16,3), cex=0.4, cols=2:3) plot(gordon, main="People in Gordon Square", pch=16) plot(gorillas, which.marks=1, chars=c(1,3), cols=2:3, main="Gorilla nest sites") plot(hamster, cols=c(2,4)) plot(heather) plot(humberside) plot(hyytiala, add=TRUE, cols=2:5) plot(japanesepines) plot(lansing) plot(split(lansing)) plot(longleaf) plot(mucosa, chars=c(1,3), cols=c("red", "green")) plot(mucosa.subwin, add=TRUE, lty=3) plot(murchison, main="Murchison data") plot(murchison$greenstone, main="Murchison data", col="lightgreen") plot(murchison$gold, add=TRUE, pch=3, col="blue") plot(murchison$faults, add=TRUE, col="red") plot(nbfires, use.marks=FALSE, pch=".") plot(split(nbfires), use.marks=FALSE, chars=".") plot(split(nbfires)$"2000", which.marks="fire.type", main=c("New Brunswick fires 2000", "by fire type"), cols=c("blue", "green", "red", "cyan"), leg.side="left") plot(nztrees) plot(trim.rectangle(as.owin(nztrees), c(0,5), 0), add=TRUE, lty=3) enable3d <- ("scatterplot3d" %in% row.names(installed.packages())) if(enable3d) { plot(osteo[1:10,], tick.marks=FALSE, xlab="", ylab="", zlab="") } plot(paracou, cols=2:3, chars=c(16,3)) ponderosa.extra$plotit() pyr <- pyramidal pyr$grp <- abbreviate(pyramidal$group, minlength=7) plot(pyr, quote(plot(Neurons, pch=16, main=grp)), main="Pyramidal Neurons") rm(pyr) plot(redwood) redwoodfull.extra$plotit() plot(residualspaper$Fig1) plot(residualspaper$Fig4a) plot(residualspaper$Fig4b) plot(residualspaper$Fig4c) shapley.extra$plotit(main="Shapley") plot(simdat) plot(spruces, maxsize=min(nndist(spruces))) plot(swedishpines) plot(urkiola, cex=0.5, cols=2:3) plot(waka, markscale=0.04, main=c("Waka national park", "tree diameters")) plot(waterstriders) par(oldpar) options(oldoptions) spatstat/demo/00Index0000755000176000001440000000032412324667727014245 0ustar ripleyusersspatstat Demonstration of spatstat library diagnose Demonstration of diagnostic capabilities for models in spatstat data Datasets in spatstat sumfun Demonstration of nonparametric summary functions in spatstat spatstat/demo/spatstat.R0000755000176000001440000004767112330634221015075 0ustar ripleyusersif(dev.cur() <= 1) { dd <- getOption("device") if(is.character(dd)) dd <- get(dd) dd() } oldpar <- par(ask = interactive() && dev.interactive(orNone=TRUE)) oldoptions <- options(warn=-1) fanfare <- function(stuff) { plot(c(0,1),c(0,1),type="n",axes=FALSE, xlab="", ylab="") text(0.5,0.5, stuff, cex=2.5) } par(mar=c(1,1,2,1)+0.1) fanfare("Spatstat demonstration") fanfare("I. Types of data") plot(swedishpines, main="Point pattern") plot(demopat, cols=c("green", "blue"), main="Multitype point pattern") plot(longleaf, fg="blue", main="Marked point pattern") plot(finpines, main="Point pattern with multivariate marks") a <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) plot(a, main="Line segment pattern") marks(a) <- sample(letters[1:4], 20, replace=TRUE) plot(a, main="Multitype line segment pattern") marks(a) <- runif(20) plot(a, main="Marked line segment pattern") plot(owin(), main="Rectangular window") plot(letterR, main="Polygonal window") plot(as.mask(letterR), main="Binary mask window") Z <- as.im(function(x,y){ sqrt((x - 1)^2 + (y-1)^2)}, square(2)) plot(Z, main="Pixel image") X <- runifpoint(42) plot(dirichlet(X), main="Tessellation") enable3d <- ("scatterplot3d" %in% row.names(installed.packages())) if(enable3d) plot(rpoispp3(100), main="Three-dimensional point pattern") plot(simplenet, main="Linear network (linnet)") X <- rpoislpp(20, simplenet) plot(X, main="Point pattern on linear network (lpp)") fanfare("II. Graphics") plot(letterR, col="green", border="red", lwd=2, main="Polygonal window with colour fill") plot(letterR, hatch=TRUE, spacing=0.15, angle=30, main="Polygonal window with line shading") plot(letterR, hatch=TRUE, hatchargs=list(texture=8, spacing=0.12), main="Polygonal window with texture fill") plot(amacrine, chars=c(1,16), main="plot(X, chars = c(1,16))") plot(amacrine, cols=c("red","blue"), chars=16, main="plot(X, cols=c(\"red\", \"blue\"))") opa <- par(mfrow=c(1,2)) plot(longleaf, markscale=0.03, main="markscale=0.03") plot(longleaf, markscale=0.09, main="markscale=0.09") par(opa) plot(longleaf, pch=21, cex=1, bg=colourmap(terrain.colors(128), range=c(0,80)), main="colourmap for numeric mark values") Z <- as.im(function(x,y) { r <- sqrt(x^2+y^2); r * exp(-r) }, owin(c(-5,5),c(-5,5))) plot(Z, main="pixel image: image plot") plot(Z, main="pixel image: image plot (heat colours)", col=heat.colors(256)) plot(Z, main="pixel image: logarithmic colour map", log=TRUE, col=rainbow(128, end=5/6)) contour(Z, main="pixel image: contour plot", axes=FALSE) plot(Z, main="pixel image: image + contour plot") contour(Z, add=TRUE) persp(Z, colmap=terrain.colors(128), shade=0.3, phi=30,theta=100, main="pixel image: perspective plot") ct <- colourmap(rainbow(20), breaks=seq(-1,1,length=21)) plot(ct, main="Colour map for real numbers") ca <- colourmap(rainbow(8), inputs=letters[1:8]) plot(ca, main="Colour map for discrete values") Z <- as.im(nnfun(runifpoint(8))) plot(Z, main="colour image for discrete values") textureplot(Z, main="texture plot for discrete values") W <- owin(c(1,5),c(0,4.5)) Lout <- scaletointerval(distmap(rebound.owin(letterR, W))) Lin <- scaletointerval(distmap(complement.owin(letterR, W))) L <- scaletointerval(eval.im(Lin-Lout)) D <- scaletointerval(density(runifpoint(30, W), adjust=0.3)) X <- scaletointerval(as.im(function(x,y){ x }, W=W)) plot(listof(L=L, D=D, X=X), main="Multiple images") pairs(L, D, X, main="Multiple images: pairs plot") plot(rgbim(D,X,L,maxColorValue=1), valuesAreColours=TRUE, main="Three images: RGB display") plot(hsvim(D,L,X), valuesAreColours=TRUE, main="Three images: HSV display") fanfare("III. Conversion between types") W <- as.owin(chorley) plot(W, "window W") plot(as.mask(W)) plot(as.mask(W, dimyx=1000)) plot(as.im(W, value=3)) plot(as.im(W, value=3, na.replace=0), ribbon=TRUE) plot(as.im(function(x,y) {x^2 + y}, W=square(1)), main="as.im(function(x,y){x^2+y})") V <- delaunay(runifpoint(12)) plot(V, main="Tessellation V") plot(as.im(V, dimyx=256), main="as.im(V)") plot(as.owin(V)) X <- swedishpines plot(X, "point pattern X") plot(as.im(X), col=c("white","red"), ribbon=FALSE, xlab="", ylab="") plot(as.owin(X), add=TRUE) fanfare("IV. Subsetting and splitting data") plot(X, "point pattern X") subset <- 1:20 plot(X[subset], main="subset operation: X[subset]") subwindow <- owin(poly=list(x=c(0,96,96,40,40),y=c(0,0,100,100,50))) plot(X[subwindow], main="subset operation: X[subwindow]") plot(lansing, "Lansing Woods data") plot(split(lansing), main="split operation: split(X)") plot(longleaf, main="Longleaf Pines data") plot(cut(longleaf, breaks=3), main=c("cut operation", "cut(longleaf, breaks=3)")) Z <- dirichlet(runifpoint(16)) X <- runifpoint(100) plot(cut(X,Z), main="points cut by tessellation", leg.side="left") plot(Z, add=TRUE) plot(split(X, Z), main="points split by tessellation") W <- square(1) X <- as.im(function(x,y){sqrt(x^2+y^2)}, W) Y <- dirichlet(runifpoint(12, W)) plot(split(X,Y), main="image split by tessellation") fanfare("V. Exploratory data analysis") par(mar=c(3,3,3,2)+0.1) plot(swedishpines, main="Quadrat counts", pch="+") tab <- quadratcount(swedishpines, 4) plot(tab, add=TRUE, lty=2, cex=2, col="blue") par(mar=c(5,3,3,2)+0.1) plot(swedishpines, main="", pch="+") title(main=expression(chi^2 * " test"), cex.main=2) tes <- quadrat.test(swedishpines, 3) tes plot(tes, add=TRUE, col="red", cex=1.5, lty=2, lwd=3) title(sub=paste("p-value =", signif(tes$p.value,3)), cex.sub=1.4) par(mar=c(4,4,3,2)+0.1) tesk <- kstest(nztrees, "x") tesk plot(tesk) mur <- lapply(murchison, rescale, s=1000) mur <- lapply(mur, "unitname<-", value="km") X <- mur$gold D <- distfun(mur$faults) plot(X, main="Murchison gold deposits", cols="blue") plot(mur$faults, add=TRUE, col="red") rh <- rhohat(X,D) plot(rh, main="Smoothed rate estimate", xlab="Distance to nearest fault (km)", legend=FALSE) plot(predict(rh), main="predict(rhohat(X,D))") Z <- density(cells, 0.07) plot(Z, main="Kernel smoothed intensity of point pattern") plot(cells, add=TRUE) plot(redwood, main="Redwood data") te <- scan.test(redwood, 0.1, method="poisson") plot(te, main=c("Scan Statistic for redwood data", paste("p-value =", signif(te$p.value,3)))) plot(redwood, add=TRUE) te X <- unique(unmark(shapley)) plot(X, "Shapley galaxy concentration", pch=".") pa <- function(i, ...) { if(i == 1) list(chars=c(".", "+"), cols=1:2) else list(size=0.5, pch=21, bg=colourmap(topo.colors(128), range=c(0,1))) } plot(nnclean(X, k=17), panel.args=pa, main="Byers-Raftery nearest neighbour cleaning") Y <- sharpen(X, sigma=0.5, edgecorrect=TRUE) plot(Y, main="Choi-Hall data sharpening", pch=".") owpa <- par(mfrow=c(1,2)) W <- grow.rectangle(as.rectangle(letterR), 1) X <- superimpose(runifpoint(300, letterR), runifpoint(50, W), W=W) plot(W, main="clusterset(X, 'm')") plot(clusterset(X, 'marks', fast=TRUE), add=TRUE, chars=c("o", "+"), cols=1:2) plot(letterR, add=TRUE) plot(W, main="clusterset(X, 'd')") plot(clusterset(X, 'domain', exact=FALSE), add=TRUE) plot(letterR, add=TRUE) par(owpa) D <- density(a, sigma=0.05) plot(D, main="Kernel smoothed intensity of line segment pattern") plot(a, add=TRUE) X <- runifpoint(42) plot(dirichlet(X)) plot(X, add=TRUE) plot(delaunay(X)) plot(X, add=TRUE) parsave <- par(mfrow=c(2,2)) plot(longleaf, main="Longleaf Pines data") plot(nnmark(longleaf), main="Nearest mark") plot(Smooth(longleaf, 10), main="Kernel smoothing of marks") plot(idw(longleaf), main=c("Inverse distance weighted","smoothing of marks")) par(parsave) fryplot(cells, main=c("Fry plot","cells data"), pch="+") miplot(longleaf, main="Morishita Index plot", pch=16, col="blue") plot(swedishpines, main="Swedish Pines data") K <- Kest(swedishpines) plot(K, main="K function for Swedish Pines", legendmath=TRUE) en <- envelope(swedishpines, fun=Kest, nsim=10, correction="translate") plot(en, main="Envelopes of K function based on CSR", shade=c("hi", "lo")) pc <- pcf(swedishpines) plot(pc, main="Pair correlation function") plot(swedishpines, main="nearest neighbours") m <- nnwhich(swedishpines) b <- swedishpines[m] arrows(swedishpines$x, swedishpines$y, b$x, b$y, angle=12, length=0.1, col="red") plot(swedishpines %mark% nndist(swedishpines), markscale=1, main="Stienen diagram", legend=FALSE, fg="blue") plot(Gest(swedishpines), main=c("Nearest neighbour distance function G", "Gest(swedishpines)"), legendmath=TRUE) Z <- distmap(swedishpines, dimyx=512) plot(swedishpines$window, main="Distance map") plot(Z, add=TRUE) points(swedishpines) plot(Fest(swedishpines), main=c("Empty space function F", "Fest(swedishpines)"), legendmath=TRUE) W <- rebound.owin(letterR, square(5)) plot(distmap(W), main="Distance map") plot(W, add=TRUE) a <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) contour(distmap(a), main="Distance map") plot(a, add=TRUE,col="red") plot(Jest(swedishpines), main=c("J-function", "J(r)=(1-G(r))/(1-F(r))")) X <- swedishpines X <- X[sample(1:npoints(X))] Z <- nnfun(X) plot(as.owin(X), main="Nearest neighbour map") plot(Z, add=TRUE) points(X) plot(allstats(swedishpines)) Fig4b <- residualspaper$Fig4b plot(Fig4b, main="Inhomogeneous point pattern") plot(Kinhom(Fig4b), main="Inhomogeneous K-function") plot(pcfinhom(Fig4b, stoyan=0.1), main="Inhomogeneous pair correlation") plot(Ginhom(Fig4b, sigma=0.06), main="Inhomogeneous G-function") plot(Jinhom(Fig4b, sigma=0.06), main="Inhomogeneous J-function") X <- unmark(bronzefilter) plot(X, "Bronze filter data") lam <- predict(ppm(X ~x)) plot(Kscaled(X, lam), xlim=c(0, 1.5), main="Locally-scaled K function") plot(urkiola) plot(split(urkiola)) plot(density(split(urkiola))) contour(density(split(urkiola)), panel.begin=as.owin(urkiola)) plot(relrisk(urkiola), main="Relative risk (cross-validated)") plot(bramblecanes) br <- rescale(bramblecanes) plot(alltypes(br, "K"), mar.panel=c(4,5,2,2)+0.1) ama <- rescale(amacrine) plot(alltypes(ama, Lcross, envelope=TRUE, nsim=9), . - r ~ r, ylim=c(-25, 5)) ponderosa.extra$plotit(main="Ponderosa Pines") L <- localL(ponderosa) pL <- plot(L, lty=1, col=1, legend=FALSE, main="neighbourhood density functions for Ponderosa Pines") parsave <- par(mfrow=c(1,2)) ponderosa.extra$plotit() par(pty="s") plot(L, iso007 ~ r, main="point B") ponderosa.extra$plotit() L12 <- localL(ponderosa, rvalue=12) P12 <- ponderosa %mark% L12 Z12 <- Smooth(P12, sigma=5, dimyx=128) plot(Z12, col=topo.colors(128), main="smoothed neighbourhood density") contour(Z12, add=TRUE) points(ponderosa, pch=16, cex=0.5) plot(amacrine, main="Amacrine cells data") par(pty="s") mkc <- markcorr(amacrine, correction="translate", method="density", kernel="epanechnikov") plot(mkc, main="Mark correlation function", legend=FALSE) par(parsave) plot(alltypes(amacrine, markconnect), title="Mark connection functions for amacrine cells") parsave <- par(mfrow=c(1,2)) plot(spruces, cex.main=0.75) par(pty="s") plot(markcorr(spruces), main="Mark correlation", legendpos="bottomright") plot(spruces, cex.main=0.75) plot(markvario(spruces), main="Mark variogram", legendpos="topright") par(parsave) plot(as.listof(list("Emark(spruces)"=Emark(spruces), "Vmark(spruces)"=Vmark(spruces))), main="Independence diagnostics", ylim.covers=0, legendpos="bottom") if(enable3d) { par3 <- par(mfrow=c(1,2)) X <- rpoispp3(100) plot(X, main="3D point pattern X") plot(K3est(X), main="K-function in 3D") plot(X, main="3D point pattern X") plot(G3est(X), main="G-function in 3D", legendpos="bottomright") par(par3) } par2 <- par(mfrow=c(1,3)) X <- unmark(chicago) plot(as.linnet(X), main="Chicago Street Crimes",col="green") plot(as.ppp(X), add=TRUE, col="red") plot(linearK(X, correction="none"), main="Network K-function") plot(linearK(X, correction="Ang"), main="Corrected K-function") par(par2) fanfare("VI. Model-fitting") plot(japanesepines) fit <- ppm(japanesepines ~1) print(fit) fit <- ppm(japanesepines ~polynom(x,y,2)) print(fit) plot(fit, how="image", se=FALSE, main=c("Inhomogeneous Poisson model", "fit by maximum likelihood", "Fitted intensity")) plot(fit, how="image", trend=FALSE, main="Standard error of fitted intensity") plot(leverage(fit)) plot(influence(fit)) plot(mur$gold, main="Murchison gold deposits", cols="blue") plot(mur$faults, add=TRUE, col="red") fit <- ppm(mur$gold ~D, covariates=list(D=distfun(mur$faults))) plot(parres(fit, "D"), main="Partial residuals from loglinear Poisson model", xlab="Distance to nearest fault (km)", ylab="log intensity of gold", legend=FALSE) legend("bottomleft", legend=c("partial residual", "loglinear fit"), col=c(1,4), lty=c(1,4)) parsave <- par(mfrow=c(1,2)) plot(redwood) fitT <- kppm(redwood ~1, clusters="Thomas") plot(simulate(fitT)[[1]], main="simulation from fitted Thomas model") oop <- par(pty="s") plot(fitT, main=c("Thomas model","minimum contrast fit")) os <- objsurf(fitT) plot(os, main="Minimum contrast objective function", col=terrain.colors(128)) contour(os, add=TRUE) par(oop) plot(swedishpines) fit <- ppm(swedishpines ~1, Strauss(r=7)) print(fit) plot(fit, how="image", main=c("Strauss model", "fit by maximum pseudolikelihood", "Conditional intensity plot")) # fitted interaction plot(swedishpines) fit <- ppm(swedishpines ~1, PairPiece(c(3,5,7,9,11,13))) plot(fitin(fit), legend=FALSE, main=c("Pairwise interaction model", "fit by maximum pseudolikelihood")) # simulation plot(swedishpines) Xsim <- rmh(model=fit, start=list(n.start=80), control=list(nrep=100)) plot(Xsim, main="Simulation from fitted Strauss model") # model compensator plot(swedishpines) fit <- ppm(swedishpines ~1, Strauss(r=7)) plot(Kcom(fit), cbind(iso, icom, pois) ~ r, legend=FALSE, main="model compensators") legend("topleft", legend=c("empirical K function", "Strauss model compensator of K", "Poisson theoretical K"), lty=1:3, col=1:3, inset=0.05) par(parsave) # Multitype data dpat <- rescale(demopat, 8) unitname(dpat) <- c("mile", "miles") dpat plot(dpat, cols=c("red", "blue")) fit <- ppm(dpat ~marks + polynom(x,y,2), Poisson()) plot(fit, trend=TRUE, se=TRUE) fanfare("VII. Simulation") plot(letterR, main="Poisson random points") lambda <- 10/area.owin(letterR) points(rpoispp(lambda, win=letterR)) points(rpoispp(9 * lambda, win=letterR)) points(rpoispp(90 * lambda, win=letterR)) plot(rpoispp(100)) plot(rpoispp(function(x,y){1000 * exp(-3*x)}, 1000)) plot(rMaternII(200, 0.05)) plot(rSSI(0.05, 200)) plot(rThomas(10, 0.2, 5)) plot(rMatClust(10, 0.05, 4)) plot(rCauchy(30, 0.01, 5)) plot(rVarGamma(30, 2, 0.02, 5)) plot(rGaussPoisson(30, 0.05, 0.5)) if(require(RandomFields) && RandomFieldsSafe()) { param <- c(0, variance=0.2, nugget=0, scale=.1) mu <- 4 plot(rLGCP("exp", mu, param)) X <- rLGCP("exp", mu, param) plot(attr(X, "Lambda"), main="log-Gaussian Cox process") plot(X, add=TRUE, pch=16) } plot(rStrauss(200, 0.3, 0.07)) plot(rDiggleGratton(200,0.03,0.08)) plot(rDGS(300, 0.05)) plot(redwood, main="random thinning - rthin()") points(rthin(redwood, 0.5), col="green", cex=1.4) plot(rcell(nx=15)) plot(rsyst(nx=5)) abline(h=(1:4)/5, lty=2) abline(v=(1:4)/5, lty=2) plot(rstrat(nx=5)) abline(h=(1:4)/5, lty=2) abline(v=(1:4)/5, lty=2) X <- rsyst(nx=10) plot(rjitter(X, 0.02)) Xg <- rmh(list(cif="geyer", par=list(beta=1.25, gamma=1.6, r=0.2, sat=4.5), w=c(0,10,0,10)), control=list(nrep=1e4), start=list(n.start=200)) plot(Xg, main=paste("Geyer saturation process\n", "rmh() with cif=\"geyer\"")) L <- as.psp(matrix(runif(20), 5, 4), window=square(1)) plot(L, main="runifpointOnLines(30, L)") plot(runifpointOnLines(30, L), add=TRUE, pch="+") plot(L, main="rpoisppOnLines(3, L)") plot(rpoisppOnLines(3, L), add=TRUE, pch="+") plot(runiflpp(20, simplenet)) plot(rpoislpp(5, simplenet)) plot(rpoisline(10)) plot(rlinegrid(30, 0.1)) spatstat.options(npixel=256) X <- dirichlet(runifpoint(30)) plot(rMosaicSet(X, 0.4), col="green", border=NA) plot(X, add=TRUE) plot(rMosaicField(X, runif)) plot(rMosaicSet(rpoislinetess(3), 0.5), col="green", border=NA, main="Switzer's random set") spatstat.options(npixel=100) plot(Halton(512, c(2,3)), main="quasirandom pattern") plot(Halton(16384, c(2,3)), main="quasirandom pattern", pch=".") fanfare("VIII. Geometry") A <- letterR B <- shift(letterR, c(0.2,0.1)) plot(bounding.box(A,B), main="shift", type="n") plot(A, add=TRUE) plot(B, add=TRUE, border="red") B <- rotate(letterR, 0.2) plot(bounding.box(A,B), main="rotate", type="n") plot(A, add=TRUE) plot(B, add=TRUE, border="red") mat <- matrix(c(1.1, 0, 0.3, 1), 2, 2) B <- affine(letterR, mat=mat, vec=c(0.2,-0.1)) plot(bounding.box(A,B), main="affine", type="n") plot(A, add=TRUE) plot(B, add=TRUE, border="red") par1x2 <- par(mfrow=c(1,2)) L <- rpoisline(10, owin(c(1.5,4.5),c(0.2,3.6))) plot(L, main="Line segment pattern") plot(L$window, main="L[window]", type="n") plot(L[letterR], add=TRUE) plot(letterR, add=TRUE, border="red") par(par1x2) a <- psp(runif(20),runif(20),runif(20),runif(20), window=owin()) plot(a, main="Self-crossing points") plot(selfcrossing.psp(a), add=TRUE, col="red") a <- as.psp(matrix(runif(20), 5, 4), window=square(1)) b <- rstrat(square(1), 5) plot(a, lwd=3, col="green", main="project points to segments") plot(b, add=TRUE, col="red", pch=16) v <- project2segment(b, a) Xproj <- v$Xproj plot(Xproj, add=TRUE, pch=16) arrows(b$x, b$y, Xproj$x, Xproj$y, angle=10, length=0.15, col="red") plot(a, main="pointsOnLines(L)") plot(pointsOnLines(a, np=100), add=TRUE, pch="+") parry <- par(mfrow=c(1,3)) X <- tess(xgrid=seq(2, 4, length=10), ygrid=seq(0, 3.5, length=8)) plot(X) plot(letterR) plot(intersect.tess(X, letterR)) X <- dirichlet(runifpoint(10)) plot(X) L <- infline(0.3,0.5) plot(owin(), main="L") plot(L, col="red", lwd=2) plot(chop.tess(X,L)) par(parry) W <- chorley$window plot(W, main="simplify.owin") WS <- simplify.owin(W, 2) plot(WS, add=TRUE, border="green") nopa <- par(mfrow=c(2,2)) Rbox <- grow.rectangle(as.rectangle(letterR), 0.3) v <- erode.owin(letterR, 0.25) plot(Rbox, type="n", main="erode.owin", cex.main=0.75) plot(letterR, add=TRUE, col="red", cex.main=0.75) plot(v, add=TRUE, col="blue") v <- dilate.owin(letterR, 0.25) plot(Rbox, type="n", main="dilate.owin", cex.main=0.75) plot(v, add=TRUE, col="blue") plot(letterR, add=TRUE, col="red") v <- closing.owin(letterR, 0.3) plot(Rbox, type="n", main="closing.owin", cex.main=0.75) plot(v, add=TRUE, col="blue") plot(letterR, add=TRUE, col="red") v <- opening.owin(letterR, 0.3) plot(Rbox, type="n", main="opening.owin", cex.main=0.75) plot(letterR, add=TRUE, col="red") plot(v, add=TRUE, col="blue") par(nopa) fanfare("IX. Operations on pixel images") Z <- distmap(swedishpines, dimyx=512) plot(Z, main="An image Z") plot(levelset(Z, 4)) plot(cut(Z, 5)) plot(eval.im(sqrt(Z) - 3)) plot(solutionset(abs(Z - 6) <= 1)) nopa <- par(mfrow=c(1,2)) plot(Z) segments(0,0,96,100,lwd=2) plot(transect.im(Z)) par(nopa) d <- distmap(cells, dimyx=256) W <- levelset(d, 0.06) nopa <- par(mfrow=c(1,2)) plot(W) plot(connected(W)) par(nopa) Z <- as.im(function(x,y) { 4 * x^2 + 3 * y }, letterR) plot(Z) plot(letterR, add=TRUE) plot(blur(Z, 0.3, bleed=TRUE)) plot(letterR, add=TRUE) plot(blur(Z, 0.3, bleed=FALSE)) plot(letterR, add=TRUE) plot(blur(Z, 0.3, bleed=FALSE)) plot(letterR, add=TRUE) fanfare("X. Programming tools") showoffK <- function(Y, current, ..., fullpicture,rad) { plot(fullpicture, main=c("Animation using `applynbd'", "explaining the K function")) points(Y, cex=2) u <- current points(u[1],u[2],pch="+",cex=3) theta <- seq(0,2*pi,length=100) polygon(u[1]+ rad * cos(theta),u[2]+rad*sin(theta)) text(u[1]+rad/3,u[2]+rad/2,Y$n,cex=3) if(runif(1) < 0.2) Sys.sleep(runif(1, max=0.4)) return(Y$n) } par(ask=FALSE) applynbd(redwood, R=0.2, showoffK, fullpicture=redwood, rad=0.2, exclude=TRUE) par(oldpar) options(oldoptions) spatstat/NEWS0000644000176000001440000045265012332613006012654 0ustar ripleyusers CHANGES IN spatstat VERSION 1.37-0 OVERVIEW o Ege Rubak is now a joint author of spatstat. o We thank Peter Forbes, Tom Lawrence and Mikko Vihtakari for contributions. o Spatstat now exceeds 100,000 lines of code. o New syntax for point process models (ppm, kppm, lppm) equivalent to syntax of lm, glm, ... o Covariates in ppm and kppm can now be tessellations. o Confidence intervals and prediction intervals for fitted models. o Quasirandom point patterns and sequences. o Plots using texture fill. o Support for mappings from data to graphical symbols and textures. o Automatic re-fitting of model in Ginhom, Kinhom, Finhom, Jinhom. o Support for Mixed Poisson distribution. o Interpretation of mark scale parameters has changed in plot.ppp o Syntax of multitype interactions (eg MultiStrauss) has changed. o Bug fix in Metropolis-Hastings simulation of 'StraussHard' models o Changed default behaviour of perfect simulation algorithms. o Improvements to layout of text output. o Version nickname: 'Model Prisoner' NEW CLASSES o symbolmap An object of class 'symbolmap' represents a mapping from data to graphical symbols o texturemap An object of class 'texturemap' represents a mapping from data to graphical textures. NEW FUNCTIONS o split.hyperframe, split<-.hyperframe methods for split and split<- for hyperframes. o dmixpois, pmixpois, qmixpois, rmixpois (log-)normal mixture of Poisson distributions. o vdCorput, Halton, Hammersley, rQuasi quasirandom sequences and quasirandom point patterns. o Smoothfun create a function(x,y) equivalent to the result of Smooth.ppp o minnndist, maxnndist Faster ways to compute min(nndist(X)), max(nndist(X)) o add.texture Draw a simple texture inside a specified region. o textureplot Display a factor-valued pixel image using texture fill. o texturemap Create a texture map o plot.texturemap Plot a texture map in the style of a legend o symbolmap Create a symbol map o update.symbolmap Modify a symbol map o invoke.symbolmap Apply symbol map to data values, and plot them o plot.symbolmap Plot the symbol map in the style of a legend o as.owin.boxx Converts a 'boxx' to an 'owin' if possible. o ellipse Create an elliptical window. o clickbox Interactively specify a rectangle, by point-and-click on a graphics device. o complementarycolour Compute the complementary colour value of a given colour value, or the complementary colour map of a given colour map. o gauss.hermite Gauss-Hermite quadrature approximation to the expectation of any function of a normally-distributed random variable. o boundingbox Generic function, replaces bounding.box o edges Extract boundary edges of a window. Replaces and extends 'as.psp.owin' o pixelcentres Extract centres of pixels as a point pattern. SIGNIFICANT USER-VISIBLE CHANGES o ppm, kppm, lppm NEW SYNTAX FOR POINT PROCESS MODELS The model-fitting functions 'ppm', 'kppm' and 'lppm' now accept a syntax similar to 'lm' or 'glm', for example ppm(X ~ Z), but still accept the older syntax ppm(X, ~Z). To support both kinds of syntax, the functions 'ppm' and 'kppm' are now generic, with methods for the classes 'formula', 'ppp' and 'quad'. The 'formula' method handles a syntax like ppm(X ~ Z) while the 'ppp' method handles the old syntax ppm(X, ~Z). Similarly 'lppm' is generic with methods for 'formula' and 'lpp'. o ppm, kppm, lppm Covariates appearing in the model formula can be objects which exist in the R session, instead of always having to be elements of the list `covariates'. o ppm.formula, kppm.formula, lppm.formula Formulae involving polynom() are now expanded, symbolically, so that polynom(x, 3) becomes x + I(x^2) + I(x^3) and polynom(x,y,2) becomes x + y + I(x^2) + I(x*y) + I(y^2). This neatens the model output, and also makes it possible for anova() and step() to add or delete single terms in the polynomial. o predict.ppm New argument 'interval' allows confidence intervals or prediction intervals to be calculated. o predict.ppm New argument 'total' allows for prediction of the total number of points in a specified region. o plot.ppp, plot.lpp For marked point patterns, a legend is automatically added to the plot, by default. Arguments have changed: new arguments include parameters of the legend, and an optional symbol map. Result has changed: it is now an object of class 'symbolmap'. o plot.ppp, plot.lpp Interpretation of the parameters 'markscale' and 'maxsize' has changed. The size of a circle in the plot is now defined as the circle's diameter instead of its radius. (Size of a square is measured, as before, by its side length). o parres Now handles the case where the fitted model is not separable but its restriction to the given 'subregion' is separable. o envelope Now issues a warning if the usage of envelope() appears to be `invalid' in the sense that the simulated patterns and the data pattern have not been treated equally. o Kinhom, Finhom, Ginhom, Jinhom New argument 'update'. If 'lambda' is a fitted model (class ppm or kppm) and update=TRUE, the model is re-fitted to the data pattern, before the intensities are computed. o rDiggleGratton, rDGS, rHardcore, rStrauss, rStraussHard By default the point pattern is now generated on a larger window, and trimmed to the original window. New argument expand=TRUE. o MultiStrauss, MultiHard, MultiStraussHard The syntax of these functions has changed. The new code should still accept the old syntax. o rhohat rhohat.ppp and rhohat.quad have new argument 'baseline' o ippm Algorithm improved. Argument syntax changed. o default.dummy, quadscheme Dummy points can now be generated by a quasirandom sequence. o plot.owin The window can now be filled with one of 8 different textures. Arguments changed. o ppm, kppm Covariates in the model can now be tessellations. o [.im New argument 'tight' allows the resulting image to be trimmed to the smallest possible rectangle. o [.psp, rlinegrid, rpoisline These functions now handle binary mask windows. o rotate The user can specify the centre of rotation. o rescale rescale() and all its methods now have argument 'unitname' which can be used to change the name of the unit of length. o anova.ppm Output format has been improved. Number of columns of result has changed. o print.ppp, print.summary.ppp, print.owin, print.summary.owin, print.im, print.summary.im, print.fv, print.msr, print.profilepl These functions now avoid over-running the text margin (i.e. they respect options('width') where possible). o layerplotargs<- Now handles any spatial object, converting it to a 'layered' object. o effectfun Improved display in case se.fit=TRUE. o scaletointerval New argument 'xrange' o contour.im New argument 'show.all'. Default value of 'axes' changed to FALSE. o identify.ppp Now handles multivariate marks. o plot.listof Improved layout. New arguments 'hsep', 'vsep'. Argument 'mar.panel' may have length 1, 2 or 4. o plot.splitppp This function is no longer identical to plot.listof. Instead it is a much simpler function which just calls plot.listof with equal.scales=TRUE. o anova.ppm Output is neater. o plot.layered New argument 'do.plot' o plot.psp New argument 'do.plot' o as.psp.owin New argument 'window' o plot.im, contour.im, textureplot New argument 'clipwin' o plot.ppp New argument 'clipwin' o plot.msr New argument 'how' allows density to be plotted as image and/or contour o diagnose.ppm, plot.diagppm More options for 'plot.neg' o plot.leverage.ppm, plot.influence.ppm, plot.msr Argument 'clipwin' can now be used to restrict the display to a subset of the full data. o [.hyperframe, [<-.hyperframe, $.hyperframe, $<-.hyperframe These functions are now documented. o leverage.ppm, influence.ppm, dfbetas.ppm Resulting objects are now smaller (in memory size). o print.ppm Now indicates whether the irregular parameters 'covfunargs' were optimised (by profilepl or ippm) or whether they were simply provided by the user. o plot.ppp A point pattern with numerical marks can now be plotted as filled dots with colours determined by the marks, by setting pch=21 and bg= o colourmap Now handles dates and date-time values (of class 'Date' or 'POSIXt'). o plot.ppp, print.ppp, summary.ppp Improved handling of dates and date-time values (of class 'Date' or 'POSIXt') in the marks of a point pattern. o cut.im Now refuses to handle images whose pixel values are factor, logical or character. o centroid.owin New argument 'as.ppp' o superimpose Improved default names for columns of marks. o Softcore() Improved printout. o kppm, lgcp.estpcf, lgcp.estK Adjusted to new structure of RandomFields package. No change in syntax. o data(murchison) This dataset now belongs to class 'listof' so that it can be plotted directly. o data(clmfires) The format of the covariate data has changed. The objects 'clmcov100' and 'clmcov200' are now elements of a list 'clmfires.extra'. o bounding.box This function is now Deprecated; it has been replaced by the generic boundingbox(). o as.psp.owin This function is now Deprecated; it has been replaced and extended by the function edges(). o plot.kstest Changed defaults so that the two curves are distinguishable. o with.fv Improved mathematical labels. BUG FIXES o intensity.quadratcount Values were incorrect for a rectangular tessellation (the matrix of intensities was transposed). Fixed. o rmh, simulate.ppm Simulation of the Strauss-hard core model (StraussHard) was incorrect (intensity of the simulated process was about 15% too low). Bug introduced in spatstat 1.31-0 (January 2013). because of a o intensity.quadratcount Crashed for a rectangular tessellation with only a single row or column. Fixed. o model.images.ppm Crashed sometimes if the argument W was given. Fixed. o eval.im Crashed when applied to images with only a single row or column. Fixed. o ppp, marks<-.ppp If the marks were a vector of dates, they were erroneously converted to numbers. Fixed. o ippm Crashed if the model formula included an offset term that was not a function. Fixed. o leverage.ppm Crashed sometimes when the model had irregular parameters ('covfunargs'). Fixed. o residuals.ppm Crashed sometimes when type='score'. Fixed. o scaletointerval Did not handle dates and date-time values correctly. Fixed. o rbind.hyperframe, as.list.hyperframe Gave incorrect results for hyperframes with 1 row. Fixed. o Kinhom Did not renormalise the result (even when renormalise=TRUE), in some cases. Spotted by Peter Forbes. Fixed. o disc If mask=TRUE the disc was erroneously clipped to the square [-1,1] x [-1,1]. Fixed. o plot.fv Sometimes shaded the wrong half of the graph when the 'shade' coordinates were infinite. Fixed. o print.ppm Gave an error message if the coefficient vector had length zero. Fixed. o vcov.ppm Gave an error message if the coefficient vector had length zero. Fixed. o plot.msr Ignored certain additional arguments such as 'pch'. Fixed. o cut.im Crashed if the image had 1 row or 1 column of pixels. Fixed. o iplot.ppp Crashed with message about missing object 'vals'. Fixed. o effectfun Demanded a value for every covariate supplied in the original call to ppm, even for covariates which were not used in the model. Fixed. o plot.listof, plot.hyperframe When plotting 3D point patterns (class pp3), these functions issued warnings about 'add' being an unrecognised graphics argument. Fixed. CHANGES IN spatstat VERSION 1.36-0 OVERVIEW o We thank Sebastian Meyer, Kevin Ummer, Jean-Francois Coeurjolly, Ege Rubak, Rasmus Waagepetersen, Oscar Garcia and Sourav Das for contributions. o Important change to package dependencies. o Geometrical inconsistencies in polygons are now repaired automatically. o Improved quadrature schemes and reduced bias in ppm. o New vignette 'Summary of Recent Changes to Spatstat'. o Approximation to K function and pcf for Gibbs models. o Counterpart of 'apply' for lists of images. o Hexagonal grids and tessellations. o Extensions to scan test and Allard-Fraley cluster set estimator. o Change the parameters of a fitted model before simulating it. o Accelerated Kest, Kinhom for rectangular windows. o Extensions and improvements to plotting functions. o Improvements to labelling of 'fv' objects. o New demo of summary functions. o More methods for 'intensity'. o Version nickname: 'Intense Scrutiny' NEW FUNCTIONS o Kmodel.ppm, pcfmodel.ppm Compute approximation to K-function or pair correlation function of a Gibbs point process model. o im.apply Apply a function to corresponding pixel values in several images. o hexgrid, hextess Create a hexagonal grid of points, or a tessellation of hexagonal tiles o shift.tess, rotate.tess, reflect.tess, scalardilate.tess, affine.tess Apply a geometrical transformation to a tessellation. o quantile.ewcdf Extract quantiles from a weighted cumulative distribution function. o scanLRTS Evaluate the spatially-varying test statistic for the scan test. o pcfmulti General multitype pair correlation function o intensity.splitppp Estimate intensity in each component of a split point pattern. o intensity.quadratcount Use quadrat counts to estimate intensity in each quadrat. o as.owin.quadratcount, as.owin.quadrattest Extract the spatial window in which quadrat counts were performed. o reload.or.compute Utility function for R scripts: either reload results from file, or compute them. o to.grey Convert colour to greyscale. o Smooth.im Method for Smooth() for pixel images. Currently identical to blur(). o demo(sumfun) Demonstration of nonparametric summary functions in spatstat. SIGNIFICANT USER-VISIBLE CHANGES o Package Dependencies spatstat now "Imports" (rather than "Depends" on) the libraries mgcv, deldir, abind, tensor, polyclip. This means that these libraries are not accessible to the user unless the user explicitly loads them by typing 'library(mgcv)' and so on. o owin, as.owin Polygon data are no longer subjected to strict checks on geometrical validity (self-crossing points, overlaps etc.) Instead, polygon geometry is automatically repaired. o ppm The default quadrature scheme for a point pattern has been improved (in the case of a non-rectangular window) to remove a possible source of bias. o Performance various parts of spatstat now run slightly faster. o scan.test Now handles multiple values of circle radius 'r'. o plot.scan.test, as.im.scan.test These functions can now give the optimal value of circle radius 'r'. o pcfcross, pcfdot Algorithms have been reimplemented using a single-pass kernel smoother and now run much faster. Bandwidth selection rule improved. o plot.listof, plot.splitppp Default behaviour has changed: panels are now plotted on different scales. o plot.listof, plot.splitppp When 'equal.scales=TRUE' the panels are plotted on exactly equal scales and are exactly aligned (under certain conditions). o ppp, marks.ppp, marks<-.ppp New argument 'drop' determines whether a data frame with a single column will be converted to a vector. o simulate.ppm, rmh.ppm, rmhmodel.ppm New argument 'new.coef' allows the user to change the parameters of a fitted model, before it is simulated. o logLik.ppm New argument 'new.coef' allows the user to evaluate the loglikelihood for a different value of the parameter. o clusterset The argument 'result' has been renamed 'what'. It is now possible to give multiple values to 'what' so that both types of result can be computed together. o residuals.ppm Argument 'coefs' has been renamed 'new.coef' for consistency with fitted.ppm etc. o residuals.ppm If drop=TRUE the window associated with the residuals is now taken to be the domain of integration of the composite likelihood. o intensity.ppp Now has argument 'weights' o density.ppp, Smooth.ppp, markmean, markvar, intensity.ppp Argument 'weights' can now be an 'expression'. o pcf New argument 'domain' causes the computation to be restricted to a subset of the window. o nnclean The result now has attributes which give the fitted parameter values, information about the fitting procedure, and the histogram bar heights. o nnclean Extra arguments are now passed to hist.default. o plot.tess For a tessellation represented by a pixel image, plot.tess no longer treats the pixel labels as palette colours. o relrisk New argument 'case' allows the user to specify which mark value corresponds to the cases in a case-control dataset. o Kinhom Now accepts correction="good" o spatstat.options New option ('monochrome') controls whether plots generated by spatstat will be displayed in colour or in greyscale. This will eventually be applied to all plot commands in spatstat. o plot.im, persp.im, contour.im, plot.owin, plot.psp, plot.fv, plot.fasp These functions now obey spatstat.options('monochrome') o plot.ppp, plot.owin, plot.im, plot.psp, plot.tess, plot.layered New universal argument 'show.all' determines what happens when a plot is added to an existing plot. If show.all = TRUE then everything is plotted, including the main title and colour ribbon. o plot.ppp New argument 'show.window' o plot.im New arguments 'add' and 'do.plot'. More arguments recognised by 'ribargs' o plot.layered New arguments 'add', 'main' Better argument handling. o plot.fv Improved handling of argument 'shade' o layered, layerplotargs, plot.layered The plotting argument can now be a list of length 1, which will be replicated to the correct length. o varblock Ugly legends have been repaired. o quad.ppm New argument 'clip' o edge.Trans New arguments 'dx', 'dy' o disc Argument 'centre' can be in various formats. o affine, shift Argument 'vec' can be in various formats. o Geyer, BadGey A warning is no longer issued when the parameter 'sat' is fractional. o adaptive.density Now has argument 'verbose' o Smooth.ppp 'sigma' is now a formal argument of Smooth.ppp o plot.quadratcount, plot.quadrattest These functions have now been documented. o Summary functions and envelopes Improved mathematical labels in plots. o Kest Accelerated, in the case of a rectangular window. o Kscaled Argument 'lambda' can now be a fitted model (class ppm) o print.fv Improved layout. o plot.bermantest Improved graphics. o which.max.im This function is now deprecated. which.max.im(x) is superseded by im.apply(x, which.max) o smooth.ppp, smooth.fv, smooth.msr