SGL-backend: Sparse-group-lasso Cox backend

In peperr: Parallelised Estimation of Prediction Error

Sparse-group-lasso Cox backend

Description

Wrappers for Cox models fitted with SGL.

Usage

fit.SGL.cox(response, x, cplx, index, ...)
complexity.cvSGL.cox(response, x, full.data, index, ...)
## S3 method for class 'SGL_cox'
predictProb(object, response, x, times, complexity = NULL, ...)
## S3 method for class 'SGL_cox'
PLL(object, newdata, newtime, newstatus, complexity = NULL, ...)

Arguments

response	survival response as a Surv object or a two-column time/status matrix.
x	covariate matrix.
cplx	selected lambda value.
index	group membership vector for the columns of x.
full.data	full data frame, accepted for the peperr complexity-function contract.
object	a fitted SGL_cox object.
times	evaluation times for survival probabilities.
complexity	selected lambda value.
newdata	new covariate matrix.
newtime	vector of follow-up times.
newstatus	vector of event indicators.
...	additional arguments passed to SGL or cvSGL.

Details

The SGL predictor returns relative risks. The peperr wrapper reconstructs survival probabilities from those risks with a Breslow baseline estimated on the stored training data.

Value

Fitted SGL_cox objects, selected lambda values, survival-probability matrices, and numeric predictive partial log-likelihood values, respectively.

See Also

peperr, SGL, cvSGL

peperr documentation built on March 25, 2026, 9:06 a.m.