View source: R/methods_BayesFactor.R
model_parameters.BFBayesFactor | 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,
es_type = NULL,
include_proportions = FALSE,
verbose = TRUE,
...
)
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. |
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
|
verbose |
Toggle off warnings. |
... |
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
(\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmp.2012.08.001")}).
A data frame of indices related to the model's parameters.
# 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"
)
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