ELMSurv (version 0.6)

CoxBAR: CoxBAR

Description

Cox's Regression with broken adaptive ridge (CoxBAR)

Usage

CoxBAR(trainx, y, weight = NULL, maxiter = 5, standardize = TRUE)

Arguments

trainx

The covariates(predictor variables) of training data.

y

Survival time and censored status of training data. Must be a Surv survival object

weight

In ELMCoxBAR, we set this to a random Cox-Lasso estimate.

maxiter

Maximum values of iterations to update the CoxBAR estimator. Default is 5.

standardize

Logical flag for trainx variable standardization, prior to fitting the model sequence. Default is standardize=TRUE

Value

Object of class CoxBAR with elements

meanx	Mean values of original trainx if standardization is TRUE.
sdx	Standard deviation values of original trainx if standardization is TRUE.
standardize	The standardization status.
beta	The point estimates of \(\beta\).
logLik	Log Likelihood.

References

• Wang, H, Li, G. Extreme learning machine Cox model for high-dimensional survival analysis. Statistics in Medicine.2019; 38:2139-2156.

Examples

# NOT RUN {
set.seed(123)
require(ELMSurv)
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)
# A randon weight for illustration purpose.
p=dim(lung)[2]-2
myweight=rep(0.5,p)
coxbarmodel=CoxBAR(trainx=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]),weight=myweight)
# }