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Linear and generalized linear mixed models
2015AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
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On Inverse Prediction in Mixed Linear Models
Communications in Statistics - Simulation and Computation, 2014Given training data, a model relating a multivariate response y to x, and y* from a mystery specimen, the objective is to infer what values x* might have given rise to y*. Two approaches are investigated and illustrated here. In one, inverse prediction, tenable values of x* are those at which y* does not test as an outlier.
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Robustness of linear mixed‐effects models to violations of distributional assumptions
Methods in Ecology and Evolution, 2020Holger Schielzeth +2 more
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Best practice guidance for linear mixed-effects models in psychological science
Journal of Memory and Language, 2020Lotte Meteyard
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Consistent Estimators in Generalized Linear Mixed Models
Journal of the American Statistical Association, 1998Jiming Jiang
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A general and simple method for obtaining R 2 from generalized linear mixed‐effects models
Methods in Ecology and Evolution, 2013Shinichi Nakagawa, Holger Schielzeth
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