parameters (version 0.4.0)

model_parameters.befa: Format PCA/FA from the psych package

Description

Format PCA/FA objects from the psych package (Revelle, 2016).

Usage

# S3 method for befa
model_parameters(
  model,
  sort = FALSE,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.89,
  ci_method = "hdi",
  test = NULL,
  ...
)

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 .89 (89%) for Bayesian models and .95 (95%) for frequentist models.

ci_method

The type of index used for Credible Interval. Can be "HDI" (default, see hdi) or "ETI" (see eti).

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.

...

Arguments passed to or from other methods.

Value

A data.frame of loadings.

Examples

# NOT RUN {
library(parameters)
# }
# NOT RUN {
library(BayesFM)
efa <- BayesFM::befa(mtcars, iter = 1000)
results <- model_parameters(efa, sort = TRUE)

results
attributes(results)$loadings_long
efa_to_cfa(results)
# }
# NOT RUN {
# }