PCA on high dimentional data
... and adding to what has already been said, PCA can be distorted by non-ellipsoidal distributions or small numbers of unusual values. Careful (chiefly graphical) examination of results is therefore essential, and usually fairly easy to do. There are robust/resistant versions of PCA in R, but they come with their own issues. As you have already been told, you need to do some homework -- or get some local advice. Also, you need to post on some other list, e.g. stats.stackexchange.com, as you have wandered outside the realm of R issues. -- Bert
On Sat, Dec 10, 2011 at 10:40 AM, Mark Difford <mark_difford at yahoo.co.uk> wrote:
On Dec 10, 2011 at 5:56pm deb wrote:
My question is, is there any way I can map the PC1, PC2, PC3 to the original conditions, so that i can still have a reference to original condition labels after PCA?
deb, To add to what Stephen has said. Best to do read up on principal component analysis. Briefly, each PCA is composite variable, composed of different "amounts" of each and every one of your column variables, i.e. cond1, ..., cond1000. So the short answer to your question is no. There is no way to do this mapping, except as loadings on each principal component (PC). Regards, Mark. ----- Mark Difford (Ph.D.) Research Associate Botany Department Nelson Mandela Metropolitan University Port Elizabeth, South Africa -- View this message in context: http://r.789695.n4.nabble.com/PCA-on-high-dimentional-data-tp4180467p4180890.html Sent from the R help mailing list archive at Nabble.com.
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Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm