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#'
#' evalcovar.R
#'
#' evaluate covariate values at data points and at pixels
#'
#' $Revision: 1.44 $ $Date: 2022/05/12 04:08:30 $
#'
evalCovar <- function(model, covariate, ...) {
UseMethod("evalCovar")
}
evalCovar.ppm <- local({
evalCovar.ppm <- function(model, covariate, ...,
lambdatype=c("cif", "trend", "intensity"),
dimyx=NULL, eps=NULL,
interpolate=TRUE,
jitter=TRUE, jitterfactor=1,
modelname=NULL, covname=NULL,
dataname=NULL, subset=NULL, clip.predict=TRUE) {
lambdatype <- match.arg(lambdatype)
#' evaluate covariate values at data points and at pixels
ispois <- is.poisson(model)
csr <- ispois && is.stationary(model)
#' determine names
if(is.null(modelname))
modelname <- if(csr) "CSR" else short.deparse(substitute(model))
if(is.null(covname)) {
if(is.character(covariate)) covname <- covariate else
covname <- singlestring(short.deparse(substitute(covariate)))
}
if(is.null(dataname))
dataname <- model$Qname
info <- list(modelname=modelname, covname=covname,
dataname=dataname, csr=csr, ispois=ispois,
spacename="two dimensions")
X <- data.ppm(model)
W <- as.owin(model)
#' explicit control of pixel resolution
if(!is.null(dimyx) || !is.null(eps))
W <- as.mask(W, dimyx=dimyx, eps=eps)
Wfull <- Zfull <- NULL
if(!is.null(subset)) {
#' restrict to subset
if(!clip.predict) {
## use original window for prediction
Wfull <- W
}
X <- X[subset]
W <- W[subset, drop=FALSE]
}
#' evaluate covariate
if(is.character(covariate)) {
#' One of the characters 'x' or 'y'
#' Turn it into a function.
ns <- length(covariate)
if(ns == 0) stop("covariate is empty")
if(ns > 1) stop("more than one covariate specified")
covname <- covariate
covariate <- switch(covariate,
x=xcoordfun,
y=ycoordfun,
stop(paste("Unrecognised covariate",
dQuote(covariate))))
}
if(!is.marked(model)) {
#' ................... unmarked .......................
if(is.im(covariate)) {
type <- "im"
if(!interpolate) {
#' look up covariate values
ZX <- safelookup(covariate, X)
} else {
#' evaluate at data points by interpolation
ZX <- interp.im(covariate, X$x, X$y)
#' fix boundary glitches
if(any(uhoh <- is.na(ZX)))
ZX[uhoh] <- safelookup(covariate, X[uhoh])
}
#' covariate values for pixels inside window (for calculation)
Z <- covariate[W, drop=FALSE]
#' covariate values for pixels inside window (for prediction)
if(!is.null(Wfull)) Zfull <- covariate[Wfull, drop=FALSE]
#' corresponding mask
W <- as.owin(Z)
} else if(is.function(covariate)) {
type <- "function"
#' evaluate exactly at data points
ZX <- covariate(X$x, X$y)
if(!all(is.finite(ZX)))
warning("covariate function returned NA or Inf values")
#' window
W <- as.mask(W)
#' covariate in window
Z <- as.im(covariate, W=W)
if(!is.null(Wfull)) Zfull <- as.im(covariate, W=Wfull)
#' collapse function body to single string
covname <- singlestring(covname)
} else if(is.null(covariate)) {
stop("The covariate is NULL", call.=FALSE)
} else stop(paste("The covariate should be",
"an image, a function(x,y)",
"or one of the characters",
sQuote("x"), "or", sQuote("y")),
call.=FALSE)
#' values of covariate in window
Zvalues <- as.vector(Z[W, drop=TRUE])
#' corresponding fitted [conditional] intensity values
lambda <- as.vector(predict(model, locations=W,
type=lambdatype)[W, drop=TRUE])
#' pixel area (constant)
pixelarea <- with(Z, xstep * ystep)
} else {
#' ................... marked .......................
