The linear mixed model (LMM), sometimes referred to as the multi-level model, stands as one of the most widely used tools for analyses involving clustered data. Various de?nitions of R-squared have been proposed for the LMM, but several limitations prevail. Presently there is no de?nition of R-squared in the LMM that accomodates (1) an interpretation based on variance partitioning, (2) a method to quantify uncertainty and produce con?dence limits, and (3) a capacity to conduct covariance model selection in a manner similar to information criteria. We introduce an R-squared for the LMM with each of these characteristics. The proposed R-squared measures the proportion of generalized variance explained by ?xed predictors in the model. Simulated and real longitudinal data are used to illustrate the statistical properties of the proposed R-squared and its capacity to assist analysts with covariance model selection and assessment of model goodness-of-fit.