The Spatstat Package

Description

This is a summary of the features of spatstat, a family of R packages for the statistical analysis of spatial point patterns.

Details

spatstat is a family of R packages for the statistical analysis of spatial data. Its main focus is the analysis of spatial patterns of points in two-dimensional space.

spatstat is designed to support a complete statistical analysis of spatial data. It supports

• creation, manipulation and plotting of point patterns;

• exploratory data analysis;

• spatial random sampling;

• simulation of point process models;

• parametric model-fitting;

• non-parametric smoothing and regression;

• formal inference (hypothesis tests, confidence intervals);

• model diagnostics.

Apart from two-dimensional point patterns and point processes, spatstat also supports point patterns in three dimensions, point patterns in multidimensional space-time, point patterns on a linear network, patterns of line segments in two dimensions, and spatial tessellations and random sets in two dimensions.

The package can fit several types of point process models to a point pattern dataset:

• Poisson point process models (by Berman-Turner approximate maximum likelihood or by spatial logistic regression)

• Gibbs/Markov point process models (by Baddeley-Turner approximate maximum pseudolikelihood, Coeurjolly-Rubak logistic likelihood, or Huang-Ogata approximate maximum likelihood)

• Cox/cluster point process models (by Waagepetersen's two-step fitting procedure and minimum contrast, composite likelihood, or Palm likelihood)

• determinantal point process models (by Waagepetersen's two-step fitting procedure and minimum contrast, composite likelihood, or Palm likelihood)

The models may include spatial trend, dependence on covariates, and complicated interpoint interactions. Models are specified by a formula in the R language, and are fitted using a function analogous to lm and glm . Fitted models can be printed, plotted, predicted, simulated and so on.

Getting Started

For a quick introduction to spatstat, read the package vignette Getting started with spatstat installed with spatstat. To read that document, you can either

• visit https://cran.r-project.org/package=spatstat and click on Getting Started with Spatstat

• start R, type library(spatstat) and vignette('getstart')

• start R, type help.start() to open the help browser, and navigate to Packages > spatstat > Vignettes.

Once you have installed spatstat, start R and type library(spatstat). Then type beginner for a beginner's introduction, or demo(spatstat) for a demonstration of the package's capabilities.

For a complete course on spatstat, and on statistical analysis of spatial point patterns, read the book by Baddeley, Rubak and Turner (2015). Other recommended books on spatial point process methods are Diggle (2014), Gelfand et al (2010) and Illian et al (2008).

The spatstat package includes over 50 datasets, which can be useful when learning the package. Type demo(data) to see plots of all datasets available in the package. Type vignette('datasets') for detailed background information on these datasets, and plots of each dataset.

For information on converting your data into spatstat format, read Chapter 3 of Baddeley, Rubak and Turner (2015). This chapter is available free online, as one of the sample chapters at the book companion website, https://book.spatstat.org/.

Structure of the spatstat family

The original spatstat package grew to be very large. It has now been divided into several sub-packages:

• spatstat.utils containing basic utilities

• spatstat.sparse containing linear algebra utilities

• spatstat.data containing datasets

• spatstat.univar containing functions for estimating probability distributions of random variables

• spatstat.geom containing functionality for geometrical operations, and defining the main classes of spatial objects

• spatstat.explore containing the main functions for exploratory analysis of spatial data

• spatstat.model containing the main functions for parametric statistical modelling and analysis, and formal inference, for spatial data

• spatstat.linnet containing functions for spatial data on a linear network

• spatstat, which simply loads the other sub-packages listed above, and provides documentation.

The breakup has been done in such a way that the user should not notice any difference. Source code that worked with the old spatstat package should work with the new spatstat family. Code that is documented in our books, journal articles and vignettes should still work.

When you install spatstat, the sub-packages listed above are also installed. Then if you load the spatstat package by typing library(spatstat), the other sub-packages listed above will automatically be loaded or imported.

This help file covers all the functionality and datasets that are provided in the sub-packages listed above.

