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Related Experiment Video

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

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An Overview of Current Software Procedures for Fitting Linear Mixed Models.

Brady T West¹, Andrzej T Galecki

¹Institute for Social Research, Center for Statistical Consultation and Research, University of Michigan-Ann Arbor, Ann Arbor, MI, 48109.

The American Statistician

|April 23, 2013

Summary

This summary is machine-generated.

This study compares software for fitting linear mixed models (LMMs) to complex data. It guides statisticians in selecting the best LMM software for their specific research needs.

Keywords:

Covariance Structures Longitudinal Data Analysis Models for Clustered Data Statistical Software

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Area of Science:

Statistics
Biostatistics
Data Science

Background:

Linear mixed models (LMMs) are essential for analyzing clustered or longitudinal data.
Existing software offers various capabilities for fitting LMMs, but features can differ significantly.
Understanding these differences is crucial for appropriate statistical analysis.

Purpose of the Study:

To provide a comprehensive comparison of current software for fitting LMMs.
To guide statisticians in selecting appropriate software based on their analytical objectives.
To highlight specific features like crossed random effects, variance component testing, diagnostics, and sampling weights.

Main Methods:

Systematic review and comparison of available statistical software packages for LMMs.
Evaluation of features relevant to complex data structures (clustered, longitudinal).
Assessment of capabilities for handling missing data (MAR) and time-varying covariates.

Main Results:

Significant variation exists in advanced LMM features across different software packages.
No single software package excels in all aspects of LMM analysis.
Key differences identified in handling crossed random effects, hypothesis testing, diagnostics, and sampling weights.

Conclusions:

Statisticians must carefully consider their specific analytical needs when choosing LMM software.
The comparison serves as a practical guide for selecting the most suitable tool for complex data analysis.
Awareness of software limitations and strengths is vital for robust statistical inference.