Description Usage Arguments Details Value Note Author(s) See Also
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## S3 method for class '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 class 'ppp'
spseg(pts, h, opt, ...)

pts 
an object that contains the points. This could be a twocolumn 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 crossvalidated loglikelihood function. 
opt 
integer, 1 to select bandwidth; 2 to calculate typespecific 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 
delta 
spacing distance of grid points at which to calculate the estimated
typespecific probabilities for 
proc 
logical with default 
... 
other arguments concerning 
obj 
list of the returning value of 
types 
numeric/character types of the marks of data points to plot the estimated typespecific probabilities, default to plot all types. 
sup 
logical with default 
col 
list of colors such as that generated by 
breaks 
a set of breakpoints for the 
quan 
numeric, the pointwise significance levels to add contours to

spseg
implements a complete spatial segregation analysis by
selecting bandwidth, calculating the typespecific 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 spatialkernelpackage
.
This is the details of the S3 generic method
Does spseg for marked ppp objects
A list with components
bandwidth selected by the crossvalidated loglikelihood function.
x, y
coordinate vectors at which the grid points
are generated at which to calculate the typespecific probabilities
and pointwise segregation test pvalue.
estimated typespecific probabilities at grid points generated
by vectors gridx, gridy
.
pvalue of the Monte Carlo spatial segregation test.
pointwise pvalue of the Monte Carlo spatial segregation test.
copy of pts, marks, h, opt
.
spseg results
an spseg object
Setting h
to a unique value may force spseg
to skip the
selecting bandwidth step, go straight to calculate the typespecific
probabilities and then test the spatial segregation with this fixed
value of bandwidth.
Barry Rowlingson
Barry Rowlingson
cvloglk
, phat
, mcseg.test
,
pinpoly
, risk.colors
, and metre
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