PLmixed - R Package

Estimate GLMMs w/ Factor Structures



The purpose of PLmixed is to extend the capabilities of the R package lme4 (Bates, Machler, Bolker, & Walker, 2015) to allow factor structures (i.e., factor loadings, weights, discrimination parameters) to be freely estimated. Thus, factor analysis and item response theory models with multiple hierarchical levels and/or crossed random effects can be estimated using code that requires little more input than that required by lme4. All of the strengths of lme4, including the ability to add (possibly random) covariates and an arbitrary number of crossed random effects, are encompassed within PLmixed. In fact, PLmixed uses lme4 and optim to estimate the model using nested maximizations. Details of this approach can be found in Jeon and Rabe-Hesketh (2012) doi:10.3102/1076998611417628. A manuscript documenting the use of PLmixed is currently in preparation.


Rockwood, N. J. & Jeon, M. (2019). Estimating complex measurement and growth models using the R package PLmixed. Multivariate Behavioral Research. [link]

Jeon, M. & Rockwood, N. J. (2017). PLmixed: Estimate (Generalized) Linear Mixed Models with Factor Structures. R package version 0.1.0.


You can install PLmixed from CRAN in R with:



A vignette containing two example analyses can be found here.