作者
Holger Schielzeth, Niels J Dingemanse, Shinichi Nakagawa, David F Westneat, Hassen Allegue, Céline Teplitsky, Denis Réale, Ned A Dochtermann, László Zsolt Garamszegi, Yimen G Araya‐Ajoy
发表日期
2020/9
期刊
Methods in Ecology and Evolution
卷号
11
期号
9
页码范围
1141-1152
简介
- 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 random effect and residual variances, of missing random effect terms and of correlated fixed effect predictors. We focus on bias and prediction error on estimates of fixed and random effects.
- Model …
学术搜索中的文章
H Schielzeth, NJ Dingemanse, S Nakagawa… - Methods in ecology and evolution, 2020