CoxBAR: CoxBAR

In ELMSurv: Extreme Learning Machine for Survival Analysis

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 β.
logLik	Log Likelihood.

Author(s)

Hong Wang

References

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

Examples

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)

ELMSurv documentation built on May 27, 2019, 9:04 a.m.