Parameters from BayesFactor objects
Parameters from BayesFactor objects.
## S3 method for class 'BFBayesFactor'
model_parameters(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 0.89,
priors = TRUE,
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. |
verbose |
Toggle warnings and messages. |
... |
Additional arguments to be passed to or from methods. |
The meaning of the extracted parameters:
For ttestBF: Difference is the raw
difference between the means.
For
correlationBF: rho is the linear
correlation estimate (equivalent to Pearson's r).
For
lmBF / generalTestBF
/ regressionBF /
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")) {
model <- ttestBF(x = rnorm(100, 1, 1))
model_parameters(model)
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