00spatstat.Rd
\name{spatstat}
\alias{spatstat}
\alias{spatstat-package}
\docType{package}
\title{
The Spatstat Package
}
\description{
This is a summary of the features of
\pkg{spatstat}, a package in \code{R}
for the statistical analysis of spatial point patterns.
}
\details{
\pkg{spatstat} is a package for the statistical analysis
of spatial data. Currently, it deals mainly with the analysis of
patterns of points in the plane. The points may carry `marks',
and the spatial region in which the points were recorded
may have arbitrary shape.
The package supports
\itemize{
\item creation, manipulation and plotting of point patterns
\item exploratory data analysis
\item simulation of point process models
\item parametric model-fitting
\item hypothesis tests and diagnostics
}
The point process models to be fitted
may be quite general Gibbs/Markov models; they may include spatial trend,
dependence on covariates, and interpoint interactions of any order (i.e.
not restricted to pairwise interactions). Models are specified by
a \code{formula} in the \code{R} language, and are fitted using
a single function \code{\link{ppm}} analogous to
\code{\link{lm}} and \code{\link{glm}}.
It is also possible to fit cluster process models by the method of
minimum contrast.
}
\section{Getting Started}{
Type \code{demo(spatstat)} for an overall demonstration
of the package.
For a readable introduction to \pkg{spatstat}, see the paper by
Baddeley and Turner (2005a), available online.
Type \code{demo(data)} to see all the datasets
available in the package.
}
\section{FUNCTIONS AND DATASETS}{
Following is a summary of the main functions and datasets
in the \pkg{spatstat} package.
Alternatively an alphabetical list of all functions and
datasets is available by typing \code{library(help=spatstat)}.
For further information on any of these,
type \code{help(name)} where \code{name} is the name of the function
or dataset.
Type \code{demo(data)} to see all the datasets
installed with the package.
}
\section{CONTENTS:}{
\tabular{ll}{
I. \tab Creating and manipulating data \cr
II. \tab Exploratory Data Analysis \cr
III. \tab Model fitting (cluster models) \cr
IV. \tab Model fitting (Gibbs models) \cr
V. \tab Tests and diagnostics\cr
VI. \tab Documentation
}
}
\section{I. CREATING AND MANIPULATING DATA}{
\bold{To create a point pattern:}
\tabular{ll}{
\code{\link{ppp}} \tab
create a point pattern from \eqn{(x,y)} and window information
\cr
\tab
\code{ppp(x, y, xlim, ylim)} for rectangular window\cr
\tab
\code{ppp(x, y, poly)} for polygonal window \cr
\tab
\code{ppp(x, y, mask)} for binary image window \cr
\code{\link{as.ppp}} \tab
convert other types of data to a \code{ppp} object \cr
\code{\link{clickppp}} \tab
interactively add points to a plot \cr
\code{\link{setmarks}}, \code{\%mark\%} \tab
attach/reassign marks to a point pattern
}
\bold{To simulate a random point pattern:}
\tabular{ll}{
\code{\link{runifpoint}} \tab
generate \eqn{n} independent uniform random points \cr
\code{\link{rpoint}} \tab
generate \eqn{n} independent random points \cr
\code{\link{rmpoint}} \tab
generate \eqn{n} independent multitype random points \cr
\code{\link{rpoispp}} \tab
simulate the (in)homogeneous Poisson point process \cr
\code{\link{rmpoispp}} \tab
simulate the (in)homogeneous multitype Poisson point process \cr
\code{\link{runifdisc}} \tab
generate \eqn{n} independent uniform random points in disc\cr
\code{\link{rstrat}} \tab
stratified random sample of points \cr
\code{\link{rsyst}} \tab
systematic random sample of points \cr
\code{\link{rjitter}} \tab
apply random displacements to points in a pattern\cr
\code{\link{rMaternI}} \tab
simulate the Mat\'ern Model I inhibition process\cr
\code{\link{rMaternII}} \tab
simulate the Mat\'ern Model II inhibition process\cr
\code{\link{rSSI}} \tab
simulate Simple Sequential Inhibition process\cr
\code{\link{rStrauss}} \tab
simulate Strauss process (perfect simulation)\cr
\code{\link{rNeymanScott}} \tab
simulate a general Neyman-Scott process\cr
\code{\link{rMatClust}} \tab
simulate the Mat\'ern Cluster process\cr
\code{\link{rThomas}} \tab
simulate the Thomas process \cr
\code{\link{rGaussPoisson}} \tab
simulate the Gauss-Poisson cluster process\cr
\code{\link{rthin}} \tab random thinning \cr
\code{\link{rcell}} \tab
simulate the Baddeley-Silverman cell process \cr
\code{\link{rmh}} \tab
simulate Gibbs point process using Metropolis-Hastings
}
\bold{To randomly change an existing point pattern:}
\tabular{ll}{
\code{\link{rlabel}} \tab random (re)labelling of a multitype
point pattern \cr
\code{\link{rshift}} \tab random shift (including toroidal shifts)
}
\bold{Standard point pattern datasets:}
Remember to say \code{\link{data}(bramblecanes)} etc.
