library(rstan) knitr::opts_chunk$set( echo = TRUE, comment = NA, fig.align = "center", fig.height = 5, fig.width = 7 )

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)

J <- 8 y <- c(28, 8, -3, 7, -1, 1, 18, 12) sigma <- c(15, 10, 16, 11, 9, 11, 10, 18) fit <- stan( file= "schools.stan", model_name = "eight_schools", data = c("y", "J", "sigma"), refresh = 0 )

```
class(fit)
```

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.

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))

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$mu) head(list_of_draws$tau) head(list_of_draws$theta)

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

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))

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)

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))

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)

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)

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)

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)

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

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)
```

A readable version can be printed using `cat`

:

```
cat(code)
```

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)

The `get_seed`

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

```
print(get_seed(fit))
```

The `get_elapsed_time`

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

```
print(get_elapsed_time(fit))
```

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