# Parameters from BayesFactor objects

Source:`R/methods_BayesFactor.R`

`model_parameters.BFBayesFactor.Rd`

Parameters from `BFBayesFactor`

objects from `{BayesFactor}`

package.

## Usage

```
# 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,
...
)
```

## 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"`

(see`map_estimate()`

),`"trimmed"`

(which is just`mean(x, trim = threshold)`

),`"mode"`

or`"all"`

.- dispersion
Logical, if

`TRUE`

, computes indices of dispersion related to the estimate(s) (`SD`

and`MAD`

for`mean`

and`median`

, respectively). Dispersion is not available for`"MAP"`

or`"mode"`

centrality indices.- ci
Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to

`0.95`

(`95%`

).- ci_method
The type of index used for Credible Interval. Can be

`"ETI"`

(default, see`eti()`

),`"HDI"`

(see`hdi()`

),`"BCI"`

(see`bci()`

),`"SPI"`

(see`spi()`

), or`"SI"`

(see`si()`

).- 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()`

or`p_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 to`x +- 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.

- 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

`BayesFactor::contingencyTableBF()`

). Defaults to`FALSE`

, as this information is often redundant.- verbose
Toggle off warnings.

- ...
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{
# Bayesian t-test
model <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
model_parameters(model)
#> Bayesian t-test
#>
#> Parameter | Median | 95% CI | pd | Prior | BF
#> -------------------------------------------------------------------------
#> Difference | 0.83 | [0.63, 1.02] | 100% | Cauchy (0 +- 0.71) | 6.78e+10
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a MCMC distribution approximation.
model_parameters(model, es_type = "cohens_d", ci = 0.9)
#> Bayesian t-test
#>
#> Parameter | Median | 90% CI | Cohen's d | d 90% CI | pd | Prior | BF
#> ----------------------------------------------------------------------------------------------------
#> Difference | 0.83 | [0.67, 1.00] | 0.85 | [0.65, 1.04] | 100% | Cauchy (0 +- 0.71) | 6.78e+10
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a MCMC distribution approximation.
# 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"
)
#> Bayesian contingency table analysis
#>
#> Parameter | SD | Cramer's V (adj.) | Cramers 95% CI | Prior | BF
#> -----------------------------------------------------------------------------------------------
#> Ratio | 0.08 | 0.14 | [0.00, 0.30] | Independent multinomial (0 +- 1) | 1.81
# }
```