EFAtools (version 1.1.0)

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EKC() has been superseded by efa_ekc(), which is the recommended interface going forward.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 = lifecycle::deprecated()
)

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. Must be larger than the number of variables.

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). Default is "pearson". Note that the EKC reference values rest on the Marchenko-Pastur law for the eigenvalues of a sample correlation matrix of independent variables, which assumes the sampling behaviour of product-moment correlations; with "poly" / "tetra" (and, to a lesser degree, the rank-based methods) the reference series is therefore an approximation.

type

Accepted and ignored. It selected between two ways to compute the reference values. The "AM2019" reference values do not depend on the observed eigenvalues, so they do not apply the empirical correction that defines the criterion, and they are no longer computed.

See Also

efa_ekc()