lmeresampler: lmeresampler: A package for bootstrapping nested linear...

In lmeresampler: Bootstrap Methods for Nested Linear Mixed-Effects Models

lmeresampler: A package for bootstrapping nested linear mixed-effects models

Description

The lme4 and nlme packages have made fitting nested linear mixed-effects (LME) models quite easy. Using the the functionality of these packages we can easily use maximum likelihood or restricted maximum likelihood to fit a model and conduct inference using our parametric toolkit. In practice, the assumptions of our model are often violated to such a degree that leads to biased estimators and incorrect standard errors. In these situations, resampling methods such as the bootstrap can be used to obtain consistent estimators and standard errors for inference. lmeresampler provides an easy way to bootstrap nested linear-mixed effects models using either fit using either lme4 or nlme.

Details

A variety of bootstrap procedures are available:

• the parametric bootstrap: parametric_bootstrap

• the residual bootstrap: resid_bootstrap

• the cases (i.e. non-parametric) bootstrap: case_bootstrap

• the random effects block (REB) bootstrap: reb_bootstrap

• the Wild bootstrap: wild_bootstrap

In addition to the individual bootstrap functions, lmeresampler provides a unified interface to bootstrapping LME models in its bootstrap function.

lmeresampler documentation built on Feb. 16, 2023, 6:53 p.m.