Print method for stanreg objects
# S3 method for stanreg print(x, digits = 1, ...)
# S3 method for stanmvreg print(x, digits = 3, ...)
A fitted model object returned by one of the rstanarm modeling functions. See
Number of digits to use for formatting numbers.
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
approximations, draws from the variational approximation to the posterior are
used. In all cases, the point estimates reported are the same as the values
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
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_PPDis 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_PPDis 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.