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MIXREGLS: A Program for Mixed-Effects Location Scale Analysis.

Donald Hedeker1, Rachel Nordgren

  • 1Center for Biostatistical Development, Division of Epidemiology & Biostatistics (M/C 923), School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Room 955, Chicago, IL, 60612-4336.

Journal of Statistical Software
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PubMed
Summary
This summary is machine-generated.

MIXREGLS is a statistical program for mixed-effects location scale models. It analyzes complex data structures, modeling both mean and variance, including between-subject and within-subject variations.

Keywords:
FortranRSASecological momentary assessmentheteroscedasticityintensive longitudinal datamixed modelsmultilevelvariance modeling

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Area of Science:

  • Statistics
  • Statistical Software

Background:

  • Mixed-effects models are essential for analyzing hierarchical or clustered data.
  • Modeling both the mean and variance structures is crucial for a comprehensive understanding of data variability.

Purpose of the Study:

  • Introduce MIXREGLS, a program for estimating mixed-effects location scale models.
  • Facilitate the analysis of complex data with nested structures and covariate effects on variance.

Main Methods:

  • Utilizes maximum likelihood estimation with the EM algorithm and Newton-Raphson methods.
  • Employs empirical Bayes methods for the estimation of random effects.
  • Models both between-subject and within-subject variances based on specified covariates.

Main Results:

  • Provides estimates for mixed-effects location scale models with normally distributed dependent variables.
  • Accommodates analysis of repeated measures and clustered data.
  • Allows covariates to influence both the mean and variance components.

Conclusions:

  • MIXREGLS offers a robust tool for analyzing complex variance structures in statistical modeling.
  • The program supports standalone use and integration with SAS and R software packages.
  • Facilitates advanced statistical analysis for researchers dealing with nested or longitudinal data.