# print.stanreg

##### Print method for stanreg objects

The `print`

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

method.

##### Usage

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

##### Arguments

- 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.

##### Details

### Point estimates

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`

.

### Uncertainty estimates (MAD_SD)

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`

.

### Additional output

The median and MAD_SD are also reported for

`mean_PPD`

, the sample average posterior predictive distribution of the outcome. This is useful as a quick diagnostic. A useful heuristic is to check if`mean_PPD`

is plausible when compared to`mean(y)`

. If it is plausible then this does*not*mean that the model is good in general (only that it can reproduce the sample mean), however if`mean_PPD`

is implausible then it is a sign that something is wrong (severe model misspecification, problems with the data, computational issues, etc.).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.

##### Value

Returns `x`

, invisibly.

##### See Also

*Documentation reproduced from package rstanarm, version 2.18.2, License: GPL (>= 3)*