grmtree.control: Control Parameters for GRM Trees

In grmtree: Recursive Partitioning for Graded Response Models

Control Parameters for GRM Trees

Description

Creates a control object for grmtree containing various parameters that control the tree growing process.

Usage

grmtree.control(
  minbucket = 20,
  p_adjust = "none",
  alpha = 0.05,
  initial_alpha = NULL,
  ...
)

Arguments

minbucket	Minimum number of observations in a terminal node (default: 20).
p_adjust	Method for p-value adjustment. One of: "none", "bonferroni", "holm", "BH", "BY", "hochberg", or "hommel" (default: "none").
alpha	Significance level for splitting (default: 0.05).
initial_alpha	For post-hoc adjustment methods (holm, BH, BY, hochberg, hommel), the significance threshold for initial tree construction before pruning. Must satisfy alpha < initial_alpha < 1. Default is min(3 * alpha, 0.20). Lower values produce more conservative results but run faster; higher values provide more power but require more computation and may increase Type I error. Ignored for "none" and "bonferroni" methods.
...	Additional arguments passed to partykit::mob_control().

Value

A list of control parameters with class grmtree_control.

See Also

grmtree fits a Graded Response Model Tree

Examples

# Use Bonferroni correction with alpha = 0.01
ctrl <- grmtree.control(p_adjust = "bonferroni", alpha = 0.01)

grmtree documentation built on Sept. 2, 2026, 1:07 a.m.