#statstab #450 Fitting GAMs with brms

Thoughts: Assuming linearity of your continuous predictors is not needed when you can add wiggles!

#gam #glmm #linearmodel #modelling #brms #rstats #bayes #tutorial #splines #r

https://fromthebottomoftheheap.net/2018/04/21/fitting-gams-with-brms/

#statstab #401 Common issues, conundrums, and other things that might come up when implementing mixed models

Thoughts: GLMMs are cool, but come with their own quirks.

#glmm #lmer #brms #mixedeffects #hierarchicalmodels #r

https://m-clark.github.io/mixed-models-with-R/issues.html

【💡High Cited 2020-2022 】
glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models

#CommonalityAnalysis | #FixedEffect | #GLMM | #HierarchicalPartitioning | #RelativeImportance | #VariancePartitioning

https://doi.org/10.1093/jpe/rtac096

Extremely nice review of REML estimation for generalized linear mixed models. Covers a couple of important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #GAM #GLMM

Restricted maximum likelihood ...

Extremely nice review of REML estimation for generalized linear mixed models.
Covers a couple of important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #GAM #GLMM
https://buff.ly/405IaB0

Extremely nice review of REML estimation for generalized linear mixed models.
Covers a couple of historically important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #GAM #GLMM
https://buff.ly/405IaB0

Extremely nice review of REML estimation for generalized linear mixed models.
Covers a couple of historically important papers that I was not aware of (e.g. Schall 1991 and Stiratelli 1984) until today, but also the work of Simon Wood in #rstats mgcv #GAM #GLMM
https://buff.ly/405IaB0

#statstab #204 GLMMadaptive: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Thoughts: No clue what this package is does, but seems useful. Maybe someone can explain some use cases.

#glmm #gaussian #modelling #r #stats

https://drizopoulos.github.io/GLMMadaptive/

There is only 1 seat left for the #GLMM in R course in October: https://www.physalia-courses.org/courses-workshops/glmms-in-r/

🚨New preprint on #deception detection analysis 🔍 We provide a tutorial on #Bayesian Mixed Effects Models for veracity data; no more aggregating & converting data to % 😤 (conflating acc w/ bias), just model the lie/truth answers directly! Bonus: they are SDT models 🧐 w/ @matti

[link below]
Bayesian Generalized Linear Mixed Effects Models for Deception Detection Analyses http://osf.io/fdh5b/

#Bayesian #glmm #MixedEffects #statistics #methods #signaldetectiontheory

📢 Calling all data wizards! 🧙‍♀️🧙‍♂️The second edition of our #GLMM in R course is a must-attend if you want to harness the power of mixed models for complex data analysis. Don't miss out!: https://physalia-courses.org/courses-workshops/glmms-in-r/ 🌟

#Rstats #DataScience

🥁 Registration is now open for the 2nd edition of the #GLMM in R course with @florianhartig
& @_Max_Pichler in October

If interested, please check it out: https://physalia-courses.org/courses-workshops/glmms-in-r/

#Rstats #DataScience #Statistics #GLMM