RealVAMS: An R package for fitting a multivariate value-added model (VAM)

Jennifer Broatch, Jennifer Green, Andrew Karl

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)22-30
Number of pages9
JournalR Journal
Issue number1
StatePublished - Jul 1 2018

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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