
Parameters from BayesFactor objects
Source:R/methods_BayesFactor.R
model_parameters.BFBayesFactor.Rd
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 = c("pd", "rope"),
rope_range = "default",
rope_ci = 0.95,
priors = TRUE,
cohens_d = NULL,
cramers_v = 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"
or"all"
.- dispersion
Logical, if
TRUE
, computes indices of dispersion related to the estimate(s) (SD
andMAD
formean
andmedian
, respectively).- ci
Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to
.95
(95%
).- ci_method
The type of index used for Credible Interval. Can be
"ETI"
(default, seeeti()
),"HDI"
(seehdi()
),"BCI"
(seebci()
),"SPI"
(seespi()
), or"SI"
(seesi()
).- 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()
orp_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 tox +- 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.
- cohens_d
If
TRUE
, compute Cohens' d as index of effect size. Only applies to objects fromttestBF()
. Seeeffectsize::cohens_d()
for details.- cramers_v
Compute Cramer's V or phi as index of effect size. Can be
"raw"
or"adjusted"
(effect size will be bias-corrected). Only applies to objects fromchisq.test()
.- include_proportions
Logical that decides whether to include posterior cell proportions/counts for Bayesian contingency table analysis (from
BayesFactor::contingencyTableBF()
). Defaults toFALSE
, as this information is often redundant.- verbose
Toggle warnings and messages.
- ...
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 (doi:10.1016/j.jmp.2012.08.001 ).
Examples
# \donttest{
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,
cramers_v = TRUE
)
}
#> Loading required package: BayesFactor
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.3. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
#>
#> Type BFManual() to open the manual.
#> ************
#> Bayesian contingency table analysis
#>
#> Parameter | 95% CI | Cramer's V | Cramers 95% CI | Prior | BF
#> ------------------------------------------------------------------------------------------
#> Ratio | | 0.16 | [0.02, 0.31] | Independent multinomial (0 +- 1) | 1.81
# }