Mastodawn

#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)

egghead9029 20h ago

Rekomendasi Trading bulan ini adalah BRMS & PSAB kembali mencuri momentum seiring harga emas.

Mulai dari info insider trading dan perspektif investor.

Apakah Sentimen komoditas menguat, peluang masih terbuka?

Tetap perhatikan volatilitas pasar di Bareksa.

#Bareksa #TradingSaham #BRMS #PSAB #HargaEmas #GoldUpdate #SahamTambang #MarketToday

https://www.bareksa.com/berita/saham/2025-11-11/rekomendasi-trading-saham-hari-ini-brms-psab-harga-emas-kembali-tembus-us4100

Rekomendasi Trading Saham Hari Ini BRMS & PSAB, Harga Emas Kembali Tembus US$4.100

Secara teknikal, IHSG diperkirakan bergerak di rentang 8.043 (support) – 8.332 (resistance), dengan potensi penutupan lebih tinggi hari ini (11/11)

Bareksa.com

Alejandro_P Nov 1

To the Bayesian pros: Is this is an OK-ish loo_ribbon plot?
Im using pp_check from #brms
Maybe @paul_buerkner could help 😅 ?

The ppd looks like this.
Im not super happy because that small bimodality is not well captured, but perhaps is too small and it doesnt matter?

#Bayesian #BayesInference #BRMS

Dr Mircea Zloteanu 🍁 🍃Oct 31

#statstab #450 Fitting GAMs with brms

Thoughts: Assuming linearity of your continuous predictors is not needed when you can add wiggles!

#gam #glmm #linearmodel #modelling #brms #rstats #bayes #tutorial #splines #r

https://fromthebottomoftheheap.net/2018/04/21/fitting-gams-with-brms/

Fitting GAMs with brms: part 1

Regular readers will know that I have a somewhat unhealthy relationship with GAMs and the mgcv package. I use these models all the time in my research but recently we’ve been hitting the limits of the range of models that mgcv can fit. So I’ve been looking into alternative ways...

From the Bottom of the Heap

Dr Mircea Zloteanu 🍁 🍃Oct 14

#statstab #437 Speeding up categorical models in {brms}

Thoughts: As I'm currently annoyed with how long some models take, I'm sharing resources to help others.

#brms #categorical #efficiency #rstats #modelling #r #stan

https://rpubs.com/mvuorre/faster-categorical-brms-models

RPubs - Speed up categorical brms models with weights

Dr Mircea Zloteanu 🍁 🍃Oct 13

#statstab #436 {chkptstanr} Checkpoint MCMC Sampling with Stan

Thoughts: This! Allows you to stop and start the sampling in {brms}. Can be a lifesaver.

#rstats #stan #brms #mcmc #efficiency #stanr #hmc #bayesian

https://donaldrwilliams.github.io/chkptstanr/

chkptstanr

Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> with checkpointing, that is, the ability to stop the MCMC sampler at will, and then pick right back up where the MCMC sampler left off. Custom Stan models can be fitted, or the popular package brms <doi: 10.18637/jss.v080.i01> can be used to generate the Stan code. This package is fully compatible with the R packages brms, posterior, cmdstanr, and bayesplot.

Dr Mircea Zloteanu 🍁 🍃Oct 10

#statstab #435 Guide to understanding the intuition behind the Dirichlet distribution

Thoughts: Useful for composite proportions, but take ages in brms.

#brms #Dirichlet #proportions #modelling #rstats #r #betareg

https://www.andrewheiss.com/blog/2023/09/18/understanding-dirichlet-beta-intuition/

Guide to understanding the intuition behind the Dirichlet distribution | Andrew Heiss

Learn about the Dirichlet distribution and explore how it’s just a fancier version of the Beta distribution

Andrew Heiss

Dr Mircea Zloteanu 🍁 🍃Oct 10

R: how many cores do you want to use?
Me: Yes.

#R #brms #bayesian #rstats

Dr Mircea Zloteanu 🍁 🍃Aug 18

#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

Issues | Mixed Models with R

This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mixed models, Bayesian approaches, and realms beyond.

Dr Mircea Zloteanu 🍁 🍃Jul 30

#statstab #398 Eta^2 for bayesian models {effectsize}

Thoughts: Great resource, but scroll to "Eta Squared from Posterior Predictive Distribution"

#effectsize #eta2 #bayesian #brms #r

https://easystats.github.io/effectsize/reference/eta_squared.html#eta-squared-from-posterior-predictive-distribution

$\eta^2$ and Other Effect Size for ANOVA — eta_squared

Functions to compute effect size measures for ANOVAs, such as Eta- ($\eta$), Omega- ($\omega$) and Epsilon- ($\epsilon$) squared, and Cohen's f (or their partialled versions) for ANOVA tables. These indices represent an estimate of how much variance in the response variables is accounted for by the explanatory variable(s). When passing models, effect sizes are computed using the sums of squares obtained from anova(model) which might not always be appropriate. See details.