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Summary method for stanfit objects

stanfit-method-summary.Rd

Summarize the distributions of estimated parameters and derived quantities using the posterior draws.

Usage

# S4 method for class 'stanfit'
summary(object, pars, probs = c(0.025, 0.25, 0.50, 0.75, 0.975),
  use_cache = TRUE, ...)

Arguments

object

An instance of class stanfit.

pars

A character vector of parameter names. Defaults to all parameters as well as the log-posterior (lp__).

probs

A numeric vector of quantiles of interest. The default is c(0.025,0.25,0.5,0.75,0.975).

use_cache

Logical, defaulting to TRUE. When use_cache=TRUE the summary quantities for all parameters are computed and cached for future use. Setting use_cache=FALSE can be used to avoid performing the summary computations for all parameters if pars is given as some specific parameters.

...

Currently unused.

Value

The summary method returns a named list with elements summary and c_summary, which contain summaries for for all chains merged and individual chains, respectively. Included in the summaries are quantiles, means, standard deviations (sd), effective sample sizes (n_eff), and split Rhats (the potential scale reduction derived from all chains after splitting each chain in half and treating the halves as chains). For the summary of all chains merged, Monte Carlo standard errors (se_mean) are also reported.

See also

  • monitor, which computes similar summaries but accepts an array of MCMC draws as its input rather than a stanfit object.

  • The RStan vignettes for more example usage.

Examples

# \dontrun{
ecode <- '
  parameters {
    array[2] real<lower=0> y;
  } 
  model {
    y ~ exponential(1);
  }
'
fit <- stan(model_code = ecode)
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.03 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: 0.005 seconds (Warm-up)
#> Chain 1:                0.005 seconds (Sampling)
#> Chain 1:                0.01 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.01 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: 0.006 seconds (Warm-up)
#> Chain 2:                0.005 seconds (Sampling)
#> Chain 2:                0.011 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.01 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: 0.005 seconds (Warm-up)
#> Chain 3:                0.005 seconds (Sampling)
#> Chain 3:                0.01 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.01 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: 0.006 seconds (Warm-up)
#> Chain 4:                0.005 seconds (Sampling)
#> Chain 4:                0.011 seconds (Total)
#> Chain 4: 
s <- summary(fit, probs = c(0.1, 0.9))
s$summary  # all chaines merged
#>            mean    se_mean        sd        10%       90%     n_eff     Rhat
#> y[1]  0.9595214 0.01819913 0.9581889  0.1054167  2.159105 2772.0503 1.000208
#> y[2]  0.9816504 0.01882405 0.9677815  0.1013469  2.232863 2643.1905 1.000009
#> lp__ -3.1484041 0.03789293 1.1233914 -4.6473975 -2.117413  878.9128 1.001904
s$c_summary  # individual chains
#> , , chains = chain:1
#> 
#>          stats
#> parameter       mean        sd        10%       90%
#>      y[1]  0.9986302 0.9678030  0.1078815  2.232374
#>      y[2]  0.9767451 0.9441381  0.1024321  2.237703
#>      lp__ -3.1136490 1.0916640 -4.6080260 -2.112247
#> 
#> , , chains = chain:2
#> 
#>          stats
#> parameter       mean        sd        10%       90%
#>      y[1]  0.9039287 0.8824154  0.1171884  2.018244
#>      y[2]  0.9613771 0.9842030  0.1082879  2.286797
#>      lp__ -3.1312855 1.1349815 -4.4844423 -2.117125
#> 
#> , , chains = chain:3
#> 
#>          stats
#> parameter       mean        sd         10%       90%
#>      y[1]  0.9667037 0.9991372  0.09625611  2.198628
#>      y[2]  1.0474490 0.9950988  0.11646897  2.343141
#>      lp__ -3.1101334 1.0805761 -4.52121870 -2.104319
#> 
#> , , chains = chain:4
#> 
#>          stats
#> parameter       mean        sd         10%       90%
#>      y[1]  0.9688230 0.9782246  0.10038614  2.197503
#>      y[2]  0.9410304 0.9447339  0.06700093  2.115310
#>      lp__ -3.2385485 1.1803231 -4.83945038 -2.149419
#> 
# }