mboost-backend: mboost Cox backend

In peperr: Parallelised Estimation of Prediction Error

mboost Cox backend

Description

Wrappers for Cox boosting models fitted with mboost.

Usage

fit.mboost.coxph(response, x, cplx, ...)
complexity.cvrisk.mboost(response, x, full.data, max.mstop = 100L, folds = NULL, ...)
## S3 method for class 'mboost_coxph'
predictProb(object, response, x, times, complexity = NULL, ...)
## S3 method for class 'mboost_coxph'
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 stopping iteration mstop.
full.data	full data frame, accepted for the peperr complexity-function contract.
max.mstop	maximum number of boosting iterations considered during cross-validation.
folds	optional mboost fold specification passed to cvrisk.
object	a fitted mboost_coxph object.
times	evaluation times for survival probabilities.
complexity	selected stopping iteration.
newdata	new covariate matrix.
newtime	vector of follow-up times.
newstatus	vector of event indicators.
...	additional arguments passed to glmboost, cvrisk, or prediction helpers.

Details

If mboost provides native survival-curve predictions for the fitted model, predictProb.mboost_coxph uses them. Otherwise it falls back to a Breslow baseline estimated from the stored training data and linear predictors.

Value

Fitted mboost_coxph objects, selected stopping iterations, survival-probability matrices, and numeric predictive partial log-likelihood values, respectively.

See Also

peperr, glmboost, cvrisk

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