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#'
#' rhohatlpp.R
#'
#' rhohat.lpp and rhohat.lppm
#'
#' Moved from rhohat.R to separate file rhohatlpp.R on 16 june 2020
#'
#' Copyright (c) Adrian Baddeley 2015-2019
#' GNU Public Licence GPL >= 2.0
rhohat.lpp <- rhohat.lppm <-
function(object, covariate, ...,
weights=NULL,
method=c("ratio", "reweight", "transform"),
horvitz=FALSE,
smoother=c("kernel", "local",
"decreasing", "increasing",
"mountain", "valley",
"piecewise"),
subset=NULL,
do.CI=TRUE,
jitter=TRUE, jitterfactor=1, interpolate=TRUE,
nd=1000, eps=NULL, random=TRUE,
n=512, bw="nrd0", adjust=1, from=NULL, to=NULL,
bwref=bw, covname, confidence=0.95, positiveCI, breaks=NULL) {
callstring <- short.deparse(sys.call())
smoother <- match.arg(smoother)
method <- match.arg(method)
if(missing(positiveCI))
positiveCI <- (smoother == "local")
if(missing(covname))
covname <- sensiblevarname(short.deparse(substitute(covariate)), "X")
if(is.null(adjust))
adjust <- 1
# validate model
if(is.lpp(object)) {
X <- object
model <- eval(substitute(
lppm(object, ~1, eps=eps, nd=nd, random=random, subset=SUBSET),
list(SUBSET=subset)))
reference <- "Lebesgue"
modelcall <- NULL
} else if(inherits(object, "lppm")) {
if(!is.null(subset)) object <- update(object, subset=subset)
model <- object
X <- model$X
reference <- "model"
modelcall <- model$call
} else stop("object should be of class lpp or lppm")
if("baseline" %in% names(list(...)))
warning("Argument 'baseline' ignored: not available for ",
if(is.lpp(object)) "rhohat.lpp" else "rhohat.lppm")
if(is.character(covariate) && length(covariate) == 1) {
covname <- covariate
switch(covname,
x={
covariate <- function(x,y) { x }
},
y={
covariate <- function(x,y) { y }
},
stop("Unrecognised covariate name")
)
covunits <- unitname(X)
} else if(inherits(covariate, "distfunlpp")) {
covunits <- unitname(covariate)
} else {
covunits <- NULL
}
S <- as.psp(as.linnet(X))
if(!is.null(subset)) S <- S[subset]
totlen <- sum(lengths_psp(S))
rhohatEngine(model, covariate, reference, totlen, ...,
do.CI=do.CI,
subset=subset,
weights=weights,
method=method,
horvitz=horvitz,
smoother=smoother,
resolution=list(nd=nd, eps=eps, random=random),
spatCovarArgs=list(clip.predict=FALSE,
jitter=jitter,
jitterfactor=jitterfactor,
interpolate=interpolate),
n=n, bw=bw, adjust=adjust, from=from, to=to,
bwref=bwref, covname=covname, covunits=covunits,
confidence=confidence, positiveCI=positiveCI,
breaks=breaks,
modelcall=modelcall, callstring=callstring)
}
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