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https://en.wikipedia.org/wiki/Generalized_linear_mixed_model
Generalized linear mixed model. In statistics, a generalized linear mixed model ( GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. [1] [2] [3] They also inherit from generalized linear models the idea of extending linear mixed models to non

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https://stats.oarc.ucla.edu/other/mult-pkg/introduction-to-generalized-linear-mixed-models/
Generalized linear mixed models (or GLMMs) are an extension of linear mixed models to allow response variables from different distributions, such as binary responses. Alternatively, you could think of GLMMs as an extension of generalized linear models (e.g., logistic regression) to include both fixed and random effects (hence mixed models).

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866838/
Generalized linear mixed models (GLMM) have become increasingly popular for the analysis of non-normal data with random effects commonly encountered in medical research and across many disciplines. This popularity has generated a lot of methodological research in the past two decades. Unlike linear mixed models for which the likelihood function

https://www.sciencedirect.com/science/article/pii/S0169534709000196
Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM

https://link.springer.com/chapter/10.1007/978-3-031-32800-8_9
A GLMM with repeated measures is a generalization of the standard linear model, and this generalization is due to (1) the presence of more than one response variable that can be binary, ordinal, count, and so on and (2) the nonconstant correlation and/or variability exhibited by the data. The linear mixed model, therefore, gives you the

https://online.stat.psu.edu/stat504/lesson/generalized-linear-mixed-models
The philosophy of GEE is to treat the covariance structure as a nuisance. An alternative to GEE is the class of generalized linear mixed models (GLMM). These are fully parametric and model the within-subject covariance structure more explicitly. GLMM is a further extension of GLMs that permits random effects as well as fixed effects in the

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https://www.library.virginia.edu/data/articles/getting-started-with-binomial-generalized-linear-mixed-models
Using a binomial GLMM, we could model the probability of eating vegetables daily given various predictors such as sex of the student, race of the student, and/or some "treatment" we applied to a subset of the students, such as a nutrition class. Since each student is observed over the course of multiple days, we have repeated measures and

https://bookdown.org/ks6017/GLM_bookdown3/chapter-5-introduction-to-generalized-linear-mixed-models.html
5.1.3 Problem with clustered data. Observations that belong to the same cluster tend to be correlated due to cluster effect (they belong to the same group). For example, students assigned to the classroom with a more effective teacher tend to have higher test scores than students assigned to a different classroom with less effective teacher.

https://www.edutopia.org/article/am-i-your-favorite-student
The Serious Approach. The opposite of the "You were" response came up rather often—an earnest seriousness intended to show the student something like unconditional acceptance. It was a minority vote, but still sizable, exemplified by @jharrisl: "You aren't just a favorite student—you are one of my favorite humans. Period.".

https://stats.stackexchange.com/questions/185491/diagnostics-for-generalized-linear-mixed-models-specifically-residuals
Fit the full GLMM. Insufficient computer memory o r too slow: reduce model complexity. If estimation succeeds on a subset of the data, try a more efficient estimation algorithm (e.g. PQL if appropriate). Failure to converge (warnings or errors): reduce model complexity or change optimization settings (make sure the resulting answers make sense

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https://www.rdocumentation.org/packages/lme4/versions/1.1-35.5/topics/glmer
Fit a generalized linear mixed-effects model (GLMM). Both fixed effects and random effects are specified via the model formula .

https://pages.stat.wisc.edu/~larget/Stat998/Fall2015/Bolker-et-al-2009-TREE.pdf
Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are pre-sent. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for prac-titioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM

https://ja.wikipedia.org/wiki/%E4%B8%80%E8%88%AC%E5%8C%96%E7%B7%9A%E5%BD%A2%E6%B7%B7%E5%90%88%E3%83%A2%E3%83%87%E3%83%AB
一般化線形混合モデル（いっぱんかせんけいこんごうモデル、英: Generalized linear mixed model, GLMM ）とは、統計学において一般化線形モデルを拡張した統計解析モデルである。 さらにこの一般化線形混合モデルを拡張し、事前分布に含まれる母数の事前分布を導入する場合には、階層ベイズモデル

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