Summarize Posterior draws

Summarizes posterior draws based on point estimates (mean or median), estimation errors (SD or MAD) and quantiles. This function mainly exists to retain backwards compatibility. It will eventually be replaced by functions of the posterior package (see examples below).

Usage

posterior_summary(x, ...)

# Default S3 method
posterior_summary(x, probs = c(0.025, 0.975), robust = FALSE, ...)

# S3 method for class 'brmsfit'
posterior_summary(
  x,
  pars = NA,
  variable = NULL,
  probs = c(0.025, 0.975),
  robust = FALSE,
  ...
)

Arguments

x: An R object.
...: More arguments passed to or from other methods.
probs: The percentiles to be computed by the quantile function.
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.
pars: Deprecated alias of variable. For reasons of backwards compatibility, pars is interpreted as a vector of regular expressions by default unless fixed = TRUE is specified.
variable: A character vector providing the variables to extract. By default, all variables are extracted.

Value

A matrix where rows indicate variables and columns indicate the summary estimates.

Examples

# \dontrun{
fit <- brm(time ~ age * sex, data = kidney)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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: 0.085 seconds (Warm-up)
#> Chain 1:                0.026 seconds (Sampling)
#> Chain 1:                0.111 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.04 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: 0.089 seconds (Warm-up)
#> Chain 2:                0.04 seconds (Sampling)
#> Chain 2:                0.129 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.083 seconds (Warm-up)
#> Chain 3:                0.028 seconds (Sampling)
#> Chain 3:                0.111 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.089 seconds (Warm-up)
#> Chain 4:                0.033 seconds (Sampling)
#> Chain 4:                0.122 seconds (Total)
#> Chain 4: 
posterior_summary(fit)
#>                     Estimate   Est.Error        Q2.5       Q97.5
#> b_Intercept       18.5005937  87.7179795 -152.248782  184.178764
#> b_age              0.8126862   1.8874627   -2.756836    4.487141
#> b_sexfemale      170.2873881 103.3346862  -27.120354  367.237210
#> b_age:sexfemale   -2.5858524   2.2207673   -6.859541    1.639408
#> sigma            128.8130098  10.7099134  110.125291  151.409339
#> Intercept         96.0940203  14.7152594   67.391819  124.716570
#> lprior           -12.3308598   0.4007541  -13.184889  -11.617766
#> lp__            -485.1741313   1.6258448 -489.164938 -483.037219

# recommended workflow using posterior
library(posterior)
draws <- as_draws_array(fit)
summarise_draws(draws, default_summary_measures())
#> # A tibble: 8 × 7
#>   variable            mean   median      sd     mad      q5      q95
#>   <chr>              <dbl>    <dbl>   <dbl>   <dbl>   <dbl>    <dbl>
#> 1 b_Intercept       18.5     20.7    87.7    88.3   -125.    160.   
#> 2 b_age              0.813    0.748   1.89    1.88    -2.22    3.95 
#> 3 b_sexfemale      170.     170.    103.    103.       1.66  338.   
#> 4 b_age:sexfemale   -2.59    -2.55    2.22    2.20    -6.26    0.923
#> 5 sigma            129.     128.     10.7    10.7    113.    148.   
#> 6 Intercept         96.1     96.2    14.7    14.4     71.2   120.   
#> 7 lprior           -12.3    -12.3     0.401   0.388  -13.0   -11.7  
#> 8 lp__            -485.    -485.      1.63    1.42  -488.   -483.   
# }

Usage

Arguments

Value

See also

Examples