fals: Unweighted least squares factor analysis

In fungible: Psychometric Functions from the Waller Lab

Unweighted least squares factor analysis

Description

Unweighted least squares factor analysis

Usage

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

See Also

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()

Examples

Rbig <- fungible::rcor(120)                   
out1 <- fals(R = Rbig, 
             nfactors = 2,
             TreatHeywood = TRUE)

fungible documentation built on May 29, 2024, 8:28 a.m.