On Oct 10, 10:27 pm, mkei...@WHARTON.UPENN.EDU ("Keintz, H. Mark") wrote: > Song [soun...@YAHOO.COM] said: > > > > > > > > > > > On Oct 10, 4:23 pm, peterflomconsult...@mindspring.com (Peter Flom) > > wrote: > > > Song <soun...@YAHOO.COM> wrote > > > > >Can anyone please explain to me how running a simple PCA in SAS and > > R > > > >can produce conflicting results? You will notice that signs of the > > > >component score are different only in the 4th component. What is > > > >troubling to me is that this is not consistent. On a different > > > >dataset, the signs become different starting with the 2nd > component. > > > > The signs of the principal components are arbitrary. It's like > > > saying "John is taller than Mary" or "Mary is shorter than John". > > > > If the magnitudes were noticeably different, that would be > troubling, > > but changes in the signs are no big deal > > > > Peter > > > Hi Peter -- > > > I understand what you are saying...however this does matter if I'm > > trying to construct biplots using say the first two PCs. This > > wouldn't be a problem if I knew a priori that the sign will differ > > only after the second component...but it changes for different > > dataset. So the biplots that I get from SAS and R may or may not be > > the same. > > > Song > > I doubt if you're going to be able to force SAS (or R, I suppose) to > pick a particular sign pattern for score coefficients. > > So why not set your own rule to standardize? Such as, if the first > coefficient for a given factor is negative, reverse the sign of all > coefficients for that factor. Then, no matter what you start with, > you'll end up with each factor beginning with a positive score > coefficient. The R and SAS coefficients should then be entirely > equivalent. > > Regards, > Mark- Hide quoted text - > > - Show quoted text -

Hi Mark --

If you simply reverse the signs of the coefficients manually and obtain the Pearson correlation for each principal component score versus the individual variables, how can one maintain consistency in the interpretation of the same dataset analyzed by two different individual? Although the correlation coefficients are the same in magnitude...saying two variables have a correlation of 0.8 is different than saying they have a correlation of -0.8.

Best regards,

Song