We present RealVAMS, an R package for fitting a generalized linear mixed model to multimembership data with partially crossed and partially nested random effects. RealVAMS utilizes a multivariate generalized linear mixed model with pseudo-likelihood approximation for fitting normally distributed continuous response(s) jointly with a binary outcome. In an educational context, the model is referred to as a multidimensional value-added model, which extends previous theory to estimate the relationships between potential teacher contributions toward different student outcomes and to allow the consideration of a binary, real-world outcome such as graduation. The simultaneous joint modeling of continuous and binary outcomes was not available prior to RealVAMS due to computational difficulties. In this paper, we discuss the multidimensional model, describe RealVAMS, and demonstrate the use of this package and its modeling options with an educational data set.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-033.zip
Bayesian, Econometrics, Environmetrics, OfficialStatistics, Psychometrics, SocialSciences, SpatioTemporal
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Broatch, et al., "RealVAMS: An R Package for Fitting a Multivariate Value-added Model (VAM)", The R Journal, 2018
BibTeX citation
@article{RJ-2018-033,
author = {Broatch, Jennifer and Green, Jennifer and Karl, Andrew},
title = {RealVAMS: An R Package for Fitting a Multivariate Value-added Model (VAM)},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-033},
doi = {10.32614/RJ-2018-033},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {22-30}
}