waterstriders

The territorial behaviour of an insect group
 called waterstriders was studied in 
 a series of laboratory experiments by Dr Matti Nummelin
 (University of Helskini). The data were analysed in
 the pioneering PhD thesis of Antti Penttinen (1984).
The dataset <code>waterstriders</code> is 
 a list of three point patterns. Each point pattern gives
 the locations of larvae of the waterstrider
 Limnoporus (Gerris) rufoscutellatus (larval stage V)
 in a homogeneous area about 48 cm square. The point 
 patterns can be assumed to be independent.
It is known that this species of waterstriders
 exhibits territorialism at older larvae stages and at the
 adult stage. Therefore, if any deviation from Complete Spatial
 Randomness exists in these three point patterns, it is expected
 to be towards inhibition.
The data were obtained from photographs which were scanned manually.
 The waterstriders are in a pool which is larger than the picture.
 A guard area (width about 2.5 cm) has been deleted because 
 it is a source of inhomogeneity to interactions.
Penttinen (1984, chapter 5) fitted a pairwise interaction model with
 a Strauss/hardcore interaction (see <code><a rd-options="" href="/link/StraussHard?package=spatstat&version=1.52-1" data-mini-rdoc="spatstat::StraussHard">StraussHard</a></code>)
 with hard core radius 1.5 cm and interaction radius 4.5 cm.

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

waterstriders function

<code>waterstriders</code> is a list of three point patterns
 (objects of class <code>"ppp"</code>). It also has class <code>"listof"</code>
 so that it can be plotted and printed directly. The point pattern
 coordinates are in centimetres.

Format

The territorial behaviour of an insect group
 called waterstriders was studied in 
 a series of laboratory experiments by Dr Matti Nummelin
 (University of Helskini). The data were analysed in
 the pioneering PhD thesis of Antti Penttinen (1984).
The dataset <code>waterstriders</code> is 
 a list of three point patterns. Each point pattern gives
 the locations of larvae of the waterstrider
 Limnoporus (Gerris) rufoscutellatus (larval stage V)
 in a homogeneous area about 48 cm square. The point 
 patterns can be assumed to be independent.
It is known that this species of waterstriders
 exhibits territorialism at older larvae stages and at the
 adult stage. Therefore, if any deviation from Complete Spatial
 Randomness exists in these three point patterns, it is expected
 to be towards inhibition.
The data were obtained from photographs which were scanned manually.
 The waterstriders are in a pool which is larger than the picture.
 A guard area (width about 2.5 cm) has been deleted because 
 it is a source of inhomogeneity to interactions.
Penttinen (1984, chapter 5) fitted a pairwise interaction model with
 a Strauss/hardcore interaction (see <code><a rd-options='' href='StraussHard'>StraussHard</a></code>)
 with hard core radius 1.5 cm and interaction radius 4.5 cm.

waterstriders: Waterstriders data. Three independent replications of a point pattern formed by insects.

Description

Usage

Arguments

Format

References