# fals: Unweighted least squares factor analysis In fungible: Psychometric Functions from the Waller Lab

## Description

Unweighted least squares factor analysis

## Usage

 `1` ```fals(R, nfactors, TreatHeywood = TRUE) ```

## Arguments

 `R` Input correlation matrix. `nfactors` Number of factors to extract. `TreatHeywood` If TreatHeywood = TRUE then a penalized least squares function is used to bound the commonality estimates below 1.0. Default(TreatHeywood = TRUE).

## Value

 `loadings` Unrotated factor loadings. If a Heywood case is present in the initial solution then the model is re-estimated via non-iterated principal axes with max(rij^2) as fixed communaility (h2) estimates. `h2` Vector of final commonality estimates. `uniqueness` Vector of factor uniquenesses, i.e. (1 - h2). `Heywood` (logical) TRUE if a Heywood case was produced in the LS solution. `TreatHeywood` (logical) Value of the TreatHeywood argument. `converged` (logical) TRUE if all values of the gradient are sufficiently close to zero. `MaxAbsGrad` The maximum absolute value of the gradient at the solution. `f.value` The discrepancy value associated with the final solution.

## Author(s)

Niels Waller

Other Factor Analysis Routines: `BiFAD()`, `Box26`, `GenerateBoxData()`, `Ledermann()`, `SLi()`, `SchmidLeiman()`, `faAlign()`, `faEKC()`, `faIB()`, `faLocalMin()`, `faMB()`, `faMain()`, `faScores()`, `faSort()`, `faStandardize()`, `faX()`, `fapa()`, `fareg()`, `fsIndeterminacy()`, `orderFactors()`, `print.faMB()`, `print.faMain()`, `promaxQ()`, `summary.faMB()`, `summary.faMain()`
 ```1 2 3 4``` ```Rbig <- fungible::rcor(120) out1 <- fals(R = Rbig, nfactors = 2, TreatHeywood = TRUE) ```