Accessing the contents of a stanfit object

This vignette demonstrates how to access most of data stored in a stanfit object. A stanfit object (an object of class "stanfit") contains the output derived from fitting a Stan model using Markov chain Monte Carlo or one of Stan’s variational approximations (meanfield or full-rank). Throughout the document we’ll use the stanfit object obtained from fitting the Eight Schools example model:

library(rstan)
fit <- stan_demo("eight_schools", refresh = 0)

Trying to compile a simple C file

class(fit)

[1] "stanfit"
attr(,"package")
[1] "rstan"

Posterior draws

There are several functions that can be used to access the draws from the posterior distribution stored in a stanfit object. These are extract, as.matrix, as.data.frame, and as.array, each of which returns the draws in a different format.

extract()

The extract function (with its default arguments) returns a list with named components corresponding to the model parameters.

list_of_draws <- extract(fit)
print(names(list_of_draws))

[1] "mu"    "tau"   "eta"   "theta" "lp__"

In this model the parameters mu and tau are scalars and theta is a vector with eight elements. This means that the draws for mu and tau will be vectors (with length equal to the number of post-warmup iterations times the number of chains) and the draws for theta will be a matrix, with each column corresponding to one of the eight components:

head(list_of_draws$mu)

[1]  5.22198096 10.25431910 11.48222576  0.07918646 -3.30819970 13.58606549

head(list_of_draws$tau)

[1]  5.996095  5.909190  2.802256 11.933985  5.368431  6.944701

head(list_of_draws$theta)

          
iterations      [,1]      [,2]      [,3]       [,4]       [,5]      [,6]
      [1,]  2.283421  8.578929  3.168522  6.7439015  4.6031011  6.907852
      [2,] 11.359170  6.478249 12.446666  9.9424217  6.7237383  2.968103
      [3,] 12.249217 10.539249  8.504100 12.0364981  8.5697172  7.527797
      [4,] -8.860330 10.688263  6.029505  3.8369223 -0.8734694 10.821918
      [5,] -4.906261 -3.894452 -4.229853  0.4010875 -4.9593446 -7.039061
      [6,] 23.468212  5.095051  1.616788 13.2977748  5.4846604  9.664369
          
iterations      [,7]       [,8]
      [1,]  3.594139 -0.4996312
      [2,] 12.851494 18.5077162
      [3,] 12.471018 13.9139741
      [4,]  2.494871  8.9744022
      [5,] -5.959557  1.5743520
      [6,] 15.388620 13.4485342

as.matrix(), as.data.frame(), as.array()

The as.matrix, as.data.frame, and as.array functions can also be used to retrieve the posterior draws from a stanfit object:

matrix_of_draws <- as.matrix(fit)
print(colnames(matrix_of_draws))

 [1] "mu"       "tau"      "eta[1]"   "eta[2]"   "eta[3]"   "eta[4]"  
 [7] "eta[5]"   "eta[6]"   "eta[7]"   "eta[8]"   "theta[1]" "theta[2]"
[13] "theta[3]" "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
[19] "lp__"

df_of_draws <- as.data.frame(fit)
print(colnames(df_of_draws))

 [1] "mu"       "tau"      "eta[1]"   "eta[2]"   "eta[3]"   "eta[4]"  
 [7] "eta[5]"   "eta[6]"   "eta[7]"   "eta[8]"   "theta[1]" "theta[2]"
[13] "theta[3]" "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
[19] "lp__"

array_of_draws <- as.array(fit)
print(dimnames(array_of_draws))

$iterations
NULL

$chains
[1] "chain:1" "chain:2" "chain:3" "chain:4"

$parameters
 [1] "mu"       "tau"      "eta[1]"   "eta[2]"   "eta[3]"   "eta[4]"  
 [7] "eta[5]"   "eta[6]"   "eta[7]"   "eta[8]"   "theta[1]" "theta[2]"
[13] "theta[3]" "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"
[19] "lp__"

The as.matrix and as.data.frame methods essentially return the same thing except in matrix and data frame form, respectively. The as.array method returns the draws from each chain separately and so has an additional dimension:

print(dim(matrix_of_draws))
print(dim(df_of_draws))
print(dim(array_of_draws))

[1] 4000   19
[1] 4000   19
[1] 1000    4   19

By default all of the functions for retrieving the posterior draws return the draws for all parameters (and generated quantities). The optional argument pars (a character vector) can be used if only a subset of the parameters is desired, for example:

mu_and_theta1 <- as.matrix(fit, pars = c("mu", "theta[1]"))
head(mu_and_theta1)

          parameters
iterations        mu  theta[1]
      [1,] 16.808565 19.377383
      [2,]  9.085071  5.254745
      [3,]  9.490775 12.740426
      [4,]  7.024909  2.712334
      [5,] 10.325071 -1.006234
      [6,] 12.216922 13.034301

Posterior summary statistics and convergence diagnostics

Summary statistics are obtained using the summary function. The object returned is a list with two components:

fit_summary <- summary(fit)
print(names(fit_summary))

[1] "summary"   "c_summary"

In fit_summary$summary all chains are merged whereas fit_summary$c_summary contains summaries for each chain individually. Typically we want the summary for all chains merged, which is what we’ll focus on here.

