spseg.matrix
Integrated Functions for Spatial Segregation Analysis
Spatial segregation analysis to be performed by a single function and
presentations by associated 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
- Keywords
- hplot, spatial, nonparametric
Usage
# 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, ...)
Arguments
- pts
an object that contains the points. This could be a two-column matrix or a ppp object from spatstat.
- marks
numeric/character vector of the types of the point in the data.
- h
numeric vector of the kernel smoothing bandwidth at which to calculate the cross-validated log-likelihood function.
- opt
integer, 1 to select bandwidth; 2 to calculate type-specific probabilities; and 3 to do the Monte Carlo segregation test.
- ntest
integer with default 100, number of simulations for the Monte Carlo test.
- poly
matrix containing the
x,y-coordinates of the polygonal boundary of the data.- delta
spacing distance of grid points at which to calculate the estimated type-specific probabilities for
imageplot.- proc
logical with default
TRUEto print the processing message.- …
- obj
list of the returning value of
spseg.- types
numeric/character types of the marks of data points to plot the estimated type-specific probabilities, default to plot all types.
- sup
logical with default
FALSE, ifTRUEto superimpose data points on the estimated type-specific probability surface.- col
list of colors such as that generated by
risk.colors.- breaks
a set of breakpoints for the
col: must give one more breakpoint than colour.- quan
numeric, the pointwise significance levels to add contours to
imageplot of the estimated type-specific probability surface, with default ofc(0.05, 0.95).
Details
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
Value
A list with components
- hcv
bandwidth selected by the cross-validated log-likelihood function.
- gridx,gridy
x, ycoordinate vectors at which the grid points are generated at which to calculate the type-specific probabilities and pointwise segregation test p-value.- p
estimated type-specific probabilities at grid points generated by vectors
gridx, gridy.- pvalue
p-value of the Monte Carlo spatial segregation test.
- stpvalue
pointwise p-value of the Monte Carlo spatial segregation test.
- ...
copy of
pts, marks, h, opt.
spseg results
an spseg object
Note
Setting h to a unique value may force spseg to skip the
selecting bandwidth step, go straight to calculate the type-specific
probabilities and then test the spatial segregation with this fixed
value of bandwidth.
See Also
cvloglk, phat, mcseg.test,
pinpoly, risk.colors, and metre