These functions are now deprecated, in favour of 'Smooth' with a capital 'S' BUG FIXES o bw.ppl Crashed if the point pattern had multiple points at the same location. Fixed. o quantile Crashed when applied to the result of 'ewcdf'. Fixed. o marks<-.ppp Crashed with a message about 'unrecognised format' if the current or replacement values of marks were date/time values (belonging to class 'Date' or 'POSIXt'). Fixed. o plot.im Crashed in case log=TRUE if the window was not a rectangle. Fixed. o vcov.ppm Crashed sometimes for models with a hard core term (Hardcore, StraussHard, MultiHard or MultiStrauss interactions). Spotted by Rasmus Waagepetersen. Fixed. o multiplicity.data.frame Results were incorrect and included NA's (spotted by Sebastian Meyer). Fixed. o markvar Values were incorrect. Fixed. o Smooth.ppp Ignored argument 'diggle'. Fixed. o rotate.im, affine.im Factor-valued images were not handled correctly. Fixed. o shift.layered If argument 'origin' was used, different layers were shifted by different amounts. Fixed. o tile.areas Sometimes returned a list instead of a numeric vector. Fixed. o print.ppp If the marks were date/time values (belonging to class 'Date' or 'POSIXt'), print.ppp reported that they were double precision numbers. Fixed. o plot.layered Graphics were mangled if the argument 'add=FALSE' was given explicitly. Fixed. o Smooth.ppp The argument 'sigma' was only recognised if it was explicitly named. For example in 'Smooth(X, 5)' the '5' was ignored. Fixed. o clusterset The bounding frame of the result was smaller than the original bounding frame of the point pattern dataset, when result="domain" and exact=TRUE. Fixed. o plot.im Ignored argument 'col' if it was a 'function(n)'. Fixed. o Kinhom Ignored argument 'correction' if there were more than 1000 points. Fixed. o [.fv Mangled the plot label for the y axis. Fixed. o cbind.fv Mangled the plot label for the y axis. Fixed. o plot.envelope Main title was always 'x'. Fixed. o print.ppp Ran over the right margin. Fixed. o union.owin, intersect.owin, setminus.owin Sometimes deleted the name of the unit of length. Fixed. CHANGES IN spatstat VERSION 1.35-0 OVERVIEW o We thank Melanie Bell, Leanne Bischof, Ida-Maria Sintorn, Ege Rubak, Martin Hazelton, Oscar Garcia, Rasmus Waagepetersen, Abdollah Jalilian and Jens Oehlschlaegel for contributions. o Support for analysing replicated spatial point patterns. o New vignette on analysing replicated spatial point patterns. o Objective function surface plots. o Estimator of point process intensity using nearest neighbour distances. o Improved estimator of pair correlation function. o Four new datasets. o Simple point-and-click interface functions for general use. o More support for fv objects. o More support for ppx objects. o Extensions to nearest neighbour functions. o Morphological operations accelerated. o Bug fix to pair correlation functions. o Bug fix to k-th nearest neighbour distances o Version nickname: 'Multiple Personality' NEW CLASSES o mppm An object of class 'mppm' represents a Gibbs point process model fitted to several point pattern datasets. The point patterns may be treated as independent replicates of the same point process, or as the responses in an experimental design, so that the model may depend on covariates associated with the design. Methods for this class include print, plot, predict, anova and so on. o objsurf An object of class 'objsurf' contains values of the likelihood or objective function in a neighbourhood of the maximum. o simplepanel An object of class 'simplepanel' represents a spatial arrangement of buttons that respond to mouse clicks, supporting a simple, robust graphical interface. NEW FUNCTIONS o mppm Fit a Gibbs model to several point patterns. The point pattern data may be organised as a designed experiment and the model may depend on covariates associated with the design. o anova.mppm Analysis of Deviance for models of class mppm o coef.mppm Extract fitted coefficients from a model of class mppm o fitted.mppm Fitted intensity or conditional intensity for a model of class mppm o kstest.mppm Kolmogorov-Smirnov test of goodness-of-fit for a model of class mppm o logLik.mppm log likelihood or log pseudolikelihood for a model of class mppm o plot.mppm Plot the fitted intensity or conditional intensity of a model of class mppm o predict.mppm Compute the fitted intensity or conditional intensity of a model of class mppm o quadrat.test Quadrat counting test of goodness-of-fit for a model of class mppm o residuals.mppm Point process residuals for a model of class mppm o subfits Extract point process models for each individual point pattern dataset, from a model of class mppm o vcov.mppm Variance-covariance matrix for a model of class mppm o integral.msr Integral of a measure. o objsurf For a model fitted by optimising an objective function, this command computes the objective function in a neighbourhood of the optimal value. o contour.objsurf, image.objsurf, persp.objsurf, plot.objsurf Plot an 'objsurf' object. o fvnames Define groups of columns in a function value table, for use in plot.fv, etc o multiplicity New generic function for which multiplicity.ppp is a method. o unique.ppx, duplicated.ppx, multiplicity.ppx Methods for unique(), duplicated() and multiplicity() for 'ppx' objects. These also work for 'pp3' and 'lpp' objects. o closepairs, crosspairs, closepaircounts, crosspaircounts Low-level functions for finding all close pairs of points o nndensity Estimate point process intensity using k-th nearest neighbour distances o simplepanel, run.simplepanel Support for a simple point-and-click interface for general use. NEW DATASETS o pyramidal Diggle-Lange-Benes data on pyramidal neurons in cingulate cortex. 31 point patterns divided into 3 groups. o waterstriders Nummelin-Penttinen waterstriders data. Three independent replicates of a point pattern formed by insects. o simba Simulated data example for mppm. Two groups of point patterns with different interpoint interactions. o demohyper Simulated data example for mppm. Point patterns and pixel image covariates, in two groups with different regression coefficients. SIGNIFICANT USER-VISIBLE CHANGES o plot.hyperframe The argument 'e' now has a different format. Instead of plot(h, plot(XYZ)) one must now type plot(h, quote(plot(XYZ))) This is necessary in order to avoid problems with 'S4 method dispatch'. o pcf.ppp, pcfinhom New argument 'divisor' enables better performance of the estimator of pair correlation function for distances close to zero. o applynbd The arguments N, R and criterion may now be specified together. o markstat The arguments N and R may now be specified together. o ppx New argument 'simplify' allows the result to be converted to an object of class 'ppp' or 'pp3' if appropriate. o as.function.fv Now allows multiple columns to be interpolated o multiplicity.ppp This function is now a method for the generic 'multiplicity'. It has also been accelerated. o nnfun.ppp, distfun.ppp New argument 'k' allows these functions to compute k-th nearest neighbours. o rVarGamma, kppm, vargamma.estK, vargamma.estpcf New argument 'nu.pcf' provides an alternative way to specify the kernel shape in the VarGamma model, instead of the existing argument 'nu.ker'. Function calls that use the ambiguous argument name 'nu' will no longer be accepted. o nnmap Image is now clipped to the original window. o dilation, erosion, opening, closing Polygonal computations greatly accelerated. o plot.colourmap Improved appearance and increased options, for discrete colourmaps. o plot.msr Improved appearance o plot.ppp, plot.owin An `empty' plot can now be generated by setting type="n" o nndist.ppp, nnwhich.ppp, nncross.ppp Column names of the result are now more informative. BUG FIXES o nncross.ppp Results were completely incorrect when k > 1. Spotted by Jens Oehschlaegel. Bug was introduced in spatstat 1.34-1. Fixed. o rVarGamma Simulations were incorrect; they were generated using the wrong value of the parameter 'nu.ker'. Spotted by Rasmus Waagepetersen and Abdollah Jalilian. Bug was always present. Fixed. o pair correlation functions (pcf.ppp, pcfdot, pcfcross, pcfinhom, ...) The result had a negative bias at the maximum 'r' value, because contributions to the pcf estimate from interpoint distances greater than max(r) were mistakenly omitted. Spotted by Rasmus Waagepetersen and Abdollah Jalilian. Bug was always present. Fixed. o demo(spatstat) This demonstration script had some unwanted side-effects, such as rescaling the coordinates of standard datasets 'bramblecanes', 'amacrine' and 'demopat', which caused the demonstration to crash when it was repeated several times, and caused errors in demo(data). Fixed. o rmh Visual debugger crashed sometimes with message 'XI not found'. Fixed. o predict.ppm Crashed if the model was fitted using 'covfunargs'. Fixed. o bounding.box Crashed if one of the arguments was NULL. Fixed. o multiplicity.ppp Did not handle data frames of marks. Fixed. CHANGES IN spatstat VERSION 1.34-1 OVERVIEW o We thank Kurt Hornik, Ted Rosenbaum, Ege Rubak and Achim Zeileis for contributions. o Important bug fix. SIGNIFICANT USER-VISIBLE CHANGES o as.box3 Now accepts objects of class 'ppx' or 'boxx'. o crossdist.ppp, crossdist.pp3, crossdist.default New argument 'squared' allows the squared distances to be computed (saving computation time in some applications) BUG FIXES o union.owin, is.subset.owin, dilation.owin Results were sometimes completely wrong for polygons with holes. Spotted by Ted Rosenbaum. Fixed. o psstA, areaLoss Crashed in some cases, with error message 'Number of items to replace is not a multiple of replacement length'. Spotted by Achim Zeileis. Fixed. CHANGES IN spatstat VERSION 1.34-0 OVERVIEW o We thank Andrew Bevan, Ege Rubak, Aruna Jammalamadaka, Greg McSwiggan, Jeff Marcus, Jose M Blanco Moreno, and Brian Ripley for contributions. o spatstat and all its dependencies are now Free Open Source. o spatstat does not require the package 'gpclib' any more. o spatstat now depends on the packages 'tensor', 'abind' and 'polyclip' o polygon clipping is now enabled always. o Substantially more support for point patterns on linear networks. o Faster computations for pairwise interaction models. o Bug fixes in nearest neighbour calculations. o Bug fix in leverage and influence diagnostics. o Version nickname: "Window Cleaner" o spatstat now requires R version 3.0.2 or later NEW FUNCTIONS o as.lpp Convert data to a point pattern on a linear network. o distfun.lpp Distance function for point pattern on a linear network. o eval.linim Evaluate expression involving pixel images on a linear network. o linearKcross, linearKdot, linearKcross.inhom, linearKdot.inhom Multitype K functions for point patterns on a linear network o linearmarkconnect, linearmarkequal Mark connection function and mark equality function for multitype point patterns on a linear network o linearpcfcross, linearpcfdot, linearpcfcross.inhom, linearpcfdot.inhom Multitype pair correlation functions for point patterns on a linear network o linfun New class of functions defined on a linear network o nndist.lpp, nnwhich.lpp, nncross.lpp Methods for nndist, nnwhich, nncross for point patterns on a linear network o nnfun.lpp Method for nnfun for point patterns on a linear network o vcov.lppm Variance-covariance matrix for parameter estimates of a fitted point process model on a linear network. o bilinearform Computes a bilinear form o tilenames, tilenames<- Extract or change the names of tiles in a tessellation. SIGNIFICANT USER-VISIBLE CHANGES o package dependencies Previous versions of spatstat used the package 'gpclib' to perform geometrical calculations on polygons. Spatstat now uses the package 'polyclip' for polygon calculations instead. o free open-source licence The restrictive licence conditions of 'gpclib' no longer apply to users of spatstat. Spatstat and all its dependencies are now covered by a free open-source licence. o polygon clipping In previous versions of spatstat, geometrical calculations on polygons could be performed 'exactly' using gpclib or 'approximately' using pixel discretisation. Polygon calculations are now always performed 'exactly'. o intersect.owin, union.owin, setminus.owin If A and B are polygons, the result is a polygon. o erosion, dilation, opening, closing If the original set is a polygon, the result is a polygon. o intersect.tess, dirichlet The tiles of the resulting tessellation are polygons if the input was polygonal. o plot.owin Polygons with holes can now be plotted with filled colours on any device. o lppm New arguments 'eps' and 'nd' control the quadrature scheme. o pairwise interaction Gibbs models Many calculations for these models have been accelerated. BUG FIXES o nncross.pp3 Values were completely incorrect in some cases. Usually accompanied by a warning about NA values. (Spotted by Andrew Bevan.) Fixed. o nnmap, nnmark A small proportion of pixels had incorrect values. [These were the pixels lying on the boundary of a Dirichlet cell.] Fixed. o leverage.ppm, influence.ppm, dfbetas.ppm Results were incorrect for non-Poisson processes. Fixed. o distcdf Results were incorrect in some cases when W was a window and V was a point pattern. Fixed. o Kcross, Kdot, pcfcross, pcfdot Results were incorrect in some rare cases. Fixed. o as.fv.kppm Erroneously returned a NULL value. Fixed. o vcov.ppm For point process models fitted with method = 'logi', sometimes crashed with error "object 'fit' not found". (Spotted by Ege Rubak). Fixed. o vcov.ppm For multitype point process models, sometimes crashed with error "argument 'par' is missing". Fixed. o plot.im Crashed if some of the pixel values were infinite. Fixed. o owin owin(poly=..) crashed if there were NA's in the polygon coordinates. Spotted by Jeff Marcus. Fixed. o plot.fv Crashed, giving an incomprehensible error, if the plot formula contained a number with a decimal point. Fixed. o alltypes Crashed if envelopes=TRUE and global=TRUE, with error message 'csr.theo not found'. Spotted by Jose M Blanco Moreno. Fixed. o chop.tess, rMosaicField Format of result was garbled in some cases. Fixed. o vcov.ppm Sometimes gave an irrelevant warning "parallel option not available". Fixed. CHANGES IN spatstat VERSION 1.33-0 OVERVIEW o We thank Kurt Hornik and Brian Ripley for advice. o The package namespace has been modified. o Numerous internal changes. o Likelihood cross-validation for smoothing bandwidth. o More flexible models of intensity in cluster/Cox processes. o New generic function for smoothing. o Version nickname: 'Titanic Deckchair' NEW FUNCTIONS o bw.ppl Likelihood cross-validation technique for bandwidth selection in kernel smoothing. o is.lppm, is.kppm, is.slrm Tests whether an object is of class 'lppm', 'kppm' or 'slrm' o Smooth New generic function for spatial smoothing. o Smooth.ppp, Smooth.fv, Smooth.msr Methods for Smooth (identical to smooth.ppp, smooth.fv, smooth.msr respectively) o fitted.kppm Method for 'fitted' for cluster/Cox models SIGNIFICANT USER-VISIBLE CHANGES o namespace The namespace of the spatstat package has been changed. o internal functions Some undocumented internal functions are no longer visible, as they are no longer exported in the namespace. These functions can still be accessed using the form spatstat:::functionname. Functions that are not visible are not guaranteed to exist or to remain the same in future. o methods For some generic functions defined in the spatstat package, it is possible that R may fail to find one of the methods for the generic. This is a temporary problem due to a restriction on the size of the namespace in R 3.0.1. It will be fixed in future versions of R and spatstat. It only applies to methods for a generic which is a spatstat function (such as nndist) and does not apply to methods for generics defined elsewhere (such as density). In the meantime, if this problem should occur, it can be avoided by calling the method explicitly, in the form spatstat:::genericname.classname. o speed The package should run slightly faster overall, due to the improvement of the namespace, and changes to internal code. o envelope New argument 'envir.simul' determines the environment in which to evaluate the expression 'simulate'. o kppm More flexible models of the intensity, and greater control over the intensity fitting procedure, are now possible using the arguments 'covfunargs', 'use.gam', 'nd', 'eps' passed to ppm. Also the argument 'X' may now be a quadrature scheme. o distcdf Arguments W and V can now be point patterns. o Kest New option: correction = "good" selects the best edge correction that can be computed in reasonable time. o bw.diggle Accelerated. o predict.ppm Calculation of standard error has been accelerated. o smooth.ppp, smooth.fv, smooth.msr These functions will soon be 'Deprecated' in favour of the methods Smooth.ppp, Smooth.fv, Smooth.msr respectively. o stratrand, overlap.owin, update.slrm, edge.Trans, edge.Ripley These already-existing functions are now documented. BUG FIXES o kppm, matclust.estpcf, pcfmodel The pair correlation function of the Matern Cluster Process was evaluated incorrectly at distances close to 0. This could have affected the fitted parameters in matclust.estpcf() or kppm(clusters="MatClust"). Fixed. o anova.ppm Would cause an error in future versions of R when 'anova.glm' is removed from the namespace. Fixed. CHANGES IN spatstat VERSION 1.32-0 OVERVIEW o We thank Ege Rubak for major contributions. o Thanks also to Patrick Donnelly, Andrew Hardegen, Tom Lawrence, Robin Milne, Gopalan Nair and Sean O'Riordan. o New 'logistic likelihood' method for fitting Gibbs models. o Substantial acceleration of several functions including profile maximum pseudolikelihood and variance calculations for Gibbs models. o Nearest neighbours for point patterns in 3D o Nearest-neighbour interpolation in 2D o New 'progress plots' o Hard core thresholds can be estimated automatically. o More support for colour maps o More support for 'fv' objects o Spatstat now has version nicknames. The current version is "Logistical Nightmare". o Minor improvements and bug fixes. NEW FUNCTIONS o nncross.pp3 Method for 'nncross' for point patterns in 3D o nnmark Mark of nearest neighbour - can be used for interpolation o dclf.progress, mad.progress Progress plots (envelope representations) for the DCLF and MAD tests. o deriv.fv Numerical differentiation for 'fv' objects. o interp.colourmap Smooth interpolation of colour map objects - makes it easy to build colour maps with gradual changes in colour o tweak.colourmap Change individual colour values in a colour map object o beachcolourmap Colour scheme appropriate for `altitudes' (signed numerical values) o as.fv Convert various kinds of data to an 'fv' object o quadscheme.logi Generates quadrature schemes for the logistic method of ppm. o beginner Introduction for beginners. SIGNIFICANT USER-VISIBLE CHANGES o ppm New option: method = "logi" Fits a Gibbs model by the newly developed 'logistic likelihood' method which is often faster and more accurate than maximum pseudolikelihood. Code contributed by Ege Rubak. o profilepl Greatly accelerated, especially for area-interaction models. o vcov.ppm Greatly accelerated for higher-order interaction models. o smooth.ppp Now handles bandwidths equal to zero (by invoking 'nnmark') o Hardcore, StraussHard The hard core distance 'hc' can now be omitted; it will be estimated from data. o plot.ppp Now behaves differently if there are multiple columns of marks. Each column of marks is plotted, in a series of separate plots arranged side-by-side. o plot.im Argument 'col' can now be a function o lohboot Now computes confidence intervals for L-functions as well (fun="Lest" or fun="Linhom") o dclf.test, mad.test The argument X can now be an object produced by a previous call to dclf.test or mad. o plot.fv