fa(r,nfactors=1,n.obs = NA,n.iter=1, rotate="oblimin", scores=FALSE, residuals=FALSE, SMC=TRUE, covar=FALSE,missing=FALSE,impute="median",min.err = 0.001, max.iter = 50,symmetric=TRUE,warnings=TRUE,fm="minres",alpha=.1,p=.05,oblique.scores=TRUE,...)fac(r,nfactors=1,n.obs = NA, rotate="oblimin", scores=FALSE, residuals=FALSE, SMC=TRUE, covar=FALSE,missing=FALSE,impute="median",min.err = 0.001, max.iter = 50,symmetric=TRUE,warnings=TRUE,fm="minres",alpha=.1,oblique.scores=TRUE,...)
fa.poly(x,nfactors=1,n.obs = NA, n.iter=1, rotate="oblimin", SMC=TRUE, missing=FALSE, impute="median", min.err = .001, max.iter=50, symmetric=TRUE,warnings=TRUE,fm="minres",alpha=.1, p =.05, oblique.scores=TRUE,...)
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). If variables are thought to represent a ``true" or latent part then factor analysis provides an estimate of the correlations with the latent factor(s) representing the data. If variables are thought to be measured without error, then principal components provides the most parsimonious description of the data. The fa function will do factor analyses using one of four different algorithms: minimum residual (minres), principal axes, weighted least squares, or maximum likelihood.
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 solution. 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 communalities.
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. Then, factor scores are just $Fs = X W$.
In the oblique case, the factor loadings are referred to as Pattern coefficients and are related to the Structure coefficients by $S = P \Phi$ and thus $P = S \Phi^{-1}$. When estimating factor scores, fa and factanal differ in that fa finds the factors from the Structure matrix and factanal seems to find them from the Pattern matrix. Thus, although in the orthogonal case, fa and factanal agree perfectly in their factor score estimates, they do not agree in the case of oblique factors. Setting oblique.scores = FALSE will produce factor score estimate that match those of factanal.
It is sometimes useful to extend the factor solution to variables that were not factored. This may be done using fa.extension. Factor extension is typically done in the case where some variables were not appropriate to factor, but factor loadings on the original factors are still desired.
For dichotomous items or polytomous items, it is recommended to analyze the tetrachoric or polychoric correlations rather than the Pearson correlations. This is done automatically when using irt.fa or fa.poly functions. In the first case, the factor analysis results are reported in Item Response Theory (IRT) terms, although the original factor solution is returned in the results. In the later case, a typical factor loadings matrix is returned, but the tetrachoric/polychoric correlation matrix and item statitics are saved for reanalysis by irt.fa
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 slightly different results even when normalization is set. For consistency with the other rotation functions, Varimax is probably preferred.
When factor analyzing items with dichotomous or polytomous responses, the irt.fa function provides an Item Response Theory representation of the factor output.
The function fa will repeat the analysis n.iter times on a bootstrapped sample of the data (if they exist) or of a simulated data set based upon the observed correlation matrix. The mean estimate and standard deviation of the estimate are returned and will print the original factor analysis as well as the alpha level confidence intervals for the estimated coefficients. The bootstrapped solutions are rotated towards the original solution using target.rot. The factor loadings are z-transformed, averaged and then back transformed.
fa.poly will find confidence intervals for a factor solution for dichotomous or polytomous items (set n.iter > 1 to do so). Perhaps more useful is to find the Item Response Theory parameters equivalent to the factor loadings reported in fa.poly by using the irt.fa function.
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, irt.fa, VSS, ICLUST, predict.psych, fa.extension#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
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