michael watson (IAH-C) wrote: > 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?
No, obviously not "an ideal method", if used as is on the whole data. Alternatives are certainly described in the literature - I am not specialised in this field (I mean, this gene stuff), hence do not want to specify misleading references here. Uwe Ligges > >>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