Labelling of plots has been improved in some cases. o smooth.fv Further options added. o density.ppp The argument 'weights' can now be a matrix. o smooth.ppp Accelerated, when there are several columns of marks. o density.ppp Accelerated slightly. o simulate.ppm, simulate.kppm The total computation time is also returned. o simulate.kppm Now catches errors (such as 'insufficient memory'). o latest.news, licence.polygons Can now be executed by typing the name of the function without parentheses. o latest.news The text is now displayed one page at a time. BUG FIXES o Hest, Gfox, Jfox The 'raw' estimate was not computed correctly (or at least it was not the raw estimate described in the help files). Spotted by Tom Lawrence. Fixed. o edges2vees Format of result was incorrect if there were fewer than 3 edges. Fixed. o Jfox The theoretical value (corresponding to independence between X and Y) was erroneously given as 0 instead of 1. Spotted by Patrick Donnelly. Fixed. o ppm, quadscheme, default.dummy If the grid spacing parameter 'eps' was specified, the quadrature scheme was sometimes slightly incorrect (missing a few dummy points near the window boundary). Fixed. o print.timed Matrices were printed incorrectly. Fixed. CHANGES IN spatstat VERSION 1.31-3 OVERVIEW o spatstat now 'Suggests' the package 'tensor' o Code slightly accelerated. o More support for pooling of envelopes. o Bug fixes. NEW FUNCTIONS o nnmap Given a point pattern, finds the k-th nearest point in the pattern from each pixel in a raster. o coef.fii, coef.summary.fii Extract the interaction coefficients of a fitted interpoint interaction o edges2vees Low-level function for finding triples in a graph. SIGNIFICANT USER-VISIBLE CHANGES o predict.ppm New argument 'correction' allows choice of edge correction when calculating the conditional intensity. o pool.envelope New arguments 'savefuns' and 'savepatterns'. o pool.envelope Envelopes generated with VARIANCE=TRUE can now be pooled. o pool.envelope The plot settings of the input data are now respected. o Numerous functions have been slightly accelerated. BUG FIXES o predict.ppm Calculation of the conditional intensity omitted the edge correction if correction='translate' or correction='periodic'. Fixed. o shift.lpp, rotate.lpp, scalardilate.lpp, affine.lpp, shift.linnet, rotate.linnet, scalardilate.linnet, affine.linnet The enclosing window was not correctly transformed. Fixed. o rHardcore, rStraussHard, rDiggleGratton, rDGS The return value was invisible. Fixed. o ppm In rare cases the results obtained with forcefit=FALSE and forcefit=TRUE were different, due to numerical rounding effects. Fixed. CHANGES IN spatstat VERSION 1.31-2 OVERVIEW o We thank Robin Corria Anslie, Julian Gilbey, Kiran Marchikanti, Ege Rubak and Thordis Linda Thorarinsdottir for contributions. o spatstat now depends on R 3.0.0 o More support for linear networks o More functionality for nearest neighbours o Bug fix in fitting Geyer model o Performance improvements and bug fixes NEW FUNCTIONS o affine.lpp, shift.lpp, rotate.lpp, rescale.lpp, scalardilate.lpp Geometrical transformations for point patterns on a linear network o affine.linnet, shift.linnet, rotate.linnet, rescale.linnet, scalardilate.linnet Geometrical transformations for linear networks o [.linnet Subset operator for linear networks o timed Records the computation time taken SIGNIFICANT USER-VISIBLE CHANGES o nncross nncross.ppp can now find the k-th nearest neighbours, for any k. o nndist, nnwhich New argument 'by' makes it possible to find nearest neighbours belonging to specified subsets in a point pattern, for example, the nearest neighbour of each type in a multitype point pattern. o [.fv Now handles the argument 'drop'. o with.fv Argument 'drop' replaced by new argument 'fun' (with different interpretation). o [.lpp Subset index may now be a window (class 'owin') o Kest Options correction='border' and correction='none' now run about 4 times faster, thanks to Julian Gilbey. o density.ppp Numerical underflow no longer occurs when sigma is very small and 'at="points"'. A warning is no longer issued. Thanks to Robin Corria Anslie. o crossing.psp New argument 'fatal' allows the user to handle empty intersections o union.owin It is now guaranteed that if A is a subset of B, then union.owin(A,B)=B. o plot.colourmap Now passes arguments to axis() to control the plot. Appearance of plot improved. o image.listof Now passes arguments to plot.colourmap() if equal.ribbon=TRUE. o kppm Accelerated (especially for large datasets). o plot.envelope plot.envelope is now equivalent to plot.fv and is essentially redundant. o rThomas, rMatClust, rNeymanScott Improved explanations in help files. o All functions Many functions have been slightly accelerated. BUG FIXES o ppm Results were incorrect for the Geyer saturation model with a non-integer value of the saturation parameter 'sat'. Spotted by Thordis Linda Thorarinsdottir. Bug introduced in spatstat 1.20-0, July 2010. Fixed. o ppm Fitting a stationary Poisson process using a nonzero value of 'rbord', as in "ppm(X, rbord=R)" with R > 0, gave incorrect results. Fixed. o predict.slrm Crashed with message 'longer object length is not a multiple of shorter object length' if the original data window was not a rectangle. Fixed. o iplot Main title was sometimes incorrect. Fixed. o plot.layered Ignored argument 'main' in some cases. Fixed. o plot.listof, image.listof Crashed sometimes with a message 'figure margins too large' when equal.ribbon=TRUE. Fixed. o print.ppx Crashed if the object contained local coordinates. Fixed. o transect.im Crashed if the transect lay partially outside the image domain. Fixed. o rthin Crashed if X was empty. Fixed. o max.im, min.im, range.im Ignored additional arguments after the first argument. Fixed. o update.lppm Updated object did not remember the name of the original dataset. Fixed. o envelope Grey shading disappeared from plots of envelope objects when the envelopes were transformed using eval.fv or eval.fasp. Fixed. CHANGES IN spatstat VERSION 1.31-1 OVERVIEW o We thank Marcelino de la Cruz, Daniel Esser, Jason Goldstick, Abdollah Jalilian, Ege Rubak and Fabrice Vinatier for contributions. o Nonparametric estimation and tests for point patterns in a linear network. o More support for 'layered' objects. o Find clumps in a point pattern. o Connected component interaction model. o Improvements to interactive plots. o Visual debugger for Metropolis-Hastings algorithm. o Bug fix in Metropolis-Hastings simulation of Geyer process. o Faster Metropolis-Hastings simulation. o Faster computation of 'envelope', 'fv' and 'fasp' objects. o Improvements and bug fixes. NEW FUNCTIONS o connected.ppp Find clumps in a point pattern. o kstest.lpp, kstest.lppm The spatial Kolmogorov-Smirnov test can now be applied to point patterns on a linear network (class 'lpp') and point processes on a linear network (class 'lppm'). o bermantest.lpp, bermantest.lppm Berman's Z1 and Z2 tests can now be applied to point patterns on a linear network (class 'lpp') and point processes on a linear network (class 'lppm'). o rhohat.lpp, rhohat.lppm Nonparametric estimation of the dependence of a point pattern on a spatial covariate: 'rhohat' now applies to objects of class 'lpp' and 'lppm'. o intensity.lpp Empirical intensity of a point pattern on a linear network. o as.function.rhohat Converts a 'rhohat' object to a function, with extrapolation beyond the endpoints. o [.layered Subset operator for layered objects. o shift, rotate, affine, rescale, reflect, flipxy, scalardilate These geometrical transformations now work for 'layered' objects. o iplot.layered Interactive plotting for 'layered' objects. o as.owin.layered Method for as.owin for layered objects. o [.owin Subset operator for windows, equivalent to intersect.owin. o rcellnumber Generates random integers for the Baddeley-Silverman counterexample. o is.lpp Tests whether an object is a point pattern on a linear network. o is.stationary.lppm, is.poisson.lppm New methods for is.stationary and is.poisson for class 'lppm' o sessionLibs Print library names and version numbers (for use in Sweave scripts) SIGNIFICANT USER-VISIBLE CHANGES o iplot iplot is now generic, with methods for 'ppp', 'layered' and 'default'. iplot methods now support zoom and pan navigation. o rmh.default New argument 'snoop' allows the user to activate a visual debugger for the Metropolis-Hastings algorithm. o connected connected() is now generic, with methods for 'im', 'owin' and 'ppp'. o alltypes Now works for lpp objects o rlabel Now works for lpp, pp3, ppx objects o plot.kstest Can now perform P-P and Q-Q plots as well. o plot.fasp New argument 'samey' controls whether all panels have the same y limits. o plot.fasp Changed default value of 'samex'. o Objects of class 'envelope', 'fv' and 'fasp' Reduced computation time and storage required for these objects. o pcfmodel.kppm Improved calculation. o plot.fv Improved collision-avoidance algorithm (for avoiding overlaps between curves and legend) o ppm Improved error handling o envelope All methods for 'envelope' now handle fun=NULL o setminus.owin Better handling of the case where both arguments are rectangles. o rmh Simulation has been further accelerated. o lppm Accelerated. o vcov.ppm Accelerated. o marktable Accelerated. o Triplets() interaction Accelerated. o alltypes Accelerated when envelope=TRUE. BUG FIXES o rmh Simulation of the Geyer saturation process was incorrect. [Bug introduced in previous version, spatstat 1.31-0.] Fixed. o rmh Simulation of the Geyer saturation process was incorrectly initialised, so that the results of a short run (i.e. small value of 'nrep') were incorrect, while long runs were correct. [Bug introduced in spatstat 1.17-0, october 2009.] Fixed. o ppm Objects fitted with use.gam=TRUE caused fatal errors in various functions including print, summary, vcov and model.frame. Spotted by Jason Goldstick. Fixed. o lpp, runiflpp, rpoislpp Empty point patterns caused an error. Fixed. o rmh.default Crashed for hybrid models, with message 'Attempt to apply non-function'. Spotted by Ege Rubak. Fixed. o relrisk Crashed when 'at="points"' for a multitype pattern with more than 2 types. Spotted by Marcelino de la Cruz. Fixed. o erosion.owin, dilation.psp, border Ignored the arguments "..." in some cases (namely when the window was polygonal and 'gpclib' was disabled). Fixed. o rsyst, rcell Did not correctly handle the argument 'dx'. Spotted by Fabrice Vinatier. Fixed. o correction="trans" Various functions such as Kest no longer recognised 'correction = "trans"'. Fixed. o istat Crashed with an error message about envelopes. Fixed. o summary.ppm, print.ppm p-values which were exactly equal to zero were reported as NA. Fixed. o [.im Crashed if the intersection consisted of a single row or column of pixels. Fixed. o plot.im Sometimes incorrectly displayed an image consisting of a single row or column of pixels. Fixed. o plot.layered The plot region was determined by the first layer, so that objects in subsequent layers could sometimes fall outside the plot region. Fixed. o transect.im If the arguments 'from' and 'to' were numeric vectors of length 2, the result was garbled. Fixed. o Inhomogeneous K functions and pair correlation functions [Kinhom, pcfinhom, Kcross.inhom, Kdot.inhom, pcfcross.inhom, etc.] These functions reported an error 'lambda is not a vector' if the intensity argument lambda was computed using density(, at="points"). Fixed. o rlabel Did not accept a point pattern with a hyperframe of marks. Fixed. o alltypes Crashed when envelope=TRUE if the summary function 'fun' did not have default values for the marks i and j. Fixed. o Kres, Gres, psst, psstA Ignored the unit of length. Fixed. CHANGES IN spatstat VERSION 1.31-0 OVERVIEW o We thank Frederic Lavancier and Ege Rubak for contributions. o Major bug fix in simulation of area-interaction process. o Metropolis-Hastings simulations accelerated. o Rounding of spatial coordinates o clmfires dataset corrected. o Bug fixes and minor improvements. NEW FUNCTIONS o round.ppp Round the spatial coordinates of a point pattern to a specified number of decimal places. o rounding Determine whether a dataset has been rounded. SIGNIFICANT USER-VISIBLE CHANGES o rmh Simulation of the following models has been accelerated: areaint, dgs, diggra, fiksel, geyer, hardcore, lennard, multihard, strauss, straush, straussm, strausshm. o rmh The transition history of the simulation (which is saved if 'track=TRUE') now also contains the value of the Hastings ratio for each proposal. o clmfires The clmfires dataset has been modified to remove errors and inconsistencies. o plot.linim Appearance of the plot has been improved, when style='width'. o summary.ppm Now reports whether the spatial coordinates have been rounded. o dclf.test, mad.test The range of distance values ('rinterval') used in the test is now printed in the test output, and is saved as an attribute. BUG FIXES o rmh Simulation of the Area-Interaction model was completely incorrect. Spotted by Frederic Lavancier. The bug was introduced in spatstat version 1.23-6 or later. Fixed. o dclf.test The test statistic was incorrectly scaled (by a few percent). This did not affect the p-value of the test. Fixed. o ppx If argument 'coord.type' was missing, various errors occurred: a crash may have occurred, or the results may have depended on the storage type of the data. Spotted by Ege Rubak. Fixed. o plot.ppx Crashed for 1-dimensional point patterns. Spotted by Ege Rubak. Fixed. CHANGES IN spatstat VERSION 1.30-0 OVERVIEW o We thank Jorge Mateu, Andrew Bevan, Olivier Flores, Marie-Colette van Lieshout, Nicolas Picard and Ege Rubak for contributions. o The spatstat manual now exceeds 1000 pages. o Hybrids of point process models. o Five new datasets o Second order composite likelihood method for kppm. o Inhomogeneous F, G and J functions. o Delaunay graph distance o Fixed serious bug in 'lppm' for marked patterns. o bug fix in some calculations for Geyer model o Improvements to linear networks code o Pixel images can now be displayed with a logarithmic colour map. o spatstat now formally 'Depends' on the R core package 'grDevices' o miscellaneous improvements and bug fixes NEW DATASETS o clmfires Forest fires in Castilla-La Mancha o gordon People sitting on the grass in Gordon Square, London o hyytiala Mixed forest in Hyytiala, Finland (marked by species) o paracou Kimboto trees in Paracou, French Guiana (marked as adult/juvenile) o waka Trees in Waka national park (marked with diameters) NEW FUNCTIONS o Hybrid The hybrid of several point process interactions [Joint research with Jorge Mateu and Andrew Bevan] o is.hybrid Recognise a hybrid interaction or hybrid point process model. o Finhom, Ginhom, Jinhom Inhomogeneous versions of the F, G and J functions [Thanks to Marie-Colette van Lieshout] o delaunay.distance Graph distance in the Delaunay triangulation. o distcdf Cumulative distribution function of the distance between two independent random points in a given window. o bw.frac Bandwidth selection based on window geometry o shortside.owin, sidelengths.owin Side lengths of (enclosing rectangle of) a window SIGNIFICANT USER-VISIBLE CHANGES o ppm Can now fit models with 'hybrid' interactions [Joint research with Jorge Mateu and Andrew Bevan] o kppm Now has the option of fitting models using Guan's (2006) second order composite likelihood. o envelope.lpp Now handles multitype point patterns. o envelope.envelope New argument 'transform' allows the user to apply a transformation to previously-computed summary functions. o runifpointOnLines, rpoisppOnLines, runiflpp, rpoislpp Can now generate multitype point patterns. o rmhmodel, rmh, simulate.ppm Now handle point process models with 'hybrid' interactions. o kppm Accelerated, and more reliable, due to better choice of starting values in the optimisation procedure. o kppm The internal format of kppm objects has changed. o minimum contrast estimation Error messages from the optimising function 'optim' are now trapped and handled. o rhohat This command is now generic, with methods for ppp, quad, and ppm. o raster.x, raster.y, raster.xy These functions have a new argument 'drop' o summary.ppm Improved behaviour when the model covariates are a data frame. o progressreport Output improved. o second order summary functions (Kest, Lest, Kinhom, pcf.ppp, Kdot, Kcross, Ldot etc etc) These functions now accept correction="translation" as an alternative to correction = "translate", for consistency. o plot.im New argument 'log' allows colour map to be equally spaced on a log scale. o as.owin.ppm, as.owin.kppm New argument 'from' allows the user to extract the spatial window of the point data (from="points") or the covariate images (from="covariates") o dclf.test, mad.test The rule for handling tied values of the test statistic has been changed. The tied values are now randomly ordered to obtain a randomised integer rank. o with.fv New argument 'enclos' allows evaluation in other environments BUG FIXES o lppm For multitype patterns, the fitted model was completely incorrect due to an error in constructing the quadrature scheme. Fixed. o Geyer For point process models with the 'Geyer' interaction, vcov.ppm() and suffstat() sometimes gave incorrect answers. [Spotted by Ege Rubak.] Fixed. o as.im.im Did not correctly handle factor-valued images if one of the arguments 'dimyx', 'eps', 'xy' was given. Fixed. o envelope.lppm Crashed if the model was multitype. Fixed. o lpp Did not handle empty patterns. Fixed. o density.ppp If 'sigma' was a bandwidth selection function such as bw.scott() which returned a numeric vector of length 2, a warning message was issued, and the smoothing bandwidth was erroneously taken to be the first element of the vector. Fixed. o Fest, Jcross, Jdot, Jmulti If these functions were computed using correction = 'rs', plotting them would sometimes give an error, with the message "no finite x/y limits". Fixed. o pcfmodel.kppm For models with clusters="VarGamma" the value of the pcf at distance r=0 was given as NaN. Fixed. o vcov.ppm Result was incorrect in rare cases, due to numerical rounding effects. Fixed. o rLGCP, simulate.kppm For models fitted to point patterns in an irregular window, simulation sometimes failed, with a message that the image 'mu' did not cover the simulation window. (Spotted by George Limitsios.) Fixed. o rLGCP, simulate.kppm Crashed sometimes with an error about unequal x and y steps (from 'GaussRF'). Fixed. CHANGES IN spatstat VERSION 1.29-0 OVERVIEW o We thank Colin Beale, Li Haitao, Frederic Lavancier, Erika Mudrak and Ege Rubak for contributions. o random sequential packing o Allard-Fraley estimator o method for pooling several quadrat tests o better control over dummy points in ppm o more support for data on a linear network o nearest neighbour map o changes to subsetting of images o improvements and bug fixes NEW FUNCTIONS o clusterset Allard-Fraley estimator of high-density features in a point pattern o pool.quadrattest Pool several quadrat tests o nnfun Nearest-neighbour map of a point pattern or a line segment pattern o as.ppm Converts various kinds of objects to ppm o crossdist.lpp Shortest-path distances between pairs of points in a linear network o nobs.lppm Method for 'nobs' for lppm objects. o as.linim Converts various kinds of objects to 'linim' o model.images.slrm Method for model.images for slrm objects o rotate.im Rotate a pixel image SIGNIFICANT USER-VISIBLE CHANGES o "[.im" and "[<-.im" New argument 'j' allows any type of matrix indexing to be used. o "[.im" Default behaviour changed in the case of a rectangular subset. New argument 'rescue' can be set to TRUE to reinstate previous behaviour. o rSSI Performs 'Random Sequential Packing' if n=Inf. o ppm New argument 'eps' determines the spacing between dummy points. (also works for related