plot functions.Integrated Functions for Spatial Segregation Analysis
Spatial segregation analysis to be performed by a single function and
presentations by associated plot functions.
Run the spatial segregation analysis
spseg for spatstat objects
# S3 method for matrix
spseg(pts, marks, h, opt = 2, ntest = 100, poly = NULL,
delta = min(apply(apply(pts, 2, range), 2, diff))/100, proc = TRUE, ...)plotcv(obj, ...)
plotphat(obj, types = unique(obj$marks), sup = TRUE,
col = risk.colors(10), breaks = seq(0, 1, length = length(col) + 1), ...)
plotmc(obj, types = unique(obj$marks), quan = c(0.05, 0.95), sup = FALSE,
col = risk.colors(10), breaks = seq(0, 1, length = length(col) + 1), ...)
spseg(pts, ...)
# S3 method for ppp
spseg(pts, h, opt, ...)
an object that contains the points. This could be a two-column matrix or a ppp object from spatstat.
numeric/character vector of the types of the point in the data.
numeric vector of the kernel smoothing bandwidth at which to calculate the cross-validated log-likelihood function.
integer, 1 to select bandwidth; 2 to calculate type-specific probabilities; and 3 to do the Monte Carlo segregation test.
integer with default 100, number of simulations for the Monte Carlo test.
matrix containing the x,y-coordinates of the polygonal
boundary of the data.
spacing distance of grid points at which to calculate the estimated
type-specific probabilities for image plot.
logical with default TRUE to print the
processing message.
list of the returning value of spseg.
numeric/character types of the marks of data points to plot the estimated type-specific probabilities, default to plot all types.
logical with default FALSE, if TRUE to superimpose data points
on the estimated type-specific probability surface.
list of colors such as that generated by risk.colors.
a set of breakpoints for the col: must give one more
breakpoint than colour.
numeric, the pointwise significance levels to add contours to
image plot of the estimated type-specific probability
surface, with default of c(0.05, 0.95).
A list with components
bandwidth selected by the cross-validated log-likelihood function.
x, y coordinate vectors at which the grid points
are generated at which to calculate the type-specific probabilities
and pointwise segregation test p-value.
estimated type-specific probabilities at grid points generated
by vectors gridx, gridy.
p-value of the Monte Carlo spatial segregation test.
pointwise p-value of the Monte Carlo spatial segregation test.
copy of pts, marks, h, opt.
spseg results
an spseg object
spseg implements a complete spatial segregation analysis by
selecting bandwidth, calculating the type-specific probabilities, and then
carrying out the Monte Carlo test of spatial segregation and pointwise
significance. Some plot functions are also provided here so that
users can easily present the results.
These functions are provided only for the convenience of users. Users can instead use individual functions to implement the analysis step by step and plot the diagrams as they wish.
Examples of how to use spseg and present results using plot
functions are presented in spatialkernel-package.
This is the details of the S3 generic method
Does spseg for marked ppp objects
cvloglk, phat, mcseg.test,
pinpoly, risk.colors, and metre