amacrine

Austin Hughes' data: a point pattern 
of displaced amacrine cells in the retina of a rabbit.
A marked point pattern.

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

amacrine function

An object of class <code>"ppp"</code>
 representing the point pattern of cell locations.
 Entries include
 <tabular>ll</tabular>{
 <code>x</code> 	Cartesian $x$-coordinate of cell <code>y</code> 	Cartesian $y$-coordinate of cell <code>marks</code> 	factor with levels <code>off</code> and <code>on</code> 	indicating ``off'' and ``on'' cells
 }
 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.

format

source

Austin Hughes' data: a point pattern 
of displaced amacrine cells in the retina of a rabbit.
152 ``on'' cells and 142 ``off'' cells in a rectangular sampling frame.The true dimensions of the rectangle are 1060 by 662 microns.
The coordinates here are scaled to a rectangle of height 1 and width
$1060/662 = 1.601$ so the unit of measurement is approximately 662 microns.The data were analysed by Diggle (1986).

amacrine: Hughes' Amacrine Cell Data

Description

Usage

Arguments

format

source

Notes

References