functions quadscheme, default.dummy, ...) o fitted.ppm, predict.ppm Argument 'new.coef' specifies a vector of parameter values to replace the fitted coefficients of the model. o lppm Stepwise model selection using step() now works for lppm objects. o vcov.slrm Can now calculate correlation matrix or Fisher information matrix as well as variance-covariance matrix. o eval.fv Improved behaviour when plotted. o "[.fv" Improved behaviour when plotted. o lohboot When the result is plotted, the confidence limits are now shaded. o lohboot New argument 'global' allows global (simultaneous) confidence bands instead of pointwise confidence intervals. o vcov.ppm Accelerated by 30% in some cases. o quadrat.test.splitppp The result is now a single object of class 'quadrattest' o progressreport Improved output (also affects many functions which print progress reports) o Full redwood data (redwoodfull) Plot function redwoodfull.extra$plotit has been slightly improved. o nncross This function is now generic, with methods for 'ppp' and 'default'. o distfun The internal format of objects of class 'distfun' has been changed. o duplicated.ppp, unique.ppp New argument 'rule' allows behaviour to be consistent with package 'deldir' BUG FIXES o bdist.tiles Values were incorrect in some cases due to numerical error. (Spotted by Erika Mudrak.) Fixed. o vcov.ppm, suffstat These functions sometimes gave incorrect values for marked point process models. Fixed. o simulate.ppm, predict.ppm Did not correctly handle the 'window' argument. (Spotted by Li Haitao). Fixed. o smooth.ppp, markmean If sigma was very small, strange values were produced, due to numerical underflow. (Spotted by Colin Beale). Fixed. o MultiHard, MultiStrauss, MultiStraussHard Crashed if the data point pattern was empty. (Spotted by Ege Rubak). Fixed. o vcov.ppm Crashed sporadically, with multitype interactions. (Spotted by Ege Rubak). Fixed. o rStrauss, rHardcore, rStraussHard, rDiggleGratton, rDGS If the simulated pattern was empty, these functions would either crash, or return a pattern containing 1 point. (Spotted by Frederic Lavancier). Fixed. o model.matrix.slrm Crashed if the model was fitted using split pixels. Fixed. o residuals.ppm, diagnose.ppm Did not always correctly handle models that included offset terms. Fixed. o project.ppm When a model was projected by project.ppm or by ppm(project=TRUE), the edge corrections used the projected models were sometimes different from the edge corrections in the original model, so that the projected and unprojected models were not comparable. Fixed. o plot.listof, plot.splitppp Crashed sometimes due to a scoping problem. Fixed. o dclf.test, mad.test Crashed if any of the function values were infinite or NaN. Fixed. o psstA Default plot did not show the horizontal line at y=0 corresponding to a perfect fit. Fixed. o vcov.ppm names attribute was spelt incorrectly in some cases. Fixed. CHANGES IN spatstat VERSION 1.28-2 OVERVIEW o We thank Thomas Bendtsen, Ya-Mei Chang, Daniel Esser, Robert John-Chandran, Ege Rubak and Yong Song for contributions. o New code for Partial Residual Plots and Added Variable Plots. o maximum profile pseudolikelihood computations vastly accelerated. o New dataset: cells in gastric mucosa o now possible to capture every k-th state of Metropolis-Hastings algorithm. o size of 'ppm' objects reduced. o scope of 'intensity.ppm' extended. o quadrat.test can now perform Monte Carlo tests and one/two-sided tests o improvements to 'plot.fv' o improvement to 'rescale' o some datasets reorganised. o numerous bug fixes NEW DATASET o mucosa Cells in gastric mucosa Kindly contributed by Dr Thomas Bendtsen NEW FUNCTIONS o parres Partial residual plots for spatial point process models. A diagnostic for the form of a covariate effect. o addvar Added variable plots for spatial point process models. A diagnostic for the existence of a covariate effect. SIGNIFICANT USER-VISIBLE CHANGES o profilepl Accelerated (typically by a factor of 5). o rmh, rmhcontrol It is now possible to save every k-th iteration of the Metropolis-Hastings algorithm. The arguments 'nsave' and 'nburn' may be given to rmh or to rmhcontrol. They specify that the point pattern will be saved every 'nsave' iterations, after an initial burn-in of 'nburn' iterations. o simulate.ppm New argument 'singlerun' determines whether the simulated patterns are generated using independent runs of the Metropolis-Hastings algorithm or are obtained by performing one long run of the algorithm and saving every k-th iteration. o exactMPLEstrauss New argument 'project' determines whether the parameter gamma is constrained to lie in [0,1]. o intensity.ppm Now works for stationary point process models with the interactions DiggleGratton, DiggleGatesStibbard, Fiksel, PairPiece and Softcore. o plot.fv Improved algorithm for avoiding collisions between graphics and legend. o plot.fv New argument 'log' allows plotting on logarithmic axes. o envelope Can now calculate an estimate of the true significance level of the "wrong" test (which declares the observed summary function to be significant if it lies outside the pointwise critical boundary anywhere). Controlled by new argument 'do.pwrong'. o quadrat.test New argument 'alternative' allows choice of alternative hypothesis and returns one-sided or two-sided p-values as appropriate. o quadrat.test Can now perform Monte Carlo test as well (for use in small samples where the chi^2 approximation is inaccurate) o Softcore Improved numerical stability. New argument 'sigma0' for manual control over rescaling. o rescale If scale argument 's' is missing, then the data are rescaled to native units. For example if the current unit is 0.1 metres, coordinates will be re-expressed in metres. o psst Extra argument 'verbose=TRUE' o is.subset.owin Accelerated for polygonal windows o rmh.default 'track' is no longer a formal argument of rmh.default; it is now a parameter of rmhcontrol. However there is no change in usage: the argument 'track' can still be given to rmh.default. o clf.test Has been renamed 'dclf.test' to give proper attribution to Peter Diggle. o betacells This dataset has been restructured. The vector of cell profile areas, formerly given by betacells.extra$area, has now been included as a column of marks in the point pattern 'betacells'. o ants The function ants.extra$plot() has been renamed plotit() for conformity with other datasets. o redwoodfull The function redwoodfull.extra$plot() has been renamed plotit() for conformity with other datasets. o nbfires For conformity with other datasets, there is now an object nbfires.extra BUG FIXES o ripras Expansion factor was incorrect in the rectangular case. Fixed. o Triplets Crashed sometimes with error "dim(X) must have positive length". Fixed. o affine.im Crashed in the case of a diagonal transformation matrix! Spotted by Ege Rubak. Fixed. o envelope.envelope Ignored the argument 'global'. Fixed. o MultiStraussHard The printed output showed the hardcore radii as NULL. Spotted by Ege Rubak. Fixed. o "[.psp" Crashed if the data were generated by rpoisline(). Spotted by Marcelino de la Cruz. Fixed. o plot.linim If style="colour", the main title was always "x". Fixed. o plot.ppx Setting add=TRUE did not prevent the domain being plotted. Fixed. o rmh Crashed if x.start was an empty point pattern. Spotted by Ege Rubak. Fixed. o as.ppp.data.frame Crashed if any points lay outside the window. Spotted by Ege Rubak. Fixed. o Ripley isotropic edge correction Divide-by-zero error in rare cases. Spotted by Daniel Esser. Fixed. o summary functions For many of the summary functions (e.g. Kest, pcf), the result of saving the object to disc was an enormous file. Spotted by Robert John-Chandran. Fixed. o pcf.fv Default plot was wrongly coloured. Fixed. CHANGES IN spatstat VERSION 1.28-1 OVERVIEW o We thank Ege Rubak, Gopal Nair, Jens Oehlschlaegel and Mike Zamboni for contributions. o New approximation to the intensity of a fitted Gibbs model. o Minor improvements and bug fixes o spatstat now 'Suggests' the package 'gsl' NEW FUNCTIONS o intensity, intensity.ppp, intensity.ppm Calculate the intensity of a dataset or fitted model. Includes new approximation to the intensity of a fitted Gibbs model o LambertW Lambert's W-function SIGNIFICANT USER-VISIBLE CHANGES o envelope Improved plot labels for envelopes that were generated using the 'transform' argument. o plot.fv Improved algorithm for collision detection. o plot.im Now returns the colour map used. o plot.listof, plot.splitppp Slight change to handling of plot.begin and plot.end o square Now accepts vectors of length 2 o plot.fii Increased resolution of the plot obtained from plot(fitin(ppm(...))) o image.listof If equal.ribbon=TRUE, the colour ribbon will no longer be displayed repeatedly for each panel, but will now be plotted only once, at the right hand side of the plot array. BUG FIXES o vcov.ppm Results were sometimes incorrect for a Gibbs model with non-trivial trend. Spotted by Ege Rubak. Fixed. o nncross In rare cases the results could be slightly incorrect. Spotted by Jens Oehlschlaegel. Fixed. o plot.fv When add=TRUE, the x limits were sometimes truncated. Spotted by Mike Zamboni. Fixed. o plot.im Labels for the tick marks on the colour ribbon were sometimes ridiculous, e.g. "2.00000001". Fixed. CHANGES IN spatstat VERSION 1.28-0 OVERVIEW o We thank Farzaneh Safavimanesh, Andrew Hardegen and Tom Lawrence for contributions. o Improvements to 3D summary functions. o A multidimensional point pattern (ppx) can now have 'local' coordinates as well as spatial and temporal coordinates and marks. o Changed format for point patterns on a linear network (lpp). Changes are backward compatible. Many computations run faster. o More support for fitted cluster models (kppm). o split method for multidimensional point patterns (ppx) and point patterns on a linear network (lpp). o Fixed bug causing errors in plot.im o Miscellaneous improvements and bug fixes NEW FUNCTIONS o exactMPLEstrauss Fits the stationary Strauss point process model using an exact maximum pseudolikelihood technique. This is mainly intended for technical investigation of algorithms. o split.ppx Method for 'split' for multidimensional point patterns (class 'ppx'). This also works for point patterns on a linear network (class 'lpp'). o model.images This function is now generic, with methods for classes ppm, kppm, lppm o model.frame, model.matrix These generic functions now have methods for classes kppm, lppm o as.owin.kppm, as.owin.lppm New methods for 'as.owin' for objects of class kppm, lppm o as.linnet.lppm Extracts the linear network in which a point process model was fitted. SIGNIFICANT USER-VISIBLE CHANGES o class 'ppx' An object of class 'ppx' may now include 'local' coordinates as well as 'spatial' and 'temporal' coordinates, and marks. o ppx Arguments have changed. o class 'lpp' The internal format of lpp objects has been extended (but is backward-compatible). Many computations run faster. To convert an object to the new format: X <- lpp(as.ppp(X), as.linnet(X)). o F3est Calculation of theoretical Poisson curve ('theo') has changed, and is now controlled by the argument 'sphere'. o rmh, rmhstart The initial state ('start') can now be missing or null. o im, as.im The pixel coordinates in an image object are now generated more accurately. This avoids a numerical error in plot.im. o eval.fv, eval.fasp Evaluation is now applied only to columns that contain values of the function itself (rather than values of the derivative, hazard rate, etc). This is controlled by the new argument 'dotonly'. o spatstat.options New option 'nvoxel' o quad.ppm Now accepts kppm objects. o str This generic function (for inspecting the internal structure of an object) now produces sensible output for objects of class 'hyperframe', 'ppx', 'lpp' o ppx, coords.ppx, coords<-.ppx The arguments to these functions have changed. o lgcp.estK, Kmodel Computation can be greatly accelerated by setting spatstat.options(fastK.lgcp=TRUE). o G3est Computation accelerated. o envelope Computation slightly accelerated. o spatstat.options New option 'fastK.lgcp' BUG FIXES o nndist.psp Caused an error if length(k) > 1. Fixed. o plot.im Sometimes reported an error "useRaster=TRUE can only be used with a regular grid." This was due to numerical rounding effects on the coordinates of a pixel image. Fixed. o plot.fv If a formula was used to specify the plot, the names of variables in the formula were sometimes incorrectly matched to *functions*. Spotted by Farzaneh Safavimanesh. Fixed. o F3est Took a very long time if the containing box was very flat, due to the default value of 'vside'. Fixed. o rmh, rmhmodel An erroneous warning about 'outdated format of rmhmodel object' sometimes occurred. Fixed. o marks<-.ppx Names of result were incorrect. Fixed. o hyperframe class Various minor bug fixes. CHANGES IN spatstat VERSION 1.27-0 OVERVIEW o Variance estimates are now available for all Gibbs point process models. o Cressie-Loosmore-Ford test implemented o plot.fv now avoids collisions between the legend and the graphics. o Extension to predict.ppm o Improvements to envelopes and multitype summary functions. o Line transects of a pixel image. o Changes to defaults in Metropolis-Hastings simulations. o More geometrical operations o Bug fixes. o We thank Aruna Jammalamadaka for contributions. NEW FUNCTIONS o clf.test Perform the Cressie (1991)/ Loosmore and Ford (2006) test of CSR (or another model) o mad.test Perform the Maximum Absolute Deviation test of CSR (or another model). o convolve.im Compute convolution of pixel images. o Kmulti.inhom Counterpart of 'Kmulti' for spatially-varying intensity. o rmhexpand Specify a simulation window, or a rule for expanding the simulation window, in Metropolis-Hastings simulation (rmh) o transect.im Extract pixel values along a line transect. o affine.im Apply an affine transformation to a pixel image. o scalardilate Perform scalar dilation of a geometrical object relative to a specified origin. o reflect Reflect a geometrical object through the origin. o "[.lpp", "[.ppx" Subset operators for the classes "lpp" (point pattern on linear network) and "ppx" (multidimensional space-time point pattern). o is.rectangle, is.polygonal, is.mask Determine whether a window w is a rectangle, a domain with polygonal boundaries, or a binary pixel mask. o has.offset Determines whether a fitted model object (of any kind) has an offset. SIGNIFICANT USER-VISIBLE CHANGES o predict.ppm This function can now calculate the conditional intensity of a model relative to any point pattern X (not just the original data pattern). o vcov.ppm This function now handles all Gibbs point process models. o plot.fv Collisions between the legend box and the graphics are now detected and avoided. o rmh.ppm, rmh.default, simulate.ppm, qqplot.ppm, envelope.ppm These functions now have slightly different default behaviour because of changes to the handling of arguments to 'rmhcontrol'. o rmhcontrol The default value of the parameters 'periodic' and 'expand' has changed. o rmhcontrol The parameter 'expand' can now be in any format acceptable to rmhexpand(). o rmh.ppm, rmh.default, simulate.ppm Any 'rmhcontrol' parameter can now be given directly as an argument to rmh.ppm, rmh.default or simulate.ppm. o Kmulti, Gmulti, Jmulti The arguments I, J can now be any kind of subset index or can be functions that yield a subset index. o envelope.envelope In envelope(E, fun=NULL) if E does not contain simulated summary functions, but does contain simulated point patterns, then 'fun' now defaults to Kest, instead of flagging an error. o print.ppp, summary.ppp If the point pattern x was generated by Metropolis-Hastings simulation using 'rmh', then print(x) and summary(x) show information about the simulation parameters. o print.ppm Standard errors for the parameter estimates, and confidence intervals for the parameters, can now be printed for all Gibbs models (but are printed only for Poisson models by default). o eval.im Images with incompatible dimensions are now resampled to make them compatible (if harmonize=TRUE). o spatstat.options New option 'print.ppm.SE' controls whether standard errors and confidence intervals are printed for all Gibbs models, for Poisson models only, or are never printed. o inside.owin Now accepts the form list(x,y) for the first argument. o image.listof New argument 'equal.ribbon' allows several images to be plotted with the same colour map. o is.subset.owin Improved accuracy in marginal cases. o expand.owin Functionality extended to handle all types of expansion rule. o default.rmhcontrol, default.expand These functions now work with models of class 'rmhmodel' as well as 'ppm' o print.rmhcontrol Output improved. BUG FIXES o linearK, linearKinhom If any data points were located exactly at a vertex of the linear network, the weights for Ang's correction were incorrect, due to numerical error. This sometimes produced infinite or NA values of the linear K function. Fixed. o predict.ppm In some cases, predict.ppm(type="cif") generated a spurious warning that "number of rows of result is not a multiple of vector length." Fixed. o crossing.psp Results were sometimes incorrect due to numerical rounding error associated with GCC bug #323. Fixed. o MultiHard, MultiStrauss, MultiStraussHard If the mark values contained non-alphanumeric characters, the names of the interaction coefficients in coef(ppm(...)) were sometimes garbled. Fixed. o profilepl For edge corrections other than the border correction, an error message about 'rbord' would sometimes occur. Fixed. o is.marked, is.multitype These functions gave the wrong answer for 'lpp' objects. Fixed. o marks<-.lpp, marks<-.ppx Format of result was garbled if new columns of marks were added. Fixed. o reach.rmhmodel Gave the wrong answer for Geyer and BadGey models. Fixed. o envelope.envelope Ignored the argument 'savefuns'. Fixed. o BadGey Sometimes wrongly asserted that the parameter 'sat' was invalid. Occurred only in ppm(project=TRUE). Fixed. CHANGES IN spatstat VERSION 1.26-1 OVERVIEW o Variance-covariance matrix for Gibbs point process models. o Bootstrap confidence bands for pair correlation function and K function. o Bug fix in scan test. o Area-interaction model accelerated. o we thank Jean-Francois Coeurjolly and Ege Rubak for contributions. NEW FUNCTIONS o lohboot Computes bootstrap confidence bands for pair correlation function and K function using Loh's (2008) mark bootstrap. SIGNIFICANT USER-VISIBLE CHANGES o vcov.ppm Now works for all Gibbs point process models, thanks to new code (and theory) from Jean-Francois Coeurjolly and Ege Rubak o AreaInter Computations related to the area-interaction point process (ppm, predict.ppm, residuals.ppm, diagnose.ppm, qqplot.ppm) have been accelerated. BUG FIXES o scan.test Results were sometimes incorrect due to numerical instability (a 'Gibbs phenomenon'). Fixed. CHANGES IN spatstat VERSION 1.26-0 OVERVIEW o We thank Jens Oehlschlaegel for contributions. o Further substantial acceleration of spatstat functions. o Workaround for bug in RandomFields package. o Numerous modifications to internal code. NEW FUNCTIONS o RandomFieldsSafe There is a bug in the package 'RandomFields' (version <= 2.0.54) which causes a crash to occur, in the development version of R but not in R 2.15.0. To avoid crashing spatstat, we have written the temporary, undocumented function RandomFieldsSafe() which returns TRUE if it is safe to use the RandomFields package. Examples in the spatstat help files for kppm, lgcp.estK, lgcp.estpcf and rLGCP are only executed if RandomFieldsSafe() returns TRUE. SIGNIFICANT USER-VISIBLE CHANGES o Many functions Many spatstat functions now run faster, and will handle larger datasets, thanks to improvements in the internal code, following suggestions from Jens Oehlschlaegel. o Many functions The