The summary is a matrix with rows corresponding to parameters and columns to the various summary quantities. These include the posterior mean, the posterior standard deviation, and various quantiles computed from the draws. The probs argument can be used to specify which quantiles to compute and pars can be used to specify a subset of parameters to include in the summary.

For models fit using MCMC, also included in the summary are the Monte Carlo standard error (se_mean), the effective sample size (n_eff), and the R-hat statistic (Rhat).

print(fit_summary$summary)

                  mean    se_mean        sd        2.5%         25%
mu         8.095883193 0.10639687 5.0640173  -1.4324607   4.9183331
tau        6.293738721 0.14506881 5.3735697   0.2015424   2.3575233
eta[1]     0.381015597 0.01459744 0.9507598  -1.5796632  -0.2234058
eta[2]     0.009007562 0.01417983 0.9034728  -1.7880656  -0.5770494
eta[3]    -0.187210058 0.01452286 0.9459167  -2.0682485  -0.8217128
eta[4]    -0.035375600 0.01382681 0.8657225  -1.7561734  -0.5939173
eta[5]    -0.361676495 0.01413001 0.8717491  -2.0227518  -0.9591457
eta[6]    -0.214510464 0.01409324 0.8956887  -1.9366989  -0.8332017
eta[7]     0.350821227 0.01497281 0.9004367  -1.4300924  -0.2491383
eta[8]     0.052958067 0.01345953 0.9322137  -1.7588616  -0.5763021
theta[1]  11.322773164 0.15505640 8.2776572  -1.9203819   6.0332449
theta[2]   8.004101819 0.09630841 6.2422112  -4.2607888   4.0382535
theta[3]   6.299784074 0.11559691 7.5421778 -10.6624969   2.3119588
theta[4]   7.767177199 0.08539246 6.3793292  -5.0272230   3.8436346
theta[5]   5.247303714 0.09140613 6.3314446  -8.9518058   1.5579495
theta[6]   6.336858566 0.09616920 6.8222195  -8.2020483   2.3757087
theta[7]  10.608966215 0.11328368 6.8471616  -1.9536568   6.0474640
theta[8]   8.422207428 0.12279133 7.7450219  -6.6975366   3.8857270
lp__     -39.674936458 0.07277398 2.6759517 -45.6028614 -41.2705096
                  50%         75%      97.5%    n_eff      Rhat
mu         7.90163063  11.3031398  18.378286 2265.336 1.0007683
tau        5.08593552   8.8503482  19.143525 1372.075 1.0005621
eta[1]     0.39903034   1.0292366   2.190002 4242.172 0.9997237
eta[2]     0.01004062   0.5826053   1.824950 4059.646 0.9994544
eta[3]    -0.19934059   0.4211110   1.755174 4242.298 1.0009679
eta[4]    -0.04264697   0.5060588   1.721352 3920.250 1.0017374
eta[5]    -0.39280179   0.2076239   1.452278 3806.259 0.9996797
eta[6]    -0.21183422   0.3730205   1.590079 4039.175 1.0004399
eta[7]     0.37472434   0.9473720   2.146286 3616.594 0.9999667
eta[8]     0.04892119   0.6660938   1.915073 4797.014 1.0002494
theta[1]  10.27992160  15.3176698  30.916918 2849.938 0.9996924
theta[2]   7.89781756  11.7876773  21.066178 4200.960 0.9998463
theta[3]   6.76232287  11.1205656  19.795872 4256.975 1.0004553
theta[4]   7.67299298  11.8187479  20.456410 5580.986 0.9994345
theta[5]   5.75700790   9.5487355  16.429356 4797.942 0.9996267
theta[6]   6.80506899  10.7812451  18.395870 5032.449 0.9999338
theta[7]  10.09710317  14.5408069  26.214924 3653.310 0.9994559
theta[8]   8.28648790  12.7220356  25.181417 3978.413 0.9997589
lp__     -39.42810189 -37.7446463 -35.271979 1352.086 1.0019371