if(!is.multitype(model))
stop("Only implemented for multitype models (factor marks)")
marx <- marks(X, dfok=FALSE)
possmarks <- levels(marx)
npts <- npoints(X)
#' single image: replicate
if(is.im(covariate)) {
covariate <- rep(list(covariate), times=length(possmarks))
names(covariate) <- as.character(possmarks)
}
#'
if(is.list(covariate) && all(sapply(covariate, is.im))) {
#' list of images
type <- "im"
if(length(covariate) != length(possmarks))
stop("Number of images does not match number of possible marks")
#' evaluate covariate at each data point
ZX <- numeric(npts)
for(k in seq_along(possmarks)) {
ii <- (marx == possmarks[k])
covariate.k <- covariate[[k]]
if(!interpolate) {
#' look up covariate values
values <- safelookup(covariate, X)
} else {
#' interpolate
values <- interp.im(covariate.k, x=X$x[ii], y=X$y[ii])
#' fix boundary glitches
if(any(uhoh <- is.na(values)))
values[uhoh] <- safelookup(covariate.k, X[ii][uhoh])
}
ZX[ii] <- values
}
#' restrict covariate images to window
Z <- solapply(covariate, "[", i=W, drop=FALSE)
if(!is.null(Wfull)) Zfull <- solapply(covariate, "[", i=Wfull, drop=FALSE)
#' extract pixel locations and pixel values
Zframes <- lapply(Z, as.data.frame)
#' covariate values at each pixel inside window
Zvalues <- unlist(lapply(Zframes, getElement, name="value"))
#' pixel locations
locn <- lapply(Zframes, getxy)
#' tack on mark values
for(k in seq_along(possmarks))
locn[[k]] <- cbind(locn[[k]], data.frame(marks=possmarks[k]))
loc <- do.call(rbind, locn)
#' corresponding fitted [conditional] intensity values
lambda <- predict(model, locations=loc, type=lambdatype)
#' pixel areas
pixelarea <- rep(sapply(Z, pixarea), sapply(Z, npixdefined))
} else if(is.function(covariate)) {
type <- "function"
#' evaluate exactly at data points
ZX <- functioncaller(x=X$x, y=X$y, m=marx, f=covariate, ...)
#' functioncaller: function(x,y,m,f,...) { f(x,y,m,...) }
#' same window
W <- as.mask(W)
#' covariate in window
Z <- list()
for(k in seq_along(possmarks))
Z[[k]] <- as.im(functioncaller, m=possmarks[k], f=covariate, W=W, ...)
Zvalues <- unlist(lapply(Z, pixelvalues))
#' covariate in original window, for prediction
if(!is.null(Wfull)) {
Zfull <- list()
for(k in seq_along(possmarks))
Zfull[[k]] <- as.im(functioncaller, m=possmarks[k], f=covariate, W=Wfull, ...)
}
#' corresponding fitted [conditional] intensity values
lambda <- predict(model, locations=W, type=lambdatype)
lambda <- unlist(lapply(lambda, pixelvalues))
if(length(lambda) != length(Zvalues))
stop("Internal error: length(lambda) != length(Zvalues)")
#' collapse function body to single string
covname <- singlestring(covname)
#' pixel areas
pixelarea <- rep(sapply(Z, pixarea), sapply(Z, npixdefined))
} else if(is.null(covariate)) {
stop("The covariate is NULL", call.=FALSE)
} else stop(paste("For a multitype point process model,",
"the covariate should be an image, a list of images,",
"a function(x,y,m)",
"or one of the characters",
sQuote("x"), "or", sQuote("y")),
call.=FALSE)
}
#' ..........................................................
#' apply jittering to avoid ties
if(jitter) {
ZX <- jitter(ZX, factor=jitterfactor)
Zvalues <- jitter(Zvalues, factor=jitterfactor)
}
lambdaname <- if(is.poisson(model)) "intensity" else lambdatype
lambdaname <- paste("the fitted", lambdaname)
check.finite(lambda, xname=lambdaname, usergiven=FALSE)
check.finite(Zvalues, xname="the covariate", usergiven=TRUE)
#' lambda values at data points
lambdaX <- predict(model, locations=X, type=lambdatype)
#' lambda image(s)
lambdaimage <- predict(model, locations=Wfull %orifnull% W, type=lambdatype)
#' wrap up
values <- list(Zimage = Zfull %orifnull% Z,
lambdaimage = lambdaimage,
Zvalues = Zvalues,
lambda = lambda,
lambdaX = lambdaX,
weights = pixelarea,
ZX = ZX,
type = type)
return(list(values=values, info=info, X=X)) # X is possibly a subset of original
}
xcoordfun <- function(x,y,m){x}
ycoordfun <- function(x,y,m){y}
pixarea <- function(z) { z$xstep * z$ystep }
npixdefined <- function(z) { sum(!is.na(z$v)) }
pixelvalues <- function(z) { as.data.frame(z)[,3L] }
getxy <- function(z) { z[,c("x","y")] }
## Function caller used for marked locations (x,y,m) only.
functioncaller <- function(x,y,m,f,...) {
nf <- length(names(formals(f)))
if(nf < 2) stop("Covariate function must have at least 2 arguments")
if(nf == 2) return(f(x,y))
if(nf == 3) return(f(x,y,m))
argh <- list(...)