response to an 'Interrupt' signal is slightly slower. CHANGES IN spatstat VERSION 1.25-5 OVERVIEW o We thank Ya-Mei Chang, Jens Oehlschlaegel and Yong Song for contributions. o Extended functionality of 'rhohat' to local likelihood smoothing and bivariate smoothing. o Nearest neighbour distance computations accelerated. o spatstat now 'Suggests:' the package 'locfit' NEW FUNCTIONS o rho2hat Bivariate extension of 'rhohat' for estimating spatial residual risk, or intensity as a function of two covariates. SIGNIFICANT USER-VISIBLE CHANGES o rhohat Estimation can now be performed using local likelihood fitting with the 'locfit' package, or using kernel smoothing. o nncross Substantially accelerated. New arguments added to control the return value and the sorting of data. BUG FIXES o plot.msr Crashed if the argument 'box' was given. Fixed. CHANGES IN spatstat VERSION 1.25-4 OVERVIEW o We thank Jonathan Lee and Sergiy Protsiv for contributions. o Improvements and bug fixes to K function for very large datasets NEW FUNCTIONS o rStraussHard Perfect simulation for Strauss-hardcore process (with gamma <= 1) SIGNIFICANT USER-VISIBLE CHANGES o plot.im The colour ribbon can now be placed left, right, top or bottom using new argument 'ribside' o profilepl Does not generate warnings when some of the candidate models have zero likelihood - for example when fitting model with a hard core. o Kest Now includes fast algorithm for 'correction="none"' which will handle patterns containing millions of points. BUG FIXES o Kest, Lest Gave incorrect values in very large datasets, due to numerical overflow. `Very large' typically means about 1 million points in a random pattern, or 100,000 points in a tightly clustered pattern. [Overflow cannot occur unless there are at least 46,341 points.] [Spotted by Sergiy Protsiv.] Fixed. o Kest, Lest Ignored 'ratio=TRUE' if the argument 'domain' was given. [Spotted by Jonathan Lee.] Fixed. o rjitter Output was sometimes incorrect. [Spotted by Sergiy Protsiv.] Fixed. CHANGES IN spatstat VERSION 1.25-3 OVERVIEW o We thank Daniel Esser for contributions. o Improved support for fitted point process models. o Bug fixes. NEW FUNCTIONS o simulate.slrm Method for 'simulate' for spatial logistic regression models. o labels.ppm, labels.kppm, labels.slrm Methods for 'labels' for fitted point process models. o commonGrid Determine a common spatial domain and pixel resolution for several pixel images and/or binary masks SIGNIFICANT USER-VISIBLE CHANGES o effectfun Now has argument 'se.fit' allowing calculation of standard errors and confidence intervals. o [.msr Now handles character-valued indices. o print.summary.ppm Output gives a more precise description of the fitting method. o ppm, kppm, slrm Confidence intervals for the fitted trend parameters can now be obtained using 'confint' o predict.slrm New argument 'window' o union.owin Now handles a single argument: union.owin(A) returns A. BUG FIXES o selfcrossing.psp y coordinate values were incorrect. [Spotted by Daniel Esser.] Fixed. o as.im.owin Did not handle a binary mask with a 1 x 1 pixel array. Fixed. o predict.slrm Results of predict(object, newdata) were incorrect if the spatial domain of 'newdata' was larger than the original domain. Fixed. o ppm If the model was the uniform Poisson process, the argument 'rbord' was ignored. Fixed. o image subset assignment "[<-.im" Generated an error if the indexing argument 'i' was a point pattern containing zero points. Fixed. o hyperframe subset assignment "[<-.hyperframe" Did not correctly handle the case where a single column of the hyperframe was to be changed. Fixed. o help(bw.relrisk), help(rmh.ppm), help(plot.plotppm) These help files had the side-effect of changing some options in spatstat.options. Fixed. CHANGES IN spatstat VERSION 1.25-2 OVERVIEW o We thank Abdollah Jalilian and Thierry Onkelinx for contributions. o Very Important Bug fixes. o Improved mechanism for handling 'invalid' point processes NEW FUNCTIONS o as.matrix.owin Converts a window to a logical matrix. SIGNIFICANT USER-VISIBLE CHANGES o project.ppm Improved algorithm. Now handles terms in the trend formula as well as the interaction. The projected point process is now obtained by re-fitting the model, and is guaranteed to be the maximum pseudolikelihood fit. o plot.im Now handles many arguments recognised by plot.default such as 'cex.main'. Also handles argument 'box'. New argument 'ribargs' contains parameters controlling the ribbon plot only. o spatstat.options New option 'project.fast' allows a faster shortcut for project.ppm o spatstat.options New options 'rmh.p', 'rmh.q', 'rmh.nrep' determine the default values of the parameters p, q and nrep of the Metropolis-Hastings algorithm. See rmhcontrol o ppm Slightly accelerated. BUG FIXES o nncross, distfun, AreaInter Results of nncross were possibly incorrect when X and Y did not have the same window. This bug affected values of 'distfun' and may also have affected ppm objects with interaction 'AreaInter'. [Spotted by Thierry Onkelinx] Bug introduced in spatstat 1.9-4 (June 2006). Fixed. o rCauchy Simulations were incorrect in the sense that the value of 'omega' was inadvertently doubled (i.e. omega was incorrectly replaced by 2 * omega). Bug introduced in spatstat 1.25-0. Fixed. o plot.im White lines were present in the image display, on some graphics devices, due to changes in R 2.14. Fixed. o update.ppm The result of 'update(object, formula)' sometimes contained errors in the internal format. Bug introduced in spatstat 1.25-0. Fixed. o example(AreaInter), example(bw.smoothppp), example(Kest.fft), example(plot.owin), example(predict.ppm), example(simulate.ppm) Executing these examples had the side-effect of changing some of the parameters in spatstat.options. Fixed. CHANGES IN spatstat VERSION 1.25-1 OVERVIEW o We thank Neba Funwi-Gabga and Jorge Mateu for contributions. o New dataset of gorilla nest sites o New functions for perfect simulation o Bug fix for rare crashes in rStrauss o Code for ensuring a fitted point process model is a valid point process NEW DATASET o gorillas Gorilla nest sites in a National Park in Cameroon. Generously contributed by Neba Funwi-Gabga NEW FUNCTIONS o rDiggleGratton, rDGS, rHardcore Perfect simulation for the Diggle-Gratton process, Diggle-Gates-Stibbard process, and Hardcore process. o bw.scott Scott's rule of thumb for bandwidth selection in multidimensional smoothing o valid.ppm Checks whether a fitted point process model is a valid point process o project.ppm Forces a fitted point process model to be a valid point process SIGNIFICANT USER-VISIBLE CHANGES o ppm New argument 'project' determines whether the fitted model is forced to be a valid point process o linnet Substantially accelerated. o rStrauss Slightly accelerated. o summary.lpp Now prints the units of length. BUG FIXES o rStrauss Crashed rarely (once every 10 000 realisations) with a memory segmentation fault. Fixed. CHANGES IN spatstat VERSION 1.25-0 OVERVIEW o Leverage and influence for point process models o New cluster models (support for model-fitting and simulation). o Fit irregular parameters in trend of point process model o Third order summary statistic. o Improvements to speed and robustness of code. o spatstat now depends on R 2.14 o We thank Abdollah Jalilian and Rasmus Waagepetersen for contributions. NEW FUNCTIONS o leverage.ppm, influence.ppm, dfbetas.ppm Leverage and influence for point process models o ippm Experimental extension to 'ppm' which fits irregular parameters in trend by Fisher scoring algorithm. o Tstat Third order summary statistic for point patterns based on counting triangles. o rCauchy, rVarGamma simulation of a Neyman-Scott process with Cauchy clusters or Variance Gamma (Bessel) clusters. Contributed by Abdollah Jalilian. o rPoissonCluster simulation of a general Poisson cluster process o model.covariates Identify the covariates involved in a model (lm, glm, ppm etc) o as.im.distfun Converts a 'distfun' to a pixel image. o cauchy.estK, cauchy.estpcf, vargamma.estK, vargamma.estpcf Low-level model-fitting functions for the Neyman-Scott process with Cauchy or Variance-Gamma cluster kernel. Contributed by Abdollah Jalilian. SIGNIFICANT USER-VISIBLE CHANGES o kppm Now accepts clusters="Cauchy" or clusters="VarGamma" for the Neyman-Scott process with Cauchy or Variance-Gamma cluster kernel. Code contributed by Abdollah Jalilian. o rNeymanScott Argument 'rcluster' may now take a different format. o psst Argument 'funcorrection' changed to 'funargs' allowing greater flexibility. o plot.fv, plot.envelope New argument 'limitsonly' allows calculation of a common x,y scale for several plots. o overall speed spatstat is now byte-compiled and runs slightly faster. o user interrupt Long calculations in spatstat now respond to the Interrupt/Stop signal. o update.ppm Now runs faster and uses much less memory, when the update only affects the model formula (trend formula). o rNeymanScott, rThomas, rMatClust Accelerated thanks to Rasmus Waagepetersen. o multitype data and models Second order multitype statistics (such as Kcross, pcfcross) and multitype interaction models (such as MultiStrauss) now run faster, by a further 5%. BUG FIXES o distfun Some manipulations involving 'distfun' objects failed if the original data X in distfun(X) did not have a rectangular window. Fixed. CHANGES IN spatstat VERSION 1.24-2 OVERVIEW o Geyer's triplet interaction o more functionality for replicated point patterns o changed default for simulation window in point process simulation o changed default for edge correction in Kcom, Gcom o data in spatstat is now lazy-loaded o bug fixes NEW FUNCTIONS o Triplets Geyer's triplet interaction, for point process models o coef.summary.ppm New method coef.summary.ppm You can now type 'coef(summary(fit))' to extract a table of the fitted coefficients of the point process model 'fit' SIGNIFICANT USER-VISIBLE CHANGES o data in spatstat are now lazy-loaded so you don't have to type data(amacrine), etc. o rmh.default, rmh.ppm, simulate.ppm These now handle the 'triplets' interaction o fryplot Now has arguments 'to' and 'from', allowing selection of a subset of points. o fryplot, frypoints These functions now handle marked point patterns properly. o Kcross, Kdot, Kmulti New argument 'ratio' determines whether the numerator and denominator of the estimate of the multitype K-function will be stored. This enables analysis of replicated point patterns, using 'pool.rat()' to pool the K function estimates. o rmh.ppm, simulate.ppm, default.expand For point process models which have a trend depending only on x and y, the simulation window is now taken to be the same as the original window containing the data (by default). That is, `expansion' does not take place, by default. (In previous versions of spatstat the simulation window was larger than the original data window.) o rmh.ppm, simulate.ppm The argument sequence for these functions has changed. New argument 'expand' allows more explicit control over simulation domain. o Kcom, Gcom New argument 'conditional' gives more explicit control over choice of edge correction in compensator. Simplified defaults for edge correction. o Kinhom Improved plot labels. o profilepl Printed output improved. BUG FIXES o Lest The variance approximations (Lotwick-Silverman and Ripley) obtained with var.approx=TRUE, were incorrect for Lest (although they were correct for Kest) due to a coding error. Fixed. o simulate.ppm Ignored the argument 'control' in some cases. Fixed. o pcf and its relatives (pcfinhom, pcfcross.inhom, pcfdot.inhom) Sometimes gave a warning about 'extra arguments ignored'. Fixed. CHANGES IN spatstat VERSION 1.24-1 OVERVIEW o Spatial Scan Test o Functionality for replicated point patterns o Bug fixes NEW FUNCTIONS o scan.test Spatial scan test of clustering o rat New class of 'ratio objects' o pool.rat New method for 'pool'. Combines K function estimates for replicated point patterns (etc) by computing ratio-of-sums o unnormdensity Weighted kernel density with weights that do not sum to 1 and may be negative. o compatible New generic function with methods for 'fv', 'im', 'fasp' and 'units' SIGNIFICANT USER-VISIBLE CHANGES o Kest New argument 'ratio' determines whether the numerator and denominator of the estimate of the K-function will be stored. This enables analysis of replicated point patterns, using 'pool.rat()' to pool the K function estimates. o Lest Now handles theoretical variance estimates (using delta method) if var.approx=TRUE o as.mask Argument 'eps' can now be a 2-vector, specifying x and y resolutions. o default.expand Behaviour changed slightly. o plot.listof, plot.splitppp, contour.listof, image.listof The arguments 'panel.begin' and 'panel.end' can now be objects such as windows. BUG FIXES o rgbim, hsvim Did not work on images with non-rectangular domains. Fixed. o scaletointerval Did not handle NA's. Fixed. CHANGES IN spatstat VERSION 1.24-0 OVERVIEW o This version was not released publicly. CHANGES IN spatstat VERSION 1.23-6 OVERVIEW o Spatial covariance functions of windows and pixel images. o Area-interaction models can now be fitted in non-rectangular windows o Bug fix for envelope of inhomogeneous Poisson process o Bug fix for raster conversion o New vignette on 'Getting Started with Spatstat' o Code accelerated. NEW FUNCTIONS o imcov Spatial covariance function of pixel image or spatial cross-covariance function of two pixel images o harmonise.im Make several pixel images compatible by converting them to the same pixel grid o contour.listof, image.listof Methods for contour() and image() for lists of objects o dummify Convert data to numeric values by constructing dummy variables. SIGNIFICANT USER-VISIBLE CHANGES o setcov Can now compute the `cross-covariance' between two regions o AreaInter Point process models with the AreaInter() interaction can now be fitted to point pattern data X in any window. o areaGain, areaLoss These now handle arbitrary windows W. They are now more accurate when r is very small. o Kcom Computation vastly accelerated, for non-rectangular windows. o vignettes New vignette 'Getting Started with the Spatstat Package' o nncorr, nnmean, nnvario These functions now handle data frames of marks. BUG FIXES o envelope.ppm If the model was an inhomogeneous Poisson process, the resulting envelope object was incorrect (the simulations were correct, but the envelopes were calculated assuming the model was CSR). Bug was introduced in spatstat 1.23-5. Fixed. o envelope.ppm If the model was an inhomogeneous Poisson process with intensity a function of x and y only, overflow errors sometimes occurred ('insufficient storage' or 'attempting to generate a large number of random points'). Fixed. o as.im.im The result of as.im(X, W) was incorrect if 'W' did not cover 'X'. Fixed. o as.mask The result of as.mask(w, xy) was incorrect if 'xy' did not cover 'w'. Fixed. o plot.fv Legend was incorrectly labelled if 'shade' variables were not included in the plot formula. Fixed. o areaGain, areaLoss Crashed if the radius r was close to zero. Fixed. CHANGES IN spatstat VERSION 1.23-5 OVERVIEW o Bug fix to bandwidth selection. o Functions to pool data from several objects of the same class. o Improvements and bug fixes. o We thank Michael Sumner for contributions. NEW FUNCTIONS o pool Pool data from several objects of the same class o pool.envelope Pool simulated data from several envelope objects and create a new envelope o pool.fasp Pool simulated data from several function arrays and create a new array o envelope.envelope Recalculate an envelope from simulated data using different parameters SIGNIFICANT USER-VISIBLE CHANGES o bw.diggle, bw.relrisk, bw.smoothppp, bw.optim Plot method modified. o model.depends Now also recognises 'offset' terms. BUG FIXES o bw.diggle Bandwidth was too large by a factor of 2. Fixed. o plot.psp Crashed if any marks were NA. Fixed. o pointsOnLines Crashed if any segments had zero length. Ignored argument 'np' in some cases. Fixed. o stieltjes Crashed if M had only a single column of function values. Fixed. CHANGES IN spatstat VERSION 1.23-4 OVERVIEW o Bandwidth selection for density.ppp and smooth.ppp o Layered plots. o Model-handling facilities. o Improvements and bug fixes. NEW FUNCTIONS o bw.diggle Bandwidth selection for density.ppp by mean square error cross-validation. o bw.smoothppp Bandwidth selection for smooth.ppp by least-squares cross-validation. o layered, plot.layered A simple mechanism for controlling plots that consist of several successive layers of data. o model.depends Given a fitted model (of any kind), identify which of the covariates is involved in each term of the model. o model.is.additive Determine whether a fitted model (of any kind) is additive, in the sense that each term in the model involves at most one covariate. SIGNIFICANT USER-VISIBLE CHANGES o smooth.ppp Bandwidth 'sigma' is now selected by least-squares cross-validation o bw.relrisk Computation in large datasets accelerated. New arguments 'hmin', 'hmax' control the range of trial values of bandwidth. o Hest, Gfox, Jfox Improved algebraic labels for plot o spatstat.options New parameter 'n.bandwidth' o density.ppp, smooth.ppp Slightly accelerated. o point-in-polygon test Accelerated. BUG FIXES o with.fv Mathematical labels were incorrect in some cases. Fixed. o bw.relrisk Implementation of method="weightedleastsquares" was incorrect and was equivalent to method="leastsquares". Fixed. o smooth.ppp NaN values occurred if the bandwidth was very small. Fixed. CHANGES IN spatstat VERSION 1.23-3 OVERVIEW o Urgent bug fix. BUG FIXES o crossing.psp Crashed occasionally with a message about NA or NaN values. Fixed. o affine.ppp Crashed if the point pattern was empty. Fixed. CHANGES IN spatstat VERSION 1.23-2 OVERVIEW o Bug fixes. o Several functions have been accelerated. o We thank Marcelino de la Cruz and Ben Madin for contributions. NEW FUNCTIONS o sumouter, quadform Evaluate certain quadratic forms. o flipxy Exchange x and y coordinates. SIGNIFICANT USER-VISIBLE CHANGES o vcov.ppm Accelerated. o owin, as.owin Checking the validity of polygons has been accelerated. o crossing.psp, selfcrossing.psp Accelerated. BUG FIXES o split.ppp If drop=TRUE then some of the point patterns had the wrong windows. Spotted by Marcelino de la Cruz. Fixed. o split.ppp Crashed if the tessellation did not cover the point pattern. Fixed. o predict.ppm Crashed when type="se" if NA's were present. Spotted by Ben Madin. Fixed. o plot.ppp Incorrectly handled the case where both 'col' and 'cols' were present. Fixed. o polygon geometry The point-in-polygon test gave the wrong answer in some boundary cases. Fixed. CHANGES IN spatstat VERSION 1.23-1 OVERVIEW o Important bug fix to 'localpcf'. o Inverse-distance weighted smoothing. o Inhomogeneous versions of neighbourhood density functions. o Internal repairs and bug fixes. o We thank Mike Kuhn and Ben Madin for contributions. NEW FUNCTIONS o idw Inverse-distance weighted smoothing. o localKinhom, localLinhom, localpcfinhom Inhomogeneous versions of localK, localL, localpcf BUG FIXES o localpcf The columns of the result were in the wrong order. [i.e. pair correlation functions were associated with the wrong points.] Fixed. o delaunay If the union of several Delaunay triangles formed a triangle, this was erroneously included in the result of delaunay(). Fixed. o predict.ppm, plot.ppm Sometimes crashed with a warning about 'subscript out of bounds'. Fixed. o point-in-polygon test Vertices of a polygon were sometimes incorrectly classified as lying outside the polygon. Fixed. o Internal code Numerous tweaks and repairs to satisfy the package checker for the future R version 2.14. CHANGES IN spatstat VERSION 1.23-0 OVERVIEW o point patterns on a linear network: new tools including geometrically-corrected linear K function, pair correlation