If, for example, we wanted the only quantiles included to be 10% and 90%, and for only the parameters included to be mu and tau, we would specify that like this:

mu_tau_summary <- summary(fit, pars = c("mu", "tau"), probs = c(0.1, 0.9))$summary
print(mu_tau_summary)

        mean   se_mean       sd       10%      90%    n_eff     Rhat
mu  8.095883 0.1063969 5.064017 1.8547907 14.36239 2265.336 1.000768
tau 6.293739 0.1450688 5.373570 0.9052414 13.12377 1372.075 1.000562

Since mu_tau_summary is a matrix we can pull out columns using their names:

mu_tau_80pct <- mu_tau_summary[, c("10%", "90%")]
print(mu_tau_80pct)

          10%      90%
mu  1.8547907 14.36239
tau 0.9052414 13.12377

Sampler diagnostics

For models fit using MCMC the stanfit object will also contain the values of parameters used for the sampler. The get_sampler_params function can be used to access this information.

The object returned by get_sampler_params is a list with one component (a matrix) per chain. Each of the matrices has number of columns corresponding to the number of sampler parameters and the column names provide the parameter names. The optional argument inc_warmup (defaulting to TRUE) indicates whether to include the warmup period.

sampler_params <- get_sampler_params(fit, inc_warmup = FALSE)
sampler_params_chain1 <- sampler_params[[1]]
colnames(sampler_params_chain1)

[1] "accept_stat__" "stepsize__"    "treedepth__"   "n_leapfrog__" 
[5] "divergent__"   "energy__"

To do things like calculate the average value of accept_stat__ for each chain (or the maximum value of treedepth__ for each chain if using the NUTS algorithm, etc.) the sapply function is useful as it will apply the same function to each component of sampler_params:

mean_accept_stat_by_chain <- sapply(sampler_params, function(x) mean(x[, "accept_stat__"]))
print(mean_accept_stat_by_chain)

[1] 0.8228008 0.9521825 0.9379850 0.8681285

max_treedepth_by_chain <- sapply(sampler_params, function(x) max(x[, "treedepth__"]))
print(max_treedepth_by_chain)

[1] 4 4 4 4

Model code

The Stan program itself is also stored in the stanfit object and can be accessed using get_stancode:

code <- get_stancode(fit)

The object code is a single string and is not very intelligible when printed:

print(code)

[1] "data {\n  int<lower=0> J;          // number of schools\n  array[J] real y;               // estimated treatment effects\n  array[J] real<lower=0> sigma;  // s.e. of effect estimates\n}\nparameters {\n  real mu;\n  real<lower=0> tau;\n  vector[J] eta;\n}\ntransformed parameters {\n  vector[J] theta;\n  theta = mu + tau * eta;\n}\nmodel {\n  target += normal_lpdf(eta | 0, 1);\n  target += normal_lpdf(y | theta, sigma);\n}"
attr(,"model_name2")
[1] "schools"

A readable version can be printed using cat:

cat(code)

data {
  int<lower=0> J;          // number of schools
  array[J] real y;               // estimated treatment effects
  array[J] real<lower=0> sigma;  // s.e. of effect estimates
}
parameters {
  real mu;
  real<lower=0> tau;
  vector[J] eta;
}
transformed parameters {
  vector[J] theta;
  theta = mu + tau * eta;
}
model {
  target += normal_lpdf(eta | 0, 1);
  target += normal_lpdf(y | theta, sigma);
}

Initial values

The get_inits function returns initial values as a list with one component per chain. Each component is itself a (named) list containing the initial values for each parameter for the corresponding chain:

inits <- get_inits(fit)
inits_chain1 <- inits[[1]]
print(inits_chain1)

$mu
[1] -1.805984

$tau
[1] 3.709496

$eta
[1] -0.8315136  0.7793653  0.3112285 -1.2241347  1.9311699 -1.7715798 -1.0577400
[8]  1.3319387

$theta
[1] -4.8904808  1.0850681 -0.6514836 -6.3469073  5.3576824 -8.3776523 -5.7296666
[8]  3.1348370

(P)RNG seed

The get_seed function returns the (P)RNG seed as an integer:

print(get_seed(fit))

[1] 346871191

Warmup and sampling times

The get_elapsed_time function returns a matrix with the warmup and sampling times for each chain:

print(get_elapsed_time(fit))

        warmup sample
chain:1  0.022  0.017
chain:2  0.021  0.026
chain:3  0.027  0.026
chain:4  0.022  0.021

Stan Development Team

2025-12-10