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Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models

Published online by Cambridge University Press:  01 January 2025

Eric F. Lock Open the ORCID record for Eric F. Lock [Opens in a new window] ,
Nidhi Kohli  and
Maitreyee Bose
Eric F. Lock*
Affiliation:
University of Minnesota
Nidhi Kohli
Affiliation:
University of Minnesota
Maitreyee Bose
Affiliation:
University of Minnesota
*
Correspondence should be made to Eric F. Lock, Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303 420 Delaware Street, S.E., Minneapolis, MN 55455, USA. Email: elock@umn.edu; URL: http://ericfrazerlock.com/
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Abstract

Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study are as follows: (1) to develop a PGMM using a Bayesian inference approach that allows the estimation of multiple random changepoints within each class; (2) to develop a procedure to empirically detect the number of random changepoints within each class; and (3) to empirically investigate the bias and precision of the estimation of the model parameters, including the random changepoints, via a simulation study. We have developed the user-friendly package BayesianPGMM for R to facilitate the adoption of this methodology in practice, which is available at https://github.com/lockEF/BayesianPGMM. We describe an application to mouse-tracking data for a visual recognition task.

Keywords

Bayesianlongitudinal dataMarkov chain Monte Carlomixture modelnonlinear random effects modelspiecewise function
Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 3 , September 2018 , pp. 733 - 750
Copyright
Copyright © The Psychometric Society 2017

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-017-9594-5) contains supplementary material, which is available to authorized users.

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