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spatstat.model (version 3.3-2)

Parametric Statistical Modelling and Inference for the 'spatstat' Family

Description

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

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Version

Install

install.packages('spatstat.model')

Monthly Downloads

16,761

Version

3.3-2

License

GPL (>= 2)

Maintainer

Adrian Baddeley

Last Published

September 19th, 2024

Functions in spatstat.model (3.3-2)

Extract.leverage.ppm

Extract Subset of Leverage Object

The Connected Component Process Model

Diggle-Gratton model

Hybrid Geyer Point Process Model

Extract Subset of Signed or Vector Measure

Extract.influence.ppm

Extract Subset of Influence Object

Model Compensator of Nearest Neighbour Function

The Fiksel Interaction

The Area Interaction Point Process Model

DiggleGatesStibbard

Diggle-Gates-Stibbard Point Process Model

K Function or Pair Correlation Function of a Point Process Model

The Hard Core Point Process Model

The Hierarchical Strauss Point Process Model

Model Compensator of K Function

HierStraussHard

The Hierarchical Strauss Hard Core Point Process Model

Geyer's Saturation Point Process Model

Hybrid Interaction Point Process Model

K-function or Pair Correlation Function of a Determinantal Point Process Model

Residual G Function

The Hierarchical Hard Core Point Process Model

The Lennard-Jones Potential

The Multitype Hard Core Point Process Model

Arithmetic Operations on Measures

Residual K Function

K Function or Pair Correlation Function of Cluster Model or Cox model

Lambert's W Function

The Multitype Strauss Point Process Model

The Soft Core Point Process Model

Ord's Interaction model

Generic Ord Interaction model

Generic Pairwise Interaction model

Penttinen Interaction

MultiStraussHard

The Multitype/Hard Core Strauss Point Process Model

K Function or Pair Correlation Function of Gibbs Point Process model

The Strauss Point Process Model

The Piecewise Constant Pairwise Interaction Point Process Model

ANOVA for Fitted Point Process Models for Replicated Patterns

Analysis of Deviance for Spatial Logistic Regression Models

Poisson Point Process Model

The Strauss / Hard Core Point Process Model

Convert Fitted Model To Class fv

Piecewise Constant Saturated Pairwise Interaction Point Process Model

Saturated Pairwise Interaction model

ANOVA for Fitted Point Process Models

Extract Window of Spatial Object

as.function.leverage.ppm

Convert Leverage Object to Function of Coordinates

Added Variable Plot for Point Process Model

Extract Interaction Structure

Smooth a Signed or Vector-Valued Measure

Bias Correction for Fitted Model

Extract Fitted Point Process Model

closepaircounts

Count Close Pairs of Points

Convert Measure To Layered Object

clusterfield.kppm

Field of clusters

The Triplet Point Process Model

berman.test.ppm

Berman's Tests for Point Process Model

clusterradius.kppm

Compute or Extract Effective Range of Cluster Kernel

Spatial Distribution Test for Point Pattern or Point Process Model

Bessel Type Determinantal Point Process Model

Convert Data To Class owin

Residual Diagnostics for Multiple Fitted Models

Spatial Distribution Test for Multiple Point Process Model

Generalized Cauchy Determinantal Point Process Model

Fit the Neyman-Scott cluster process with Cauchy kernel

Extract Original Data from a Fitted Point Process Model

Case Deletion Effect Measure of Fitted Model

Fit Cluster or Cox Point Process Model via Minimum Contrast

dppspecdenrange

Range of Spectral Density of a Determinantal Point Process Model

Area Under ROC Curve

Diagnostic Plots for Fitted Point Process Model

Fit the Neyman-Scott cluster process with Cauchy kernel

Coefficients of Fitted Point Process Model

Coefficients of Point Process Model Fitted to Multiple Point Patterns

clusterkernel.kppm

Extract Cluster Offspring Kernel

detpointprocfamilyfun

Construct a New Determinantal Point Process Model Family Function

Parameter Influence Measure

Convert Data to Numeric Values by Constructing Dummy Variables

Coefficients of Fitted Spatial Logistic Regression Model

Simulation Envelopes of Summary Function

Power Exponential Spectral Determinantal Point Process Model

Extract Kernel from Determinantal Point Process Model Object

Gaussian Determinantal Point Process Model

exactMPLEstrauss

Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process

Whittle-Matern Determinantal Point Process Model

Intensity of Determinantal Point Process Model

Intensity of Fitted Point Process Model

dppapproxkernel

Approximate Determinantal Point Process Kernel

