# Parameters from Bayesian Exploratory Factor Analysis

Source:`R/methods_BayesFM.R`

`model_parameters.befa.Rd`

Format Bayesian Exploratory Factor Analysis objects from the BayesFM package.

## Usage

```
# S3 method for befa
model_parameters(
model,
sort = FALSE,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = NULL,
verbose = TRUE,
...
)
```

## Arguments

- model
Bayesian EFA created by the

`BayesFM::befa`

.- sort
Sort the loadings.

- 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.- verbose
Toggle warnings.

- ...
Arguments passed to or from other methods.

## Examples

```
library(parameters)
# \donttest{
if (require("BayesFM")) {
efa <- BayesFM::befa(mtcars, iter = 1000)
results <- model_parameters(efa, sort = TRUE, verbose = FALSE)
results
efa_to_cfa(results, verbose = FALSE)
}
#> Loading required package: BayesFM
#> starting MCMC sampling...
#> 5%
#> 10%
#> 15%
#> 20%
#> 25%
#> 30%
#> 35%
#> 40%
#> 45%
#> done with burn-in period
#> 50%
#> 55%
#> 60%
#> 65%
#> 70%
#> 75%
#> 80%
#> 85%
#> 90%
#> 95%
#> 100%
#> done with sampling!
#> # Latent variables
#> F1 =~ am + mpg + vs
#> F2 =~ carb + cyl + disp + hp + wt
#> F3 =~ drat + gear + qsec
# }
```