survSL.km: Wrapper function for Kaplan-Meier prediction algorithm

In tedwestling/survSuperLearner: Super Learning for Conditional Survival Functions with Right-Censored Data

Wrapper function for Kaplan-Meier prediction algorithm

Description

This prediciton algorithm ignores all covariates and simply computes the Kaplan-Meier estimator of the marginal survival function of the event as indicated by the right-censored data time and event using the survfit function.

Usage

survSL.km(time, event, X, newX, new.times, obsWeights, ...)

Arguments

time	Observed follow-up time; i.e. minimum of the event and censoring times.
event	Observed event indicator; i.e, whether the follow-up time corresponds to an event or censoring.
X	Training covariate data.frame.
newX	Test covariate data.frame to use for prediction. Should have the same variable names and structure as X.
new.times	Times at which to obtain to obtain the predicted survivals.
obsWeights	Observation weights.
...	Additional ignored arguments.

Value

pred	Matrix of predictions, with the same number of rows as newX and number of columns equal to the length of new.times. Rows index new observations, and columns index new times at which the survival was computed.
fit	One-element list including object, the fitted survfit object.

tedwestling/survSuperLearner documentation built on Dec. 12, 2024, 4:16 p.m.