psych (version 1.0-51)

factor.pa: Principal Axis Factor Analysis

Description

Among the many ways to do factor analysis, one of the most conventional is principal axes. An eigen value decomposition of a correlation matrix is done and then the communalities for each variable are estimated by the first n factors. These communalities are entered onto the diagonal and the procedure is repeated until the sum(diag(r)) does not vary. For well behaved matrices, maximum likelihood factor analysis (factanal) is probably preferred.

Usage

factor.pa(r, nfactors=1, residuals = FALSE, rotate = "varimax",n.obs = NULL,
scores = FALSE,SMC=TRUE, missing=FALSE,impute="median",min.err = 0.001, digits = 2, max.iter = 50,symmetric=TRUE,warnings=TRUE)

Arguments

r

A correlation matrix or a raw data matrix. If raw data, the correlation matrix will be found using pairwise deletion.

nfactors

Number of factors to extract, default is 1

residuals

Should the residual matrix be shown

rotate

"none", "varimax", "promax" or "oblimin" are possible rotations of the solution.

n.obs

Number of observations used to find the correlation matrix if using a correlation matrix. Used for finding the goodness of fit statistics.

scores

If TRUE, estimate factor scores

SMC

Use squared multiple correlations (SMC=TRUE) or use 1 as initial communality estimate. Try using 1 if imaginary eigen values are reported.

missing

if scores are TRUE, and missing=TRUE, then impute missing values using either the median or the mean

impute

"median" or "mean" values are used to replace missing values

min.err

Iterate until the change in communalities is less than min.err

digits

How many digits of output should be returned

max.iter

Maximum number of iterations for convergence

symmetric

symmetric=TRUE forces symmetry by just looking at the lower off diagonal values

warnings

warnings=TRUE => warn if number of factors is too many

Value

• values
  Eigen values of the final solution
• communality
  Communality estimates for each item. These are merely the sum of squared factor loadings for that item.
• rotation
  which rotation was requested?
• n.obs
  number of observations specified or found
• loadings
  An item by factor loading matrix of class ``loadings" Suitable for use in other programs (e.g., GPA rotation or factor2cluster.
• fit
  How well does the factor model reproduce the correlation matrix. (See VSS, ICLUST, and principal for this fit statistic.
• fit.off
  how well are the off diagonal elements reproduced?
• dof
  Degrees of Freedom for this model. This is the number of observed correlations minus the number of independent parameters. Let n=Number of items, nf = number of factors then $dof = n * (n-1)/2 - n * nf + nf*(nf-1)/2$
• objective
  value of the function that is minimized by maximum likelihood procedures. This is reported for comparison purposes and as a way to estimate chi square goodness of fit. The objective function is $f = log(trace ((FF'+U2)^{-1} R) - log(|(FF'+U2)^{-1} R|) - n.items$.
• STATISTIC
  If the number of observations is specified or found, this is a chi square based upon the objective function, f. Using the formula from factanal(which seems to be Bartlett's test) : $\chi^2 = (n.obs - 1 - (2 * p + 5)/6 - (2 * factors)/3)) * f$
• PVAL
  If n.obs > 0, then what is the probability of observing a chisquare this large or larger?
• phi
  If oblique rotations (using oblimin from the GPArotation package) are requested, what is the interfactor correlation.
• communality.iterations
  The history of the communality estimates. Probably only useful for teaching what happens in the process of iterative fitting.
• residual
  If residuals are requested, this is the matrix of residual correlations after the factor model is applied.

Details

Factor analysis is an attempt to approximate a correlation or covariance matrix with one of lesser rank. The basic model is that $_nR_n \approx _{n}F_{kk}F_n'+ U^2$ where k is much less than n. There are many ways to do factor analysis, and maximum likelihood procedures are probably the most preferred (see 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.

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.

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.

References

Gorsuch, Richard, (1983) Factor Analysis. Lawrence Erlebaum Associates.

See Also

principal, VSS, ICLUST

Examples

#using the Harman 24 mental tests, compare a principal factor with a principal components solution
pc <- principal(Harman74.cor$cov,4,rotate=TRUE)
pa <- factor.pa(Harman74.cor$cov,4,rotate="varimax")
round(factor.congruence(pc,pa),2)

#then compare with a maximum likelihood solution using factanal
mle <- factanal(x,4,covmat=Harman74.cor$cov)
round(factor.congruence(mle,pa),2)
#note that the order of factors and the sign of some of factors differ