On Tue, 2009-09-29 at 17:02 +0000, Paul Dennis wrote: > Dear all > > I have a data set for which PCA based between group analysis (BGA) gives > significant results but CA-BGA does not. > > I am having difficulty finding a reliable method for deciding which > ordination technique is most appropriate. > > I have been told to do a 1 table CA and if the 1st axis is>2 units go for CA > if not then PCA. > > Another approach is that described in the Canoco manual - perform DCA and > then look at the length of the axes. I used decorana in vegan and it gives > axis lengths. I assume that these are measured in SD units. Anyway the > manual say if the axis length is <3 go for PCA,>4 use CA and if intermediate > use either. > > Are either of these approaches good/valid/recommended or is there a better > method? > > Thanks > > Paul
Hi Paul I think that Ca is Correspondence Analysis and PCA is Principal Component Analysis, right? In this case, if all variables is numeric do you must use PCA, if all variables is factor do you must use CA. If you have a mixed variables do you have a problem, in most case I convert numeric variables to factor (with loss of information) and make CA -- Bernardo Rangel Tura, M.D,MPH,Ph.D National Institute of Cardiology Brazil ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
