Tidying methods for an rstanarm model

rstanarm tidiers will soon be deprecated in broom and there is no ongoing development of these functions at this time.

# S3 method for stanreg
tidy(x, parameters = "non-varying",
  intervals = FALSE, prob = 0.9, ...)

# S3 method for stanreg
glance(x, looic = FALSE, ...)

Arguments

x	Fitted model object from the rstanarm package. See rstanarm::stanreg-objects().
parameters	One or more of "non-varying", "varying", "hierarchical", "auxiliary" (can be abbreviated). See the Value section for details.
intervals	If TRUE columns for the lower and upper bounds of the 100*prob% posterior uncertainty intervals are included. See rstanarm::posterior_interval() for details.
prob	See rstanarm::posterior_interval().
...	For glance, if looic=TRUE, optional arguments to rstanarm::loo.stanreg().
looic	Should the LOO Information Criterion (and related info) be included? See rstanarm::loo.stanreg() for details. Note: for models fit to very large datasets this can be a slow computation.

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

When parameters="non-varying" (the default), tidy.stanreg returns one row for each coefficient, with three columns:

term

The name of the corresponding term in the model.

estimate

A point estimate of the coefficient (posterior median).

std.error

A standard error for the point estimate based on stats::mad(). See the Uncertainty estimates section in rstanarm::print.stanreg() for more details.

For models with group-specific parameters (e.g., models fit with rstanarm::stan_glmer()), setting parameters="varying" selects the group-level parameters instead of the non-varying regression coefficients. Additional columns are added indicating the level and group. Specifying parameters="hierarchical" selects the standard deviations and (for certain models) correlations of the group-level parameters. Setting parameters="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 rstanarm::stan_glm.nb() the overdispersion parameter is included if parameters="aux", for rstanarm::stan_lm() the auxiliary parameters include the residual SD, R^2, and log(fit_ratio), etc. If intervals=TRUE, columns for the lower and upper values of the posterior intervals computed with rstanarm::posterior_interval() are also included. glance returns one row with the columns

algorithm

The algorithm used to fit the model.

pss

The posterior sample size (except for models fit using optimization).

nobs

The number of observations used to fit the model.

sigma

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:

looic

The LOO Information Criterion.

elpd_loo

The expected log predictive density (elpd_loo = -2 * looic).

p_loo

The effective number of parameters.

Details

These methods tidy the estimates from rstanarm::stanreg-objects() (fitted model objects from the rstanarm package) into a summary.

See also

rstanarm::summary.stanreg()

Examples

if (FALSE) {
fit <- stan_glmer(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
                  iter = 300, chains = 2)
# non-varying ("population") parameters
tidy(fit, intervals = TRUE, prob = 0.5)

# hierarchical sd & correlation parameters
tidy(fit, parameters = "hierarchical")

# group-specific deviations from "population" parameters
tidy(fit, parameters = "varying")

# glance method
glance(fit)
glance(fit, looic = TRUE, cores = 1)
}