generate_node_scores_dataset: Generate Dataset with Node Assignments and Factor Scores

In grmtree: Recursive Partitioning for Graded Response Models

Generate Dataset with Node Assignments and Factor Scores

Description

Creates a dataset by augmenting the original data with node assignments and computed factor scores. Unlike the previous version which only returned model frame variables, this version merges node and score information back to the full original data frame.

Usage

generate_node_scores_dataset(object, data = NULL, method = "EAP")

Arguments

object	A grmtree or longitudinal_grmtree object.
data	The original data frame used to fit the tree. If provided, the output contains all columns from this data frame plus node and factor score columns. If NULL (default), returns only model frame variables (backward-compatible behavior).
method	Scoring method: "EAP" (default), "MAP", "ML", or "WLE".

Details

The function works by:

1. Predicting node membership for each observation using predict(object, type = "node")

2. Computing factor scores within each terminal node using the node-specific model

3. Merging the results back to the original data by row position

When data is provided, the function ensures that the output contains all columns from the original data frame, not just the variables used in the model formula. This is important when the original data contains clinical variables, identifiers, or other columns not used as partitioning variables.

Value

A data.frame containing:

All original columns

From data if provided

node

Factor indicating terminal node membership

factor_score

For cross-sectional grmtree: single latent score. For longitudinal: Theta_T1.

Theta_T1

(Longitudinal only) Latent trait at T1

Theta_T2

(Longitudinal only) Latent trait at T2

See Also

grmtree fits a Graded Response Model Tree, grmforest for GRM Forests, fscores_grmtree for computing factor scores, threshpar_grmtree for extracting threshold parameters, discrpar_grmtree for extracting discrimination parameters, itempar_grmtree for extracting item parameters, longitudinal_grmtree for longitudinal GRMTree, fscores_longitudinal_grmtree, for computing factor scores for longitudinal GRMTree

Examples

# Cross-sectional GRMTree
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))

# Generate combined dataset
scored_data <- generate_node_scores_dataset(tree, data = asti)

# Plot scores by node
boxplot(factor_score ~ node, data = scored_data)

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