I'm trying to do a linear discriminant analysis on a dataset of three classes ("Affinities"), using the MASS library:> data.frame2 <- na.omit(data.frame1) > > data.ld = lda(AFFINITY ~ ., data.frame2, prior = c(1,1,1)/3)Error in var(x - group.means[g, ]) : missing observations in cov/cor What does this error message mean and how can I get rid of it? Thanks! Pieter
Pieter Vermeesch wrote:> I'm trying to do a linear discriminant analysis on a dataset of three > classes ("Affinities"), using the MASS library: > >> data.frame2 <- na.omit(data.frame1) >> >> data.ld = lda(AFFINITY ~ ., data.frame2, prior = c(1,1,1)/3) > > Error in var(x - group.means[g, ]) : missing observations in cov/cor > > What does this error message mean and how can I get rid of it?What does str(data.frame2) tell us? Uwe Ligges> Thanks! > > Pieter > > ______________________________________________ > R-help at 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 > and provide commented, minimal, self-contained, reproducible code.
>>>>> "Pieter" == Pieter Vermeesch <pieter.vermeesch at erdw.ethz.ch> >>>>> on Mon, 16 Oct 2006 19:15:59 +0200 writes:Pieter> I'm trying to do a linear discriminant analysis on a Pieter> dataset of three classes ("Affinities"), using the Pieter> MASS library: ^^^^^^^ No, no! MASS *package* (please!) >> data.frame2 <- na.omit(data.frame1) >> >> data.ld = lda(AFFINITY ~ ., data.frame2, prior = c(1,1,1)/3) Pieter> Error in var(x - group.means[g, ]) : missing observations in cov/cor Pieter> What does this error message mean and how can I get rid of it? You have (+ or -) 'Inf' data values which na.omit() does not omit and 'x - group.means[g, ]' contains 'Inf - Inf' which is NaN. Ideally, MASS:::lda.default() would check for such a case and give a more user-friendly error message. Pieter> Thanks! you're welcome. Martin Maechler, ETH Zurich
hi, i am wondering if i could use lda$scaling (i.e. coeff) to evaluate variables' importance if all the x's are normalized before put into model? thanks. -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III