Package

r-spatstat

Description

Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests.
Comprehensive open-source toolbox for analysing Spatial Point Patterns.
Focused mainly on two-dimensional point patterns, including
multitype/marked points, in any spatial region. Also supports three-
dimensional point patterns, space-time point patterns in any number of
dimensions, point patterns on a linear network, and patterns of other
geometrical objects. Supports spatial covariate data such as pixel
images. Contains over 2000 functions for plotting spatial data,
exploratory data analysis, model-fitting, simulation, spatial sampling,
model diagnostics, and formal inference. Data types include point
patterns, line segment patterns, spatial windows, pixel images,
tessellations, and linear networks. Exploratory methods include quadrat
counts, K-functions and their simulation envelopes, nearest neighbour
distance and empty space statistics, Fry plots, pair correlation
function, kernel smoothed intensity, relative risk estimation with
cross-validated bandwidth selection, mark correlation functions,
segregation indices, mark dependence diagnostics, and kernel estimates
of covariate effects. Formal hypothesis tests of random pattern (chi-
squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford,
Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-
Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the
functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of
models include Poisson, Gibbs and Cox point processes, Neyman-Scott
cluster processes, and determinantal point processes. Models may involve
dependence on covariates, inter-point interaction, cluster formation and
dependence on marks. Models are fitted by maximum likelihood, logistic
regression, minimum contrast, and composite likelihood methods. A model
can be fitted to a list of point patterns (replicated point pattern
data) using the function mppm(). The model can include random effects
and fixed effects depending on the experimental design, in addition to
all the features listed above. Fitted point process models can be
simulated, automatically. Formal hypothesis tests of a fitted model are
supported (likelihood ratio test, analysis of deviance, Monte Carlo
tests) along with basic tools for model selection (stepwise(), AIC())
and variable selection (sdr). Tools for validating the fitted model
include simulation envelopes, residuals, residual plots and Q-Q plots,
leverage and influence diagnostics, partial residuals, and added
variable plots.

Note