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Symbolic formulae for linear mixed models. (English) Zbl 1445.62177
Nguyen, Hien (ed.), Statistics and data science. Proceedings of the research school on statistics and data science, RSSDS 2019, Melbourne, Australia, July 24–26, 2019. Singapore: Springer. Commun. Comput. Inf. Sci. 1150, 3-21 (2019).
Summary: A statistical model is a mathematical representation of an often simplified or idealised data-generating process. In this paper, we focus on a particular type of statistical model, called linear mixed models (LMMs), that is widely used in many disciplines e.g. agriculture, ecology, econometrics, psychology. Mixed models, also commonly known as multi-level, nested, hierarchical or panel data models, incorporate a combination of fixed and random effects, with LMMs being a special case. The inclusion of random effects in particular gives LMMs considerable flexibility in accounting for many types of complex correlated structures often found in data. This flexibility, however, has given rise to a number of ways by which an end-user can specify the precise form of the LMM that they wish to fit in statistical software. In this paper, we review the software design for specification of the LMM (and its special case, the linear model), focusing in particular on the use of high-level symbolic model formulae and two popular but contrasting R-packages in lme4 and asreml.
For the entire collection see [Zbl 1433.68029].
MSC:
62J05 Linear regression; mixed models
62A01 Foundations and philosophical topics in statistics
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