fareg: Regularized Factor Analysis

In Rnest: Next Eigenvalue Sufficiency Test

Regularized Factor Analysis

Description

This function applies the regularized factoring method to extract an unrotated factor structure matrix.

Usage

fareg(R, numFactors = 1, facMethod = "rls")

Arguments

R	(Matrix) A correlation matrix to be analyzed.
numFactors	(Integer) The number of factors to extract. Default: numFactors = 1.
facMethod	(Character) "rls" for regularized least squares estimation or "rml" for regularized maximum likelihood estimation. Default: facMethod = "rls".

Value

The main output is the matrix of unrotated factor loadings.

• loadings: (Matrix) A matrix of unrotated factor loadings.

• h2: (Vector) A vector of estimated communality values.

• L: (Numeric) Value of the estimated penality parameter.

• Heywood (Logical) TRUE if a Heywood case is detected (this should never happen).

Note

This function is from the fungible package of Niels Waller which has been archived december 19th 2025 (CRAN team, personal communication). The relevant function are fareg and rmsd. The documentation is from the original function.

Author(s)

Niels G. Waller ([e-mail])

References

Jung, S. & Takane, Y. (2008). Regularized common factor analysis. New trends in psychometrics, 141-149. Waller, N. G. (2024). fungible: Psychometric Functions from the Waller Lab. University of Minnesota, Minneapolis, Minnesota. R package 2.4.4, <https://CRAN.R-project.org/package=fungible>.

Examples

 # Conduct a regularized factor analysis
regOut <- fareg(R = ex_2factors, 
               numFactors = 2,
               facMethod = "rls")
regOut$L
regOut$Heywood

Rnest documentation built on Dec. 4, 2025, 5:07 p.m.