fscores_grmtree: Compute Latent Factor Scores for Each Terminal Node in a GRM...

In grmtree: Recursive Partitioning for Graded Response Models

Compute Latent Factor Scores for Each Terminal Node in a GRM Tree

Description

This function calculates latent factor scores for each terminal node in a GRM tree object using specified scoring method (EAP, MAP, ML, or WLE).

Usage

fscores_grmtree(grmtree_obj, method = "EAP")

Arguments

grmtree_obj	A GRM tree object (from grmtree() function) containing fitted models in its terminal nodes.
method	Scoring method to use: "EAP" (default), "MAP", "ML", or "WLE". See mirt::fscores() for details.

Value

A named list where each element contains the factor scores for a terminal node. Names correspond to node IDs. Returns NULL for nodes where computation fails. If no scores can be computed for any node, returns NULL with a warning.

See Also

fscores for factor scoring methods, grmtree fits a Graded Response Model Tree, grmforest for GRM Forests, threshpar_grmtree for extracting threshold parameters, discrpar_grmtree for extracting discrimination parameters, itempar_grmtree for extracting item parameters, generate_node_scores_dataset generates combined dataset with node assignments and factor scores

Examples

  library(grmtree)
  library(hlt)
  data("asti", package = "hlt")
  asti$resp <- data.matrix(asti[, 1:4])

  # Fit GRM tree with gender and group as partitioning variables
  tree <- grmtree(resp ~ gender + group,
          data = asti,
          control = grmtree.control(minbucket = 30))

# Compute EAP scores for all terminal nodes
node_scores <- fscores_grmtree(tree)

# Compute MAP scores instead
node_scores_map <- fscores_grmtree(tree, method = "MAP")

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