This is a post with executable code.
A simple distributional model from the brm’s site.
library(brms)Loading required package: RcppLoading 'brms' package (version 2.19.0). Useful instructions
can be found by typing help('brms'). A more detailed introduction
to the package is available through vignette('brms_overview').
Attaching package: 'brms'The following object is masked from 'package:stats':
argroup <- rep(c("treat", "placebo"), each = 30)
symptom_post <- c(rnorm(30, mean = 1, sd = 2), rnorm(30, mean = 0, sd = 1))
dat1 <- data.frame(group, symptom_post)
head(dat1) group symptom_post
1 treat 0.2197493
2 treat 1.1132342
3 treat 0.6705705
4 treat 3.3821604
5 treat 4.9302193
6 treat 2.7953792formula <- bf(symptom_post ~ group, sigma ~ group)
fit <- brm(
formula = formula, data = dat1,
backend = 'cmdstanr', cores = 12
)Start samplingRunning MCMC with 4 chains, at most 12 in parallel...
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Mean chain execution time: 0.1 seconds.
Total execution time: 0.2 seconds.summary(fit) Family: gaussian
Links: mu = identity; sigma = log
Formula: symptom_post ~ group
sigma ~ group
Data: dat1 (Number of observations: 60)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.24 0.21 -0.17 0.65 1.00 4671 2972
sigma_Intercept 0.11 0.13 -0.14 0.40 1.00 3765 2420
grouptreat 0.92 0.39 0.14 1.70 1.00 3314 2966
sigma_grouptreat 0.48 0.19 0.10 0.85 1.00 3637 2337
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).plot(fit)