Force Point Process Model to be Valid

Parameter Bound for a Determinantal Point Process Model

Extract Dummy Points Used to Fit a Point Process Model

Exponential Energy Marks

Test Whether An Object Is A Fitted Point Process Model

dim.detpointprocfamily

Dimension of Determinantal Point Process Model

Extract Spectral Density from Determinantal Point Process Model Object

Compute Fitted Effect of a Spatial Covariate in a Point Process Model

Force Model to be Valid

is.stationary.ppm

Recognise Stationary and Poisson Point Process Models

Make Measures Compatible

Extract Fixed Effects from Point Process Model

hierpair.family

Hierarchical Pairwise Interaction Process Family

Log Likelihood and AIC for Fitted Determinantal Point Process Model

Extract the Domain of any Spatial Object

Fit Point Process Model Involving Irregular Trend Parameters

Fit Determinantal Point Process Model

Extract the Interaction from a Fitted Point Process Model

Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model

matclust.estpcf

Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation

Approximate Pair Correlation Function of Determinantal Point Process Model

Internal function calculating eig and index

Force Spatial Logistic Regression Model to be Valid

Hybrid Interaction Family

Model selection criteria for the intensity function of a point process

Model Formulae for Gibbs Point Process Models

Recognise Fitted Determinantal Point Process Models

Fitted Conditional Intensity for Multiple Point Process Model

Fitted Conditional Intensity for Point Process Model

Improve Intensity Estimate of Fitted Cluster Point Process Model

Test Whether Object is a Hybrid

Test Whether A Point Process Model is Marked

Fit a Log-Gaussian Cox Point Process by Minimum Contrast

Extract the Hard Core Distance of a Point Process Model

Basis for Harmonic Functions

Fitted Probabilities for Spatial Logistic Regression

Fit a Log-Gaussian Cox Point Process by Minimum Contrast

Influence Measure for Spatial Point Process Model

inforder.family

Infinite Order Interaction Family

Integral of a Measure

Leverage Measure for Spatial Point Process Model

is.multitype.ppm

Test Whether A Point Process Model is Multitype

Leverage and Influence Diagnostics for Spatial Logistic Regression

Interaction Structure Family Objects

methods.leverage.ppm

Methods for Leverage Objects

is.poissonclusterprocess

Recognise Poisson Cluster Process Models

Lurking Variable Plot for Multiple Point Patterns

methods.objsurf

Methods for Objective Function Surfaces

Log Likelihood and AIC for Multiple Point Process Model

Intensity of Fitted Spatial Logistic Regression Model

Fit Cluster or Cox Point Process Model

Method of Minimum Contrast

interactionorder

Determine the Order of Interpoint Interaction in a Model

Identify Covariates Involved in each Model Term

measureContinuous

Discrete and Continuous Components of a Measure

Fit the Matern Cluster Point Process by Minimum Contrast

Methods for Spatial Logistic Regression Models

Methods for Trajectories of Function Evaluations

Log Likelihood and AIC for Point Process Model

model.frame.ppm

Extract the Variables in a Point Process Model

Saturated Pairwise Interaction Point Process Family

Compute Images of Constructed Covariates

Loglikelihood of Spatial Logistic Regression

Objective Function Surface

Methods for Determinantal Point Process Models

model.matrix.slrm

Extract Design Matrix from Spatial Logistic Regression Model

methods.influence.ppm

Methods for Influence Objects

pairwise.family

Pairwise Interaction Process Family

Plot Profile Likelihood

Ord Interaction Process Family

Probability that a Point Has Any Siblings

Lurking Variable Plot

Methods for Cluster Point Process Models

Fit Point Process Model to Several Point Patterns

Methods for Fitted Interactions

Plot a Signed or Vector-Valued Measure

measureVariation

Positive and Negative Parts, and Variation, of a Measure

Extract Model Parameters in Understandable Form

Plot a Fitted Spatial Logistic Regression

measureWeighted

Weighted Version of a Measure

Plot the Palm Intensity Diagnostic

Polynomial in One or Two Variables

Signed or Vector-Valued Measure

methods.zclustermodel

Methods for Cluster Models

model.matrix.mppm

Extract Design Matrix of Point Process Model for Several Point Patterns

methods.zgibbsmodel

Methods for Gibbs Models

Prediction for Fitted Multiple Point Process Model

Dummy Function Returns Number of Points

Fit Point Process Model to Data

model.matrix.ppm

Extract Design Matrix from Point Process Model

Plot a Recursively Partitioned Point Process Model

Class of Fitted Point Process Models

