model_parameters.BFBayesFactor {parameters} | R Documentation |
Parameters from BFBayesFactor
objects from {BayesFactor}
package.
## 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, effectsize_type = NULL, include_proportions = FALSE, verbose = TRUE, cohens_d = NULL, cramers_v = NULL, ... )
model |
Object of class |
centrality |
The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: |
dispersion |
Logical, if |
ci |
Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to |
ci_method |
The type of index used for Credible Interval. Can be
|
test |
The indices of effect existence to compute. Character (vector) or
list with one or more of these options: |
rope_range |
ROPE's lower and higher bounds. Should be a list of two
values (e.g., |
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. |
effectsize_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
|
verbose |
Toggle warnings and messages. |
cohens_d, cramers_v |
Deprecated. Please use |
... |
Additional arguments to be passed to or from methods. |
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).
A data frame of indices related to the model's parameters.
if (require("BayesFactor")) { # Bayesian t-test model <- ttestBF(x = rnorm(100, 1, 1)) model_parameters(model) model_parameters(model, cohens_d = TRUE, ci = .9) # Bayesian contingency table analysis data(raceDolls) bf <- contingencyTableBF(raceDolls, sampleType = "indepMulti", fixedMargin = "cols") model_parameters(bf, centrality = "mean", dispersion = TRUE, verbose = FALSE, effectsize_type = "cramers_v" ) }