\tabular{ll}{
\code{\link{amacrine}} \tab Austin Hughes' rabbit amacrine cells \cr
\code{\link{anemones}} \tab Upton-Fingleton sea anemones data\cr
\code{\link{ants}} \tab Harkness-Isham ant nests data\cr
\code{\link{bei}} \tab Tropical rainforest trees \cr
\code{\link{betacells}} \tab Waessle et al. cat retinal ganglia data \cr
\code{\link{bramblecanes}} \tab Bramble Canes data \cr
\code{\link{cells}} \tab Crick-Ripley biological cells data \cr
\code{\link{chorley}} \tab Chorley-Ribble cancer data \cr
\code{\link{copper}} \tab Berman-Huntington copper deposits data \cr
\code{\link{demopat}} \tab Synthetic point pattern \cr
\code{\link{finpines}} \tab Finnish Pines data \cr
\code{\link{hamster}} \tab Aherne's hamster tumour data \cr
\code{\link{humberside}} \tab North Humberside childhood leukaemia data \cr
\code{\link{japanesepines}} \tab Japanese Pines data \cr
\code{\link{lansing}} \tab Lansing Woods data \cr
\code{\link{longleaf}} \tab Longleaf Pines data \cr
\code{\link{nbfires}} \tab New Brunswick fires data \cr
\code{\link{nztrees}} \tab Mark-Esler-Ripley trees data \cr
\code{\link{ponderosa}} \tab Getis-Franklin ponderosa pine trees data \cr
\code{\link{redwood}} \tab Strauss-Ripley redwood saplings data \cr
\code{\link{redwoodfull}} \tab Strauss redwood saplings data (full set) \cr
\code{\link{residualspaper}} \tab Data from Baddeley et al (2005) \cr
\code{\link{simdat}} \tab Simulated point pattern (inhomogeneous, with interaction) \cr
\code{\link{spruces}} \tab Spruce trees in Saxonia \cr
\code{\link{swedishpines}} \tab Strand-Ripley swedish pines data
}
\bold{To manipulate a point pattern:}
\tabular{ll}{
\code{\link{plot.ppp}} \tab
plot a point pattern (e.g. \code{plot(X)}) \cr
\code{\link{subset.ppp}},
\code{"[.ppp"} \tab
extract or replace a subset of a point pattern \cr
\tab \code{pp[subset]} \cr
\tab \code{pp[, subwindow]} \cr
\code{\link{superimpose}} \tab
superimpose any number of point patterns \cr
\code{\link{cut.ppp}} \tab
discretise the marks in a point pattern \cr
\code{\link{unmark}} \tab
remove marks \cr
\code{\link{setmarks}} \tab
attach marks or reset marks \cr
\code{\link{split.ppp}} \tab
divide pattern into sub-patterns \cr
\code{\link{rotate}} \tab
rotate pattern \cr
\code{\link{shift} } \tab
translate pattern \cr
\code{\link{affine}} \tab
apply affine transformation\cr
\code{\link{density.ppp}} \tab
kernel smoothing\cr
\code{\link{identify.ppp}} \tab
interactively identify points \cr
\code{\link{unique.ppp}} \tab
remove duplicate points \cr
\code{\link{duplicated.ppp}} \tab
determine which points are duplicates
}
See \code{\link{spatstat.options}} to control plotting behaviour.
