model_parameters.stanreg: Parameters from Bayesian Models

Description

Parameters of Bayesian models.

Usage

# S3 method for stanreg
model_parameters(
  model,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.89,
  ci_method = "hdi",
  test = c("pd", "rope"),
  rope_range = "default",
  rope_ci = 1,
  bf_prior = NULL,
  diagnostic = c("ESS", "Rhat"),
  priors = TRUE,
  effects = "fixed",
  exponentiate = FALSE,
  standardize = NULL,
  group_level = FALSE,
  verbose = TRUE,
  ...
)
# S3 method for brmsfit
model_parameters(
  model,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.89,
  ci_method = "hdi",
  test = c("pd", "rope"),
  rope_range = "default",
  rope_ci = 1,
  bf_prior = NULL,
  diagnostic = c("ESS", "Rhat"),
  priors = TRUE,
  effects = "fixed",
  component = "all",
  exponentiate = FALSE,
  standardize = NULL,
  group_level = FALSE,
  verbose = TRUE,
  ...
)

Arguments

model

Bayesian model. May also be a data frame with posterior samples.

centrality

The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all".

dispersion

Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively).

Credible Interval (CI) level. Default to 0.89 (89%). See ci for further details.

ci_method

The type of index used for Credible Interval. Can be "HDI" (default, see hdi), "ETI" (see eti) or "SI" (see si).

test

The indices of effect existence to compute. Character (vector) or list with one or more of these options: "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope or p_direction) and its results included in the summary output.

rope_range

ROPE's lower and higher bounds. Should be a list of two values (e.g., c(-0.1, 0.1)) or "default". If "default", the bounds are set to x +- 0.1*SD(response).

rope_ci

The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE.

bf_prior

Distribution representing a prior for the computation of Bayes factors / SI. Used if the input is a posterior, otherwise (in the case of models) ignored.

diagnostic

Diagnostic metrics to compute. Character (vector) or list with one or more of these options: "ESS", "Rhat", "MCSE" or "all".

priors

Add the prior used for each parameter.

effects

Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

exponentiate

Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for, say, logistic regressions, or more generally speaking: for models with log or logit link. Note: standard errors are also transformed (by multiplying the standard errors with the exponentiated coefficients), to mimic behaviour of other software packages, such as Stata.

standardize

The method used for standardizing the parameters. Can be "refit", "posthoc", "smart", "basic", "pseudo" or NULL (default) for no standardization. See 'Details' in standardize_parameters. Note that robust estimation (i.e. robust=TRUE) of standardized parameters only works when standardize="refit".

group_level

Logical, for multilevel models (i.e. models with random effects) and when effects = "all" or effects = "random", include the parameters for each group level from random effects. If group_level = FALSE (the default), only information on SD and COR are shown.

verbose

Toggle warnings and messages.

...

Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like ci_method are passed down to describe_posterior.

component

Model component for which parameters should be shown. May be one of "conditional", "precision" (betareg), "scale" (ordinal), "extra" (glmx), "marginal" (mfx), "conditional" or "full" (for MuMIn::model.avg()) or "all".

Value

A data frame of indices related to the model's parameters.

Details

Currently supported models are brmsfit, stanreg, stanmvreg, MCMCglmm, mcmc and bcplm.

Examples

Run this code

# NOT RUN {
library(parameters)
if (require("rstanarm")) {
  model <- stan_glm(
    Sepal.Length ~ Petal.Length * Species,
    data = iris, iter = 500, refresh = 0
  )
  model_parameters(model)
}
# }