Thanks for the answers Uwe! So this is a common problem in biology - few number of cases and many, many variables (genes, proteins, metabolites, etc etc)!
Under these conditions, is discriminant function analysis not an ideal method to use then? Are there alternatives? > 1) First problem, I got this error message: > >>z <- lda(C0GRP_NA ~ ., dpi30) > > Warning message: > variables are collinear in: lda.default(x, grouping, ...) > > I guess this is not a good thing, however, I *did* get a result and it > discriminated perfectly between my groups. Can anyone explain what > this means? Does it invalidate my results? Well, 14 cases and 37 variables mean that not that many degrees of freedom are left.... ;-) Of course, you get a perfect fit - with arbitrary data. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
