robustlmm : An R Package for Robust Estimation of Linear Mixed-Effects Models

As any real-life data, data modeled by linear mixed-effects models often contain outliers or other contamination. Even little contamination can drive the classic estimates far away from what they would be without the contamination. At the same time, datasets that require mixed-effects modeling are often complex and large. This makes it difficult to spot contamination. Robust estimation methods aim to solve both problems: to provide estimates where contamination has only little influence and to detect and flag contamination. We introduce an R package, robustlmm, to robustly fit linear mixed-effects models. The package's functions and methods are designed to closely equal those offered by lme4, the R package that implements classic linear mixed-effects model estimation in R. The robust estimation method in robustlmm is based on the random effects contamination model and the central contamination model. Contamination can be detected at all levels of the data. The estimation method does not make any assumption on the data's grouping structure except that the model parameters are estimable. robustlmm supports hierarchical and non-hierarchical (e.g., crossed) grouping structures. The robustness of the estimates and their asymptotic efficiency is fully controlled through the function interface. Individual parts (e.g., fixed effects and variance components) can be tuned independently. In this tutorial, we show how to fit robust linear mixed-effects models using robustlmm, how to assess the model fit, how to detect outliers, and how to compare different fits.

• Publication year

  2016

• Venue

  Journal of Statistical Software

• Publication date

  2016-12-06

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.18637/JSS.V075.I06
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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