#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/
#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/
#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
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
#statstab #477 Simulating data for Dirichlet regression with varying estimates
Thoughts: Interesting thread about an underused model.
#Dirichlet #brms #poweranalysis #rstats #proportions #stan #forum
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...
#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
#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
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: ...
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
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?
#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/
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...