Mastodawn

#statstab #515 Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

Thoughts: A nice tutorial on how to think about confounding, Simulating data, and making inferences

#brms #bayes #IV #guide

https://rpsychologist.com/adherence-analysis-IV-brms

Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

Something that never ceases to amaze (depress) me, is how extremely common it is to see causal claims in RCTs, that are not part of the randomization. For…

Dr Mircea Zloteanu 🌺🌞🍃Mar 19

#statstab #509 Effective sample size

Thoughts: ESS often reported for bayesian models, but is it really understood?

#brms #rstats #r #bayesian #ess #diagnostics

https://statmodeling.stat.columbia.edu/2025/11/27/effective-sample-size/

Dr Mircea Zloteanu 🌺🌞🍃Mar 17

#statstab #507 Nonlinear models in {flocker}

Thoughts: I'm thinking these are useful if my theory predicts some natural limit (L) for a process. Like memory recall.

#nonlinear #glmm #brms #mem #asymtotic #flocker #r #rstats

https://www.maths.bris.ac.uk/R/web/packages/flocker/vignettes/nonlinear_models.html

Nonlinear models in flocker

Dr Mircea Zloteanu 🌺🌞🍃Feb 9

Compositional data (proportions that sum to 1) behave in ways standard models aren’t built for

I walk through why Dirichlet regression is often the right tool & what extra insight it gives, using a real example of eyetracking

#Dirichlet #r #brms #guide #eyetracking #ordbetareg #tutorial #reanalysis #substack

https://open.substack.com/pub/mzloteanu/p/where-were-you-looking-re-analysis?r=3b457w&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Where were you looking? Reanalysis of Satchell, Hall, and Jones (2025)

An illustration on analyzing composite probabilities using Dirichelt models

Figuring Stuff Out - Dr Mircea Zloteanu

Dr Mircea Zloteanu 🌺🌞🍃Dec 9

#statstab #477 Simulating data for Dirichlet regression with varying estimates

Thoughts: Interesting thread about an underused model.

#Dirichlet #brms #poweranalysis #rstats #proportions #stan #forum

https://discourse.mc-stan.org/t/simulating-data-for-dirichlet-regression-with-varying-estimates/35325

Simulating data for Dirichlet regression with varying estimates

Hello everyone, The shortest formulation of my problem is the following: I’m trying to design a power analysis by simulation for a Dirichlet regression model using brms. How could we calculate the shape parameter alpha of the Dirichlet distribution by hand from a theoretical model in order to feed it to brms::rdirichlet(n, alpha) to simulate data with chosen effects? The goal is to tweak manually the coefficients of the model to simulate data with varying effect sizes. Now for the lengthier c...

The Stan Forums

Dr Mircea Zloteanu 🌺🌞🍃Nov 24

#statstab #466 Bayesian workflow: Prior determination, predictive checks and sensitivity analyses

Thoughts: Having a good bayesian work flow can be challenging with complex models.

#priors #bayesian #sensitivityanalysis #posterior #ppc #brms

https://pablobernabeu.github.io/2022/bayesian-workflow-prior-determination-predictive-checks-and-sensitivity-analyses/

Bayesian workflow: Prior determination, predictive checks and sensitivity analyses | Pablo Bernabeu

This post presents a run-through of a Bayesian workflow in R. The content is closely based on Bernabeu (2022), which was in turn based on lots of other references, also cited here.

Pablo Bernabeu

Dr Mircea Zloteanu 🌺🌞🍃Nov 20

#statstab #464 Plotting p-check interaction {brms}

Thoughts: Annoyingly #brms doesn't natively allow plotting for interactions (that I know of). The forum has a solution.

#ppc #posterior #bayesian #modelfit #diagnostic #rstats #r #stan

https://discourse.mc-stan.org/t/plotting-pp-check-interactions/31936

Plotting pp_check interactions

I am trying to model ordinal data with an interaction of two predictors. However, I have issues with plotting interactions and individual data with the BRMS functions. My model is of the type rating ~ category*distortion+ (1|id) I can use this type of code: pp_check(mcatxdistRDpCexp, type = “bars_grouped”, group = “category”, , ndraws = 500, prob = 0.95) pp_check(mcatxdistRDpCexp, type = “bars_grouped”, group = “distortion”, , ndraws = 500, prob = 0.95) which returns these two plots: ...

The Stan Forums

Dr Mircea Zloteanu 🌺🌞🍃Nov 15

#stats Q: for a bayesian negative binomial (in #brms), who should I think about the prior on phi (shape)? How can I connect it to either theory or stat desiderada?

Examples, tutorials, resources appreciated

(it all seems a bit abstract to me)