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 R2 have been proposed for the linear mixed model, but several limitations prevail. Presently, there is no method to compute R2 for the linear mixed model that accommodates an interpretation based on variance partitioning, a method to quantify uncertainty and produce confidence limits for the R2 statistic, and a capacity to use the R2 statistic to conduct covariance model selection in a manner similar to information criteria. In this article, we introduce such an R2 statistic. The proposed R2 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 R2 and its capacity to be applied to covariance model selection.