Extension packages

Additionally there are several extension packages:

• spatstat.gui for interactive graphics

• spatstat.local for local likelihood (including geographically weighted regression)

• spatstat.Knet for additional, computationally efficient code for linear networks

• spatstat.sphere (under development) for spatial data on a sphere, including spatial data on the earth's surface

The extension packages must be installed separately and loaded explicitly if needed. They also have separate documentation.

Updates

New versions of spatstat are released every 8 weeks. Users are advised to update their installation of spatstat regularly.

Type latest.news to read the news documentation about changes to the current installed version of spatstat.

See the Vignette Summary of recent updates, installed with spatstat, which describes the main changes to spatstat since the book (Baddeley, Rubak and Turner, 2015) was published. It is accessible as vignette('updates').

Type news(package="spatstat") to read news documentation about all previous versions of the package.

FUNCTIONS AND DATASETS

Following is a summary of the main functions and datasets in the spatstat package. Alternatively an alphabetical list of all functions and datasets is available by typing library(help=spatstat).

For further information on any of these, type help(name) or ?name where name is the name of the function or dataset.

CONTENTS:

I. CREATING AND MANIPULATING DATA

Types of spatial data:

The main types of spatial data supported by spatstat are:

To create a point pattern:

To simulate a random point pattern:

To randomly change an existing point pattern:

Standard point pattern datasets:

Datasets in spatstat are lazy-loaded, so you can simply type the name of the dataset to use it; there is no need to type data(amacrine) etc.

Type demo(data) to see a display of all the datasets installed with the package.

Type vignette('datasets') for a document giving an overview of all datasets, including background information, and plots.

To display a 2D point pattern:

To manipulate a 2D point pattern:

See spatstat.options to control plotting behaviour.

To create a window:

An object of class "owin" describes a spatial region (a window of observation).

To manipulate a window:

Digital approximations:

See spatstat.options to control the approximation

Geometrical computations with windows:

Pixel images: An object of class "im" represents a pixel image. Such objects are returned by some of the functions in spatstat including Kmeasure , setcov and density.ppp .

Line segment patterns

An object of class "psp" represents a pattern of straight line segments.

Tessellations

An object of class "tess" represents a tessellation.

Three-dimensional point patterns

An object of class "pp3" represents a three-dimensional point pattern in a rectangular box. The box is represented by an object of class "box3".

Multi-dimensional space-time point patterns

An object of class "ppx" represents a point pattern in multi-dimensional space and/or time.

Point patterns on a linear network

An object of class "linnet" represents a linear network (for example, a road network).

An object of class "lpp" represents a point pattern on a linear network (for example, road accidents on a road network).

Hyperframes

A hyperframe is like a data frame, except that the entries may be objects of any kind.

Layered objects

A layered object represents data that should be plotted in successive layers, for example, a background and a foreground.

Colour maps

A colour map is a mechanism for associating colours with data. It can be regarded as a function, mapping data to colours. Using a colourmap object in a plot command ensures that the mapping from numbers to colours is the same in different plots.

II. EXPLORATORY DATA ANALYSIS

Inspection of data:

Classical exploratory tools:

Smoothing:

Modern exploratory tools:

Summary statistics for a point pattern: Type demo(sumfun) for a demonstration of many of the summary statistics.

Related facilities:

Summary statistics for a multitype point pattern: A multitype point pattern is represented by an object X of class "ppp" such that marks(X) is a factor.

Summary statistics for a marked point pattern: A marked point pattern is represented by an object X of class "ppp" with a component X$marks. The entries in the vector X$marks may be numeric, complex, string or any other atomic type. For numeric marks, there are the following functions:

For marks of any type, there are the following:

Alternatively use cut.ppp to convert a marked point pattern to a multitype point pattern.

Programming tools:

Summary statistics for a point pattern on a linear network:

These are for point patterns on a linear network (class lpp). For unmarked patterns:

For multitype patterns:

Related facilities:

It is also possible to fit point process models to lpp objects. See Section IV.

Summary statistics for a three-dimensional point pattern:

These are for 3-dimensional point pattern objects (class pp3).

