Linear models

Get a simple coefficient plot⁴

stanplot(attendance_brms, pars = c('math', 'gender', 'prog'))

For linear models, one might be interested in some notion of \(R^2\)

Automatically get an interval estimate as well

fit <- brm(mpg ~ wt + cyl, data = mtcars, refresh = 0)
bayes_R2(fit, digits=2)

    Estimate  Est.Error      Q2.5     Q97.5
R2 0.8180078 0.03055521 0.7410065 0.8528425

Mixed models can include random effects or not

bayes_R2(sleepstudy_brms, re_formula = NA) # not included

    Estimate  Est.Error      Q2.5     Q97.5
R2 0.2815599 0.06296109 0.1561139 0.4018669

bayes_R2(sleepstudy_brms) # included

    Estimate  Est.Error      Q2.5     Q97.5
R2 0.7908612 0.01784671 0.7497048 0.8206132

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4. I prefer my own package visibly for this. There are a couple other plotting functions for some of the common brms models (e.g. glm, mixed)↩