fungible (version 2.4.4)

fals: Unweighted least squares factor analysis

Description

Unweighted least squares factor analysis

Usage

fals(R, nfactors, 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.

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).

Author

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)