R/methods_BayesFactor.R
model_parameters.BFBayesFactor.Rd
Parameters from BayesFactor objects.
# S3 method for 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 pointestimates (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 off warnings. 
...  Additional arguments to be passed to or from methods. 
A data frame of indices related to the model's parameters.
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 (meancentered) 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
).
# \donttest{
if (require("BayesFactor")) {
model < ttestBF(x = rnorm(100, 1, 1))
model_parameters(model)
}
#> Loading required package: BayesFactor
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.124.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
#>
#> Type BFManual() to open the manual.
#> ************
#> Bayesian ttest
#>
#> Parameter  Median  89% CI  pd  % in ROPE  Prior  BF
#> 
#> Difference  0.96  [1.12, 0.81]  100%  0%  Cauchy (0 + 0.71)  > 1000
#> Cohen's D  0.99  [ 0.80, 1.19]  100%  0%  
#>
#> Using highest density intervals as credible intervals.
# }