R/calculate.cind.R

In CatPredi: Optimal Categorisation of Continuous Variables in Prediction Models

calculate.cind <-
function(point, formula, cat.var, data.f, range, min.p.cat) {
	var.names <- c(all.vars(formula), cat.var)
	X <- data.f[,cat.var]
	if (all(sapply(point, function(X, range){ res <- if(X> range[1] & X<range[2]){TRUE} else {FALSE}}, range = range))) {
		cutoffs <- sort(unique(c(max(X), min(X), point)))
		x.cut <- cut(X, cutoffs, include.lowest=TRUE,right=TRUE)
		if(length(levels(x.cut)) > 1 & all(table(x.cut)>min.p.cat)) {
			data.f[,"x.cut_"] <- x.cut
			formula.n <- update(formula, as.formula("~ . + x.cut_"))
			fit <- try(cph(formula.n, data = data.f))     
			#if(class(fit) == "try-error"){
			if("try-error" %in% class(fit)){
				cind <- NA
			} else {
				cind <- cindex.categorization(fit$linear.predictors, Surv(data.f[,var.names[1]], data.f[,var.names[2]]))         
			}
		} else {
			cind <- NA
		}
	} else {
		cind <- NA
	}
	cind
}