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
_________________________________________________________________
Get the best of MSN on your mobile
http://clk.atdmt.com/UKM/go/147991039/direct/01/
______________________________________________
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.