Related facilities:

Computations for multi-dimensional point pattern:

These are for multi-dimensional space-time point pattern objects (class ppx).

Summary statistics for random sets:

These work for point patterns (class ppp), line segment patterns (class psp) or windows (class owin).

III. MODEL FITTING (COX AND CLUSTER MODELS)

Cluster process models (with homogeneous or inhomogeneous intensity) and Cox processes can be fitted by the function kppm . Its result is an object of class "kppm". The fitted model can be printed, plotted, predicted, simulated and updated.

For model selection, you can also use the generic functions step , drop1 and AIC on fitted point process models. For variable selection, see sdr .

The theoretical models can also be simulated, for any choice of parameter values, using rThomas , rMatClust , rCauchy , rVarGamma , and rLGCP .

Lower-level fitting functions include:

IV. MODEL FITTING (POISSON AND GIBBS MODELS)

Types of models

Poisson point processes are the simplest models for point patterns. A Poisson model assumes that the points are stochastically independent. It may allow the points to have a non-uniform spatial density. The special case of a Poisson process with a uniform spatial density is often called Complete Spatial Randomness.

Poisson point processes are included in the more general class of Gibbs point process models. In a Gibbs model, there is interaction or dependence between points. Many different types of interaction can be specified.

For a detailed explanation of how to fit Poisson or Gibbs point process models to point pattern data using spatstat, see Baddeley and Turner (2005b) or Baddeley (2008).

To fit a Poisson or Gibbs point process model:

Model fitting in spatstat is performed mainly by the function ppm . Its result is an object of class "ppm".

Here are some examples, where X is a point pattern (class "ppp"):

It is also possible to fit models that depend on other covariates.

Manipulating the fitted model:

For model selection, you can also use the generic functions step , drop1 and AIC on fitted point process models. For variable selection, see sdr .

See spatstat.options to control plotting of fitted model.

To specify a point process model:

The first order “trend” of the model is determined by an R language formula. The formula specifies the form of the logarithm of the trend.

The higher order (“interaction”) components are described by an object of class "interact". Such objects are created by:

Note that it is also possible to combine several such interactions using Hybrid .

Finer control over model fitting:

A quadrature scheme is represented by an object of class "quad". To create a quadrature scheme, typically use quadscheme .

To inspect a quadrature scheme:

A quadrature scheme consists of data points, dummy points, and weights. To generate dummy points:

To compute weights:

Simulation and goodness-of-fit for fitted models:

Point process models on a linear network:

An object of class "lpp" represents a pattern of points on a linear network. Point process models can also be fitted to these objects. Currently only Poisson models can be fitted.

V. MODEL FITTING (DETERMINANTAL POINT PROCESS MODELS)

Code for fitting determinantal point process models has recently been added to spatstat.

For information, see the help file for dppm .

VI. MODEL FITTING (SPATIAL LOGISTIC REGRESSION)

Logistic regression

Pixel-based spatial logistic regression is an alternative technique for analysing spatial point patterns that is widely used in Geographical Information Systems. It is approximately equivalent to fitting a Poisson point process model.

In pixel-based logistic regression, the spatial domain is divided into small pixels, the presence or absence of a data point in each pixel is recorded, and logistic regression is used to model the presence/absence indicators as a function of any covariates.

Facilities for performing spatial logistic regression are provided in spatstat for comparison purposes.

Fitting a spatial logistic regression

Spatial logistic regression is performed by the function slrm . Its result is an object of class "slrm". There are many methods for this class, including methods for print, fitted, predict, simulate, anova, coef, logLik, terms, update, formula and vcov.

For example, if X is a point pattern (class "ppp"):

Manipulating a fitted spatial logistic regression

There are many other undocumented methods for this class, including methods for print, update, formula and terms. Stepwise model selection is possible using step or stepAIC. For variable selection, see sdr .

VII. SIMULATION

There are many ways to generate a random point pattern, line segment pattern, pixel image or tessellation in spatstat.

Random point patterns:

Resampling a point pattern:

See also varblock for estimating the variance of a summary statistic by block resampling, and lohboot for another bootstrap technique.

