Rnest (version 1.3)

fareg: 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")

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).

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".

Author

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