Robustness of linear mixed‐effects models to violations of distributional assumptions

Abstract Linear mixed‐effects models are powerful tools for analysing complex datasets with repeated or clustered observations, a common data structure in ecology and evolution. Mixed‐effects models involve complex fitting procedures and make several assumptions, in particular about the distribution of residual and random effects. Violations of these assumptions are common in real datasets, yet it is not always clear how much these violations matter to accurate and unbiased estimation. Here we address the consequences of violations in distributional assumptions and the impact of missing random effect components on model estimates. In particular, we evaluate the effects of skewed, bimodal and heteroscedastic…

• NS
  National Science Foundation

  Awards: IOS1557951, IOS1257718
• DF
  Deutsche Forschungsgemeinschaft

  Awards: INST 215/543‐1, DI 1694/1‐1, 396782608