fa(r, nfactors=1, residuals = FALSE, rotate = "oblimin",n.obs = NA,
scores = FALSE,SMC=TRUE, covar=FALSE,missing=FALSE,impute="median",min.err = 0.001, max.iter = 50,symmetric=TRUE,warnings=TRUE,fm="minres", ...)factor.pa(r, nfactors=1, residuals = FALSE, rotate = "varimax",n.obs = NA,
scores = FALSE,SMC=TRUE, missing=FALSE,impute="median",min.err = 0.001, digits = 2, max.iter = 50,symmetric=TRUE,warnings=TRUE,fm="pa")
factor.minres(r, nfactors=1, residuals = FALSE, rotate = "varimax",n.obs = NA,
scores = FALSE,SMC=TRUE, missing=FALSE,impute="median",min.err = 0.001, digits = 2, max.iter = 50,symmetric=TRUE,warnings=TRUE,fm="minres")
factor.wls(r,nfactors=1,residuals=FALSE,rotate="varimax",n.obs = NA,
scores=FALSE,SMC=TRUE,missing=FALSE,impute="median", min.err = .001,digits=2,max.iter=50,symmetric=TRUE,warnings=TRUE,fm="wls")
print.psych with sort=TRUEVSS, ICLUST, and principal for this fit statistic.factanal(which seems to be Bartlett's test) :
$\chi^2 = (n.obs - 1 - (2 * p + 5)/6 - (2 * factors)/3)) * f$factanal ). The existence of uniquenesses is what distinguishes factor analysis from principal components analysis (e.g., principal). Principal axes factor analysis has a long history in exploratory analysis and is a straightforward procedure. Successive eigen value decompositions are done on a correlation matrix with the diagonal replaced with diag (FF') until sum(diag(FF')) does not change (very much). The current limit of max.iter =50 seems to work for most problems, but the Holzinger-Harmon 24 variable problem needs about 203 iterations to converge for a 5 factor solution.
Principal axes may be used in cases when maximum likelihood solutions fail to converge.
A problem in factor analysis is to find the best estimate of the original communalities. Using the Squared Multiple Correlation (SMC) for each variable will underestimate the communalities, using 1s will over estimate. By default, the SMC estimate is used. In either case, iterative techniques will tend to converge on a stable sollution. If, however, a solution fails to be achieved, it is useful to try again using ones (SMC =FALSE).
The algorithm does not attempt to find the best (as defined by a maximum likelihood criterion) solution, but rather one that converges rapidly using successive eigen value decompositions. The maximum likelihood criterion of fit and the associated chi square value are reported, and will be worse than that found using maximum likelihood procedures.
The minimum residual (minres) solution is an unweighted least squares solution that takes a slightly different approach. It uses the optim function and adjusts the diagonal elements of the correlation matrix to mimimize the squared residual when the factor model is the eigen value decomposition of the reduced matrix. MINRES and PA will both work when ML will not, for they can be used when the matrix is singular. At least on a number of test cases, the MINRES solution is slightly more similar to the ML solution than is the PA solution. To a great extent, the minres and wls solutions follow ideas in the factanal function.
The weighted least squares (wls) solution weights the residual matrix by 1/ diagonal of the inverse of the correlation matrix. This has the effect of weighting items with low communalities more than those with high commumnalities.
The generalized least squares (gls) solution weights the residual matrix by the inverse of the correlation matrix. This has the effect of weighting those variables with low communalities even more than those with high communalities.
The maximum likelihood solution takes yet another approach and finds those communality values that minimize the chi square goodness of fit test. The fm="ml" option provides a maximum likelihood solution following the procedures used in factanal but does not provide all the extra features of that function.
Test cases comparing the output to SPSS suggest that the PA algorithm matches what SPSS calls uls, and that the wls solutions are equivalent in their fits. The wls and gls solutions have slightly larger eigen values, but slightly worse fits of the off diagonal residuals than do the minres or maximum likelihood solutions.
Although for items, it is typical to find factor scores by scoring the salient items (using, e.g.,score.items factor scores can be estimated by regression. There are multiple approaches that are possible (see Grice, 2001) and the one taken here is Thurstone's least squares regression where the weights are found by $W = R^(-1)S$ where R is the correlation matrix of the variables ans S is the structure matrix.
Of the various rotation/transformation options, varimax, Varimax, quartimax, bentlerT and geominT do orthogonal rotations. Promax transforms obliquely with a target matix equal to the varimax solution. oblimin, quartimin, simplimax, bentlerQ, and geominQ are oblique transformations. Most of these are just calls to the GPArotation package. The ``cluster'' option does a targeted rotation to a structure defined by the cluster representation of a varimax solution. With the optional "keys" parameter, the "target" option will rotate to a target supplied as a keys matrix. (See target.rot.)
There are two varimax rotation functions. One, Varimax, in the GPArotation package does not by default apply Kaiser normalization. The other, varimax, in the stats package, does. It appears that the two rotation functions produce sllightly different results even when normalization is set. For consistency with the other rotation functions, Varimax is probably preferred.
Grice, James W. (2001), Computing and evaluating factor scores. Psychological Methods, 6, 430-450
Harman, Harry and Jones, Wayne (1966) Factor analysis by minimizing residuals (minres), Psychometrika, 31, 3, 351-368.
Revelle, William. (in prep) An introduction to psychometric theory with applications in R. Springer. Working draft available at
principal, VSS, ICLUST#using the Harman 24 mental tests, compare a principal factor with a principal components solution
pc <- principal(Harman74.cor$cov,4,rotate="varimax")
pa <- fa(Harman74.cor$cov,4,fm="pa" ,rotate="varimax") #principal axis
uls <- fa(Harman74.cor$cov,4,rotate="varimax") #unweighted least squares is minres
wls <- fa(Harman74.cor$cov,4,fm="wls") #weighted least squares
#to show the loadings sorted by absolute value
print(uls,sort=TRUE)
#then compare with a maximum likelihood solution using factanal
mle <- factanal(covmat=Harman74.cor$cov,factors=4)
factor.congruence(list(mle,pa,pc,uls,wls))
#note that the order of factors and the sign of some of factors differ
#finally, compare the unrotated factor, ml, uls, and wls solutions
wls <- factor.wls(Harman74.cor$cov,4,rotate="none")
pa <- factor.pa(Harman74.cor$cov,4,rotate="none")
mle <- factanal(factors=4,covmat=Harman74.cor$cov,rotation="none")
uls <- factor.minres(Harman74.cor$cov,4,rotate="none")
factor.congruence(list(mle,pa,uls,wls))
#note that the order of factors and the sign of some of factors differ
#an example of where the ML and PA and MR models differ is found in Thurstone.33.
#compare the first two factors with the 3 factor solution
data(bifactor)
Thurstone.33 <- as.matrix(Thurstone.33)
mle2 <- factanal(covmat=Thurstone.33,factors=2,rotation="none")
mle3 <- factanal(covmat=Thurstone.33,factors=3 ,rotation="none")
pa2 <- factor.pa(Thurstone.33,2,rotate="none")
pa3 <- factor.pa(Thurstone.33,3,rotate="none")
mr2 <- fa(Thurstone.33,2,rotate="none")
mr3 <- fa(Thurstone.33,3,rotate="none")
factor.congruence(list(mle2,mle3,pa2,pa3,mr2,mr3))Run the code above in your browser using DataLab