\bold{To create a window:}
An object of class \code{"owin"} describes a spatial region
(a window of observation).
\tabular{ll}{
\code{\link{owin}} \tab Create a window object \cr
\tab \code{owin(xlim, ylim)} for rectangular window \cr
\tab \code{owin(poly)} for polygonal window \cr
\tab \code{owin(mask)} for binary image window \cr
\code{\link{as.owin}} \tab
Convert other data to a window object \cr
\code{\link{square}} \tab make a square window \cr
\code{\link{disc}} \tab make a circular window \cr
\code{\link{ripras}} \tab
Ripley-Rasson estimator of window, given only the points \cr
\code{\link{letterR}} \tab
polygonal window in the shape of the {\sf R} logo
}
\bold{To manipulate a window:}
\tabular{ll}{
\code{\link{plot.owin}} \tab plot a window. \cr
\tab \code{plot(W)}\cr
\code{\link{bounding.box}} \tab
Find a tight bounding box for the window \cr
\code{\link{erode.owin}} \tab
erode window by a distance r\cr
\code{\link{dilate.owin}} \tab
dilate window by a distance r\cr
\code{\link{complement.owin}} \tab
invert (swap inside and outside)\cr
\code{\link{rotate}} \tab rotate window \cr
\code{\link{shift} } \tab translate window \cr
\code{\link{affine}} \tab apply affine transformation
}
\bold{Digital approximations:}
\tabular{ll}{
\code{\link{as.mask}} \tab
Make a discrete pixel approximation of a given window \cr
\code{\link{nearest.raster.point}} \tab
map continuous coordinates to raster locations\cr
\code{\link{raster.x}} \tab
raster x coordinates \cr
\code{\link{raster.y}} \tab
raster y coordinates
}
See \code{\link{spatstat.options}} to control the approximation
\bold{Geometrical computations with windows:}
\tabular{ll}{
\code{\link{intersect.owin}} \tab intersection of two windows\cr
\code{\link{union.owin}} \tab union of two windows\cr
\code{\link{inside.owin}} \tab determine whether a point is inside a window\cr
\code{\link{area.owin}} \tab compute window's area \cr
\code{\link{diameter}} \tab compute window frame's diameter\cr
\code{\link{eroded.areas}} \tab compute areas of eroded windows\cr
\code{\link{bdist.points}} \tab compute distances from data points to window boundary \cr
\code{\link{bdist.pixels}} \tab compute distances from all pixels to window boundary \cr
\code{\link{distmap.owin}} \tab distance transform image \cr
\code{\link{centroid.owin}} \tab compute centroid (centre of mass) of window\cr
\code{\link{is.subset.owin}} \tab determine whether one window contains another
}
\bold{Pixel images:}
An object of class \code{"im"} represents a pixel image.
Such objects are returned by some of the functions in
\pkg{spatstat} including \code{\link{Kmeasure}},
\code{\link{setcov}} and \code{\link{density.ppp}}.
\tabular{ll}{
\code{\link{im}} \tab create a pixel image\cr
\code{\link{as.im}} \tab convert other data to a pixel image\cr
\code{\link{as.matrix.im}} \tab convert pixel image to matrix\cr
\code{\link{plot.im}} \tab plot a pixel image on screen as a digital image\cr
\code{\link{contour.im}} \tab draw contours of a pixel image \cr
\code{\link{persp.im}} \tab draw perspective plot of a pixel image \cr
\code{\link{[.im}} \tab extract a subset of a pixel image\cr
\code{\link{[<-.im}} \tab replace a subset of a pixel image\cr
\code{\link{shift.im}} \tab apply vector shift to pixel image \cr
\code{X} \tab print very basic information about image \code{X}\cr
\code{\link{summary}(X)} \tab summary of image \code{X} \cr
\code{\link{hist.im}} \tab histogram of image \cr
\code{\link{mean.im}} \tab mean pixel value of image \cr
\code{\link{quantile.im}} \tab quantiles of image \cr
\code{\link{cut.im}} \tab convert numeric image to factor image \cr
\code{\link{is.im}} \tab test whether an object is a pixel image\cr
\code{\link{interp.im}} \tab interpolate a pixel image\cr
\code{\link{compatible.im}} \tab test whether two images have
compatible dimensions \cr
\code{\link{eval.im}} \tab evaluate any expression involving images\cr
\code{\link{levelset}} \tab level set of an image\cr
\code{\link{solutionset}} \tab region where an expression is true
}
\bold{Line segment patterns}
An object of class \code{"psp"} represents a pattern of line
segments.
