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##
## auc.R
##
## Calculate ROC curve or area under it
##
## $Revision: 1.11 $ $Date: 2022/02/12 09:13:11 $
roc <- function(X, ...) { UseMethod("roc") }
roc.ppp <- function(X, covariate, ..., high=TRUE) {
nullmodel <- ppm(X)
result <- rocData(covariate, nullmodel, ..., high=high)
return(result)
}
rocData <- function(covariate, nullmodel, ..., high=TRUE) {
d <- spatialCDFframe(nullmodel, covariate, ...)
U <- d$values$U
ec <- if(high) ecdf(1-U) else ecdf(U)
p <- seq(0,1,length=1024)
df <- data.frame(p=p, fobs=ec(p), fnull=p)
result <- fv(df,
argu="p",
ylab=quote(roc(p)),
valu="fobs",
fmla= . ~ p,
desc=c("fraction of area",
"observed fraction of points",
"expected fraction if no effect"),
fname="roc")
fvnames(result, ".") <- c("fobs", "fnull")
return(result)
}
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)
}
rocModel <- function(lambda, nullmodel, ..., high) {
if(!missing(high))
warning("Argument 'high' is ignored when computing ROC for a fitted model")
d<- spatialCDFframe(nullmodel, lambda, ...)
U <- d$values$U
ec <- ecdf(1-U)
p <- seq(0,1,length=1024)
fobs <- ec(p)
FZ <- d$values$FZ
lambdavalues <- if(is.im(lambda)) lambda[] else unlist(lapply(lambda, "["))
F1Z <- ewcdf(lambdavalues, lambdavalues/sum(lambdavalues))
pZ <- get("y", environment(FZ))
qZ <- get("x", environment(FZ))
FZinverse <- approxfun(pZ, qZ, rule=2)
ftheo <- 1 - F1Z(FZinverse(1-p))
df <- data.frame(p=p, fobs=fobs, ftheo=ftheo, fnull=p)
result <- fv(df,
argu="p",
ylab=quote(roc(p)),
valu="fobs",
fmla = . ~ p,
desc=c("fraction of area",
"observed fraction of points",
"expected fraction of points",
"expected fraction if no effect"),
fname="roc")
fvnames(result, ".") <- c("fobs", "ftheo", "fnull")
return(result)
}
# ......................................................
auc <- function(X, ...) { UseMethod("auc") }
auc.ppp <- function(X, covariate, ..., high=TRUE) {
d <- spatialCDFframe(ppm(X), covariate, ...)
U <- d$values$U
EU <- mean(U)
result <- if(high) EU else (1 - EU)
return(result)
}
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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