The original spatstat package has been split into
several sub-packages (See spatstat/spatstat)
This package spatstat.model is one of the
sub-packages. It contains all the main user-level functions that perform
parametric statistical modelling of spatial data,
with the exception of data on linear networks.
Most of the functionality is for spatial point patterns in two dimensions. There is a very modest amount of functionality for 3D and higher dimensional patterns and space-time patterns.
spatstat.model supports
- parametric modelling (fitting models to point pattern data, model selection, model prediction)
- formal inference (hypothesis tests, confidence intervals)
- informal validation (model diagnostics)
For a full list of functions, see the help file for spatstat.model-package.
- fitting Poisson point process models to point pattern data (
ppm) - fitting spatial logistic regression models to point pattern data (
slrm) - fitting Cox point process models to point pattern data (
kppm) - fitting Neyman-Scott cluster process models to point pattern data (
kppm) - fitting Gibbs point process models to point pattern data (
ppm) - fitting recursively partitioned models to point patterns (
rppm) - class support for fitted models (
update,print,summary,predict,plot,simulate,coef,confint,vcov,anova,residuals,fitted,deviance,AIC,logLik,terms,formula,model.matrix) - minimum contrast estimation (generic algorithm)
- simulation of fitted point process models
- hypothesis tests (quadrat test, 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 of a model
- prediction intervals for point counts
- residuals
- leverage
- influence
- partial residual plot
- added variable plot
- diagnostic plots
- pseudoscore residual plots
- model compensators of summary functions
- Q-Q plots