EFAtools (version 1.0.0)

EKC: Empirical Kaiser criterion

Description

EKC() has been superseded by efa_ekc(), which is the recommended interface going forward. It remains available and unchanged so existing code keeps working.

Usage

EKC(
  x,
  N = NA,
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra"),
  type = "BvA2017"
)

Value

An object of class efa_retention, identical to the value of efa_ekc(); see there for the components.

Arguments

x

data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations.

N

numeric. The number of observations. Only needed if x is a correlation matrix.

use

character. Passed to stats::cor() if raw data is given as input. Default is "pairwise.complete.obs".

cor_method

character. Correlation computed from raw data: "pearson", "spearman", or "kendall" (passed to stats::cor()), or "poly" / "tetra" for polychoric / tetrachoric correlations of ordinal / binary data (a two-step estimator with no empty-cell continuity correction). Default is "pearson".

type

character. The calculation of EKC. type "BvA2017" is the original implementation; type "AM2019" differs from the original implementation but was used in simulation studies (Auerswald & Moshagen, 2019; Caron, 2025). See details. Use type = c("BvA2017", "AM2019") for both implementations. Make sure to report which version you used.

See Also

efa_ekc()