#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

#statstab #329 Bayesian versus frequentist approaches in multilevel single-case designs: on power and type I error rate

Thoughts: An interesting project highlighting some benefits of #bayesian methods for #nof1 designs.

#stats #r #sced #mixedeffects

https://osf.io/k7b82/files/osfstorage

#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 ⚠️ Bad model are scarier 👹

#lmer #modelfit #mixedeffects #r #randomslopes

https://statmodeling.stat.columbia.edu/2025/01/23/slopes/

#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

https://discourse.mc-stan.org/t/confusion-on-difference-between-posterior-epred-and-posterior-predict-in-a-mixed-effects-modelling-context/28813

#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

https://pedermisager.org/blog/mixed_model_equivalence/

#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).

#r #simulation #power #NHST #mixedeffects #lmer #stats

https://cjungerius.github.io/powersim/

#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 #68 Minimum number of levels for a random effect

Thoughts: Fixed effects and random effects are not always intuitive differences. Apparently >5 is ok for an re.

#r #MLM #mixedeffects #rstats #stats

https://stats.stackexchange.com/questions/37647/what-is-the-minimum-recommended-number-of-groups-for-a-random-effects-factor

#statstab #51 R Functions for Variance Decomposition {varde}

Thoughts: A useful package to get more insight into your mixed effects model.

#r #rstats #mixedeffects #lmm #research #modelcomparison

https://github.com/jmgirard/varde

#statstab #45 Visualizing Hierarchical Models

Thoughts: One of the coolest visualisation websites for mixed effects models I had saved.

#stats #mixedeffects #hierarchicalmodels #rstats #MLM

http://mfviz.com/hierarchical-models/

🚨New paper: A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

w/ @matti

A better way to analyse lie data.

#deception #SDT #mixedeffects #brms #rstats

https://link.springer.com/article/10.1007/s10919-024-00456-x

#statstab #19 Model Estimation Options, Problems, and Troubleshooting

Thoughts: A nice guide on model convergence issues, REML vs FIML, and model comparison (frequentist s)

#mixedeffects #rstats #stats #statstodon

https://www.learn-mlms.com/07-module-7.html

#statstab #16 Maximal Random Structure (mixed effects models)

Thoughts: A very nice tutorial and explanation on the benefits of Mixed effects models, including some useful advice and advanced tips.

#stats #lmm #mixedeffects #randomeffects #power

https://www.alexanderdemos.org/Mixed7.html#23_Maximal_Random_Structure_vs_Simpler_Random_effects_(Counterbalanced_Designs)

🚨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

#RatingNorms should be calculated from #CumulativeLink #MixedEffects models

"Inter-item relations on ordinal scales can be appropriately modelled by cumulative link mixed effects models (#CLMMs). The authors show that CLMMs can be used to more accurately norm items, and can provide summary statistics analogous to the traditionally reported means and SDs, but which are disentangled from participants’ response biases"

https://link.springer.com/article/10.3758/s13428-022-01814-7