fals | R Documentation |
Unweighted least squares factor analysis
fals(R, nfactors, TreatHeywood = TRUE)
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). |
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. |
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()
Rbig <- fungible::rcor(120)
out1 <- fals(R = Rbig,
nfactors = 2,
TreatHeywood = TRUE)
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