Fitted point process models:

If you have fitted a point process model to a point pattern dataset, the fitted model can be simulated.

Cluster process models are fitted by the function kppm yielding an object of class "kppm". To generate one or more simulated realisations of this fitted model, use simulate.kppm .

Gibbs point process models are fitted by the function ppm yielding an object of class "ppm". To generate a simulated realisation of this fitted model, use rmh . To generate one or more simulated realisations of the fitted model, use simulate.ppm .

Other random patterns:

Simulation-based inference

VIII. TESTS AND DIAGNOSTICS

Hypothesis tests:

More recently-developed tests:

Sensitivity diagnostics:

Classical measures of model sensitivity such as leverage and influence have been adapted to point process models.

Diagnostics for covariate effect:

Classical diagnostics for covariate effects have been adapted to point process models.

Residual diagnostics:

Residuals for a fitted point process model, and diagnostic plots based on the residuals, were introduced in Baddeley et al (2005) and Baddeley, Rubak and Moller (2011).

Type demo(diagnose) for a demonstration of the diagnostics features.

Resampling and randomisation procedures

You can build your own tests based on randomisation and resampling using the following capabilities:

IX. DOCUMENTATION

The online manual entries are quite detailed and should be consulted first for information about a particular function.

The book Baddeley, Rubak and Turner (2015) is a complete course on analysing spatial point patterns, with full details about spatstat.

Older material (which is now out-of-date but is freely available) includes Baddeley and Turner (2005a), a brief overview of the package in its early development; Baddeley and Turner (2005b), a more detailed explanation of how to fit point process models to data; and Baddeley (2010), a complete set of notes from a 2-day workshop on the use of spatstat.

Type citation("spatstat") to get a list of these references.

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.

Acknowledgements

Kasper Klitgaard Berthelsen, Ottmar Cronie, Tilman Davies, Yongtao Guan, Ute Hahn, Abdollah Jalilian, Marie-Colette van Lieshout, Greg McSwiggan, Tuomas Rajala, Suman Rakshit, Dominic Schuhmacher, Rasmus Waagepetersen and Hangsheng Wang made substantial contributions of code.

