Unbalanced repeated-measures models with structured covariance matrices.

R I Jennrich; M D Schluchter

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PubMed · 3814725

Unbalanced repeated-measures models with structured covariance matrices.

R I Jennrich·M D Schluchter

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Abstract

The question of how to analyze unbalanced or incomplete repeated-measures data is a common problem facing analysts. We address this problem through maximum likelihood analysis using a general linear model for expected responses and arbitrary structural models for the within-subject covariances. Models that can be fit include standard univariate and multivariate models with incomplete data, random-effects models, and models with time-series and factor-analytic error structures. We describe Newton-Raphson and Fisher scoring algorithms for computing maximum likelihood estimates, and generalized EM algorithms for computing restricted and unrestricted maximum likelihood estimates. An example fitting several models to a set of growth data is included.

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Algorithms Analysis of Variance Humans Longitudinal Studies Models, Theoretical Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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R I Jennrich, M D Schluchter. 1986. Unbalanced repeated-measures models with structured covariance matrices.. https://pubmed.ncbi.nlm.nih.gov/3814725/

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