Nothing
# Internal function to predict value of a tree
#
# Function is not exported
#
# @param x A data.frame object {n x p}. The model.frame for covariates to be
# considered in splitting
#
# @param params A Parameters object. All information that regulates tree and
# specifies analysis preferences.
#
# @param nCat An integer object. Number of categories in each covariate. If
# covariate i is continuous, nCat[i] = 0; if covariate i is an ordered factor,
# nCat[i] = 1; if covariate i is an unordered factor, nCat[i] = levels()
#
# @param nodes A list object. The nodes of a tree.
#
#' @include class_Parameters.R
#
.predictSurvTree <- function(...,
x,
params,
nCat,
nodes) {
if (is.null(x = x)) return( NULL )
x <- data.matrix(frame = x)
usedNodes <- !is.na(x = nodes$nodes[,1L])
nUsed <- sum(usedNodes)
n <- nrow(x = x)
np <- ncol(x = x)
nTimes <- .NTimes(params)
nodes$nodes[is.na(nodes$nodes)] <- 0.0
res <- .Fortran("predictSurvTree",
n = as.integer(n),
np = as.integer(np),
xt = as.double(x = x),
nCat = as.integer(x = nCat),
nt = as.integer(x = nrow(x = nodes$survFunc)),
nNodes = as.integer(x = nUsed),
tsurvFunc = as.double(x = nodes$survFunc[,usedNodes]),
mean = as.double(nodes$mean[usedNodes]),
survProb = as.double(nodes$survProb[usedNodes]),
nCols = as.integer(x = ncol(x = nodes$nodes)),
tnodes = as.double(x = nodes$nodes[usedNodes,]),
predSurvFunc = as.double(numeric(length = n*nTimes)),
predMean = as.double(numeric(length = n)),
predSurvProb = as.double(numeric(length = n)),
PACKAGE = "dtrSurv")
isSurv <- .CriticalValueCriterion(params) %in% c("surv.mean", "surv.prob")
valueObj <- list()
valueObj[[ "survFunc" ]] <- matrix(data = res$predSurvFunc, nrow = nTimes, ncol = n)
valueObj[[ "mean" ]] <- res$predMean
if (isSurv) valueObj[[ "survProb" ]] <- res$predSurvProb
return( valueObj )
}
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