EKC {Rnest} | R Documentation |
Empirical Kaiser Criterion (EKC)
Description
Empirical Kaiser Criterion (EKC)
Usage
EKC(.data = NULL, n = NULL, nv = NULL, lowest.eig = 1, ...)
Arguments
.data |
a data frame, a numeric matrix, covariance matrix or correlation matrix from which to determine the number of factors. |
n |
the number of cases (subjects, participants, or units) if a covariance matrix is supplied in |
nv |
the number of variables if the critical values are required. |
lowest.eig |
minimal eigenvalues to retain. Default is Kaiser's suggestion of 1. |
... |
further argument for |
Value
The number of factors to retain or the crititical eigenvalues.
Note
As Rnest version >= 1.2, a correction to EKC was done which was reported by Marcel van Assen (personnal communication, june 2025), which was found in Rnest and other packages as well. There was a confusion in the sample and critical eigenvalues in equation 2 (Braeken & van Assen, 2017, p. 454).
References
Braeken, J., & van Assen, M. A. L. M. (2017). An empirical Kaiser criterion. Psychological Methods, 22(3), 450–466. doi:10.1037/met0000074
Examples
EKC(ex_4factors_corr, n = 42)