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Extract Group-Level Estimates

Source: R/brmsfit-methods.R
ranef.brmsfit.Rd

Extract the group-level ('random') effects of each level from a brmsfit object.

Usage

# S3 method for class 'brmsfit'
ranef(
  object,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  pars = NULL,
  groups = NULL,
  ...
)

Arguments

object

An object of class brmsfit.

summary

Should summary statistics be returned instead of the raw values? Default is TRUE.

robust

If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.

probs

The percentiles to be computed by the quantile function. Only used if summary is TRUE.

pars

Optional names of coefficients to extract. By default, all coefficients are extracted.

groups

Optional names of grouping variables for which to extract effects.

...

Currently ignored.

Value

A list of 3D arrays (one per grouping factor). If summary is TRUE, the 1st dimension contains the factor levels, the 2nd dimension contains the summary statistics (see posterior_summary), and the 3rd dimension contains the group-level effects. If summary is FALSE, the 1st dimension contains the posterior draws, the 2nd dimension contains the factor levels, and the 3rd dimension contains the group-level effects.

Examples

# \dontrun{
fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit),
           data = epilepsy, family = gaussian(), chains = 2)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.837 seconds (Warm-up)
#> Chain 1:                0.557 seconds (Sampling)
#> Chain 1:                1.394 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.776 seconds (Warm-up)
#> Chain 2:                0.745 seconds (Sampling)
#> Chain 2:                1.521 seconds (Total)
#> Chain 2: 
#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
ranef(fit)
#> $visit
#> , , Intercept
#> 
#>      Estimate Est.Error      Q2.5    Q97.5
#> 1  0.30467081 0.8750871 -1.214209 2.499196
#> 2  0.02825514 0.8187142 -1.690913 1.847562
#> 3  0.08762618 0.8016888 -1.578298 1.942982
#> 4 -0.21487766 0.8408538 -2.183174 1.372078
#> 
#> , , Trt1
#> 
#>     Estimate Est.Error      Q2.5    Q97.5
#> 1  0.1678512  1.091097 -1.966271 2.887353
#> 2  0.2875135  1.123628 -1.756602 3.154275
#> 3  0.1174871  1.043511 -1.930729 2.721896
#> 4 -0.2282879  1.103589 -2.918115 1.886437
#> 
#> 
# }