I now understand better why wiener waffeln exist.... every time my initial values are rejected I eat one
#rstats #brms #rstan #ddm

#statstab #350 Communicating causal effect heterogeneity
By @matti

Thoughts: Cool guide on properly communicating uncertainty in effects.

#bayesian #uncertainty #ggplot #r #brms #tidybayes #heterogeneity

https://vuorre.com/heterogeneity-uncertainty/

#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 #299 The role of "max_treedepth" in No-U-Turn?

Thoughts: Once you start using more complex models you will run into issues at some point; this is one; good solution guide.

#brms #bayesian #modeling #stats #issues #solutions #stan #forum

https://discourse.mc-stan.org/t/the-role-of-max-treedepth-in-no-u-turn/24155

Incidentally, our companion #rstats Reacnorm package is now live in CRAN, so it's as easy as `install.packages("Reacnorm")` and `vignette("TutoReacnorm")` to access our nice tutorial on analyse reaction norms using the #brms and Reacnorm package.

https://cran.r-project.org/package=Reacnorm

what are your best tips to fit shifted lognormal models (in #brms / Stan)? I'm using: - checking the long tails (few long RTs make the tail estimation unwieldy) - low initial values for ndt - careful prior checks - pathfinder estimation of initial values still with increasing data, chains get stuck

A very interesting workshop on "Hierarchical models in preclinical research" finished today in Göttingen. This was a joint undertaking of the IBS-DR working groups "Non-clinical statistics" and "Bayes Methods", and included an extensive Tutorial on #brms by Sebastian Weber and Lukas Widmer. Some of the material is available on the meeting website:

https://www.biometrische-gesellschaft.de/arbeitsgruppen/bayes-methodik/workshops/2024-goettingen.html

#Bayesian #rstats #MedStatGoe

#statstab #228 Applied Modelling in Drug Development - Setting priors in {brms}

Thoughts: Part of a larger book, useful bit for understanding how to set priors & check them for bayesian models & meta-analyses

#stats #brms #priors #metaanalysis #bayesian #r #bayes #drugs #clinicaltrials

https://opensource.nibr.com/bamdd/src/01c_priors.html

#statstab #227 Parameterization of Response Distributions in {brms}

Thoughts: If you use #brms and can read mathematical notation (who can't, right?), this page will be useful.

#r #bayes #models #distributions #likelihood #stats

https://cran.r-project.org/web/packages/brms/vignettes/brms_families.html

#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

#stats Q about sum scores: Is it better to analyse sum scores (4 items, range 4-20) using a cumulative model or a ordered beta? And how can i compare fit bw the two? just loo?

#brms #orderedbetareg #sumscores

Online free book: Introduction to Bayesian Data Analysis for Cognitive Science

https://bayes.club/@ShravanVasishth/113330289055047281

#bayes #Rstats #STAN #brms #OpenAccess #OA #CognitiveScience #CogSci @cogsci

#statstab #201 Missing Data and DAGs and other stuff

Thoughts: #missingdata is difficult to handle, but maybe if we build theoretical models using #DAGs will help. Also measurement error.

#brms #rethinking #r #stats #mice #measurement #error

https://bookdown.org/content/4857/missing-data-and-other-opportunities.html#measurement-error

#statstab #198 Bayesian mixed effects (aka multi-level) ordinal regression models with {brms}

Thoughts: Useful tutorial also for frequentists, as it covers checking multiple links at once in {ordinal}.

#ordinal #brms #clmm #probit #cloglog #r #cauchit

https://kevinstadler.github.io/notes/bayesian-ordinal-regression-with-random-effects-using-brms/

#statstab #181 Comparing Bayesian Approaches by @jebyrnes

Thoughts: Compares running models in #rethinking, #stan, #brms, #inla, and #glmmTMB via #TMBstan. It's nice to have options.

#bayes #bayesian #rstan #r #stats #rstats

https://biol609.github.io/lab/alt_to_rethinking.html

oh, low E-BFMI warning ⚠, why do you persist? just let me sleep...

#stan #brms #modelwoes

#statstab #169 {priorsense} prior diagnostics and sensitivity analysis

Thoughts: Bayesian modelling requires more scrutiny of how one's choices impact outcomes. This packages has handy functions + plots.

#brms #bayesian #rstan #priors
#mcmc #diagnostics

https://n-kall.github.io/priorsense/

@danwwilson @lwpembleton #brms by @paul_buerkner has made Bayesian models incredibly fun and intuitive for me. I love the combination of well thought out defaults and API with a lot of depth and power, should you need it. Other than that, I think #lubridate needs some love! Oh and #igraph, which just works ™️ plus it's lovely descendant #tidygraph 🕸️
#packagelove

#statstab #164 Ordinal regression models to analyze Likert scale data

Thoughts: One of the clearest tutorial for ordinal, cumulative (probit), models I've seen. Reports probabilities and expected mean ratinga, w/ plots!

#ordinal #brms #likert #probit #r

https://dibsmethodsmeetings.github.io/ordinal-regression/

First version of my first #R package is out and working! 🥳

https://gabewinter.github.io/VarDecomp/

It has functions to:
- produce and evaluate #brms models
- do variance decomposition
- summarize and plot results

Let me know if you have some data to test it out and help improve it!