Dr Mircea Zloteanu ๐บ๐๐#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.
#statstab #328 How to Assess Task Reliability using Bayesian Mixed Models
by @Dom_Makowski
Thoughts: Nice walkthrough using {brms}, with code, data gen, and plots.
#r #bayesian #mixedeffects #reliability #brms
https://realitybending.github.io/post/2024-03-18-signaltonoisemixed/
#statstab #264 When estimating a treatment effect with a cluster design, you need to include varying slopes, even if the fit gives warning messages
Thoughts: Warnings are scary โ ๏ธ Bad model are scarier ๐น
#statstab #221 #brms posterior_epred() vs posterior_predict()
Thoughts: When starting off with bayesian mixed models you'll run across this issue. Here's one of the best forum posts on it.
#bayesian #mixedeffects #models #posterior #effects #prediction
Confusion on difference between posterior_epred() and posterior_predict() in a mixed effects modelling context
Hi everyone, This is my first time posting to this forum but I am hopeful someone here can help me understand the difference between the functions posterior_epred() and posterior_predict() in the context of mixed effects modelling. These functions are used in various Bayesian R packages (e.g., rstanarm, brms, marginaleffects, brmsmargins) but it is not clear to me how they differ and, most importantly, when to use one versus the other in a mixed effects modelling context. I tried to get some ...
#statstab #199 Mixed model equivalence test using R and PANGEA
Thoughts: While there are easier ways to compute #EQ tests for such models now, it is nice to see how you'd do so manually.
#equivalencetests #NHST #mixedeffects #r #stats #nullresults
#statstab #130 Power Simulation in a Mixed Effects design using R
Thoughts: I used {faux} in my last blog post. Useful package if you think you can anticipate your data (v onerous in mixed effects).
#statstab #112 Mixed Models with R
Thoughts: A very nice overview of what mixed models are, how to use them, and how to interpret the results (even mentions issues with p-values).
#rstats #r #lmer #mixedeffects
https://m-clark.github.io/mixed-models-with-R/random_intercepts.html
#statstab #51 R Functions for Variance Decomposition {varde}
Thoughts: A useful package to get more insight into your mixed effects model.
