The `print`

method for stanreg objects displays a compact summary of the
fitted model. See the Details section below for a description of the printed
output. For additional summary statistics and diagnostics use the
`summary`

method.

```
# S3 method for stanreg
print(x, digits = 1, ...)
```# S3 method for stanmvreg
print(x, digits = 3, ...)

x

A fitted model object returned by one of the
rstanarm modeling functions. See `stanreg-objects`

.

digits

Number of digits to use for formatting numbers.

...

Ignored.

Returns `x`

, invisibly.

Regardless of the estimation algorithm, point estimates are medians computed
from simulations. For models fit using MCMC (`"sampling"`

) the posterior
sample is used. For optimization (`"optimizing"`

), the simulations are
generated from the asymptotic Gaussian sampling distribution of the
parameters. For the `"meanfield"`

and `"fullrank"`

variational
approximations, draws from the variational approximation to the posterior are
used. In all cases, the point estimates reported are the same as the values
returned by `coef`

.

The standard deviations reported (labeled MAD_SD in the print output) are
computed from the same set of draws described above and are proportional to
the median absolute deviation (`mad`

) from the median.
Compared to the raw posterior standard deviation, the MAD_SD will be more
robust for long-tailed distributions. These are the same as the values
returned by `se`

.

For models fit using MCMC or a variational approximation, the median and
MAD_SD are also reported for `mean_PPD`

, the sample average posterior
predictive distribution of the outcome.

For GLMs with group-specific terms (see `stan_glmer`

) the printed
output also shows point estimates of the standard deviations of the group
effects (and correlations if there are both intercept and slopes that vary by
group).

For analysis of variance models (see `stan_aov`

) models, an
ANOVA-like table is also displayed.

For joint longitudinal and time-to-event (see `stan_jm`

) models
the estimates are presented separately for each of the distinct submodels.