In R the
factanal() are somewhat similar. At least their output looks pretty similar. I learned that this is not surprising since the print function of
princomp comes from
factanal. I understand that SS loadings do not make much sense for
princomp as it is bounded to
1 anyway. Moreover, as Joris stated on nabble, the proportion of variance is only printed because of the common print function, but does not contain valuable information when princomp is used.
What I do not understand is rather not an R question but more a multivariate stats question what is the conceptual difference between these PCA and Factor Analysis functions as they are used in R? This question relates particularly to the scores (let's assume "regression" scores for FA) respectively the difference between scores in both concepts? What should I rather use when I want to use to resulting scores in a regression model (for example in order to circumvent multicollinearity)? I also understand that PCA has a fixed number of components while FA has fewer factors than variables.
richiemorrisroe's answer in the thread suggested by Rob Hyndman might go into that direction.