function, point process models, envelopes o changes to renormalisation of estimates in Kinhom and pcfinhom o new dataset: Chicago street crime o spatstat now 'Suggests:' the package RandomFields o spatstat now has a Namespace o we thank Mike Kuhn, Monia Mahling, Brian Ripley for contributions. NEW DATASET o chicago Street crimes in the University district of Chicago. A point pattern on a linear network. NEW FUNCTIONS o envelope.lpp Simulation envelopes for point patterns on a linear network o lineardisc Compute the 'disc' of radius r in a linear network o linearpcf Pair correlation for point pattern on a linear network o linearKinhom, linearpcfinhom Inhomogeneous versions of the K function and pair correlation function for point patterns on a linear network o lppm Fit point process models on a linear network. o anova.lppm Analysis of deviance for point process models on a linear network. o predict.lppm Prediction for point process models on a linear network. o envelope.lppm Simulation envelopes for point process models on a linear network. o linim Pixel image on a linear network o plot.linim Plot a pixel image on a linear network SIGNIFICANT USER-VISIBLE CHANGES o linearK New argument 'correction'. Geometrically-corrected estimation is performed by default (based on forthcoming paper by Ang, Baddeley and Nair) o Kinhom New argument 'normpower' allows different types of renormalisation. o pcfinhom Now performs renormalisation of estimate. Default behaviour changed - estimates are now renormalised by default. BUG FIXES o density.ppp Crashed if argument 'varcov' was given. Fixed. CHANGES IN spatstat VERSION 1.22-4 OVERVIEW o new diagnostics based on score residuals o new dataset o improvements to plotting summary functions o We thank Ege Rubak, Jesper Moller, George Leser, Robert Lamb and Ulf Mehlig for contributions. NEW FUNCTIONS o Gcom, Gres, Kcom, Kres New diagnostics for fitted Gibbs or Poisson point process models based on score residuals. Gcom is the compensator of the G function Gres is the residual of the G function Kcom is the compensator of the K function Kres is the residual of the K function o psst, psstA, psstG New diagnostics for fitted Gibbs or Poisson point process models based on pseudoscore residuals. psst is the pseudoscore diagnostic for a general alternative psstA is the pseudoscore diagnostic for an Area-interaction alternative psstG is the pseudoscore diagnostic for a Geyer saturation alternative o compareFit Computes and compares several point process models fitted to the same dataset, using a chosen diagnostic. o as.interact Extracts the interpoint interaction structure (without parameters) from a fitted point process model or similar object. NEW DATASET o flu Spatial point patterns giving the locations of influenza virus proteins on cell membranes. Kindly released by Dr George Leser and Dr Robert Lamb. SIGNIFICANT USER-VISIBLE CHANGES o pixel images and grids The default size of a pixel grid, given by spatstat.options("npixel"), has been changed from 100 to 128. A power of 2 gives faster and more accurate results in many cases. o residuals.ppm New arguments 'coefs' and 'quad' for advanced use (make it possible to compute residuals from a modified version of the fitted model.) o relrisk New argument 'casecontrol' determines whether a bivariate point pattern should be treated as case-control data. o plot.fv Further improvements in mathematical labels. o plot.fv The formula can now include the symbols .x and .y as abbreviation for the function argument and the recommended function value, respectively. o plot.fv New argument 'add' BUG FIXES o multitype summary functions (Kcross, Kdot, Gcross, Gdot, .....) Plotting these functions generated an error if the name of one of the types of points contained spaces, e.g. "Escherichia coli". Fixed. CHANGES IN spatstat VERSION 1.22-3 OVERVIEW o Important bug fix to simulation code o Miscellaneous improvements o spatstat now depends on R 2.13.0 or later o We thank Ege Rubak, Kaspar Stucki, Vadim Shcherbakov, Jesper Moller and Ben Taylor for contributions. NEW FUNCTIONS o is.stationary, is.poisson New generic functions for testing whether a point process model is stationary and/or Poisson. Methods for ppm, kppm, slrm etc o raster.xy raster coordinates of a pixel mask o zapsmall.im 'zapsmall' for pixel images SIGNIFICANT USER-VISIBLE CHANGES o density.ppp New argument 'diggle' allows choice of edge correction o rotate.owin, affine.owin These functions now handle binary pixel masks. New argument 'rescue' determines whether rectangles will be preserved BUG FIXES o rmh, simulate.ppm Serious bug - simulation was completely incorrect in the case of a multitype point process with an interaction that does not depend on the marks, such as ppm(betacells, ~marks, Strauss(60)) The calling parameters were garbled. Fixed. o effectfun Crashed if the covariate was a function(x,y). Fixed. o lurking Gave erroneous error messages about 'damaged' models. Fixed. o envelope.ppm Did not recognise when the fitted model was equivalent to CSR. Fixed. o plot.ppx Crashed in some cases. Fixed. CHANGES IN spatstat VERSION 1.22-2 OVERVIEW o Fitting and simulation of log-Gaussian Cox processes with any covariance function o More support for 'kppm' and 'rhohat' objects o K-function for point patterns on a linear network o Metropolis-Hastings algorithm now saves its transition history o Easier control of dummy points in ppm o Convert an 'fv' object to an R function o spatstat now depends on the package 'RandomFields' o We thank Abdollah Jalilian, Shen Guochun, Rasmus Waagepetersen, Ege Rubak and Ang Qi Wei for contributions. NEW FUNCTIONS o linearK Computes the Okabe-Yamada network K-function for a point pattern on a linear network. o pairdist.lpp Shortest-path distances between each pair of points on a linear network. o vcov.kppm Asymptotic variance-covariance matrix for regression parameters in kppm object. [Contributed by Abdollah Jalilian and Rasmus Waagepetersen] o rLGCP Simulation of log-Gaussian Cox processes [Contributed by Abdollah Jalilian and Rasmus Waagepetersen] o predict.rhohat Method for 'predict' for objects of class 'rhohat' Computes a pixel image of the predicted intensity. o Kmodel, pcfmodel Generic functions that compute the K-function or pair correlation function of a point process *model*. So far the only methods are for the class 'kppm'. o as.function.fv Converts a function value table (class 'fv') to a function in R o coef.kppm Method for 'coef' for objects of class 'kppm' o unitname, unitname<- These generic functions now have methods for fitted model objects (classes ppm, slrm, kppm, minconfit) and quadrature schemes (quad). o nobs.ppm Method for 'nobs' for class 'ppm'. Returns the number of points in the original data. SIGNIFICANT USER-VISIBLE CHANGES o kppm Can now fit a log-Gaussian Cox process o simulate.kppm Can now simulate a fitted log-Gaussian Cox process o lgcp.estK, lgcp.estpcf These functions previously fitted a log-Gaussian Cox process with exponential covariance. They can now fit a log-Gaussian Cox process with any covariance function implemented by the RandomFields package. o rmh If track=TRUE, the history of transitions of the Metropolis-Hastings algorithm is saved and returned. o ppm New argument 'nd' controls the number of dummy points. o as.fv Now handles objects of class kppm or minconfit. o rhohat If covariate = "x" or "y", the resulting object has the same 'unitname' as the original point pattern data. o rhohat Now has arguments 'eps, 'dimyx' to control pixel resolution. o MultiStrauss, MultiHard, MultiStraussHard Default value of 'types' has been changed to NULL. o data(ants) The auxiliary data 'ants.extra' now includes a function called 'side' determining whether a given location is in the scrub or field region. Can be used as a covariate in ppm, kppm, slrm. o print.ppm Now has argument 'what' to allow only selected information to be printed. BUG FIXES o profilepl Crashed in some cases involving multitype interactions. Fixed. o plot.splitppp Behaved incorrectly if 'main' was an expression. Fixed. o effectfun Crashed in trivial cases. Fixed. o kppm, thomas.estpcf, matclust.estpcf, lgcp.estpcf Gave a spurious warning message. Fixed. o step When applied to ppm objects this gave a spurious warning. Fixed. CHANGES IN spatstat VERSION 1.22-1 OVERVIEW o marked line segment patterns can now be plotted o multitype point process models are now 'self-starting' o new functions to manipulate colour images NEW FUNCTIONS o rgbim, hsvim Specify three colour channels. These functions convert three pixel images with numeric values into a single image whose pixel values are strings representing colours. o scaletointerval Generic utility function to rescale data (including spatial data) to a specified interval SIGNIFICANT USER-VISIBLE CHANGES o plot.im Can now plot images whose pixel values are strings representing colours. New argument 'valuesAreColours' o plot.psp Now handles marked line segment patterns and plots the marks as colours. o MultiHard, MultiStrauss, MultiStraussHard The argument 'types' can now be omitted; it will be inferred from the point pattern data. o rhohat Improved mathematical labels (when the result of rhohat is plotted) o plot.fv Minor improvements in graphics BUG FIXES o several minor bug fixes and improvements to satisfy R-devel CHANGES IN spatstat VERSION 1.22-0 OVERVIEW o support for point patterns on a linear network o 'superimpose' is now generic o improved mathematical labels when plotting functions NEW CLASSES o linnet An object of class 'linnet' represents a linear network, i.e. a connected network of line segments, such as a road network. Methods for this class include plot, print, summary etc. o lpp An object of class 'lpp' represents a point pattern on a linear network, such as a record of the locations of road accidents on a road network. Methods for this class include plot, print, summary etc. NEW FUNCTIONS o runiflpp Uniformly distributed random points on a linear network o rpoislpp Poisson point process on a linear network o clickjoin Interactive graphics to create a linear network o superimpose The function 'superimpose' is now generic, with methods for ppp, psp and a default method. o as.ppp.psp New method for as.ppp extracts the endpoints and marks from a line segment pattern NEW DATASETS o simplenet Simple example of a linear network SIGNIFICANT USER-VISIBLE CHANGES o superimposePSP This function is now deprecated in favour of 'superimpose' o superimpose Now handles data frames of marks. o plot.fv Argument 'legendmath' now defaults to TRUE. New argument 'legendargs' gives more control over appearance of legend. Increased default spacing between lines in legend. o eval.fv, with.fv Functions computed using eval.fv or with.fv now have better labels when plotted. o summary functions (Kest, Kest.fft, Kcross, Kdot, Kmulti, Kinhom, Kcross.inhom, Kdot.inhom, Kmulti.inhom, Lest, Lcross, Ldot, pcf, pcfcross, pcfdot, pcfinhom, pcfcross.inhom, pcfdot.inhom, Fest, Gest, Gcross, Gdot, Gmulti, Jest, Jcross, Jdot, Jmulti, Iest, localL, localK, markcorr, markvario, markconnect, Emark, Vmark, allstats, alltypes) Improved plot labels. BUG FIXES o superimpose If the marks components of patterns consisted of character vectors (rather than factors or non-factor numeric vectors) an error was triggered. Fixed. o plot.fv The y axis limits did not always cover the range of values if the argument 'shade' was used. Fixed. o plot.rhohat The y axis label was sometimes incorrect. Fixed. o plot.rhohat If argument 'xlim' was used, a warning was generated from 'rug'. Fixed. CHANGES IN spatstat VERSION 1.21-6 OVERVIEW o A line segment pattern can now have a data frame of marks. o Various minor extensions and alterations in behaviour NEW FUNCTIONS o nsegments Number of segments in a line segment pattern SIGNIFICANT USER-VISIBLE CHANGES o psp class A line segment pattern (object of class 'psp') can now have a data frame of marks. o density.ppp New argument 'adjust' makes it easy to adjust the smoothing bandwidth o plot.envelope If the upper envelope is NA but the lower envelope is finite, the upper limit is now treated as +Infinity o msr Argument 'continuous' renamed 'density' BUG FIXES o [.psp In X[W] if X is a line segment pattern and W is a polygonal window, marks were sometimes discarded, leading to an error. Fixed. o [.psp In X[W] if X is a line segment pattern and W is a rectangular window, if the marks of X were factor values, they were converted to integers. Fixed. o superimposePSP If the marks were a factor, they were mistakenly converted to integers. Fixed. o is.marked.ppp Did not generate a fatal error when na.action="fatal" as described in the help file. Fixed. CHANGES IN spatstat VERSION 1.21-5 OVERVIEW o Increased numerical stability. o New 'self-starting' feature of interpoint interactions. SIGNIFICANT USER-VISIBLE CHANGES o ppm Interaction objects may now be 'self-starting' i.e. initial parameter estimates can be computed from the point pattern dataset. So far, only the LennardJones() interaction has a self-starting feature. o LennardJones Increased numerical stability. New (optional) scaling argument 'sigma0'. Interpoint distances are automatically rescaled using 'self-starting' feature. o vcov.ppm New argument 'matrix.action' controls what happens when the matrix is ill-conditioned. Changed name of argument 'gamaction' to 'gam.action' o rmhmodel.ppm Default resolution of trend image has been increased. o is.poisson.ppm Accelerated. o ppm, kppm, qqplot.ppm Improved robustness to numerical error CHANGES IN spatstat VERSION 1.21-4 OVERVIEW o Urgent bug fix BUG FIXES o print.summary.ppm exited with an error message, if the model had external covariates. Fixed. CHANGES IN spatstat VERSION 1.21-3 OVERVIEW o Point process model covariates may now depend on additional parameters. o New class of signed measures, for residual analysis. o Miscellaneous improvements and bug fixes. NEW FUNCTIONS o clarkevans.test Classical Clark-Evans test of randomness o msr New class 'msr' of signed measures and vector-valued measures supporting residual analysis. o quadrat.test.quadratcount Method for 'quadrat.test' for objects of class 'quadratcount' (allows a chi-squared test to be performed on quadrat counts rather than recomputing from the original data) o tile.areas Computes areas of tiles in a tessellation (efficiently) SIGNIFICANT USER-VISIBLE CHANGES o ppm The spatial trend can now depend on additional parameters. This is done by allowing spatial covariate functions to have additional parameters: function(x, y, ...) where ... is controlled by the new argument 'covfunargs' to ppm o profilepl Can now maximise over trend parameters as well as interaction parameters o residuals.ppm The value returned by residuals.ppm is now an object of class 'msr'. It can be plotted directly. o eval.im When the argument 'envir' is used, eval.im() now recognises functions as well as variables in 'envir' o colourmap The argument 'col' can now be any kind of colour data o persp.im The 'colmap' argument can now be a 'colourmap' object o ppm The print and summary methods for 'ppm' objects now show standard errors for parameter estimates if the model is Poisson. o quadrat.test The print method for 'quadrattest' objects now displays information about the quadrats o lurking Improved format of x axis label o distmap.ppp Internal code is more robust. BUG FIXES o im Did not correctly handle 1 x 1 arrays. Fixed. o as.mask, pixellate.ppp Weird things happened if the argument 'eps' was set to a value greater than the size of the window. Fixed. CHANGES IN spatstat VERSION 1.21-2 OVERVIEW o New multitype hardcore interaction. o Nonparametric estimation of covariate effects on point patterns. o Output of 'Kmeasure' has been rescaled. o Numerous improvements and bug fixes. NEW FUNCTIONS o MultiHard multitype hard core interaction for use in ppm() o coords<- Assign new coordinates to the points in a point pattern o rhohat Kernel estimate for the effect of a spatial covariate on point process intensity SIGNIFICANT USER-VISIBLE CHANGES o as.ppp.matrix, as.ppp.data.frame These methods for 'as.ppp' now accept a matrix or data frame with any number of columns (>= 2) and interpret the additional columns as marks. o Kmeasure The interpretation of the output has changed: the pixel values are now density estimates. o rmh.ppm, rmhmodel.ppm These functions now accept a point process model fitted with the 'MultiHard' interaction o rmh.default, rmhmodel.default These functions now accept the option: cif='multihard' defining a multitype hard core interaction. o markcorr Now handles a data frame of marks o varblock Improved estimate in the case of the K function o colourmap, lut New argument 'range' makes it easier to specify a colour map or lookup table o [<-.hyperframe Now handles multiple columns o plot.fv Improved y axis labels o spatstat.options New option 'par.fv' controls default parameters for line plotting o rmhmodel More safety checks on parameter values. o quadratresample New argument 'verbose' o smooth.fv Default value of 'which' has been changed. BUG FIXES o Kest If the argument 'domain' was used, the resulting estimate was not correctly normalised. Fixed. o Kest The Lotwick-Silverman variance approximation was incorrectly calculated. (Spotted by Ian Dryden and Igor Chernayavsky). Fixed. o plot.owin, plot.ppp Display of binary masks was garbled if the window was empty or if it was equivalent to a rectangle. Fixed. o plot.bermantest One of the vertical lines for the Z1 test was in the wrong place. Fixed. o marks<-.ppx Crashed in some cases. Fixed. o is.convex An irrelevant warning was issued (for non-convex polygons). Fixed. CHANGES IN spatstat VERSION 1.21-1 OVERVIEW o Confidence intervals for K-function and other statistics o Bug fixes for smoothing and relative risk estimation NEW FUNCTIONS o varblock Variance estimation (and confidence intervals) for summary statistics such as Kest, using subdivision technique o bw.stoyan Bandwidth selection by Stoyan's rule of thumb. o which.max.im Applied to a list of images, this determines which image has the largest value at each pixel. o as.array.im Convert image to array SIGNIFICANT USER-VISIBLE CHANGES o smooth.ppp, markmean, sharpen.ppp, relrisk, bw.relrisk Further acceleration achieved. o Kest Argument 'correction' now explicitly overrides automatic defaults o plot.fv More robust handling of 'shade' BUG FIXES o relrisk Format of relrisk(at="points") was incorrect. Fixed. o bw.relrisk Result was incorrect in the default case method="likelihood" because of previous bug. Fixed. o Jdot, Jcross, Jmulti Return value did not include the hazard function, when correction="km" Fixed. o Jdot, Jcross, Jmulti Format of output was incompatible with format of Jest. Fixed. CHANGES IN spatstat VERSION 1.21-0 OVERVIEW o Implemented Spatial Logistic Regression o Implemented nonparametric estimation of relative risk with bandwidth selection by cross-validation. o Smoothing functions can handle a data frame of marks. o New options in Kinhom; default behaviour has changed. NEW FUNCTIONS o slrm Fit a spatial logistic regression model o anova.slrm, coef.slrm, fitted.slrm, logLik.slrm, plot.slrm, predict.slrm Methods for spatial logistic regression models o relrisk Nonparametric estimation of relative risk o bw.relrisk Automatic bandwidth selection by cross-validation o default.rmhcontrol Sets default values of Metropolis-Hastings parameters SIGNIFICANT USER-VISIBLE CHANGES o smooth.ppp, markmean These functions now accept a data frame of marks. o Kinhom Default behaviour has changed. New argument 'renormalise=TRUE' determines scaling of estimator and affects bias and variance in small samples. o residuals.ppm Now also computes the score residuals. o plot.im New argument 'ribscale' o plot.listof, plot.splitppp New arguments panel.begin, panel.end