Package

r-spatstat-model

Description

Parametric Statistical Modelling and Inference for the 'spatstat'
Family. 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.

Note