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Nonlinear Random-Effects Mixture Models for Repeated Measures

Published online by Cambridge University Press:  01 January 2025

Casey L. Codd  and
Robert Cudeck
Casey L. Codd*
Affiliation:
The Ohio State University
Robert Cudeck
Affiliation:
The Ohio State University
*
Requests for reprints should be sent to Casey L. Codd, Psychology Department, Ohio State University, 240D Lazenby Hall, Columbus, OH 43210, USA. E-mail: codd.2@buckeyemail.osu.edu
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Abstract

A mixture model for repeated measures based on nonlinear functions with random effects is reviewed. The model can include individual schedules of measurement, data missing at random, nonlinear functions of the random effects, of covariates and of residuals. Individual group membership probabilities and individual random effects are obtained as empirical Bayes predictions. Although this is a complicated model that combines a mixture of populations, nonlinear regression, and hierarchical models, it is straightforward to estimate by maximum likelihood using SAS PROC NLMIXED. Many different models can be studied with this procedure. The model is more general than those that can be estimated with most special purpose computer programs currently available because the response function is essentially any form of nonlinear regression. Examples and sample code are included to illustrate the method.

Keywords

mixture modelrepeated measureshierarchical modelnonlinear model

Information

Type
Original Paper
Information
Psychometrika , Volume 79 , Issue 1 , January 2014 , pp. 60 - 83
Copyright
Copyright © 2013 The Psychometric Society

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