brms.md
In parameters: Processing of Model Parameters

mp, footer exp

Code
  print(out)
Output
  # Fixed Effects

  Parameter          | Median |        95% CI |     pd |  Rhat |     ESS
  ----------------------------------------------------------------------
  (Intercept)        |  -0.25 | [-1.28, 0.75] | 68.62% | 0.999 | 3459.00
  var_binom1         |  -0.64 | [-2.09, 0.64] | 83.20% | 1.000 | 2820.00
  groupsb            |  -0.22 | [-1.35, 0.87] | 64.75% | 1.000 | 3332.00
  var_cont           |  -0.06 | [-0.14, 0.00] | 96.65% | 1.000 | 3528.00
  var_binom1:groupsb |   0.53 | [-1.70, 2.69] | 69.25% | 1.000 | 2699.00
Message

  Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
    using a MCMC distribution approximation.

  The model has a log- or logit-link. Consider using `exponentiate =
    TRUE` to interpret coefficients as ratios.

Code
  print(out)
Output
  # Fixed Effects

  Parameter          | Median |        95% CI |     pd |  Rhat |     ESS
  ----------------------------------------------------------------------
  (Intercept)        |   0.78 | [0.28,  2.11] | 68.62% | 0.999 | 3459.00
  var_binom1         |   0.53 | [0.12,  1.90] | 83.20% | 1.000 | 2820.00
  groupsb            |   0.80 | [0.26,  2.38] | 64.75% | 1.000 | 3332.00
  var_cont           |   0.94 | [0.87,  1.00] | 96.65% | 1.000 | 3528.00
  var_binom1:groupsb |   1.69 | [0.18, 14.80] | 69.25% | 1.000 | 2699.00
Message

  Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
    using a MCMC distribution approximation.