\tabular{ll}{
\code{\link{psp}} \tab create a line segment pattern \cr
\code{\link{as.psp}} \tab convert other data into a line segment pattern \cr
\code{\link{plot.psp}} \tab plot a line segment pattern \cr
\code{\link{print.psp}} \tab print basic information \cr
\code{\link{summary.psp}} \tab print summary information \cr
\code{\link{subset.psp}} \tab \cr
\code{\link{[.psp}}
\tab extract a subset of a line segment pattern \cr
\code{\link{midpoints.psp}} \tab compute the midpoints of line segments \cr
\code{\link{endpoints.psp}} \tab extract the endpoints of line segments \cr
\code{\link{lengths.psp}} \tab compute the lengths of line segments \cr
\code{\link{angles.psp}} \tab compute the orientation angles of line segments \cr
\code{\link{rotate.psp}} \tab rotate a line segment pattern \cr
\code{\link{shift.psp}} \tab shift a line segment pattern \cr
\code{\link{affine.psp}} \tab apply an affine transformation \cr
\code{\link{distmap.psp}} \tab compute the distance map of a line
segment pattern \cr
\code{\link{density.psp}} \tab kernel smoothing of line segments\cr
\code{\link{selfcrossing.psp}} \tab find crossing points between
line segments \cr
\code{\link{crossing.psp}} \tab find crossing points between
two line segment patterns
}
}
\section{II. EXPLORATORY DATA ANALYSIS}{
\bold{Inspection of data:}
\tabular{ll}{
\code{\link{summary}(X)} \tab
print useful summary of point pattern \code{X}\cr
\code{X} \tab
print basic description of point pattern \code{X} \cr
\code{any(duplicated(X))} \tab
check for duplicated points in pattern \code{X}
}
\bold{Summary statistics for a point pattern:}
\tabular{ll}{
\code{\link{quadratcount}} \tab Quadrat counts \cr
\code{\link{Fest}} \tab empty space function \eqn{F} \cr
\code{\link{Gest}} \tab nearest neighbour distribution function \eqn{G} \cr
\code{\link{Kest}} \tab Ripley's \eqn{K}-function\cr
\code{\link{Lest}} \tab Ripley's \eqn{L}-function\cr
\code{\link{Jest}} \tab \eqn{J}-function \eqn{J = (1-G)/(1-F)} \cr
\code{\link{localL}} \tab Getis-Franklin neighbourhood density function\cr
\code{\link{localK}} \tab neighbourhood K-function\cr
\code{\link{pcf}} \tab pair correlation function \cr
\code{\link{Kinhom}} \tab \eqn{K} for inhomogeneous point patterns \cr
\code{\link{Kest.fft}} \tab fast \eqn{K}-function using FFT for large datasets \cr
\code{\link{Kmeasure}} \tab reduced second moment measure \cr
\code{\link{allstats}} \tab all four functions \eqn{F}, \eqn{G}, \eqn{J}, \eqn{K} \cr
\code{\link{envelope}} \tab simulation envelopes for a summary
function
}
Related facilities:
\tabular{ll}{
\code{\link{plot.fv}} \tab plot a summary function\cr
\code{\link{eval.fv}} \tab evaluate any expression involving
summary functions\cr
\code{\link{eval.fasp}} \tab evaluate any expression involving
an array of functions\cr
\code{\link{with.fv}} \tab evaluate an expression for a
summary function\cr
\code{\link{nndist}} \tab nearest neighbour distances \cr
\code{\link{nnwhich}} \tab find nearest neighbours \cr
\code{\link{pairdist}} \tab distances between all pairs of points\cr
\code{\link{crossdist}} \tab distances between points in two patterns\cr
\code{\link{nncross}} \tab nearest neighbours between two point patterns \cr
\code{\link{exactdt}} \tab distance from any location to nearest data point\cr
\code{\link{distmap}} \tab distance map image\cr
\code{\link{density.ppp}} \tab kernel smoothed density\cr
\code{\link{smooth.ppp}} \tab spatial interpolation of marks
}
\bold{Summary statistics for a multitype point pattern:}
A multitype point pattern is represented by an object \code{X}
of class \code{"ppp"} with a component \code{X$marks} which is a factor.
