srstepwise: Stepwise Variable Selection Procedure for survreg
In BART: Bayesian Additive Regression Trees

srstepwise

R Documentation

Stepwise Variable Selection Procedure for survreg

Description

This stepwise variable selection procedure can be applied to obtain the best candidates for a survreg fit.

Usage

srstepwise(x, times, delta, sle = 0.15, sls = 0.15, dist='lognormal')

Arguments

`x`	Matrix of variables to consider.
`times`	The time to an event, if any.
`delta`	The event indicator: 1 for event, 0 for no event.
`sle`	The chosen significance level for entering.
`sls`	The chosen significance level for staying.
`dist`	The distribution to be used by `survreg`.

Details

Unfortunately, no stepwise procedure exists for survreg models. Therefore, we provide this brute force method.

Value

Returns a list of indices of variables which have entered and stayed.

Examples


names. <- names(lung)[-(2:3)]
status1 <- ifelse(lung$status==2,1,0)
X <- as.matrix(lung)[ , names.]
vars=srstepwise(X, lung$time, status1)
print(names.[vars])

BART documentation built on June 22, 2024, 11:33 a.m.