https://www.youtube.com/watch?v=1e7IHZH2XGk
some of this are not original credits to @RosieAndMariaOfficial

https://www.youtube.com/watch?v=c6PNzexlzI0
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https://www.youtube.com/watch?v=v-LqFa5SqYU
Chelsea is a teenage pageant queen. She's been performing in pageants alongside her best friend, Scarlett, ever since she could remember and she's always bee

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 GLMs the idea of extending linear mixed models to non- normal data.

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://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

https://samuel-watson.github.io/glmmr-web/docs/glmm/
A generalised linear mixed model (GLMM) is a flexible statistical model that allows for correlation between observations through the incoporation of "random effects" into the model. There may be different reasons for including the random effects in a statistical model. In some cases, we are interested in estimating the effect of a covariate

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://nhcooper123.github.io/pcm-primer-online/phylogenetic-generalised-linear-mixed-models-in-r.html
Luckily, there is a package in R called MCMCglmm that performs generalised linear mixed models (GLMM), i.e. mixed effects models that are generalised to deal with response variables that result in non-normally distributed residuals. This package also allows us to add our phylogenetic information as a random effect in the model.

https://www.r-bloggers.com/2014/03/generalized-linear-mixed-models-in-ecology-and-in-r/
So this post is just to give around the R script I used to show how to fit GLMM, how to assess GLMM assumptions, when to choose between fixed and mixed effect models, how to do model selection in GLMM, and how to draw inference from GLMM. As a teaser here are two cool graphs that you can do with this code:

https://www.jmp.com/en_us/events/statistically-speaking/on-demand/the-what-why-and-how-of-generalized-linear-mixed-models.html
The Playback API request failed for an unknown reason. What are Generalized Linear Mixed Models (GLMM)? In this Statistically Speaking we explore the what, why and how of this powerful and flexible class of models. Learn from one of the world's foremost experts in GLMMs, Walt Stroup, and a lead developer at JMP, Dr. Elizabeth Claassen, who

https://www.tensorflow.org/probability/examples/Linear_Mixed_Effects_Model_Variational_Inference
Generalized linear mixed-effect models (GLMM) are similar to generalized linear models (GLM) except that they incorporate a sample specific noise into the predicted linear response. This is useful in part because it allows rarely seen features to share information with more commonly seen features.

https://www.theanalysisfactor.com/what-is-the-purpose-of-a-generalized-linear-mixed-model/
The GLMM gives us the information we need to make an informed determination about whether individual variation among doctors is notably related to remission probabilities—even after we take account of their measurable traits, such as experience and education. It is probably clear that this information could be of much interest to patients

https://www.theanalysisfactor.com/regression-diagnostics-glmm/
What are the best methods for checking a generalized linear mixed model (GLMM) for proper fit?This question comes up frequently when using generalized linear mixed effects models.Unfortunately, it isn't as straightforward as it is for a general linear model, where the requirements are easy to outline: linear relationships of numeric predictors to outcomes, normally distributed and

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://stats.stackexchange.com/questions/478793/how-to-interpret-glmm-results
However, in this case I change the model that the GLMM follow. It follows a log family and as there are many zeros in my dataset,so I used a zero inflation method. I would like to get the variation (variance component) in incidence (inc.) within each habitat while being mindful of random factors such as season and site.

https://greatlakesscrapbookevents.com/category/glmm/2024-glmm/
GLMM May 2024 - Class Sneak Peek - Quick Quotes. Make your cards Pop Up or Waterfall, and create layouts with amazing interactive features - all with Quick Quotes! Interested in taking one of these classes? Join us May 9-11, 2024 in Novi, Michigan at the Great Lakes Mega Meet Paper Crafting Convention.

https://stats.stackexchange.com/questions/185491/diagnostics-for-generalized-linear-mixed-models-specifically-residuals
The DHARMa package uses a simulation-based approach to create readily interpretable scaled residuals from fitted generalized linear mixed models. Currently supported are all 'merMod' classes from 'lme4' ('lmerMod', 'glmerMod'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes.

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://www.youtube.com/watch?v=csQluFcQH18
this is not original but I change some parts of the mini movie-,- and also credits to the original GLMM of this mini movie🫶🏻 credits to @StaryWxlfOfficialC

https://cran.r-project.org/web//packages//glmm/vignettes/vignettes.pdf
The R package glmm approximates the likelihood function for generalized linear mixed models (GLMMs) with a canonical link. glmm calculates and maximizes the Monte Carlo likelihood approximation (MCLA) to nd Monte Carlo maximum likelihood estimates (MCMLEs) for the xed e ects and variance components. The value, gradient vector, and Hessian

https://stats.stackexchange.com/questions/289226/what-are-the-differences-between-anovas-and-glms
Jul 6, 2017 at 23:56. 3. So an ANOVA is GLM. It is a bit semantic now. 1) GLM includes ordinary least squares. You can't really say OLS is a GLM (since GLM is general and OLS is not). Better would be to say that OLS is GLM with identity link and variance functions. 2) ANOVA is not exactly the same as OLS.

https://worldstar.com/videos/wshh92Xk3fK05VgD31I8/her-glow-up-is-serious-20-yr-old-girl-transforms-from-a-nerdy-build-then-hits-you-with-the-kitana-fatality
Her Glow-Up Is Serious.... 20 Yr Old Girl Transforms From A Nerdy Build Then Hits You With The Kitana Fatality! 167,260. May 23, 2024. Took Flight: Distracted Driver Went Up A Ramp Of A Tow Truck! 91,350. May 31, 2023. White Girl Transforms Herself Into A Black Rapper With Makeup! 119,468.