parameters (version 0.22.0)

model_parameters.BFBayesFactor: Parameters from BayesFactor objects

Description

Parameters from BFBayesFactor objects from {BayesFactor} package.

Usage

# S3 method for BFBayesFactor
model_parameters(
  model,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "eti",
  test = "pd",
  rope_range = "default",
  rope_ci = 0.95,
  priors = TRUE,
  es_type = NULL,
  include_proportions = FALSE,
  verbose = TRUE,
  effectsize_type = NULL,
  ...
)

Value

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

Arguments

model

Object of class BFBayesFactor.

centrality

The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" (see map_estimate()), "trimmed" (which is just mean(x, trim = threshold)), "mode" or "all".

dispersion

Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively). Dispersion is not available for "MAP" or "mode" centrality indices.

ci

Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to 0.95 (95%).

ci_method

The type of index used for Credible Interval. Can be "ETI" (default, see eti()), "HDI" (see hdi()), "BCI" (see bci()), "SPI" (see spi()), 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.

priors

Add the prior used for each parameter.

es_type

The effect size of interest. Not that possibly not all effect sizes are applicable to the model object. See 'Details'. For Anova models, can also be a character vector with multiple effect size names.

include_proportions

Logical that decides whether to include posterior cell proportions/counts for Bayesian contingency table analysis (from BayesFactor::contingencyTableBF()). Defaults to FALSE, as this information is often redundant.

verbose

Toggle off warnings.

effectsize_type

Deprecated. Use es_type instead.

...

Additional arguments to be passed to or from methods.

Details

The meaning of the extracted parameters:

• For BayesFactor::ttestBF(): Difference is the raw difference between the means.

• For BayesFactor::correlationBF(): rho is the linear correlation estimate (equivalent to Pearson's r).

• For BayesFactor::lmBF() / BayesFactor::generalTestBF() / BayesFactor::regressionBF() / BayesFactor::anovaBF(): in addition to parameters of the fixed and random effects, there are: mu is the (mean-centered) intercept; sig2 is the model's sigma; g / g_* are the g parameters; See the Bayes Factors for ANOVAs paper (tools:::Rd_expr_doi("10.1016/j.jmp.2012.08.001")).

Examples

if (FALSE) { # require("BayesFactor")
# \donttest{
# Bayesian t-test
model <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
model_parameters(model)
model_parameters(model, es_type = "cohens_d", ci = 0.9)

# Bayesian contingency table analysis
data(raceDolls)
bf <- BayesFactor::contingencyTableBF(
  raceDolls,
  sampleType = "indepMulti",
  fixedMargin = "cols"
)
model_parameters(bf,
  centrality = "mean",
  dispersion = TRUE,
  verbose = FALSE,
  es_type = "cramers_v"
)
# }
}