An R 2 statistic for covariance model selection in the linear mixed model

Academic Article


  • © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. The linear mixed model, sometimes referred to as the multi-level model, is one of the most widely used tools for analyses involving clustered data. Various definitions of R 2 have been proposed for the linear mixed model, but several limitations prevail. Presently, there is no method to compute R 2 for the linear mixed model that accommodates an interpretation based on variance partitioning, a method to quantify uncertainty and produce confidence limits for the R 2 statistic, and a capacity to use the R 2 statistic to conduct covariance model selection in a manner similar to information criteria. In this article, we introduce such an R 2 statistic. The proposed R 2 measures the proportion of generalized variance explained by fixed effects in the linear mixed model. Simulated and real longitudinal data are used to illustrate the statistical properties of the proposed R 2 and its capacity to be applied to covariance model selection.
  • Published In

    Digital Object Identifier (doi)

    Pubmed Id

  • 2265596
  • Author List

  • Jaeger BC; Edwards LJ; Gurka MJ
  • Start Page

  • 164
  • End Page

  • 184
  • Volume

  • 46
  • Issue

  • 1