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#'
#' evalcovarslrm.R
#'
#' method for 'evalCovar' for class 'slrm'
#'
#' $Revision: 1.4 $ $Date: 2021/06/28 05:56:35 $
evalCovar.slrm <- function(model, covariate, ...,
lambdatype=c("probabilities", "intensity"),
jitter=TRUE, jitterfactor=1,
modelname=NULL, covname=NULL,
dataname=NULL, subset=NULL) {
lambdatype <- match.arg(lambdatype)
#' trap misuse
badargs <- intersect(c("eps", "dimyx"), names(list(...)))
nbad <- length(badargs)
if(nbad > 0)
warning(paste(ngettext(nbad, "Argument", "Arguments"),
commasep(sQuote(badargs)),
ngettext(nbad, "is", "are"),
"ignored by rhohat.slrm"),
call.=FALSE)
#' evaluate covariate values at presence pixels and all pixels
#' determine names
if(is.null(modelname))
modelname <- short.deparse(substitute(model))
if(covNotNamed <- is.null(covname)) {
covname <- singlestring(short.deparse(substitute(covariate)))
if(is.character(covariate)) covname <- covariate
}
if(is.null(dataname))
dataname <- model$CallInfo$responsename
csr <- is.stationary(model)
info <- list(modelname=modelname, covname=covname,
dataname=dataname, csr=csr, ispois=TRUE,
spacename="two dimensions")
FIT <- model$Fit$FIT
link <- model$CallInfo$link
## original point pattern
X <- model$Data$response
W <- Window(X)
## extract data from each pixel (or split pixel)
df <- model$Data$df
## restrict to subset if required
if(!is.null(subset)) {
ok <- inside.owin(df$x, df$y, subset)
df <- df[ok, drop=FALSE]
X <- X[subset]
W <- W[subset, drop=FALSE]
}
## presence/absence values
responsename <- model$CallInfo$responsename
presence <- as.logical(df[[responsename]])
## areas of pixels or split pixels
pixelareas <- exp(df$logpixelarea)
## pixel centres as a point pattern
P <- ppp(df$x, df$y, window=W)
#' parse covariate argument
if(is.character(covariate)) {
#' One of the characters 'x' or 'y'
#' Turn it into a function.
ns <- length(covariate)
if(ns == 0) stop("covariate is empty")
if(ns > 1) stop("more than one covariate specified")
covname <- covariate
covNotNamed <- FALSE
covariate <- switch(covname,
x=function(x,y) { x },
y=function(x,y) { y },
stop(paste("Unrecognised covariate",
dQuote(covariate))))
}
if(is.im(covariate)) {
type <- "im"
ZP <- safelookup(covariate, P)
Z <- covariate[W, drop=FALSE]
W <- as.owin(Z)
} else if(is.function(covariate)) {
type <- "function"
ZP <- covariate(P$x, P$y)
if(!all(is.finite(ZP)))
warning("covariate function returned NA or Inf values")
#' window
W <- as.mask(W)
#' covariate in window
Z <- as.im(covariate, W=W)
#' collapse function body to single string
if(covNotNamed) covname <- singlestring(covname)
} else if(is.null(covariate)) {
stop("The covariate is NULL", call.=FALSE)
} else stop(paste("The covariate should be",
"an image, a function(x,y)",
"or one of the characters",
sQuote("x"), "or", sQuote("y")),
call.=FALSE)
#' values of covariate at pixels or split pixels
Zvalues <- ZP
#'values of covariate at 'presence' pixels
ZX <- Zvalues[presence]
#' fitted probability/intensity values at all pixels or split pixels
switch(lambdatype,
probabilities = {
lambda <- predict(FIT, newdata=df, type="response")
},
intensity = {
if(link == "cloglog") {
linkvalues <- predict(FIT, newdata=df, type="link")
lambda <- exp(linkvalues)/pixelareas
} else {
probs <- predict(FIT, newdata=df, type="response")
lambda <- -log(1-probs)/pixelareas
}
})
#' apply jittering to avoid ties
if(jitter) {
ZX <- jitter(ZX, factor=jitterfactor)
Zvalues <- jitter(Zvalues, factor=jitterfactor)
}
lambdaname <- paste("the fitted", lambdatype)
check.finite(lambda, xname=lambdaname, usergiven=FALSE)
check.finite(Zvalues, xname="the covariate", usergiven=TRUE)
#' lambda values at data points
lambdaX <- lambda[presence]
#' lambda image(s)
lambdaimage <- predict(model, window=W, type=lambdatype)
#' wrap up
values <- list(Zimage = Z,
lambdaimage = lambdaimage,
Zvalues = Zvalues,
lambda = lambda,
lambdaX = lambdaX,
weights = pixelareas,
ZX = ZX,
type = type)
return(list(values=values, info=info))
}
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