Additional contributions and suggestions from Mohomed Abraj, Monsuru Adepeju, Corey Anderson, Ang Qi Wei, Ryan Arellano, Jens Astrom, Robert Aue, Marcel Austenfeld, Sandro Azaele, Guy Bayegnak, Colin Beale, Melanie Bell, Thomas Bendtsen, Ricardo Bernhardt, Andrew Bevan, Brad Biggerstaff, Anders Bilgrau, Leanne Bischof, Christophe Biscio, Roger Bivand, Jose M. Blanco Moreno, Florent Bonneu, Jordan Brown, Ian Buller, Julian Burgos, Simon Byers, Ya-Mei Chang, Jianbao Chen, Igor Chernayavsky, Y.C. Chin, Bjarke Christensen, Lucia Cobo Sanchez, Jean-Francois Coeurjolly, Kim Colyvas, Hadrien Commenges, Rochelle Constantine, Robin Corria Ainslie, Richard Cotton, Marcelino de la Cruz, Peter Dalgaard, Mario D'Antuono, Sourav Das, Peter Diggle, Patrick Donnelly, Ian Dryden, Murray Efford, Stephen Eglen, Ahmed El-Gabbas, Belarmain Fandohan, Olivier Flores, David Ford, Peter Forbes, Shane Frank, Janet Franklin, Funwi-Gabga Neba, Oscar Garcia, Agnes Gault, Jonas Geldmann, Marc Genton, Shaaban Ghalandarayeshi, Julian Gilbey, Jason Goldstick, Pavel Grabarnik, C. Graf, Ute Hahn, Andrew Hardegen, Martin Bogsted Hansen, Martin Hazelton, Juha Heikkinen, Mandy Hering, Markus Herrmann, Maximilian Hesselbarth, Paul Hewson, Hamidreza Heydarian, Kassel Hingee, Stephanie Hogg, Kurt Hornik, Philipp Hunziker, Jack Hywood, Ross Ihaka, Cenk Icos, Aruna Jammalamadaka, Jakob Jentschke, Robert John-Chandran, Devin Johnson, Mahdieh Khanmohammadi, Bob Klaver, Lily Kozmian-Ledward, Peter Kovesi, Mike Kuhn, Jeff Laake, Robert Lamb, Frederic Lavancier, Tom Lawrence, Tomas Lazauskas, Jonathan Lee, George Leser, Angela Li, Li Haitao, George Limitsios, Andrew Lister, Nestor Luambua, Bethany Macdonald, Ben Madin, Martin Maechler, Daniel Manrique-Castano, Kiran Marchikanti, Jeff Marcus, Robert Mark, Peter McCullagh, Monia Mahling, Jorge Mateu Mahiques, Ulf Mehlig, Frederico Mestre, Sebastian Wastl Meyer, Mi Xiangcheng, Lore De Middeleer, Robin Milne, Enrique Miranda, Jesper Moller, Annie Mollie, Ines Moncada, Mehdi Moradi, Virginia Morera Pujol, Erika Mudrak, Gopalan Nair, Nader Najari, Nicoletta Nava, Linda Stougaard Nielsen, Felipe Nunes, Jens Randel Nyengaard, Jens Oehlschlaegel, Thierry Onkelinx, Sean O'Riordan, Evgeni Parilov, Jeff Picka, Nicolas Picard, Tim Pollington, Mike Porter, Sergiy Protsiv, Adrian Raftery, Suman Rakshit, Ben Ramage, Pablo Ramon, Xavier Raynaud, Nicholas Read, Matt Reiter, Ian Renner, Tom Richardson, Brian Ripley, Yonatan Rosen, Ted Rosenbaum, Barry Rowlingson, Jason Rudokas, Tyler Rudolph, John Rudge, Christopher Ryan, Farzaneh Safavimanesh, Aila Sarkka, Cody Schank, Katja Schladitz, Sebastian Schutte, Bryan Scott, Olivia Semboli, Francois Semecurbe, Vadim Shcherbakov, Shen Guochun, Shi Peijian, Harold-Jeffrey Ship, Tammy L Silva, Ida-Maria Sintorn, Yong Song, Malte Spiess, Mark Stevenson, Kaspar Stucki, Jan Sulavik, Michael Sumner, P. Surovy, Ben Taylor, Thordis Linda Thorarinsdottir, Leigh Torres, Berwin Turlach, Torben Tvedebrink, Kevin Ummer, Medha Uppala, Malissa Usher, Andrew van Burgel, Tobias Verbeke, Mikko Vihtakari, Alexendre Villers, Fabrice Vinatier, Maximilian Vogtland, Sasha Voss, Sven Wagner, Hao Wang, H. Wendrock, Jan Wild, Carl G. Witthoft, Selene Wong, Maxime Woringer, Luke Yates, Mike Zamboni, Achim Zeileis and Tingting Zhan.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.

References

Baddeley, A. (2010) Analysing spatial point patterns in R. Workshop notes, Version 4.1. Online technical publication, CSIRO. https://research.csiro.au/software/wp-content/uploads/sites/6/2015/02/Rspatialcourse_CMIS_PDF-Standard.pdf

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

Baddeley, A. and Turner, R. (2005a) Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 12:6, 1–42. DOI: 10.18637/jss.v012.i06.

Baddeley, A. and Turner, R. (2005b) Modelling spatial point patterns in R. In: A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan, editors, 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. Journal of the Royal Statistical Society, Series B 67, 617–666.

Baddeley, A., Rubak, E. and Moller, J. (2011) Score, pseudo-score and residual diagnostics for spatial point process models. Statistical Science 26, 613–646.

Baddeley, A., Turner, R., Mateu, J. and Bevan, A. (2013) Hybrids of Gibbs point process models and their implementation. Journal of Statistical Software 55:11, 1–43. https://www.jstatsoft.org/v55/i11/

Diggle, P.J. (2003) Statistical analysis of spatial point patterns, Second edition. Arnold.

