plot.lpp


    Point pattern on a linear network (object of class <code>"lpp"</code>).
  


    Additional arguments passed to <code><a rd-options="" href="/link/plot.linnet?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::plot.linnet">plot.linnet</a></code>
    or <code><a rd-options="" href="/link/plot.ppp?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::plot.ppp">plot.ppp</a></code>.
  

main


    Logical value indicating whether the plot is to be added to the
    existing plot (<code>add=TRUE</code>) or whether a new plot should be
    initialised (<code>add=FALSE</code>, the default).
  


    logical flag; if <code>TRUE</code>, plot points using a different
    plotting symbol for each mark;
    if <code>FALSE</code>, only the locations of the points will be plotted,
    using <code><a rd-options="" href="/link/points?package=spatstat&version=1.48-0" data-mini-rdoc="spatstat::points">points</a>()</code>.
  

use.marks


    Index determining which column of marks to use,
    if the marks of <code>x</code> are a data frame.
    A character or integer vector identifying one or more
    columns of marks. 
    If <code>add=FALSE</code> then
    the default is to plot all columns of marks, in a series of separate
    plots.
    If <code>add=TRUE</code> then only one column of marks can be plotted,
    and the default is <code>which.marks=1</code>
    indicating the first column of marks.
  

which.marks


    Logical value indicating whether to plot everything
    including the main title and the window containing the network.
  

show.all


    Logical value indicating whether to plot the
    window containing the network. Overrides <code>show.all</code>.
  

show.window


    Logical value indicating whether to plot the network.
  

show.network


    Logical value determining whether to actually perform the plotting.
  

do.plot


    Logical value giving permission to display multiple plots.
  

multiplot


  Plots a point pattern on a linear network.
  Plot method for the class <code>"lpp"</code> of point patterns on a linear network.


hplot

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

plot.lpp function


    Additional arguments passed to <code><a rd-options='' href='plot.linnet'>plot.linnet</a></code>
    or <code><a rd-options='' href='plot.ppp'>plot.ppp</a></code>.
  


    logical flag; if <code>TRUE</code>, plot points using a different
    plotting symbol for each mark;
    if <code>FALSE</code>, only the locations of the points will be plotted,
    using <code><a rd-options='' href='points'>points</a>()</code>.
  

plot.lpp: Plot Point Pattern on Linear Network

Description

Usage

Arguments

Value

Details

See Also

Examples