https://github.com/cran/GsymPoint
Tip revision: 158c572b6991bd851086fb65dec3157d2ba15647 authored by Mónica López-Ratón on 31 October 2023, 18:30:02 UTC
version 1.1.2
version 1.1.2
Tip revision: 158c572
summary.gsym.point.R
summary.gsym.point <-
function(object, ...) {
methods <- object[object$methods]
levels.cat <- if(is.null(object$levels.cat)) {"Global"} else {object$levels.cat}
conf.level <- ifelse(is.null(object$call$confidence.level), 0.95, object$call$confidence.level)
p.results <- names(methods[[1]][[1]][["optimal.result"]])
ci.legend <- paste(paste(conf.level*100, "% CI", sep = ""), c("lower limit", "upper limit"))
res <- vector("list", length(levels.cat))
for (i in 1:length(levels.cat)) {
for(j in 1:length(methods)) {
row.names <- c(p.results)
col.names <- c("Estimate", ci.legend)
res[[i]][[j]] <- vector("list", length(methods[[j]][[i]][["optimal.result"]][["cutoff"]][[1]]))
if (length(methods[[j]][[i]][["optimal.result"]][["cutoff"]][[1]]) != 0) {
for(k in 1:length(methods[[j]][[i]][["optimal.result"]][["cutoff"]][[1]])){
m <- matrix(ncol = 3, nrow = length(row.names), dimnames = list(row.names,col.names ))
m[1,] <- methods[[j]][[i]][["optimal.result"]][[1]][k,]
for (l in 2:length(p.results)) {
m[l,] <- methods[[j]][[i]][["optimal.result"]][[l]][k,]
}
res[[i]][[j]][[k]] <- m
}
}
}
res[[i]][[length(methods)+1]] <- round(methods[[1]][[i]][["AUC"]][[1]], 3)
names(res[[i]]) <- c(names(methods), "AUC")
}
names(res) <- levels.cat
object$p.table <- res
class(object) <- "summary.gsym.point"
object
}