extra <- intersect(names(argh),
names(formals(f))[-(1:3)])
value <- do.call(f, append(list(x,y,m), argh[extra]))
return(value)
}
evalCovar.ppm
})
evalCovar.ppp <- local({
evalCovar.ppp <- function(model, covariate, ...,
lambdatype=c("cif", "trend", "intensity"),
dimyx=NULL, eps=NULL,
interpolate=TRUE,
jitter=TRUE, jitterfactor=1,
modelname=NULL, covname=NULL,
dataname=NULL, subset=NULL, clip.predict=TRUE) {
lambdatype <- match.arg(lambdatype)
dont.complain.about(lambdatype)
if(is.null(modelname)) modelname <- "CSR"
if(is.null(dataname)) dataname <- "data"
if(is.null(covname)) {
if(is.character(covariate)) covname <- covariate else
covname <- singlestring(short.deparse(substitute(covariate)))
}
info <- list(modelname=modelname, covname=covname,
dataname=dataname, csr=TRUE, ispois=TRUE,
spacename="two dimensions")
X <- model
W <- Window(X)
LambdaBar <- intensity(X) ## numeric value or vector
#' explicit control of pixel resolution
if(!is.null(dimyx) || !is.null(eps))
W <- as.mask(W, dimyx=dimyx, eps=eps)
Wfull <- Zfull <- NULL
if(!is.null(subset)) {
#' restrict to subset
if(!clip.predict) {
#' use original window for prediction
Wfull <- W
}
X <- X[subset]
W <- W[subset, drop=FALSE]
}
#' evaluate covariate
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
covariate <- switch(covariate,
x=xcoordfun,
y=ycoordfun,
stop(paste("Unrecognised covariate",
dQuote(covariate))))
}
if(!is.marked(X)) {
#' ................... unmarked .......................
if(is.im(covariate)) {
type <- "im"
if(!interpolate) {
#' look up covariate values
ZX <- safelookup(covariate, X)
} else {
#' evaluate at data points by interpolation
ZX <- interp.im(covariate, X$x, X$y)
#' fix boundary glitches
if(any(uhoh <- is.na(ZX)))
ZX[uhoh] <- safelookup(covariate, X[uhoh])
}
#' covariate values for pixels inside window
Z <- covariate[W, drop=FALSE]
if(!is.null(Wfull)) Zfull <- covariate[Wfull, drop=FALSE]
#' corresponding mask
W <- as.owin(Z)
} else if(is.function(covariate)) {
type <- "function"
#' evaluate exactly at data points
ZX <- covariate(X$x, X$y)
if(!all(is.finite(ZX)))
warning("covariate function returned NA or Inf values")
#' window
W <- as.mask(W)
#' covariate in window
Z <- as.im(covariate, W=W)
if(!is.null(Wfull)) Zfull <- as.im(covariate, W=Wfull)
#' collapse function body to single string
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 in window
Zvalues <- as.vector(Z[W, drop=TRUE])
#' corresponding intensity values
lambda <- rep.int(LambdaBar, length(Zvalues))
#' pixel area (constant)
pixelarea <- with(Z, xstep * ystep)
#' lambda values at data points
lambdaX <- rep.int(LambdaBar, npoints(X))
#' lambda image
lambdaimage <- as.im(LambdaBar, Wfull %orifnull% W)
} else {
#' ................... marked .......................
