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.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
- Zheng, Peng; Barber, Ryan; Sorensen, Reed J. D.; Murray, Christopher J. L.; Aravkin, Aleksandr Y.: Trimmed constrained mixed effects models: formulations and algorithms (2021)
- Yu, Dalei; Ding, Chang; He, Na; Wang, Ruiwu; Zhou, Xiaohua; Shi, Lei: Robust estimation and confidence interval in meta-regression models (2019)
- Heritier, Stephane; Victoria-Feser, Maria-Pia: Discussion of: “The power of monitoring: how to make the most of a contaminated multivariate sample” (2018)
- Ting Wang, Edgar C. Merkle: merDeriv: Derivative Computations for Linear Mixed Effects Models with Application to Robust Standard Errors (2018) not zbMATH
- Manuel Koller: robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models (2016) not zbMATH