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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Testing for group differences in multilevel vector autoregressive models.

Jonas M B Haslbeck^1,2, Sacha Epskamp³, Lourens J Waldorp⁴

¹Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands. jonashaslbeck@protonmail.com.

Behavior Research Methods

|February 20, 2025

Summary

This summary is machine-generated.

Researchers developed new statistical tests for comparing multilevel Vector Autoregressive (VAR) models between groups. These methods, implemented in R, enable robust inferences on group differences in complex time series data.

Keywords:

Group comparison Network models Time series modeling VAR models Vector Autoregressive models

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Area of Science:

Computational Statistics
Time Series Analysis
Multilevel Modeling

Background:

Multilevel Vector Autoregressive (VAR) models are widely used for analyzing longitudinal data from multiple subjects.
Investigating group differences (e.g., patients vs. controls) in these models is crucial but lacks standardized inferential methods.
Existing methods for comparing multilevel VAR models across groups are not readily accessible or widely adopted.

Purpose of the Study:

To introduce and evaluate novel statistical tests for inferring group differences in multilevel VAR models.
To provide practical implementations of these tests within the R statistical environment.
To assess the performance of the proposed methods in detecting group differences using simulation studies.

Main Methods:

Development and explanation of a parametric test for group comparisons in multilevel VAR models.
Development and explanation of a nonparametric permutation test for robust group comparisons.
Implementation of both tests using the mlVAR R-package and a tutorial with the mnet R-package.

Main Results:

The study successfully implemented and evaluated two distinct statistical tests for group differences in multilevel VAR models.
Simulation studies demonstrated the performance of these tests in accurately recovering known group differences.
The R-package mnet provides a reproducible framework for applying these methods to empirical emotion data.

Conclusions:

The presented parametric and nonparametric tests offer accessible and reliable methods for group comparisons in multilevel VAR analysis.
These tools enhance the ability to investigate inter-individual differences in dynamic network structures.
The provided R implementations and tutorials facilitate the application of these advanced statistical techniques in psychological and neuroscientific research.