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Abstract:
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Mixed-effects models are commonly used to analyze clustered data, such as split plot experiments, longitudinal studies, and stratified samples. In R, there are two primary packages to fit such models: nlme and lme4. In this talk, we present an extension of the nlme and lme4 packages to include methods for bootstrapping model fits. The lmeresampler packages implements several bootstrap methods for mixed-effects models with nested dependence structures using a unified framework: the cases bootstrap resamples entire clusters or observations within clusters (or both); the parametric bootstrap simulates data from the model fit; the residual bootstrap resamples both the predicted random effects and the predicted error terms; and the random effect block bootstrap utilizes the marginal residuals to calculate nonparametric predicted random effects as part of the resampling process. We will discuss and demonstrate the implementation of these bootstrap procedures, and outline plans for future development.
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