bei.extra

A point pattern giving the locations of 3605
 trees in a tropical rain forest.
 Accompanied by covariate data giving the elevation (altitude)
 and slope of elevation in the study region.

datasets

spatial

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()). 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.

Adrian Baddeley

spatstat

Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

bei function

<code>bei</code> is an object of class <code>"ppp"</code>
 representing the point pattern of tree locations.
 See <code><a rd-options="" href="/link/ppp.object?package=spatstat&version=1.52-1" data-mini-rdoc="spatstat::ppp.object">ppp.object</a></code> for details of the format.
<code>bei.extra</code> is a list containing
 two pixel images, <code>elev</code> (elevation in metres) and
 <code>grad</code> (norm of elevation gradient). These pixel images are objects
 of class <code>"im"</code>, see <code><a rd-options="" href="/link/im.object?package=spatstat&version=1.52-1" data-mini-rdoc="spatstat::im.object">im.object</a></code>.

Format

The dataset <code>bei</code> gives the positions of 3605 trees
 of the species Beilschmiedia pendula (Lauraceae)
 in a 1000 by 500 metre rectangular sampling region
 in the tropical rainforest of Barro Colorado Island.
The accompanying dataset <code>bei.extra</code> gives information
 about the altitude (elevation) in the study region. It is a list
 containing two pixel images, <code>elev</code> (elevation in metres) and
 <code>grad</code> (norm of elevation gradient).
These data are part of a much larger dataset containing the positions of
 hundreds of thousands of trees belong to thousands of species;
 see Hubbell and Foster (1983), Condit, Hubbell and Foster (1996)
 and Condit (1998).
The present data were analysed by Moller and Waagepetersen (2007).

Notes

Tropical rain forest trees — bei

<code>bei</code> is an object of class <code>"ppp"</code>
 representing the point pattern of tree locations.
 See <code><a rd-options='' href='ppp.object'>ppp.object</a></code> for details of the format.
<code>bei.extra</code> is a list containing
 two pixel images, <code>elev</code> (elevation in metres) and
 <code>grad</code> (norm of elevation gradient). These pixel images are objects
 of class <code>"im"</code>, see <code><a rd-options='' href='im.object'>im.object</a></code>.

bei: Tropical rain forest trees

Description

Usage

Arguments

Format

Notes

References