boruta: Run the Boruta algorithm on a sem tree

In brandmaier/semtree: Recursive Partitioning for Structural Equation Models

Run the Boruta algorithm on a sem tree

Description

Grows a series of SEM Forests following the boruta algorithm to determine feature importance as moderators of the underlying model.

Usage

boruta(
  model,
  data,
  control = NULL,
  predictors = NULL,
  maxRuns = 30,
  pAdjMethod = "none",
  alpha = 0.05,
  verbose = FALSE,
  quant = 1,
  ...
)

Arguments

model	A template SEM. Same as in semtree.
data	A dataframe to boruta on. Same as in semtree.
control	A semforest control object to set forest parameters.
predictors	An optional list of covariates. See semtree code example.
maxRuns	Maximum number of boruta search cycles
pAdjMethod	A value from stats::p.adjust.methods defining a multiple testing correction method
alpha	p-value cutoff for decisionmaking. Default .05
verbose	Verbosity level for boruta processing similar to the same argument in semtree.control and semforest.control
...	Optional parameters to undefined subfunctions

Value

A vim object with several elements that need work. Of particular note, '$importance' carries mean importance; '$decision' denotes Accepted/Rejected/Tentative; '$impHistory' has the entire varimp history; and '$details' has exit values for each parameter.

Author(s)

Priyanka Paul, Timothy R. Brick, Andreas Brandmaier

See Also

semtree semforest

brandmaier/semtree documentation built on June 11, 2025, 2:52 a.m.