It always amazes me how much we can explain using mixed-effects models.

#Statistics #modeling #lmer #rstats #lme4 #Bioinformatics #DataScience

#statstab #415 Analysis of means: Examples using package ANOM

Thoughts: Ever wonder why we run Analyses of Variance and not Means? Well, now you can do both! Using this #R package.

#ANOM #ANOVA #lmer #means #ranking #design

https://cran.r-project.org/web/packages/ANOM/vignettes/ANOM.pdf

#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 #286 Post-Hoc Power Sensitivity Analysis using the SESOI

Thoughts: Not a great title or great explanation of key concepts, but having R code outlined is useful.

#lmer #simulation #sample #power #SESOI #sensitivity #R

https://thechangelab.stanford.edu/tutorials/power-analysis/post-hoc-power-sensitivity-analysis-using-the-sesoi/

#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 #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

#rstats Q: Any beginner resources on "prediction intervals" for #lmer/#brms models?

& how are conformal predictions (cv+) different from posterior_predict()

*PIs: a range of probable values where a *new* data point (participant) would fall (e.g., 90%)