Tidying methods for an rstanarm model

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

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

# S3 method for 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")
...: 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.

Examples


if (require("rstanarm")) {
if (FALSE) {
#'     ## original model
    fit <- stan_glmer(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
                      iter = 300, chains = 2)
  }
## load example data
fit <- readRDS(system.file("extdata", "rstanarm_example.rds", package="broom.mixed"))

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

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

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

  # glance method
   glance(fit)
  if (FALSE) {
     glance(fit, looic = TRUE, cores = 1)
  }
} ## if require("rstanarm")
#> Loading required package: rstanarm
#> Warning: there is no package called ‘rstanarm’

Arguments

Value

See also

Examples