Recursive Partitioning for Graded Response Models.

grmtree: Recursive Partitioning for Graded Response Models

Overview

The grmtree package implements advanced tree-based recursive partitioning methods for Graded Response Models (GRM), providing a powerful framework for detecting and analyzing differential item functioning (DIF) in polytomous items from patient-reported outcome measures (PROMs) and other psychological assessments. DIF occurs when individuals with the same underlying latent trait (e.g., health status, quality of life, or psychological attribute) respond differently to assessment items based on extraneous characteristics such as age, gender, education level, or clinical subgroups. This measurement bias can compromise the validity and fairness of assessments across diverse populations.

The GRMTree methodology combines the psychometric rigor of item response theory with the interpretability of decision trees to:

• Identify specific covariates associated with DIF

• Detect complex interaction effects between patient characteristics

• Provide visual representations of how item functioning varies across population subgroups

• Support the development of equitable assessment instruments

Key Features

• DIF Detection: Advanced algorithms for detecting differential item functioning in polytomous items using model-based recursive partitioning
• Visual Analytics: Specialized plotting functions for visualizing threshold parameters, discrimination parameters, and factor score distributions across terminal nodes
• Parameter Extraction: Comprehensive methods for extracting and analyzing item parameters (thresholds, discrimination), factor scores, and node-specific characteristics
• Ensemble Methods: GRM Forests implementation for robust variable importance analysis and enhanced DIF detection stability
• Multiple Testing Corrections: Flexible p-value adjustment methods (Bonferroni, Holm, Benjamini-Hochberg, etc.) to control Type I error inflation
• Seamless Integration: Full compatibility with the mirt package for GRM estimation and the partykit ecosystem for tree visualization

This package is particularly valuable for researchers, psychometricians, and health outcomes specialists who require robust methods for ensuring measurement invariance and equity in their assessment instruments across diverse populations.

Installation

Install from CRAN (when available):

install.packages("grmtree")

Install the development version from GitHub

# Install devtools if not already installed
install.packages("devtools")

# Install the grmtree
devtools::install_github("Predicare1/grmtree")

# Install with vignettes (takes longer but includes vignettes)
devtools::install_github("Predicare1/grmtree", build_vignettes = TRUE)

Quick Start

# Load the package
library(grmtree)

# Load the data
data("grmtree_data") # Sample dataset included with package

# Prepare the data
resp.data <- grmtree_data %>% 
  mutate_at(vars(starts_with("MOS")), as.ordered) %>% 
  mutate_at(vars(c(sex, Education)), as.factor) 

# Create response as outcomes
resp.data$resp <- data.matrix(resp.data[, 1:8])

## GRMTree control parameters with Benjamini-Hochberg
grm_control <- grmtree.control(
  minbucket = 350,
  p_adjust = "BH", alpha = 0.05)

# Fit a GRM tree
tree <- grmtree(resp ~ sex + age + Education,
                       data = resp.data,
                       control = grm_control)

# Print the GRMTree model
print(tree)

# Visualize the tree
plot(tree) # Default regions plot
plot(tree, type = "profile") # Parameter profiles
plot(tree, type = "histogram") # Factor score distributions

# Extract parameters
threshpar_grmtree(tree) # Threshold parameters
discrpar_grmtree(tree) # Discrimination parameters

Vignettes

Detailed interactive tutorials are available:

browseVignettes("grmtree")

1. Getting Started with the grmtree Package: Basic GRM tree implementation

2. GRM Forests for Robust DIF Detection: Ensemble methods for robust DIF detection

GRM Forests Example

# Fit a forest with 100 trees
forest <- grmforest(resp ~ sex + age + Education,
                       data = resp.data,
                   ctrl = grmforest.control(control = grm_control, n_tree = 100))

# Variable importance
vim <- varimp(forest)
print(vim)
plot(vim)

# Examine individual trees
plot(forest$trees[[1]]) # First tree

References

Methodological Foundations

• Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34, 100-114.

• Strobl, C., Kopf, J., & Zeileis, A. (2015). Rasch trees: A new method for detecting differential item functioning in the Rasch model. Psychometrika, 80(2), 289-316.

• Komboz, B., Strobl, C., & Zeileis, A. (2018). Tree-based global model tests for polytomous Rasch models. Educational and psychological measurement, 78(1), 128–166. doi.org/10.1177/0013164416664394.

• Arimoro, O. I., Lix, L. M., Patten, S. B., Sawatzky, R., Sebille, V., Liu, J., Wiebe, S., Josephson, C. B., & Sajobi, T. T. (2025). Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a simulation study. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 10.1007/s11136-025-04018-6. Advance online publication. https://doi.org/10.1007/s11136-025-04018-6.

Applied Examples

• Arimoro, O. I., Josephson, C. B., James, M. T., Patten, S. B., Wiebe, S., Lix, L. M., & Sajobi, T. T. (2024). Screening for depression in patients with epilepsy: same questions but different meaning to different patients. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 33(12), 3409–3419. https://doi.org/10.1007/s11136-024-03782-1.

Authors

Olayinka Imisioluwa Arimoro ([email protected]{.email}), Lisa M. Lix, Tolulope T. Sajobi

Contributing

Contributions are welcome! Please submit issues and pull requests via GitHub: https://github.com/Predicare1/grmtree/issues.