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spatstat (version 1.9-3)

Spatial Point Pattern analysis, model-fitting, simulation, tests

Description

A package for analysing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Contains functions for exploratory data analysis, model-fitting, simulation, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, and pixel images. Point process models can be fitted to point pattern data. Cluster type models are fitted by the method of minimum contrast. Very general Gibbs point process models can be fitted to point pattern data using a function ppm similar to lm or glm. Models may include dependence on covariates, interpoint interaction and dependence on marks. Fitted models can be simulated automatically. Also provides facilities for formal inference (such as chi-squared tests) and model diagnostics (including simulation envelopes, residuals, residual plots and Q-Q plots).

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Version

Install

install.packages('spatstat')

Monthly Downloads

16,590

Version

1.9-3

License

GPL version 2 or newer

Maintainer

Adrian Baddeley

Last Published

March 22nd, 2025

Functions in spatstat (1.9-3)

Apply Affine Transformation To Window

Combine Two Line Segment Patterns

Multitype J Function (i-to-any)

Test Whether Two Function Objects Are Compatible

Pixel Image Approximation of a Window

Evaluate Expression Involving Pixel Images

Crossing Points of Two Line Segment Patterns

Humberside Data on Childhood Leukaemia and Lymphoma

Window in Shape of Letter R

Midpoints of Line Segment Pattern

Convert Point Pattern Marks from Numeric to Factor

Exponential Energy Marks

Inhomogeneous K-function

Class of Images

pairwise.family

Pairwise Interaction Process Family

Distance to Boundary of Window

New Zealand Trees Point Pattern

Print Brief Details of an Image

Estimate the I-function

Area of a Window

Lengths of Line Segments

Generate Poisson Point Pattern

Pairwise distances

Pair Correlation Function obtained from K Function

Apply Vector Translation To Window

Pairwise distances

Saturated Pairwise Interaction Point Process Family

Swedish Pines Point Pattern

Mark Correlation Function

Systematic random point pattern

Class of Line Segment Patterns

Sample Quantiles of Pixel Image

Generate Multitype Poisson Point Pattern

Residuals for Fitted Point Process Model

Random Re-Labelling of Point Pattern

The Strauss Point Process Model

Random Thinning

Divide Point Pattern into Sub-patterns

Apply Vector Translation To Point Pattern

Orientation Angles of Line Segments

Apply Vector Translation To Pixel Image

Saturated Pairwise Interaction model

MultiStraussHard

The Multitype/Hard Core Strauss Point Process Model

Apply Function to Every Neighbourhood in a Point Pattern

Ord's Interaction model

Generate Poisson Random Line Process

Generate a Default Pattern of Dummy Points

The Soft Core Point Process Model

Test Whether Points Are Inside A Window

Test Whether An Object Is A Point Pattern

Distance to Boundary of Window

Pairwise distances between line segments

Longleaf Pines Point Pattern

crossdist.default

Pairwise distances between two different sets of points

Profile Maximum Pseudolikelihood

Extract Subset of Function Values

Extract Original Data from a Fitted Point Process Model

Evaluate Logical Expression Involving Pixel Images and Return Region Where Expression is True

Summary of a Line Segment Pattern Dataset

spatstat-deprecated

Deprecated spatstat functions

Distance Map of Line Segment Pattern

Summarizing a Pixel Image

multiplicity.ppp

Count Multiplicity of Duplicate Points

Geyer's Saturation Point Process Model

Convert Pixel Image to Matrix

Nearest-Neighbour Correlation of Marked Point Pattern

Create a Line Segment Pattern

The Piecewise Constant Pairwise Interaction Point Process Model

Quadrat counting for a point pattern

Poisson Point Process Model

Pair Correlation Function of Point Pattern

The Multitype Strauss Point Process Model

Plot a plotppm Object Created by plot.ppm

Chi-Squared Dispersion Test for Spatial Point Pattern Based on Quadrat Counts

Class of Quadrature Schemes

Print Brief Details of a Point Pattern Dataset

Apply Affine Transformation

complement.owin

Take Complement of a Window

Convert Data To Class psp

Convert Data To Class ppp

Multitype K Function (i-to-any)

Simulate Point Process Models using the Metropolis-Hastings Algorithm.

Centroid of a window

Hutchings' Bramble Canes data

Simulate Neyman-Scott Process

Spruces Point Pattern

Diagnostic Plots for Fitted Point Process Model

Diameter of a Window Frame

Expand Window By Factor

Aherne's hamster tumour data

Basis for Harmonic Functions

Create a Window

rescue.rectangle

Convert Window Back To Rectangle

Endpoints of Line Segment Pattern

Nearest neighbour distances

Interaction Distance of a Point Process

K-function using FFT

ANOVA for Fitted Point Process Models

Pair Correlation Function

Plot Function Valuesn

Extract Subset of Function Array

Areas of Morphological Erosions

Simulate Thomas Process

Q-Q Plot of Residuals from Fitted Point Process Model

Data and Code From JRSS Discussion Paper on Residuals

Test Whether An Object Is A Pixel Image

Remove Marks from a Marked Point Pattern

Marked Nearest Neighbour Distance Function

Extract Unique Points from a Spatial Point Pattern

Apply Affine Transformation To Line Segment Pattern

Corners of a rectangle

Plot a Spatial Window

Plot a List of Point Patterns

Pairwise distances between two different point patterns

Set Control Parameters for Metropolis-Hastings Algorithm.

plot a Fitted Point Process Model

Create a Point Pattern

Determine Initial State for Metropolis-Hastings Simulation.

