Nothing
##
## auc.R
##
## Calculate ROC curve or area under it
##
## $Revision: 1.17 $ $Date: 2023/08/15 07:44:11 $
## Code for roc() and roc.ppp() and internals is moved to spatstat.explore
roc.ppm <- function(X, ...) {
stopifnot(is.ppm(X))
model <- X
lambda <- predict(model, ...)
Y <- data.ppm(model)
nullmodel <- ppm(Y)
result <- rocModel(lambda, nullmodel, ...)
return(result)
}
roc.kppm <- function(X, ...) {
stopifnot(is.kppm(X))
model <- as.ppm(X)
lambda <- predict(model, ...)
Y <- data.ppm(model)
nullmodel <- ppm(Y)
result <- rocModel(lambda, nullmodel, ...)
return(result)
}
roc.slrm <- function(X, ...) {
stopifnot(is.slrm(X))
model <- X
lambda <- predict(model, ..., type="probabilities")
Y <- response(model)
nullmodel <- slrm(Y ~ 1)
dont.complain.about(Y)
result <- rocModel(lambda, nullmodel, ..., lambdatype="probabilities")
return(result)
}
# ......................................................
## Code for auc(), auc.ppp() is moved to spatstat.explore
auc.kppm <- function(X, ...) { auc(as.ppm(X), ...) }
auc.ppm <- function(X, ...) {
model <- X
if(is.multitype(model)) {
# cheat
ro <- roc(model, ...)
aobs <- with(ro, mean(fobs))
atheo <- with(ro, mean(ftheo))
} else if(is.stationary(model)) {
aobs <- atheo <- 1/2
} else {
lambda <- intensity(model)
Fl <- ecdf(lambda[])
lambda <- as.im(lambda, Window(model))
X <- data.ppm(model)
lamX <- lambda[X]
aobs <- mean(Fl(lamX))
atheo <- mean(lambda[] * Fl(lambda[]))/mean(lambda)
}
result <- c(aobs, atheo)
names(result) <- c("obs", "theo")
return(result)
}
auc.slrm <- function(X, ...) {
ro <- roc(X, ...)
result <- with(ro, list(obs=mean(fobs), theo=mean(ftheo)))
return(unlist(result))
}
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