# fareg: Regularized Factor Analysis In fungible: Psychometric Functions from the Waller Lab

 fareg R Documentation

## 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

• 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).

### Author(s)

Niels G. Waller (nwaller@umn.edu)

### References

Jung, S. & Takane, Y. (2008). Regularized common factor analysis. New trends in psychometrics, 141-149.

Other Factor Analysis Routines: `BiFAD()`, `Box26`, `GenerateBoxData()`, `Ledermann()`, `SLi()`, `SchmidLeiman()`, `faAlign()`, `faEKC()`, `faIB()`, `faLocalMin()`, `faMB()`, `faMain()`, `faScores()`, `faSort()`, `faStandardize()`, `faX()`, `fals()`, `fapa()`, `fsIndeterminacy()`, `orderFactors()`, `print.faMB()`, `print.faMain()`, `promaxQ()`, `summary.faMB()`, `summary.faMain()`

### Examples

``````
data("HW")

# load first HW data set

RHW <- cor(x = HW\$HW6)

# Compute principal axis factor analysis
fapaOut <- faMain(R = RHW,
numFactors = 3,
facMethod = "fapa",
rotate = "oblimin",
faControl = list(treatHeywood = FALSE))

fapaOut\$faFit\$Heywood
round(fapaOut\$h2, 2)

# Conduct a regularized factor analysis
regOut <- fareg(R = RHW,
numFactors = 3,
facMethod = "rls")
regOut\$L
regOut\$Heywood

# population structure
rotate = "oblimin")

# ALign

colnames(AllSolutions) <- c("F1", "F2", "F3", "Fr1", "Fr2", "Fr3",
"Fhw1", "Fhw2", "Fhw3")
AllSolutions

IncludeDiag = FALSE,
Symmetric = FALSE)