Superimpose Several Point Patterns

Read Point Pattern From Data File

Nearest neighbour

Marked J Function

Convert Data To Class owin

Simulated Point Pattern

Sufficient Statistic of Point Process Model

Artificial Data Point Pattern

Nearest Neighbour Distance Function G

Beta Ganglion Cells in Cat Retina

Calculate four standard summary functions of a point pattern.

Concatenate x,y Coordinate Vectors

Pine saplings in Finland.

dirichlet.weights

Compute Quadrature Weights Based on Dirichlet Tessellation

Distance Map of Point Pattern

Test Whether An Object Is A Window

Data Frames of Function Values

Histogram of Pixel Values in an Image

Function Arrays for Spatial Patterns

Test Whether Marks Are Present

Compute Quadrature Weights Based on Grid Counts

Estimate the J-function

Diggle-Gratton model

Print a Fitted Point Process Model

Simulation envelopes of summary function

Intersection or Union of Two Windows

Kaplan-Meier Estimator using Histogram Data

Generate N Uniform Random Points

Plot a Pixel Image

Summary of a Point Pattern Dataset

Method of Minimum Contrast

Simulate Baddeley-Silverman Cell Process

Class of Point Patterns

rshift.splitppp

Randomly Shift a List of Point Patterns

selfcrossing.psp

Crossing Points in a Line Segment Pattern

Summary of a Spatial Window

Apply Vector Translation

plot a Spatial Point Pattern

Cartesian Coordinates for a Pixel Raster

Extract Quadrature Scheme Used to Fit a Point Process Model

Simple Sequential Inhibition

Apply Vector Translation To Line Segment Pattern

Set or Reset the Marks in a Point Pattern

Union of Data and Dummy Points

Simulate from a Fitted Point Process Model

Multitype Nearest Neighbour Distance Function (i-to-any)

Inhomogeneous Cross K Function

Variance-Covariance Matrix for a Fitted Point Process Model

Bounding Box of a Window

Reduced Second Moment Measure

Rotate a Point Pattern

spatstat.options

Internal Options in Spatstat Package

Randomly Shift a Line Segment Pattern

Summarizing a Fitted Point Process Model

Extract Dummy Points Used to Fit a Point Process Model

Evaluate Expression Involving Functions

Determine Duplicated Points in a Spatial Point Pattern

pairdist.default

Pairwise distances

plot a Spatial Line Segment Pattern

Print Brief Details of a Spatial Window

Print Brief Details of a Line Segment Pattern Dataset

Simulate point patterns using the Metropolis-Hastings algorithm.

Define Point Process Model for Metropolis-Hastings Simulation.

Multitype K Function (Cross-type)

Berman-Huntington points and lines data

Kernel Smoothed Intensity of Point Pattern

Test Whether An Object Is A Fitted Point Process Model

Japanese Pines Point Pattern

Kaplan-Meier and Reduced Sample Estimator using Histograms

nearest.raster.point

Find Pixel Nearest to a Given Point

Plot a Function Array

Lurking variable plot

Print a Quadrature Scheme

Prediction from a Fitted Point Process Model

Harkness-Isham ants' nests data

Test Whether Two Pixel Images Are Compatible

Identify Points in a Point Pattern

Lansing Woods Point Pattern

Generate a Quadrature Scheme from a Point Pattern

Estimate window from points alone

Randomly Shift a Point Pattern

Stratified random point pattern

Summarizing a Quadrature Scheme

Pairwise distances

Level Set of a Pixel Image

California Redwoods Point Pattern (Ripley's Subset)

Rotate a Line Segment Pattern

Extract or Replace Subset of Point Pattern

Fit the Thomas Point Process by Minimum Contrast

Calculate Statistic for All Types in a Multitype Point Pattern

The Strauss / Hard Core Point Process Model

Hughes' Amacrine Cell Data

Kernel Smoothing of Line Segment Pattern

Coefficients of Fitted Point Process Model

spatstat-internal

Internal spatstat functions

Fit the Matern Cluster Point Process by Minimum Contrast

Distance Map of Window

Ord Interaction Process Family

Estimate the empty space function F

Inhomogeneous Multitype K Dot Function

Set Covariance of a Window

Marked K-Function

Generic Pairwise Interaction model

Chorley-Ribble Cancer Data

Convert to Pixel Image

Biological Cells Point Pattern

Multitype J Function (i-to-j)

Multitype Nearest Neighbour Distance Function (i-to-j)

The Lennard-Jones Potential

Pairwise distances between two different line segment patterns

California Redwoods Point Pattern (Entire Dataset)

Test Whether A Point Pattern is Marked

Mean Pixel Value in an Image

Test Whether A Point Process Model is Marked

The Spatstat Package

Fitted Conditional Intensity for Point Process Model

Summarise Marks in Every Neighbourhood in a Point Pattern

Create a Pixel Image Object

Rectangular grid of points

Simulate Matern Model II

Determine Whether One Window is Contained In Another

plot a Spatial Quadrature Scheme

Reduced Sample Estimator using Histogram Data

Rotate a Window

Fit Point Process Model to Data

Class of Fitted Point Process Models

Generate N Random Multitype Points

Spokes pattern of dummy points

Generate N Random Points

Simulate Matern Cluster Process

Extract Subset of Line Segment Pattern

Simulate Matern Model I

Dilate a Window

Fit a Log-Gaussian Cox Point Process by Minimum Contrast

Pair Correlation Function obtained from array of K functions

Apply Affine Transformation To Point Pattern

Perspective Plot of Pixel Image

Beta Ganglion Cells in Cat Retina, Old Version

Extract Subset of Image

Generic Ord Interaction model

Convert Pixel Image from Numeric to Factor

Update a Fitted Point Process Model