predict.ELMCoxEN: SurvELM predict.ELMCoxEN

In whcsu/SurvELM: Survival Anlalysis with Extreme Learning Machines

Description

Predicting from an Ensemble of Regularized Cox Extreme Learning Machine Model

Usage

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

Arguments

object	An object that inherits from class ELMCoxEN.
testx	A data frame in which to look for variables with which to predict.
enlen	Number of based models used for prediction, shouble be less than and equal to the number for training.
...	Additional arguments for glmnet.

Value

produces a vector of predictions or a matrix of predictions

Author(s)

Hong Wang

See Also

predict.glmnet

Examples

set.seed(123)
require(SurvELM)
require(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)
# with ridge penalty and RBF kernel, alpha has the same meaning as in glmnet
elmsurvmodel=ELMCoxEN(x=trset[,-rii],y=Surv(trset[,rii[1]],trset[,rii[2]]),
enlen=10,Kernel_type="RBF_kernel",Kernel_para=c(2,1),alpha=0)
#The second base model
fit2=elmsurvmodel$elmcoxfit[[2]]
#The predicted linear predictor
testprelin=predict(elmsurvmodel,teset[,-c(rii)],type="link")
#The predicted  relative-risk
testpreres=predict(elmsurvmodel,teset[,-c(rii)],type="response")

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