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MIXREGLS: A Program for Mixed-Effects Location Scale Analysis

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  • Hedeker, Donald
  • Nordgren, Rachel

Abstract

MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable. This model can be used for analysis of data in which subjects may be measured at many observations and interest is in modeling the mean and variance structure. In terms of the variance structure, covariates can by specified to have effects on both the between-subject and within-subject variances. Another use is for clustered data in which subjects are nested within clusters (e.g. clinics, hospitals, schools, etc.) and interest is in modeling the between-cluster and within-cluster variances in terms of covariates. MIXREGLS was written in Fortran and uses maximum likelihood estimation, utilizing both the EM algorithm and a Newton-Raphson solution. Estimation of the random effects is accomplished using empirical Bayes methods. Examples illustrating stand-alone usage and features of MIXREGLS are provided, as well as use via the SAS and R software packages.

Suggested Citation

  • Hedeker, Donald & Nordgren, Rachel, 2013. "MIXREGLS: A Program for Mixed-Effects Location Scale Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i12).
    • Handle: RePEc:jss:jstsof:v:052:i12
    DOI: http://hdl.handle.net/10.18637/jss.v052.i12
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    References listed on IDEAS

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    1. Murray Aitkin, 1987. "Modelling Variance Heterogeneity in Normal Regression Using GLIM," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 332-339, November.
    2. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    3. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2002. "Reliable estimation of generalized linear mixed models using adaptive quadrature," Stata Journal, StataCorp LP, vol. 2(1), pages 1-21, February.
    4. Donald Hedeker & Robin J. Mermelstein & Hakan Demirtas, 2008. "An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data," Biometrics, The International Biometric Society, vol. 64(2), pages 627-634, June.
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    2. D. Betsy McCoach & Graham G. Rifenbark & Sarah D. Newton & Xiaoran Li & Janice Kooken & Dani Yomtov & Anthony J. Gambino & Aarti Bellara, 2018. "Does the Package Matter? A Comparison of Five Common Multilevel Modeling Software Packages," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 594-627, October.
    3. George Leckie & Robert French & Chris Charlton & William Browne, 2014. "Modeling Heterogeneous Variance–Covariance Components in Two-Level Models," Journal of Educational and Behavioral Statistics, , vol. 39(5), pages 307-332, October.
    4. Shelley A. Blozis, 2022. "Bayesian two-part multilevel model for longitudinal media use data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 311-328, December.
    5. Ian Brunton-Smith & Patrick Sturgis & George Leckie, 2017. "Detecting and understanding interviewer effects on survey data by using a cross-classified mixed effects location–scale model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 551-568, February.

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