\tabular{ll}{
\code{\link{Gcross},\link{Gdot},\link{Gmulti}} \tab
multitype nearest neighbour distributions
\eqn{G_{ij}, G_{i\bullet}}{Gij, Gi.} \cr
\code{\link{Kcross},\link{Kdot}, \link{Kmulti}} \tab
multitype \eqn{K}-functions
\eqn{K_{ij}, K_{i\bullet}}{Kij, Ki.} \cr
\code{\link{Jcross},\link{Jdot},\link{Jmulti}} \tab
multitype \eqn{J}-functions
\eqn{J_{ij}, J_{i\bullet}}{Jij,Ji.} \cr
\code{\link{alltypes}} \tab estimates of the above
for all \eqn{i,j} pairs \cr
\code{\link{Iest}} \tab multitype \eqn{I}-function\cr
\code{\link{Kcross.inhom},\link{Kdot.inhom}} \tab
inhomogeneous counterparts of \code{Kcross}, \code{Kdot}
}
\bold{Summary statistics for a marked point pattern:}
A marked point pattern is represented by an object \code{X}
of class \code{"ppp"} with a component \code{X$marks}.
The entries in the vector \code{X$marks} may be numeric, complex,
string or any other atomic type.
\tabular{ll}{
\code{\link{markcorr}} \tab mark correlation function \cr
\code{\link{Gmulti}} \tab multitype nearest neighbour distribution \cr
\code{\link{Kmulti}} \tab multitype \eqn{K}-function \cr
\code{\link{Jmulti}} \tab multitype \eqn{J}-function
}
Alternatively use \code{\link{cut.ppp}} to convert a marked point pattern
to a multitype point pattern.
\bold{Programming tools:}
\tabular{ll}{
\code{\link{applynbd}} \tab apply function to every neighbourhood\cr
\tab in a point pattern \cr
\code{\link{markstat}} \tab apply function to the marks of neighbours\cr
\tab in a point pattern \cr
\code{\link{marktable}} \tab tabulate the marks of neighbours\cr
\tab in a point pattern \cr
\code{\link{pppdist}} \tab find the optimal match between two point
patterns
}
}
\section{III. MODEL FITTING (CLUSTER MODELS)}{
Several kinds of clustered point process models can be fitted
using the Method of Minimum Contrast.
\tabular{ll}{
\code{\link{thomas.estK}} \tab fit the Thomas process model \cr
\code{\link{matclust.estK}} \tab fit the Matern Cluster process model \cr
\code{\link{lgcp.estK}} \tab fit a log-Gaussian Cox process model\cr
\code{\link{mincontrast}} \tab general algorithm for fitting models
\cr \tab by the method of minimum contrast
}
The Thomas and Matern models can also be simulated,
using \code{\link{rThomas}} and \code{\link{rMatClust}} respectively.
}
\section{IV. MODEL FITTING (GIBBS MODELS)}{
For a detailed explanation of how to fit models to point pattern data
using \pkg{spatstat}, see Baddeley and Turner (2005b).
\bold{To fit a Gibbs point process model:}
Model fitting in \pkg{spatstat} is performed mainly by the function
\code{\link{ppm}}. Its result is an object of class \code{"ppm"}.