Diggle, P.J. (2014) Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third edition. Chapman and Hall/CRC.

Gelfand, A.E., Diggle, P.J., Fuentes, M. and Guttorp, P., editors (2010) Handbook of Spatial Statistics. CRC Press.

Huang, F. and Ogata, Y. (1999) Improvements of the maximum pseudo-likelihood estimators in various spatial statistical models. Journal of Computational and Graphical Statistics 8, 510–530.

Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) Statistical Analysis and Modelling of Spatial Point Patterns. Wiley.

Waagepetersen, R. An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63 (2007) 252–258.

Print Introduction For Beginners

Description

Prints an introduction for beginners to the spatstat package, or another specified package.

Usage

beginner(package = "spatstat")

Arguments

Details

This function prints an introduction for beginners to the spatstat package.

The function can be executed simply by typing beginner without parentheses.

If the argument package is given, then the function prints the beginner's help file BEGINNER.txt from the specified package (if it has one).

Value

Null.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner rolfturner@posteo.net

See Also

latest.news

Examples

 beginner

List Recent Bug Fixes

Description

List all bug fixes in a package, starting from a certain date or version of the package. Fixes are sorted alphabetically by the name of the affected function. The default is to list bug fixes in the latest version of the spatstat family of packages.

Usage

 bugfixes(sinceversion = NULL, sincedate = NULL,
 package = spatstat.family(), show = TRUE)

Arguments

Details

Bug reports are extracted from the NEWS file of the specified package. Only those after a specified date, or after a specified version of the package, are retained. The bug reports are then sorted alphabetically, so that all bugs affecting a particular function are listed consecutively. Finally the table of bug reports is displayed (if show=TRUE) and returned invisibly.

The argument sinceversion should be a character string like "1.2-3". The default is the current installed version of the package.

The argument sincedate should be a character string like "2015-05-27", or a date-time object.

If sinceversion="all" or sincedate="all" then all recorded bugs will be listed.

The special options sinceversion="book" and sincedate="book" are interpreted to mean sincedate="2015-06-05", which gives all bugs reported after publication of the book by Baddeley, Rubak and Turner (2015).

Typing bugfixes without parentheses will display a table of all bugs that were fixed in the current installed version of spatstat and its sub-packages.

By default, bugs in the extension packages spatstat.local, spatstat.Knet, spatstat.gui are not reported. To include these bugs as well, set package=spatstat.family(TRUE,TRUE).

Value

(Invisibly) a data frame, belonging to the class "bugtable", which has a print method.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au.

References

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

See Also

latest.changes , latest.news , news .

Examples

 bugfixes
 ## show all bugs reported after publication of the spatstat book
 if(interactive()) bugfixes(sinceversion="book")

Foo is Not a Real Name

Description

The name foo is not a real name: it is a place holder, used to represent the name of any desired thing.

The functions defined here simply print an explanation of the placeholder name foo.

Usage

foo()
## S3 method for class 'foo'
plot(x, ...)

Arguments

Details

The name foo is used by computer scientists as a place holder, to represent the name of any desired object or function. It is not the name of an actual object or function; it serves only as an example, to explain a concept.

However, many users misinterpret this convention, and actually type the command foo or foo(). Then they email the package author to inform them that foo is not defined.

To avoid this correspondence, we have now defined an object called foo.

The function foo() prints a message explaining that foo is not really the name of a variable.

The function can be executed simply by typing foo without parentheses.

Value

Null.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.

See Also

beginner

Examples

 foo

List Recent Significant Changes to a Function

Description

List all the changes to a particular function in a package, starting from a certain date or version of the package. The default is to list changes in the latest version of the spatstat family of packages.

Usage

 latest.changes(x,
 sinceversion = NULL, sincedate = NULL,
 package = spatstat.family(), show = TRUE)

Arguments

Details

Details of changes are extracted from the NEWS file of the specified package under the heading ‘Significant User-Visible Changes’. All entries for which the first line matches x are selected. The table of changes is displayed (if show=TRUE) and returned invisibly.

