causalTree.exp: Initialization function for exponential fitting

In swager/causalForest: Causal Trees and Forests

Description

This function does the initialization step for causalTree, when the response is a survival object. It rescales the data so as to have an exponential baseline hazard and then uses Poisson methods. This function would rarely if ever be called directly by a user.

Usage

causalTree.exp(y, offset, parms, wt)

Arguments

y	the response, which will be of class Surv
offset	optional offset
parms	parameters controlling the fit. This is a list with components shrink and method. The first is the prior for the coefficient of variation of the predictions. The second is either "deviance" or "sqrt" and is the measure used for cross-validation. If values are missing the defaults are used, which are "deviance" for the method, and a shrinkage of 1.0 for the deviance method and 0 for the square root.
wt	case weights, if present

Value

a list with the necessary initialization components

Author(s)

Terry Therneau

See Also

causalTree

swager/causalForest documentation built on May 30, 2019, 9:32 p.m.