and panel.args o ppp Now checks for NA/NaN/Inf values in the coordinates o envelope.ppm Changed default value of 'control' New argument 'nrep' o qqplot.ppm Changed default value of 'control' BUG FIXES o marks<-.ppp, setmarks, %mark% A matrix of marks was accepted by ppp() but not by these assignment functions. Fixed. o density.ppp, smooth.ppp, sharpen.ppp, markmean Crashed if the bandwidth was extremely small. Fixed. CHANGES IN spatstat VERSION 1.20-5 OVERVIEW o Accelerated computations of kernel smoothing. o Implemented Choi-Hall data sharpening. NEW FUNCTIONS o sharpen.ppp Performs Choi-Hall data sharpening of a point pattern SIGNIFICANT USER-VISIBLE CHANGES o density.ppp, smooth.ppp Computation has been vastly accelerated for density(X, at="points") and smooth.ppp(X, at="points") o Kinhom Accelerated in case where lambda=NULL o Vignette 'shapefiles' updated CHANGES IN spatstat VERSION 1.20-4 OVERVIEW o New functions for inhomogeneous point patterns and local analysis. o Pair correlation function for 3D point patterns o Minor improvements and bug fixes to simulation code and image functions NEW FUNCTIONS o pcf3est Pair correlation function for 3D point patterns. o Kscaled, Lscaled Estimator of the template K function (and L-function) for a locally-scaled point process. o localpcf Local version of pair correlation function o identify.psp Method for 'identify' for line segment patterns. o as.im.matrix Converts a matrix to a pixel image SIGNIFICANT USER-VISIBLE CHANGES o rMaternI, rMaternII New argument 'stationary=TRUE' controls whether the simulated process is stationary (inside the simulation window). Default simulation behaviour has changed. o im New arguments 'xrange', 'yrange' o envelope Improvements to robustness of code. BUG FIXES o quadratcount If V was a tessellation created using a factor-valued image, quadratcount(X, tess=V) crashed with the error "Tessellation does not contain all the points of X". Fixed. o [.im If Z was a factor valued image and X was a point pattern then Z[X] was not a factor. Fixed. CHANGES IN spatstat VERSION 1.20-3 OVERVIEW o minor improvements (mostly internal). NEW FUNCTIONS o unmark.ppx Method for 'unmark' for general space-time point patterns SIGNIFICANT USER-VISIBLE CHANGES o plot.ppx Now handles marked patterns, in two-dimensional case o as.psp.psp Default value of argument 'check' set to FALSE CHANGES IN spatstat VERSION 1.20-2 OVERVIEW o Extensions to minimum contrast estimation. o Bug fix in simulation of Lennard-Jones model. o More support for distance functions. o Changes to point process simulations. NEW FUNCTIONS o thomas.estpcf Fit Thomas process model by minimum contrast using the pair correlation function (instead of the K-function). o matclust.estpcf Fit Matern Cluster model by minimum contrast using the pair correlation function (instead of the K-function). o lgcp.estpcf Fit log-Gaussian Cox process model by minimum contrast using the pair correlation function (instead of the K-function). o contour.distfun, persp.distfun Methods for 'contour' and 'persp' for distance functions o default.expand Computes default window for simulation of a fitted point process model. SIGNIFICANT USER-VISIBLE CHANGES o kppm Models can now be fitted using either the K-function or the pair correlation function. o ppm The list of covariates can now include windows (objects of class 'owin'). A window will be treated as a logical covariate that equals TRUE inside the window and FALSE outside it. o plot.distfun Pixel resolution can now be controlled. o envelope.ppm, qqplot.ppm The default value of 'control' has changed; simulation results may be slightly different. o rmh Slightly accelerated. BUG FIXES o rmh Simulation of the Lennard-Jones model (cif = 'lennard') was incorrect due to an obscure bug, introduced in spatstat 1.20-1. Fixed. o thomas.estK, matclust.estK, lgcp.estK The value of 'lambda' (if given) was ignored if X was a point pattern. Fixed. CHANGES IN spatstat VERSION 1.20-1 OVERVIEW o Further increases in speed and efficiency of ppm and rmh o New pairwise interaction model NEW FUNCTIONS o DiggleGatesStibbard Diggle-Gates-Stibbard pairwise interaction for use in ppm() SIGNIFICANT USER-VISIBLE CHANGES o ppm has been accelerated by a factor of 10 for the BadGey interaction. o rmh simulation of the Lennard-Jones model (cif='lennard') has been greatly accelerated. o rmh, rmhmodel.ppm Point process models fitted by ppm() using the DiggleGatesStibbard interaction can be simulated automatically using rmh. BUG FIXES o fitin The plot of a fitted Hardcore interaction was incorrect. Fixed. CHANGES IN spatstat VERSION 1.20-0 OVERVIEW o spatstat now contains over 1000 functions. o Substantial increase in speed and efficiency of model-fitting code. o Changes to factor-valued images. SIGNIFICANT USER-VISIBLE CHANGES o ppm has been accelerated by a factor of 10, and can handle datasets with 20,000 points, for the following interactions: DiggleGratton, Fiksel, Geyer, Hardcore, Strauss, StraussHard o predict.ppm accelerated by a factor of 3 (when type = "cif") with vastly reduced memory requirements for the following interactions: DiggleGratton, Fiksel, Geyer, Hardcore, Strauss, StraussHard o pixel images (class "im") The internal representation of factor-valued images has changed. Existing objects in the old format should still work. o im The syntax for creating a factor-valued image has changed. Argument 'lev' has been deleted. o ppm Some warnings have been reworded for greater clarity. BUG FIXES o [.im Mishandled some factor-valued images. Fixed. o hist.im Produced slightly erroneous output for some factor-valued images. Fixed. o plot.owin Filled polygons appeared to contain criss-cross lines on some graphics drivers. Fixed. o deltametric Did not handle windows with different enclosing frames (error message: 'dA and dB are incompatible') Fixed. o quadratcount Crashed if the pattern was empty and the window was a rectangle. (Noticed by Sandro Azaele) Fixed. o rNeymanScott Crashed if the parent process realisation was empty. (Noticed by Sandro Azaele) Fixed. CHANGES IN spatstat VERSION 1.19-3 ACKNOWLEDGEMENTS o We thank David Dereudre for contributions. OVERVIEW o Urgent bug fix to Metropolis-Hastings for Lennard-Jones model. o Miscellaneous additions to plotting and colour management. NEW FUNCTIONS o col2hex, rgb2hex, paletteindex, samecolour Functions for converting and comparing colours. o plot.envelope New method for plotting envelopes. By default the area between the upper and lower envelopes is shaded in grey. SIGNIFICANT USER-VISIBLE CHANGES o plot.fasp If the entries in the array are envelopes, they are plotted using plot.envelope (hence the envelope region is shaded grey). o plot.fv Now displays mathematical notation for each curve, if legendmath=TRUE. o print.fv Now prints the available range of 'r' values as well as the recommended range of 'r' values. BUG FIXES o rmh Simulation of Lennard-Jones model was incorrect; the simulations were effectively Poisson patterns. (Spotted by David Dereudre.) Fixed. o plot.fv Did not correctly handle formulas that included I( ) Fixed. CHANGES IN spatstat VERSION 1.19-2 ACKNOWLEDGEMENTS o We thank Jorge Mateu, Michael Sumner and Sebastian Luque for contributions. OVERVIEW o More support for fitted point process models and pixel images. o Improved plotting of pixel images and envelopes. o Simulation algorithm for Lennard-Jones process. o Improvements and bug fixes to envelopes. o Bug fixes to Metropolis-Hastings simulation. NEW FUNCTIONS o pairs.im Creates a scatterplot matrix for several pixel images. o model.frame.ppm Method for 'model.frame' for point process models. o sort.im Method for 'sort' for pixel images. SIGNIFICANT USER-VISIBLE CHANGES o plot.fv, plot.fasp New argument 'shade' enables confidence intervals or significance bands to be displayed as filled grey shading. o LennardJones The parametrisation of this interaction function has been changed. o rmh, rmhmodel These functions will now simulate a point process model that was fitted using the LennardJones() interaction. o rmh.default, rmhmodel.default These functions will now simulate a point process model with the Lennard-Jones interaction (cif='lennard'). o ecdf This function now works for pixel images. o dim, row, col These functions now work for pixel images. o order This function now works for pixel images. o [.im and [<-.im The subset index can now be any valid subset index for a matrix. o density.ppp, smooth.ppp The return value now has attributes 'sigma' and 'varcov' reporting the smoothing bandwidth. o plot.im The argument 'col' can now be a 'colourmap' object. This makes it possible to specify a fixed mapping between numbers and colours (e.g. so that it is consistent between plots of several different images). o rmh, spatstat.options spatstat.options now recognises the parameter 'expand' which determines the default window expansion factor in rmh. o rmh Improved handling of ppm objects with covariates. o kstest The 'covariate' can now be one of the characters "x" or "y" indicating the Cartesian coordinates. BUG FIXES o model.matrix.ppm For a fitted model that used a large number of quadrature points, model.matrix.ppm sometimes reported an internal error about mismatch between the model matrix and the quadrature scheme. Fixed. o plot.ppx Minor bugs fixed. o rmh In rare cases, the simulated point pattern included multiple points at the origin (0,0). (Bug introduced in spatstat 1.17-0.) Fixed. o rmh, rmhmodel.ppm Crashed when applied to a fitted multitype point process model if the model involved more than one covariate image. (Spotted by Jorge Mateu) Fixed. o density.psp If any segment had zero length, the result contained NaN values. (Spotted by Michael Sumner and Sebastian Luque.) Fixed. o envelope Crashed with fun=Lest or fun=Linhom if the number of points in a simulated pattern exceeded 3000. Fixed. o plot.kstest Main title was corrupted if the covariate was a function. Fixed. CHANGES IN spatstat VERSION 1.19-1 OVERVIEW o New dataset: replicated 3D point patterns. o Improvements to Metropolis-Hastings simulation code. o More support for hyperframes. o Bug fixes. NEW DATASETS o osteo: Osteocyte Lacunae data: replicated 3D point patterns NEW FUNCTIONS o rbind.hyperframe: Method for rbind for hyperframes. o as.data.frame.hyperframe: Converts a hyperframe to a data frame. SIGNIFICANT USER-VISIBLE CHANGES o Fiksel: Fitted point process models (class ppm) with the Fiksel() double exponential interaction can now be simulated by rmh. o rmh.default: Point processes with the Fiksel interaction can now be simulated by specifying parameters in rmh.default. o logLik.ppm: New argument 'warn' controls warnings. o profilepl: No longer issues spurious warnings. BUG FIXES o Hardcore, rmh: Simulation of the 'Hardcore' process was incorrect. The hard core radius was erroneously set to zero so that the simulated patterns were Poisson. Fixed. o fitin: A plot of the pairwise interaction function of a fitted model, generated by plot(fitin(model)) where model <- ppm(...), was sometimes incorrect when the model included a hard core. Fixed. CHANGES IN spatstat VERSION 1.19-0 OVERVIEW o Numerous bugs fixed in the implementation of the Huang-Ogata approximate maximum likelihood method. o New interpoint interaction model. NEW FUNCTIONS o Fiksel: new interpoint interaction: Fiksel's double exponential model. SIGNIFICANT USER-VISIBLE CHANGES o runifpoint, rpoispp, envelope These functions now issue a warning if the number of random points to be generated is very large. This traps a common error in simulation experiments. BUG FIXES o predict.ppm, fitted.ppm: Predictions and fitted values were incorrect for objects fitted using ppm(..., method="ho"). Fixed. o logLik, AIC: Values of logLik() and AIC() were incorrect for objects fitted using ppm(..., method="ho"). Fixed. o profilepl: Results were incorrect if the argument 'method="ho"' was used. Fixed. o fitin The result of fitin() was incorrect for objects fitted using ppm(..., method="ho"). Fixed. o rmhcontrol: rmhcontrol(NULL) generated an error. Fixed. CHANGES IN spatstat VERSION 1.18-4 ACKNOWLEDGEMENTS o We thank Michael Sumner for contributions. BUG FIXES o pixellate.psp: segments shorter than one pixel width were measured incorrectly if the 'weights' argument was present. Fixed. NEW FUNCTIONS o pairdist.ppx, crossdist.ppx, nndist.ppx, nnwhich.ppx: Methods for pairdist, crossdist, nndist, nnwhich for multidimensional point patterns (class 'ppx') o runifpointx, rpoisppx: Random point patterns in any number of dimensions o boxx: Multidimensional box in any number of dimensions o diameter.boxx, volume.boxx, shortside.boxx, eroded.volumes.boxx: Geometrical computations for multidimensional boxes o sum.im, max.im, min.im: Methods for sum(), min(), max() for pixel images. o as.matrix.ppx: Convert a multidimensional point pattern to a matrix SIGNIFICANT USER-VISIBLE CHANGES o plot.ppp: New argument 'zap' o diameter: This function is now generic, with methods for "owin", "box3" and "boxx" o eroded.volumes: This function is now generic, with methods for "box3" and "boxx" CHANGES IN spatstat VERSION 1.18-3 ACKNOWLEDGEMENTS o We thank Michael Sumner for contributions. BUG FIXES o pixellate.psp: segments shorter than one pixel width were measured incorrectly. Fixed. o fv: 'alim' not handled correctly. Fixed. NEW FUNCTIONS o smooth.fv: Applies spline smoothing to the columns of an fv object. CHANGES IN spatstat VERSION 1.18-2 ACKNOWLEDGEMENTS o We thank Michael Sumner for contributions. NEW FUNCTIONS o Gfox, Jfox: Foxall's G and J functions o as.owin.distfun: New method for as.owin extracts the domain of a distfun object. SIGNIFICANT USER-VISIBLE CHANGES o distfun: objects of class 'distfun', when called as functions, will now accept either two vectors (x,y) or a point pattern x. o Hest: this function can now compute the Hanisch estimator. It now has arguments 'r', 'breaks' and 'correction', like other summary functions. o Hest: new argument 'conditional'. BUG FIXES o pixellate.psp: Values were sometimes incorrect due to coding error. (Spotted by Michael Sumner) Fixed. o kstest: Crashed if the covariate contained NA's. Fixed. o kstest: Crashed if X was a multitype point pattern in which some mark values were unrepresented. Fixed. o lurking: Minor bug in handling of NA values. Fixed. o Hest: labels of columns were incorrect. Fixed. CHANGES IN spatstat VERSION 1.18-1 ACKNOWLEDGEMENTS o we thank Andrew Bevan and Ege Rubak for suggestions. NEW FUNCTIONS o Hardcore: Hard core interaction (for use in ppm) o envelope.pp3: simulation envelopes for 3D point patterns o npoints: number of points in a point pattern of any kind SIGNIFICANT USER-VISIBLE CHANGES o rmh.ppm, rmhmodel.ppm: It is now possible to simulate Gibbs point process models that are fitted to multitype point patterns using a non-multitype interaction, e.g. data(amacrine) fit <- ppm(amacrine, ~marks, Strauss(0.1)) rmh(fit, ...) o rmh.ppm, rmhmodel.ppm, rmh.default, rmhmodel.default: Hard core models can be simulated. o rmh.default, rmhmodel.default: The argument 'par' is now required to be a list, in all cases (previously it was sometimes a list and sometimes a vector). o Fest: Calculation has been accelerated in some cases. o summary.pp3 now returns an object of class 'summary.pp3' containing useful summary information. It is plotted by 'plot.summary.pp3'. o F3est, G3est, K3est: these functions now accept 'correction="best"' o union.owin, intersect.owin: these functions now handle any number of windows. o envelope.ppp, envelope.ppm, envelope.kppm: argument lists have changed slightly BUG FIXES o Fest: The result of Fest(X, correction="rs") had a slightly corrupted format, so that envelope(X, Fest, correction="rs") in fact computed the envelopes based on the "km" correction. (Spotted by Ege Rubak). Fixed. o rmh (rmh.ppm, rmhmodel.ppm): rmh sometimes failed for non-stationary point process models, with a message about "missing value where TRUE/FALSE needed". (Spotted by Andrew Bevan). Fixed. o diagnose.ppm, lurking: Calculations were not always correct if the model had conditional intensity equal to zero at some locations. Fixed. o ppm, profilepl: If data points are illegal under the model (i.e. if any data points have conditional intensity equal to zero) the log pseudolikelihood should be -Inf but was sometimes returned as a finite value. Thus profilepl did not always work correctly for models with a hard core. Fixed. o F3est, G3est: Debug messages were printed unnecessarily. Fixed. CHANGES IN spatstat VERSION 1.18-0 ACKNOWLEDGEMENTS o we thank Ege Rubak and Tyler Dean Rudolph for suggestions. HEADLINES o A point pattern is now allowed to have a data frame of marks (previously the marks had to be a vector). o Extended capabilities for 'envelope' and 'kstest'. NEW FUNCTIONS o pixellate.psp, as.mask.psp Convert a line segment pattern to a pixel image or binary mask o as.data.frame.im Convert a pixel image to a data frame SIGNIFICANT USER-VISIBLE CHANGES o A point pattern is now allowed to have a data frame of marks (previously the marks had to be a vector). o Many functions in spatstat now handle point patterns with a data frame of marks. These include print.ppp, summary.ppp, plot.ppp, split.ppp. o finpines, nbfires, shapley: The format of these datasets has changed. They are now point patterns with a data frame of marks. o envelope() is now generic, with methods for "ppp", "ppm" and "kppm". o kstest() now handles multitype point patterns and multitype point process models. o nnclean() now returns a point pattern with a data frame of marks. o plot.ppp() has new argument 'which.marks' to select one column from a data frame of marks to be plotted. o plot.ppp() now handles marks that are POSIX times. o complement.owin now handles any object acceptable to as.owin. BUG FIXES o erosion(w) and opening(w) crashed if w was not a window. Fixed. o diameter() and eroded.areas() refused to work if w was not a window. Fixed. CHANGES IN spatstat VERSION 1.17-6 ACKNOWLEDGEMENTS o We thank Simon Byers and Adrian Raftery for generous contributions. OVERVIEW o Nearest neighbour clutter removal algorithm o New documentation for the 'fv' class. o Minor improvements and bug fixes. NEW FUNCTIONS o nnclean: Nearest neighbour clutter removal for recognising features in spatial point patterns. Technique of Byers and Raftery (1998) [From original code by Simon Byers and Adrian Raftery, adapted for spatstat.] o marks.ppx, marks<-.ppx: Methods for extracting and changing marks in a multidimensional point pattern o latest.news: print news about the current version of the package SIGNIFICANT USER-VISIBLE CHANGES o news: spatstat now has a NEWS file which can be printed by typing news(package="spatstat"). o areaGain, areaLoss: New algorithms in case exact=TRUE. Syntax slightly modified. o with.hyperframe: - The result now inherits 'names' from the row names of the hyperframe. - New argument 'enclos' controls the environment in which the expression is evaluated. - The algorithm is now smarter at simplifying the result when simplify=TRUE. o update.ppm: Tweaked to improve the ability of ppm objects to be re-fitted in different contexts. ADVANCED USERS ONLY o Documentation for the class 'fv' of function value tables - fv: Creates an object of class 'fv' - cbind.fv, collapse.fv: Combine objects of class 'fv' - bind.fv: Add additional columns of data to an 'fv' object BUG FIXES o "$<-.hyperframe" destroyed the row names of the hyperframe. Fixed. o model.matrix.ppm had minor inconsistencies. Fixed. o ppm: The fitted coefficient vector had incorrect format in the default case of a uniform Poisson process. Fixed. o plot.ppx: Crashed if the argument 'main' was given. Fixed. o envelope.ppp: Crashed if the object returned by 'fun' did not include a column called "theo". 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