redwood

Locations of 62 seedlings and saplings 
 of California redwood trees.
 
 The data represent the locations of 62 seedlings and saplings 
 of California redwood trees in a square sampling region. 
 They originate from Strauss (1975);
 the present data are a subset extracted by Ripley (1977) 
 in a subregion that has been rescaled to a unit square. The coordinates are rounded to the nearest 0.01 units,
 except for one point which has an $x$ coordinate
 of 0.999, presumably to ensure that it is properly inside the window.
 
 There are many further analyses of this dataset. It is often used as a 
 canonical example of a clustered point pattern
 (see e.g. Diggle, 1983). The original, full redwood dataset is supplied in the <code>spatstat</code>
 library as <code>redwoodfull</code>.

datasets

spatial

A package for analysing spatial data, mainly Spatial Point	Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, and point patterns on a linear network. 
	Contains about 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, Diggle-Cressie-Loosmore-Ford, Dao-Genton) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov) are also supported.
	Parametric models can be fitted to point pattern data using the functions ppm, kppm, slrm similar to glm. Types of models include Poisson, Gibbs, Cox and cluster point processes. Models may involve dependence on covariates, interpoint interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. 
	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

redwood function

An object of class <code>"ppp"</code>
 representing the point pattern of tree locations.
 The window has been rescaled to the unit square. See <code><a href="/link/ppp.object?package=spatstat&version=1.39-0" rd-options="" data-mini-rdoc="spatstat::ppp.object">ppp.object</a></code> for details of the format of a
 point pattern object.

format

Strauss (1975), subset extracted by Ripley (1977)

redwood: California Redwoods Point Pattern (Ripley's Subset)

Description

Usage

Arguments

format

source

References

See Also