Package

r-spatstat-linnet

Description

Linear Networks Functionality of the 'spatstat' Family. 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.

Note