survTreeLaplaceHazardRanger: Laplace Hazards for a Competing Risk Survival Tree Object

In discSurv: Discrete Time Survival Analysis

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

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

discSurv documentation built on March 18, 2022, 7:12 p.m.