Parameters from Bayesian Exploratory Factor Analysis

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.

Value

A data frame of loadings.

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
# }