robustlmm-package {robustlmm}

Robust linear mixed effects models

Description

robustlmm provides functions for estimating linear mixed effects models in a robust way.

The main workhorse is the function rlmer; it is implemented as direct robust analogue of the popular lmer function of the lme4 package. The two functions have similar abilities and limitations. A wide range of data structures can be modeled: mixed effects models with hierarchical as well as complete or partially crossed random effects structures are possible. While the lmer function is optimized to handle large datasets efficiently, the computations employed in the rlmer function are more complex and for this reason also more expensive to compute. The two functions have the same limitations in the support of different random effect and residual error covariance structures. Both support only diagonal and unstructured random effect covariance structures.

The robustlmm package implements most of the analysis tool chain as is customary in R. The usual functions such as summary, coef, resid, etc. are provided as long as they are applicable for this type of models (see rlmerMod-class for a full list). The functions are designed to be as similar as possible to the ones in the lme4 package to make switching between the two packages easy.

Details on the implementation and example analyses are provided in the package vignette available via vignette("rlmer") (Koller 2016).

References

Manuel Koller (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24. doi:10.18637/jss.v075.i06

Koller M, Stahel WA (2022). "Robust Estimation of General Linear Mixed Effects Models.” In PM Yi, PK Nordhausen (eds.), Robust and Multivariate Statistical Methods, Springer Nature Switzerland AG.

Manuel Koller (2013). Robust estimation of linear mixed models. (Doctoral dissertation, Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 20997, 2013).