parameters (version 0.20.0)

model_parameters.befa: Parameters from Bayesian Exploratory Factor Analysis

Description

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,
  ...
)

Value

A data frame of loadings.

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" or "all".

dispersion

Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively).

ci

Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to .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 and messages.

...

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)
  results
  efa_to_cfa(results)
}
# }