\bold{Manipulating the fitted model:}
\tabular{ll}{
\code{\link{plot.ppm}} \tab Plot the fitted model\cr
\code{\link{predict.ppm}} \tab Compute the spatial trend \cr
\tab and conditional intensity\cr
\tab of the fitted point process model \cr
\code{\link{coef.ppm}} \tab Extract the fitted model coefficients\cr
\code{\link{fitted.ppm}} \tab Compute fitted conditional intensity at quadrature points \cr
\code{\link{update.ppm}} \tab Update the fit \cr
\code{\link{vcov.ppm}} \tab Variance-covariance matrix of estimates\cr
\code{\link{rmh.ppm}} \tab Simulate from fitted model \cr
\code{\link{print.ppm}} \tab Print basic information about a fitted model\cr
\code{\link{summary.ppm}} \tab Summarise a fitted model\cr
\code{\link{anova.ppm}} \tab Analysis of deviance
}
See \code{\link{spatstat.options}} to control plotting of fitted model.
\bold{To specify a point process model:}
The first order ``trend'' of the model is determined by an \code{R}
language formula. The formula specifies the form of the
\emph{logarithm} of the trend.
\tabular{ll}{
\code{~1} \tab No trend (stationary) \cr
\code{~x} \tab Loglinear trend
\eqn{\lambda(x,y) = \exp(\alpha + \beta x)}{lambda(x,y) = exp(alpha + beta * x)} \cr
\tab where \eqn{x,y} are Cartesian coordinates \cr
\code{~polynom(x,y,3)} \tab Log-cubic polynomial trend \cr
\code{~harmonic(x,y,2)} \tab Log-harmonic polynomial trend
}
The higher order (``interaction'') components are described by
an object of class \code{"interact"}. Such objects are created by:
\tabular{ll}{
\code{\link{Poisson}()} \tab the Poisson point process\cr
\code{\link{Strauss}()} \tab the Strauss process \cr
\code{\link{StraussHard}()} \tab the Strauss/hard core point process\cr
\code{\link{Softcore}()} \tab pairwise interaction, soft core potential\cr
\code{\link{PairPiece}()} \tab pairwise interaction, piecewise constant \cr
\code{\link{DiggleGratton}() } \tab Diggle-Gratton potential \cr
\code{\link{LennardJones}() } \tab Lennard-Jones potential \cr
\code{\link{Pairwise}()} \tab pairwise interaction, user-supplied potential\cr
\code{\link{AreaInter}()} \tab Area-interaction process\cr
\code{\link{Geyer}()} \tab Geyer's saturation process\cr
\code{\link{BadGey}()} \tab multiscale Geyer process\cr
\code{\link{SatPiece}()} \tab Saturated pair model, piecewise constant potential\cr
\code{\link{Saturated}()} \tab Saturated pair model, user-supplied potential\cr
\code{\link{OrdThresh}()} \tab Ord process, threshold potential\cr
\code{\link{Ord}()} \tab Ord model, user-supplied potential \cr
\code{\link{MultiStrauss}()} \tab multitype Strauss process \cr
\code{\link{MultiStraussHard}()} \tab multitype Strauss/hard core process
}
\bold{Finer control over model fitting:}
A quadrature scheme is represented by an object of
class \code{"quad"}.
\tabular{ll}{
\code{\link{quadscheme}} \tab
generate a Berman-Turner quadrature scheme\cr
\tab for use by \code{ppm } \cr
\code{\link{default.dummy}} \tab default pattern of dummy points \cr
\code{\link{gridcentres}} \tab dummy points in a rectangular grid \cr
\code{\link{rstrat}} \tab stratified random dummy pattern \cr
\code{\link{spokes}} \tab radial pattern of dummy points \cr
\code{\link{corners}} \tab dummy points at corners of the window \cr
\code{\link{gridweights}} \tab quadrature weights by the grid-counting rule \cr
\code{\link{dirichlet.weights}} \tab quadrature weights are Dirichlet tile areas \cr
\code{plot(Q)} \tab plot quadrature scheme \code{Q}\cr
\code{print(Q)} \tab print basic information about quadrature scheme \code{Q}\cr
\code{\link{summary}(Q)} \tab summary of quadrature scheme \code{Q}
}
\bold{Simulation and goodness-of-fit for fitted models:}
\tabular{ll}{
\code{\link{rmh.ppm}} \tab simulate realisations of a fitted model \cr
\code{\link{envelope}} \tab compute simulation envelopes for a
fitted model
}
}
\section{V. TESTS AND DIAGNOSTICS}{
\bold{Classical hypothesis tests:}
\tabular{ll}{
\code{\link{quadrat.test}} \tab \eqn{\chi^2}{chi^2} goodness-of-fit
test on quadrat counts \cr
\code{\link{kstest}} \tab Kolmogorov-Smirnov goodness-of-fit test\cr
\code{\link{envelope}} \tab critical envelope for Monte Carlo
test of goodness-of-fit \cr
\code{\link{anova.ppm}} \tab Analysis of Deviance for
point process models
}
\bold{Diagnostic plots:}
Residuals for a fitted point process model, and diagnostic plots
based on the residuals, were introduced in Baddeley et al (2005).
