brms: Mixed Model

We’ll start with the mixed model from before

Like rstanarm, brms follows lme4’s syntax

sleepstudy_brms <- brm(Reaction ~ Days + (1 + Days|Subject), 
                       data = sleepstudy)
summary(sleepstudy_brms)

 Family: gaussian 
  Links: mu = identity; sigma = identity 
Formula: Reaction ~ Days + (1 + Days | Subject) 
   Data: sleepstudy (Number of observations: 180) 
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup samples = 4000

Group-Level Effects: 
~Subject (Number of levels: 18) 
                    Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept)          26.74      7.04    15.36    43.05       1635 1.00
sd(Days)                6.52      1.50     4.20     9.95       1290 1.01
cor(Intercept,Days)     0.10      0.29    -0.46     0.67        877 1.01

Population-Level Effects: 
          Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept   251.42      7.39   236.86   266.19       1650 1.00
Days         10.41      1.67     7.24    13.82       1233 1.00

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sigma    25.89      1.55    23.10    29.06       3200 1.00

Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample 
is a crude measure of effective sample size, and Rhat is the potential 
scale reduction factor on split chains (at convergence, Rhat = 1).