These methods tidy the estimates from rstanarm fits
(stan_glm, stan_glmer, etc.)
into a summary.
# S3 method for stanreg
tidy(
x,
effects = c("fixed", "ran_pars"),
conf.int = FALSE,
conf.level = 0.9,
conf.method = c("quantile", "HPDinterval"),
exponentiate = FALSE,
...
)# S3 method for stanreg
glance(x, looic = FALSE, ...)
All tidying methods return a data.frame without rownames.
The structure depends on the method chosen.
When effects="fixed" (the default), tidy.stanreg returns
one row for each coefficient, with three columns:
The name of the corresponding term in the model.
A point estimate of the coefficient (posterior median).
A standard error for the point estimate based on
mad. See the Uncertainty estimates section in
print.stanreg for more details.
For models with group-specific parameters (e.g., models fit with
stan_glmer), setting effects="ran_vals"
selects the group-level parameters instead of the non-varying regression
coefficients. Addtional columns are added indicating the level and
group. Specifying effects="ran_pars" selects the
standard deviations and (for certain models) correlations of the group-level
parameters.
Setting effects="auxiliary" will select parameters other than those
included by the other options. The particular parameters depend on which
rstanarm modeling function was used to fit the model. For example, for
models fit using stan_glm the overdispersion
parameter is included if effects="aux", for
stan_lm the auxiliary parameters include the residual
SD, R^2, and log(fit_ratio), etc.
glance returns one row with the columns
The algorithm used to fit the model.
The posterior sample size (except for models fit using optimization).
The number of observations used to fit the model.
The square root of the estimated residual variance, if
applicable. If not applicable (e.g., for binomial GLMs), sigma will
be given the value 1 in the returned object.
If looic=TRUE, then the following additional columns are also
included:
The LOO Information Criterion.
The expected log predictive density (elpd_loo = -2 *
looic).
The effective number of parameters.
Fitted model object from the rstanarm package. See
stanreg-objects.
A character vector including one or more of "fixed",
"ran_vals", or "ran_pars".
See the Value section for details.
If TRUE columns for the lower (conf.low) and upper (conf.high) bounds of the
100*prob% posterior uncertainty intervals are included. See
posterior_interval for details.
See posterior_interval.
method for computing confidence intervals ("quantile" or "HPDinterval")
whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
For glance, if looic=TRUE, optional arguments to
loo.stanfit.
Should the LOO Information Criterion (and related info) be
included? See loo.stanfit for details. (This
can be slow for models fit to large datasets.)
summary,stanfit-method