if(!is.multitype(X))
stop("Only implemented for multitype patterns (factor marks)")
marx <- marks(X, dfok=FALSE)
possmarks <- levels(marx)
npts <- npoints(X)
#' single image: replicate
if(is.im(covariate)) {
covariate <- rep(list(covariate), times=length(possmarks))
names(covariate) <- as.character(possmarks)
}
#'
if(is.list(covariate) && all(sapply(covariate, is.im))) {
#' list of images
type <- "im"
if(length(covariate) != length(possmarks))
stop("Number of images does not match number of possible marks")
#' evaluate covariate at each data point
ZX <- numeric(npts)
for(k in seq_along(possmarks)) {
ii <- (marx == possmarks[k])
covariate.k <- covariate[[k]]
if(!interpolate) {
#' look up covariate values
values <- safelookup(covariate, X)
} else {
#' interpolate
values <- interp.im(covariate.k, x=X$x[ii], y=X$y[ii])
#' fix boundary glitches
if(any(uhoh <- is.na(values)))
values[uhoh] <- safelookup(covariate.k, X[ii][uhoh])
}
ZX[ii] <- values
}
#' restrict covariate images to window
Z <- solapply(covariate, "[", i=W, drop=FALSE)
if(!is.null(Wfull)) Z <- solapply(covariate, "[", i=Wfull, drop=FALSE)
#' extract pixel locations and pixel values
Zframes <- lapply(Z, as.data.frame)
#' covariate values at each pixel inside window
Zvalues <- unlist(lapply(Zframes, getElement, name="value"))
#' pixel locations
locn <- lapply(Zframes, getxy)
#' tack on mark values
for(k in seq_along(possmarks))
locn[[k]] <- cbind(locn[[k]], data.frame(marks=possmarks[k]))
loc <- do.call(rbind, locn)
#' corresponding fitted [conditional] intensity values
lambda <- LambdaBar[as.integer(loc$marks)]
#' pixel areas
pixelarea <- rep(sapply(Z, pixarea), sapply(Z, npixdefined))
} else if(is.function(covariate)) {
type <- "function"
#' evaluate exactly at data points
ZX <- functioncaller(x=X$x, y=X$y, m=marx, f=covariate, ...)
#' functioncaller: function(x,y,m,f,...) { f(x,y,m,...) }
#' same window
W <- as.mask(W)
#' covariate in window
Z <- list()
for(k in seq_along(possmarks))
Z[[k]] <- as.im(functioncaller, m=possmarks[k], f=covariate, W=W, ...)
#' covariate in original window, for prediction
if(!is.null(Wfull)) {
Zfull <- list()
for(k in seq_along(possmarks))
Zfull[[k]] <- as.im(functioncaller, m=possmarks[k], f=covariate, W=Wfull, ...)
}
Zpixvalues <- lapply(Z, pixelvalues)
Zvalues <- unlist(Zpixvalues)
#' corresponding fitted [conditional] intensity values
lambda <- rep(LambdaBar, lengths(Zpixvalues))
if(length(lambda) != length(Zvalues))
stop("Internal error: length(lambda) != length(Zvalues)")
#' collapse function body to single string
covname <- singlestring(covname)
#' pixel areas
pixelarea <- rep(sapply(Z, pixarea), sapply(Z, npixdefined))
} else if(is.null(covariate)) {
stop("The covariate is NULL", call.=FALSE)
} else stop(paste("For a multitype point pattern,",
"the covariate should be an image, a list of images,",
"a function(x,y,m)",
"or one of the characters",
sQuote("x"), "or", sQuote("y")),
call.=FALSE)
#' lambda values at data points
lambdaX <- LambdaBar[as.integer(marks(X))]
#' lambda images
lambdaimage <- solapply(LambdaBar, as.im, W=Wfull %orifnull% W)
names(lambdaimage) <- possmarks
}
#' ..........................................................
#' apply jittering to avoid ties
if(jitter) {
ZX <- jitter(ZX, factor=jitterfactor)
Zvalues <- jitter(Zvalues, factor=jitterfactor)
}
lambdaname <- "the average intensity"
check.finite(lambda, xname=lambdaname, usergiven=FALSE)
check.finite(Zvalues, xname="the covariate", usergiven=TRUE)
#' wrap up
values <- list(Zimage = Zfull %orifnull% Z,
lambdaimage = lambdaimage,
Zvalues = Zvalues,
lambda = lambda,
lambdaX = lambdaX,
weights = pixelarea,
ZX = ZX,
type = type)
return(list(values=values, info=info, X=X)) # X is possibly a subset of original
}
xcoordfun <- function(x,y,m){x}
ycoordfun <- function(x,y,m){y}
pixarea <- function(z) { z$xstep * z$ystep }
npixdefined <- function(z) { sum(!is.na(z$v)) }
pixelvalues <- function(z) { as.data.frame(z)[,3L] }
getxy <- function(z) { z[,c("x","y")] }
## Function caller used for marked locations (x,y,m) only.
functioncaller <- function(x,y,m,f,...) {
nf <- length(names(formals(f)))
if(nf < 2) stop("Covariate function must have at least 2 arguments")
if(nf == 2) return(f(x,y))
if(nf == 3) return(f(x,y,m))
argh <- list(...)
extra <- intersect(names(argh),
names(formals(f))[-(1:3)])
value <- do.call(f, append(list(x,y,m), argh[extra]))
return(value)
}
evalCovar.ppp
})
|
