library(glmnet)

n=500

p=30

nzc=trunc(p/10)

x=matrix(rnorm(n*p),n,p)

beta=rnorm(nzc)

fx=x[,seq(nzc)]%*%beta/3

hx=exp(fx)

ty=rexp(n,hx)

tcens=rbinom(n=n,prob=.3,size=1)

y=cbind(time=ty,status=1-tcens)

NFOLD = 5

foldid=sample(rep(seq(NFOLD),length=n))

fit1_cv = cv.glmnet(x,y,family="cox",foldid=foldid, thresh = 1e-16)

fit1_cv$lambda.min

library(penalized)

cv.penal <- optL1(Surv(y[, 'time'], y[, 'status']), penalized = x, fold = foldid)

cv.penal$lambda

cv.penal$fullfit@penalized

fit1_cv$cvm

whichLambda <- which( fit1_cv$lambda ==

fit1_cv$lambda.min )

fit1_cv$lambda.min, thresh = 1e-16)

res <- cbind(CV.GLMNET =

fit1_cv$
glmnet.fit

$
beta [, whichLambda],

'GLMNET.NAIF' = as.numeric(fit$beta),

 'penalized' = cv.penal$fullfit@penalized)

res