SEM Forest Variable Importance — varimp • semtree

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

varimp(
  forest,
  var.names = NULL,
  verbose = FALSE,
  eval.fun = evaluateTree,
  method = NULL,
  conditional = FALSE,
  strict = TRUE,
  ...
)

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: Character. Define the method, with which importance is computed. The default is NULL and picks the appropriate permutation-based estimation method depending on whether no focus parameters are given ("permutation") or focus parameters are given ("permutationFocus")
conditional: Conditional variable importance if TRUE, otherwise marginal variable importance.
strict: Boolean. Default is TRUE. Only consider estimates from models if there were no model convergence problems. Otherwise, partial results are used, which may incur some downward bias.
...: Optional arguments.

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

Andreas M. Brandmaier, John J. Prindle