fit_survSuperLearner: Wrapper of 'survSuperLearner::survSuperLearner'

In QIU-Hongxiang-David/SDRsurv: Sequentially Doubly Robust Estimation of Survival Curve with Time-Varying Covariates

Wrapper of survSuperLearner::survSuperLearner

Description

Wrapper of survSuperLearner::survSuperLearner

Usage

fit_survSuperLearner(
  formula,
  data,
  id.var,
  time.var,
  event.var,
  nfold = 1,
  option = list(event.SL.library = c("survSL.coxph", "survSL.weibreg", "survSL.gam",
    "survSL.rfsrc"), cens.SL.library = c("survSL.coxph", "survSL.weibreg", "survSL.gam",
    "survSL.rfsrc")),
  ...
)

Arguments

formula	formula containing all covariates to be used
data	data containing all covariates, follow-up time, event indicator and id
id.var	see SDRsurv
time.var	see SDRsurv
event.var	see SDRsurv
nfold	number of folds used when fitting survival curves with sample splitting. Default is 1, meaning no sample splitting
option	a list containing optional arguments passed to survSuperLearner::survSuperLearner. We encourage using a named list. Will be passed to survSuperLearner::survSuperLearner by running a command like do.call(survSuperLearner, option). The user should not specify time, event, X, newX or new.times. We encourage the user to specify event.SL.library and cens.SL.library.
...	ignored

Value

a pred_event_censor class containing fitted survival curves for individuals in data

QIU-Hongxiang-David/SDRsurv documentation built on March 29, 2024, 8:41 a.m.