[R] Factor Analysis by Principal Components (Code Attached)

[Brett Magill]

Hi all,

I have written some (very rudimentary) code to produce "factor analysis"
results using the principal components method.

I would appreciate it if anyone who is interested in this could could try it
out and compare it to results with other packages given known results, which I
have little ability to do.  If other find it useful, I could clean it up, make
it more general, add functionality, etc.  

With respect to interpretation, I have found my results in line with
factanal(), at least with respect to interpretation, but generally higher.  I
compared a couple of examples with some SAS results where I could find data
and results on the web--again, comparable.  There seems to be some difference
between the principal components estimated by the two packages???

I use princomp to get the loadings and the eigen values.  The fa coefficients
are then the PC loadings * the sqrt of the eigenvalues (from Johnson and
Wichern) for each retained factor.  These coeffcients are then rotated with
varimax().

The function takes a covariance or correlation matrix as its input.  If you do
not supply an n for factors, it uses the eigen > 1 criterion.

   


-----------Code below-----------------

function (xmat, factors=NULL, cor=TRUE) {

   prc <- princomp ( covmat = xmat ,cor = cor )
   eig <- prc$sdev^2

   if (is.null(factors)) factors <- sum ( eig >= 1 ) 

  loadings <- prc$loadings [ , 1:factors ]

  coefficients <- loadings [ , 1:factors ] %*% diag ( prc$sdev[1:factors] )

   rotated <- varimax ( coefficients ) $ loadings

   fct.ss  <- apply( rotated, 2 , function (x) sum (x^2) )
   pct.ss  <- fct.ss / sum (eig)
   cum.ss  <- cumsum ( pct.ss )
   ss <- t ( cbind ( fct.ss , pct.ss, cum.ss ) )

return ( coefficients , rotated , ss )
}
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