linear networks functionality of the 'spatstat' family of GNU R
Defines types of spatial data on a linear network and provides
functionality for geometrical operations, data analysis and modelling
of data on a linear network, in the 'spatstat' family of packages.
Contains definitions and support for linear networks, including
creation of networks, geometrical measurements, topological
connectivity, geometrical operations such as inserting and deleting
vertices, intersecting a network with another object, and interactive
editing of networks. Data types defined on a network include point
patterns, pixel images, functions, and tessellations. Exploratory
methods include kernel estimation of intensity on a network, K-
functions and pair correlation functions on a network, simulation
envelopes, nearest neighbour distance and empty space distance,
relative risk estimation with cross-validated bandwidth selection.
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 function lppm()
similar to glm(). Only Poisson models are implemented so far. Models
may involve dependence on covariates and dependence on marks. Models
are fitted by maximum likelihood. 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. Random point patterns on a network can be generated
using a variety of models.
