rstanarm_tidiers {broom.mixed}

Tidying methods for an rstanarm model

Description

These methods tidy the estimates from rstanarm fits (stan_glm, stan_glmer, etc.) into a summary.

Usage

## S3 method for class 'stanreg'
tidy(
  x,
  effects = "fixed",
  conf.int = FALSE,
  conf.level = 0.9,
  conf.method = c("quantile", "HPDinterval"),
  exponentiate = FALSE,
  ...
)

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

Arguments

x

Fitted model object from the rstanarm package. See stanreg-objects.

effects

A character vector including one or more of "fixed", "ran_vals", or "ran_pars". See the Value section for details.

conf.int

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.

conf.level

See posterior_interval.

conf.method

method for computing confidence intervals ("quantile" or "HPDinterval")

exponentiate

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.

looic

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.)

Value

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:

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 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

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.

See Also

summary,stanfit-method

Examples

if (require("rstanarm")) {
## Not run: 
#'     ## original models
  fit <- stan_glmer(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
                      iter = 500, chains = 2)
  fit2 <- stan_glmer((mpg>20) ~ wt + (1 | cyl) + (1 + wt | gear),
                    data = mtcars,
                    family = binomial,
                    iter = 500, chains = 2
  
## End(Not run)
## load example data
  load(system.file("extdata", "rstanarm_example.rda", package="broom.mixed"))

  # non-varying ("population") parameters
  tidy(fit, conf.int = TRUE, conf.level = 0.5)
  tidy(fit, conf.int = TRUE, conf.method = "HPDinterval", conf.level = 0.5)

  #  exponentiating (in this case, from log-odds to odds ratios)
  (tidy(fit2, conf.int = TRUE, conf.level = 0.5)
          |> dplyr::filter(term != "(Intercept)")
  )
  (tidy(fit2, conf.int = TRUE, conf.level = 0.5, exponentiate = TRUE)
          |> dplyr::filter(term != "(Intercept)")
  )

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

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

  # glance method
   glance(fit)
  ## Not run: 
     glance(fit, looic = TRUE, cores = 1)
  
## End(Not run)
} ## if require("rstanarm")