# An example adapted from glmnet package
set.seed(11011)
n = 300
p = 10
nz = 3
X = matrix(rnorm(n*p),n,p,dimnames=list(NULL,seq_len(p)))
beta = rnorm(nz)
f = X[,seq_len(nz)] %*% beta
h = exp(f) / 365.25
t = rexp(n,h)
tcens = rbinom(n=n,prob=.3,size=1) # censoring indicator
S = Surv(t, 1-tcens)
fit = rsig(S, X, "rs.prlasso", n.rep=2)
pred = predict(fit, X)
perf = rsig.eval(pred, S, X)
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