varimp: SEM Forest Variable Importance

In semtree: Recursive Partitioning for Structural Equation Models

SEM Forest Variable Importance

Description

A function to calculate relative variable importance for selecting node splits over a semforest object.

Usage

varimp(
  forest,
  var.names = NULL,
  verbose = F,
  eval.fun = evaluateTree,
  method = "permutation",
  conditional = FALSE,
  ...
)

Arguments

forest	A semforest object
var.names	Covariates used in the forest creation process. NULL value will be automatically filled in by the function.
verbose	Boolean to print messages while function is running.
eval.fun	Default is evaluateTree function. The value of the -2LL of the leaf nodes is compared to baseline overall model.
method	Experimental. Some alternative methods to compute importance. Default is "permutation".
conditional	Conditional variable importance if TRUE, otherwise marginal variable importance.
...	Optional arguments.

Author(s)

Andreas M. Brandmaier, John J. Prindle

References

Brandmaier, A.M., Oertzen, T. v., McArdle, J.J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18(1), 71-86.

semtree documentation built on May 29, 2024, 4:05 a.m.