model_parameters.BFBayesFactor: Parameters from BayesFactor objects

In parameters: Processing of Model Parameters

Parameters from BayesFactor objects

Description

Parameters from BFBayesFactor objects from {BayesFactor} package.

Usage

## S3 method for class '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,
  ...
)

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 vector of two values (e.g., c(-0.1, 0.1)), "default" or a list of numeric vectors of the same length as numbers of parameters. 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.
...	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 (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmp.2012.08.001")}).

Value

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

Examples

# 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"
)

parameters documentation built on Oct. 18, 2024, 5:13 p.m.