survSL.template: Wrapper functions for prediction algorithms in...

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

Wrapper functions for prediction algorithms in survSuperLearner

Description

This is a template function for a survSuperLearner prediction algorithm. You can use this to write your own prediction algorithms.

Usage

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

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.
id	Observation clusters.
...	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	Elements of the model fit. Typically includes fit$object, the fitted object, but may include additional elements obtained in the fitting process that would be needed for prediction.

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