The argument sinceversion should be a character string like "1.2-3". The default is the version string of the most recent version.

The argument sincedate should be a character string like "2015-05-27", or a date-time object. The default is the date of the most recent version.

If sinceversion="all" or sincedate="all" then all recorded changes will be listed.

The special options sinceversion="book" and sincedate="book" are interpreted to mean sincedate="2015-06-05", which gives all changes reported after publication of the book by Baddeley, Rubak and Turner (2015).

By default, changes in the extension packages spatstat.local, spatstat.Knet, spatstat.gui are not reported. To include these changes as well, set package=spatstat.family(TRUE,TRUE).

Value

(Invisibly) a data frame, belonging to the class "changetable", which has a print method.

Pattern matching

The first argument x is interpreted as a pattern to be matched using grepl .

For example if x = "ppp", then this will match any news items that are about ppp, but will also match any news items about plot.ppp, print.ppp, ppplist, pppmatch, [.ppp and so on.

The pattern x = "^ppp" will match only those strings which begin with "ppp", such as ppp, ppplist, pppmatch. The symbol ^ means the start of the string.

The pattern x = "^ppp$" will match only the string "ppp". The symbol $ means the end of the string.

The pattern x = "\.ppp$" will match only those strings which end with ".ppp", such as plot.ppp, print.ppp, [.ppp. The symbol . has a special meaning, but here we want the character ".", so we use \ to escape the special meaning, so that \. means the character ".", and "\.ppp$" means any string ending in ".ppp".

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au.

References

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

See Also

bugfixes , latest.news , news .

Examples

 latest.changes("plot.symbolmap")

Print News About Latest Version of Package

Description

Prints the news documentation for the current version of spatstat or another specified package.

Usage

latest.news(package = spatstat.family(), doBrowse=FALSE, major=TRUE)

Arguments

Details

This function prints the news documentation about changes in the current installed version of a package.

By default, it prints the latest news about all the sub-packages in the spatstat family.

The function can be called simply by typing its name without parentheses (see the Examples).

If major=FALSE, only information for the current minor version "x.y-z" will be printed. If major=TRUE (the default), all information for the current major version "x.y" will be printed, encompassing versions "x.y-0", "x.y-1", up to "x.y-z".

If package is given, then the function reads the news for the specified package from its NEWS file (if it has one) and prints only the entries that refer to the current version of the package.

To see the news for all previous versions as well as the current version, use the R utility news . See the Examples.

Value

Null.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.

See Also

spatstat.family lists the packages in the spatstat family.

bugfixes lists bug fixes.

news

Examples

 if(interactive()) {
 # current news
 latest.news
 # all news
 # news(package="spatstat")
 }

Internal spatstat functions

Description

Internal spatstat functions.

Usage

## S3 method for class 'autoexec'
print(x, ...)
## S3 method for class 'bugtable'
print(x, ...)
## S3 method for class 'changetable'
print(x, ...)

Details

These internal spatstat functions should not be called directly by the user. Their names and capabilities may change without warning from one version of spatstat to the next.

Value

The return values of these functions are not documented, and may change without warning.

Names of All Packages in the Spatstat Family

Description

Provides the names of all the packages belonging to the spatstat family of packages.

Usage

 spatstat.family(subpackages=TRUE, extensions=FALSE)

Arguments

Details

This function returns a character vector containing the names of the packages that belong to the spatstat family.

By default, only the sub-packages are listed, and not the extension packages.

A “sub-package” is a package which is implicitly loaded or imported when the command library(spatstat) is issued. Currently the sub-packages are:

• spatstat.utils

• spatstat.data

• spatstat.univar

• spatstat.sparse

• spatstat.geom

• spatstat.random

• spatstat.explore

• spatstat.model

• spatstat.linnet

• spatstat

An “extension package” is a package which must be loaded explicitly. The extension packages are:

• spatstat.gui

• spatstat.local

• spatstat.Knet

Value

Character vector of package names.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.

See Also

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