predict.ELMCoxBoost: SurvELM predict.ELMCoxBoost

In whcsu/SurvELM: Survival Anlalysis with Extreme Learning Machines

Description

Predicting from An Extreme Learning Machine Cox Model with Likelihood Based Boosting

Usage

## S3 method for class 'ELMCoxBoost'
predict(object, testx, ...)

Arguments

object	An object that inherits from class ELMCoxBoost.
testx	A data frame in which to look for variables with which to predict.
...	Additional arguments for CoxBoost.

Value

produces a vector of predictions or a matrix of predictions

Author(s)

Hong Wang

See Also

predict.CoxBoost

Examples

set.seed(123)
library(SurvELM)
library(survival)
#Lung DATA
data(lung)
lung=na.omit(lung)
lung[,3]=lung[,3]-1
n=dim(lung)[1]
L=sample(1:n,ceiling(n*0.5))
trset<-lung[L,]
teset<-lung[-L,]
rii=c(2,3)
elmsurvmodel=ELMCoxBoost(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
#THE predicted linear predictor
testpre=predict(elmsurvmodel,teset[,-c(rii)])
#The predicted cumulative incidence function
testprecif=predict(elmsurvmodel,teset[,-c(rii)],type="CIF")
# The predicted partial log-likelihood
testprellk=predict(elmsurvmodel,teset[,-c(rii)],newtime=teset[,rii[1]],
newstatus=teset[,rii[2]],type="logplik")
uniquetimes=sort(unique(trset$time))
# The predicted probability of not yet having had the event at the time points given in times
testprerisk=predict(elmsurvmodel,teset[,-c(rii)],times=uniquetimes,type="risk")

whcsu/SurvELM documentation built on Jan. 28, 2020, 3:07 p.m.