discSurv (version 2.0.0)

survTreeLaplaceHazardRanger: Laplace Hazards for a Competing Risk Survival Tree Object

Description

Predicts the laplace-smoothed hazards of discrete survival data based on a survival tree from class "ranger". Currently only single-risk data is supported.

Usage

survTreeLaplaceHazardRanger(treeModel, rangerdata, newdata, lambda)

Arguments

treeModel

Fitted tree object as generated by "ranger" ("class data.frame"). Must be a single ranger tree.

rangerdata

Original training data with which treeModel was fitted ("class data.frame"). Must be in long format.

newdata

Data in long format for which hazards are to be computed ("class data.frame"). Must contain the same columns that were used for tree fitting.

lambda

Smoothing parameter for laplace-smoothing ("class data.frame"). Must be a non-negative number. A value of zero corresponds to no smoothing.

Value

A m by k matrix with m being the length of newdata and k being the number of classes in treeModel. Each row corresponds to the smoothed hazard of the respective observation.

Examples

# NOT RUN {
library(pec)
library(caret)
library(ranger)
data(cost)
# Take subsample and convert time to years
cost$time <- ceiling(cost$time/365)
costSubTrain <- cost[1:50,]
costSubTest <- cost[51:70,]
# Specify column names for data augmentation
timeColumn<-"time"
eventColumn<-"status"
costSubTrainLong <- dataLong(costSubTrain, timeColumn, eventColumn)
costSubTestLong <- dataLong(costSubTest, timeColumn, eventColumn)
#create tree
formula <- y ~ timeInt + diabetes + prevStroke + age + sex
rangerTree <- ranger(formula, costSubTrainLong, num.trees = 1, mtry = 5, 
classification = TRUE, splitrule = "hellinger", replace = FALSE, 
sample.fraction = 1, max.depth = 5)
#compute laplace-smoothed hazards
laplHaz <- survTreeLaplaceHazardRanger(rangerTree, costSubTrainLong, 
costSubTestLong, lambda = 1)
laplHaz
# }