Type \code{demo(diagnose)}
for a demonstration of the diagnostics features.
\tabular{ll}{
\code{\link{diagnose.ppm}} \tab diagnostic plots for spatial trend\cr
\code{\link{qqplot.ppm}} \tab diagnostic plot for interpoint interaction\cr
\code{\link{residualspaper}} \tab examples from Baddeley et al (2005)
}
}
\section{VI. DOCUMENTATION}{
The online manual entries are quite detailed and should be consulted
first for information about a particular function.
The paper by Baddeley and Turner (2005a) describes the package.
Baddeley and Turner (2005b) is a more detailed explanation of
how to fit point process models to data.
Type \code{citation("spatstat")} to get these references.
}
\references{
Baddeley, A. and Turner, R. (2005a)
Spatstat: an R package for analyzing spatial point patterns.
\emph{Journal of Statistical Software} \bold{12}:6, 1--42.
URL: \code{www.jstatsoft.org}, ISSN: 1548-7660.
Baddeley, A. and Turner, R. (2005b)
Modelling spatial point patterns in R.
In: A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan,
editors, \emph{Case Studies in Spatial Point Pattern Modelling},
Lecture Notes in Statistics number 185. Pages 23--74.
Springer-Verlag, New York, 2006.
ISBN: 0-387-28311-0.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005)
Residual analysis for spatial point processes.
\emph{Journal of the Royal Statistical Society, Series B}
\bold{67}, 617--666.
}
\section{Licence}{
This library and its documentation are usable under the terms of the "GNU
General Public License", a copy of which is distributed with the package.
}
\author{
Adrian Baddeley
\email{adrian@maths.uwa.edu.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
and Rolf Turner
\email{r.turner@auckland.ac.nz}
}
\section{Acknowledgements}{
Marie-Colette van Lieshout, Rasmus Waagepetersen,
Dominic Schuhmacher and Kasper Klitgaard Berthelsen
made substantial contributions of code.
Additional contributions by
Colin Beale,
Brad Biggerstaff,
Roger Bivand,
Florent Bonneu,
Jianbao Chen,
Y.C. Chin,
Marcelino de la Cruz,
Peter Diggle,
Stephen Eglen,
Agnes Gault,
Marc Genton,
Pavel Grabarnik,
C. Graf,
Janet Franklin,
Ute Hahn,
Mandy Hering,
Martin Bogsted Hansen,
Martin Hazelton,
Juha Heikkinen,
Kurt Hornik,
Ross Ihaka,
Robert John-Chandran,
Devin Johnson,
Jeff Laake,
Jorge Mateu,
Peter McCullagh,
Mi Xiangcheng,
Jesper Moller,
Linda Stougaard Nielsen,
Evgeni Parilov,
Jeff Picka,
Matt Reiter,
Brian Ripley,
Barry Rowlingson,
John Rudge,
Aila Sarkka,
Katja Schladitz,
Bryan Scott,
Ida-Maria Sintorn,
Malte Spiess,
Mark Stevenson,
P. Surovy,
Berwin Turlach,
Andrew van Burgel,
Hao Wang
and
Selene Wong.
}
\keyword{spatial}
\keyword{package}