Prediction from a Fitted Point Process Model

plot.leverage.ppm

Plot Leverage Function

plot a Fitted Multiple Point Process Model

Sibling Probability of Cluster Point Process

Prune a Recursively Partitioned Point Process Model

Diagnostic based on Palm Intensity

Residuals for Fitted Determinantal Point Process Model

Pseudoscore Diagnostic For Fitted Model against General Alternative

Residuals for Fitted Cox or Cluster Point Process Model

Calculate Pseudo-R-Squared for Point Process Model

Nonparametric Estimate of Intensity as Function of a Covariate

Extract Random Effects from Point Process Model

Simulation of a Determinantal Point Process

Simulate from a Fitted Point Process Model

Partial Residuals for Point Process Model

Spatial Logistic Regression

Panel Plots using Colour Image or Contour Lines

Recursively Partitioned Point Process Model

Plot a fitted determinantal point process

spatstat.model-internal

Internal spatstat.model functions

Interaction Distance of a Point Process Model

Simulation of Determinantal Point Process Model

Fit Point Process Model to Point Pattern Data

plot.influence.ppm

Plot Influence Measure

Simulate a Fitted Cluster Point Process Model

Simulate a Point Process Model Fitted to Several Point Patterns

Plot a plotppm Object Created by plot.ppm

Fit the Thomas Point Process by Minimum Contrast

Range of Interaction for a Determinantal Point Process Model

Plot a fitted cluster point process

Leverage and Influence Measures for Spatial Point Process Model

Make Predictions From a Recursively Partitioned Point Process Model

Prediction from a Fitted Determinantal Point Process Model

Separate a Vector Measure into its Scalar Components

Simulate a Fitted Gibbs Point Process Model

update.detpointprocfamily

Set Parameter Values in a Determinantal Point Process Model

Simulate a Fitted Spatial Logistic Regression Model

Summarizing a Fitted Determinantal Point Process Model

Predicted or Fitted Values from Spatial Logistic Regression

plot a Fitted Point Process Model

quadrat.test.ppm

Dispersion Test for Spatial Point Pattern Based on Quadrat Counts

Summarizing a Fitted Cox or Cluster Point Process Model

Update a Fitted Determinantal Point Process Model

Extract trajectory of function evaluations

Print a Fitted Point Process Model

valid.detpointprocfamily

Check Validity of a Determinantal Point Process Model

quadrat.test.mppm

Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts

Prediction from a Fitted Cluster Point Process Model

Update a Fitted Cluster Point Process Model

update.interact

Update an Interpoint Interaction

Check Whether Point Process Model is Valid

Repulsiveness Index of a Determinantal Point Process Model

residualMeasure

Residual Measure for an Observed Point Pattern and a Fitted Intensity

Fit Models by Profile Maximum Pseudolikelihood or AIC

Extract the Values of the Response from a Fitted Model

Q-Q Plot of Residuals from Fitted Point Process Model

Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative

Extract Quadrature Scheme Used to Fit a Point Process Model

Richardson Extrapolation

Summarizing a Fitted Point Process Model

Range of Interaction for a Cox or Cluster Point Process Model

Parametric Estimate of Spatially-Varying Relative Risk

Pseudoscore Diagnostic For Fitted Model against Saturation Alternative

Residuals for Point Process Model Fitted to Multiple Point Patterns

Fit the Thomas Point Process by Minimum Contrast

Update a Fitted Point Process Model

Residuals for Fitted Point Process Model

Residuals for Recursively Partitioned Point Process Model

Interpret Fitted Model for Metropolis-Hastings Simulation.

Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel

Variance-Covariance Matrix for a Fitted Point Process Model

vargamma.estpcf

Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel

Variance-Covariance Matrix for a Fitted Spatial Logistic Regression

Residuals for Fitted Spatial Logistic Regression Model

Receiver Operating Characteristic

Extract List of Individual Point Process Models

spatstat.model-package

The spatstat.model Package

Sufficient Statistic of Point Process Model

Divide a Measure into Parts

Update a Recursively Partitioned Point Process Model

Triplet Interaction Family

Check Whether Point Process Model is Valid

Name for Unit of Length

Check Whether Spatial Logistic Regression Model is Valid

Predicted Variance of the Number of Points

Evaluate Expression Involving Components of a Measure

Variance-Covariance Matrix for a Fitted Cluster Point Process Model

Cluster Point Process Model

Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model