The R Journal: accepted article

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RealVAMS: An R Package for Fitting a Multivariate Value-added Model (VAM) PDF download
Jennifer Broatch, Jennifer Green and Andrew Karl

Abstract 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 indicator. 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 real-world outcome such as graduation or other binary outcomes. The simultaneous modeling of joint 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.

Received: 2017-03-03; online 2018-05-22, supplementary material, (1.4 Kb)
CRAN packages: RealVAMS, lme4
CRAN Task Views implied by cited CRAN packages: Bayesian, Econometrics, Environmetrics, OfficialStatistics, Psychometrics, SocialSciences, SpatioTemporal


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-033,
  author = {Jennifer Broatch and Jennifer Green and Andrew Karl},
  title = {{RealVAMS: An R Package for Fitting a Multivariate Value-
          added Model (VAM)}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-033/index.html}
}