| fals {fungible} | R Documentation |
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)