https://github.com/cran/spatstat
Tip revision: 3f048414cfc8c930d75516976bb25b2e4e0183d9 authored by Adrian Baddeley on 05 September 2013, 11:40:52 UTC
version 1.33-0
version 1.33-0
Tip revision: 3f04841
strausshard.R
#
#
# strausshard.S
#
# $Revision: 2.19 $ $Date: 2013/07/19 02:53:00 $
#
# The Strauss/hard core process
#
# StraussHard() create an instance of the Strauss-hardcore process
# [an object of class 'interact']
#
#
# -------------------------------------------------------------------
#
StraussHard <- local({
BlankStraussHard <-
list(
name = "Strauss - hard core process",
creator = "StraussHard",
family = "pairwise.family", # evaluated later
pot = function(d, par) {
v <- 1 * (d <= par$r)
v[ d <= par$hc ] <- (-Inf)
v
},
par = list(r = NULL, hc = NULL), # filled in later
parnames = c("interaction distance",
"hard core distance"),
selfstart = function(X, self) {
# self starter for StraussHard
nX <- npoints(X)
if(nX < 2) {
# not enough points to make any decisions
return(self)
}
r <- self$par$r
md <- min(nndist(X))
if(md == 0) {
warning(paste("Pattern contains duplicated points:",
"hard core must be zero"))
return(StraussHard(r=r, hc=0))
}
if(!is.na(hc <- self$par$hc)) {
# value fixed by user or previous invocation
# check it
if(md < hc)
warning(paste("Hard core distance is too large;",
"some data points will have zero probability"))
return(self)
}
# take hc = minimum interpoint distance * n/(n+1)
hcX <- md * nX/(nX+1)
StraussHard(r=r, hc = hcX)
},
init = function(self) {
r <- self$par$r
hc <- self$par$hc
if(length(hc) != 1)
stop("hard core distance must be a single value")
if(!is.na(hc)) {
if(!is.numeric(hc) || hc <= 0)
stop("hard core distance hc must be a positive number, or NA")
if(!is.numeric(r) || length(r) != 1 || r <= hc)
stop("interaction distance r must be a number greater than hc")
}
},
update = NULL, # default OK
print = NULL, # default OK
interpret = function(coeffs, self) {
loggamma <- as.numeric(coeffs[1])
gamma <- exp(loggamma)
return(list(param=list(gamma=gamma),
inames="interaction parameter gamma",
printable=round(gamma,4)))
},
valid = function(coeffs, self) {
loggamma <- as.numeric(coeffs[1])
return(is.finite(loggamma))
},
project = function(coeffs, self) {
loggamma <- as.numeric(coeffs[1])
if(is.finite(loggamma))
return(NULL)
hc <- self$par$hc
if(hc > 0) return(Hardcore(hc)) else return(Poisson())
},
irange = function(self, coeffs=NA, epsilon=0, ...) {
r <- self$par$r
hc <- self$par$hc
if(any(is.na(coeffs)))
return(r)
loggamma <- coeffs[1]
if(abs(loggamma) <= epsilon)
return(hc)
else
return(r)
},
version=NULL, # evaluated later
# fast evaluation is available for the border correction only
can.do.fast=function(X,correction,par) {
return(all(correction %in% c("border", "none")))
},
fasteval=function(X,U,EqualPairs,pairpot,potpars,correction, ...) {
# fast evaluator for StraussHard interaction
if(!all(correction %in% c("border", "none")))
return(NULL)
if(spatstat.options("fasteval") == "test")
message("Using fast eval for StraussHard")
r <- potpars$r
hc <- potpars$hc
hclose <- strausscounts(U, X, hc, EqualPairs)
rclose <- strausscounts(U, X, r, EqualPairs)
answer <- ifelseXB(hclose == 0, rclose, -Inf)
return(matrix(answer, ncol=1))
},
Mayer=function(coeffs, self) {
# second Mayer cluster integral
gamma <- exp(as.numeric(coeffs[1]))
r <- self$par$r
hc <- self$par$hc
return(pi * (hc^2 + (1-gamma) * (r^2 - hc^2)))
}
)
class(BlankStraussHard) <- "interact"
StraussHard <- function(r, hc=NA) {
instantiate.interact(BlankStraussHard, list(r=r, hc=hc))
}
StraussHard
})