Parameters from BayesFactor objects — model_parameters.BFBayesFactor • parameters

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 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.
...: Additional arguments to be passed to or from methods.

Value

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

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 (doi:10.1016/j.jmp.2012.08.001 ).

Examples

# \donttest{
# Bayesian t-test
model <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
model_parameters(model)
#> Bayesian t-test
#> 
#> Parameter  | Median |       95% CI |   pd |              Prior |       BF
#> -------------------------------------------------------------------------
#> Difference |   0.77 | [0.56, 0.97] | 100% | Cauchy (0 +- 0.71) | 8.55e+08
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a MCMC distribution approximation.
model_parameters(model, es_type = "cohens_d", ci = 0.9)
#> Bayesian t-test
#> 
#> Parameter  | Median |       90% CI | Cohen's d |     d 90% CI |   pd |              Prior |       BF
#> ----------------------------------------------------------------------------------------------------
#> Difference |   0.77 | [0.60, 0.94] |      0.76 | [0.57, 0.94] | 100% | Cauchy (0 +- 0.71) | 8.55e+08
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a MCMC distribution approximation.

# 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"
)
#> Bayesian contingency table analysis
#> 
#> Parameter |   SD | Cramer's V (adj.) | Cramers 95% CI |                            Prior |   BF
#> -----------------------------------------------------------------------------------------------
#> Ratio     | 0.08 |              0.14 |   [0.00, 0.30] | Independent multinomial (0 +- 1) | 1.81
# }