ganglia


  Point pattern of retinal ganglion cells identified as `on' or `off'.
  A marked point pattern.


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, 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(), 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

ganglia function

An object of class <code>"ppp"</code>
  representing the point pattern of cell locations.
  Entries include
  <table><tr><td>
    <code>x</code> </td>
</tr><tr><td> Cartesian $x$-coordinate of cell </td>
</tr><tr><td>
    <code>y</code> </td>
</tr><tr><td> Cartesian $y$-coordinate of cell </td>
</tr><tr><td>
    <code>marks</code> </td>
</tr><tr><td> factor with levels <code>off</code> and <code>on</code> </td>
</tr></table>
  See <code><a rd-options="" href="/link/ppp.object?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::ppp.object">ppp.object</a></code> for details of the format.

Format

W\"assle et al (1981), data supplied by Marie-Colette van
  Lieshout and attributed to Peter Diggle

Source

<b>Important: these data are INCORRECT.  See below.</b> The data represent a pattern of beta-type ganglion cells in the
  retina of a cat recorded in Figure 6(a) of W\"assle et al. (1981). The pattern was first analysed by W\"assle et al (1981) using
  nearest neighbour distances. The data used in their analysis
  are not available. The present dataset <code><a rd-options="" href="/link/ganglia?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::ganglia">ganglia</a></code> was
  scanned from Figure 6(a) of W\"assle et al (1981)
  in the early 1990's, but we have no further information.
  This dataset is the one analysed by Van Lieshout and Baddeley (1999)
  using multitype J functions, and by Stoyan (1995) using second
  order methods (pair correlation and mark correlation). It has now been discovered that these data are <b>incorrect</b>.
  They are not faithful to the scale in Figure 6 of W\"assle et al (1981),
  and they contain some scanning errors.
  Hence they should not be used to address the original scientific question.
  They have been retained only for comparison with other analyses
  in the statistical literature. A new, corrected dataset, scanned from the original microscope image,
  has been provided under the name <code><a rd-options="" href="/link/betacells?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::betacells">betacells</a></code>.  Use that
  dataset for any further study.

Notes

These data are incorrect.
  Use the new corrected dataset <code><a rd-options="" href="/link/betacells?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::betacells">betacells</a></code>.

Warnings

An object of class <code>"ppp"</code>
  representing the point pattern of cell locations.
  Entries include
  <table><tr><td>
    <code>x</code> </td>
</tr><tr><td> Cartesian $x$-coordinate of cell </td>
</tr><tr><td>
    <code>y</code> </td>
</tr><tr><td> Cartesian $y$-coordinate of cell </td>
</tr><tr><td>
    <code>marks</code> </td>
</tr><tr><td> factor with levels <code>off</code> and <code>on</code> </td>
</tr></table>
  See <code><a rd-options='' href='ppp.object'>ppp.object</a></code> for details of the format.

These data are incorrect.
  Use the new corrected dataset <code><a rd-options='' href='betacells'>betacells</a></code>.

ganglia: Beta Ganglion Cells in Cat Retina, Old Version

Description

Usage

Arguments

Format

Source

Notes

Warnings

References