predict.ELMCox: SurvELM predict.ELMCox

In whcsu/SurvELM: Survival Anlalysis with Extreme Learning Machines

Description

Predicting from A Regularized Cox Extreme Learning Machine Model

Usage

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

Arguments

object	An object that inherits from class ELMCox.
testx	A data frame in which to look for variables with which to predict.
...	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)
# Default with lasso penalty
elmsurvmodel=ELMCox(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
# with ridge penalty and RBF kernel, alpha has the same meaning as in glmnet
elmsurvmodel=ELMCox(x=trset[,-rii],y=Surv(trset[,rii[1]], 
trset[,rii[2]]),Kernel_type="RBF_kernel",Kernel_para=c(2,1),alpha=0)
# with elastic net penalty
elmsurvmodel=ELMCox(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]),alpha=0.5)
#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.