Region and Name are effectively the same variable cor(olive[,4:11]) will also show you that there are strong correlations between some of the variables - this is something you might want to avoid....
________________________________ From: [EMAIL PROTECTED] on behalf of Soare Marcian-Alin Sent: Wed 06/06/2007 4:45 PM To: Uwe Ligges; [email protected] Subject: Re: [R] Linear Discriminant Analysis Thanks for explaining... Im just sitting at the homework for 6 hours after taking for one week antibiotica, because i had an amygdalitis... I just wanted some tipps for solving this homework, but thanks, I will try to get help on another way :) I think i solved it, but I still get this Error :( ## Loading Data library(MASS) olive <- url(" http://www.statistik.tuwien.ac.at/public/filz/students/multi/ss07/olive.R";) print(load(olive)) dim(olive) summary(olive) index <- sample(nrow(olive), 286) train <- olive[index,-11] test <- olive[-index,-11] summary(train) summary(test) table(train$Region) table(test$Region) # Linear Discriminant Analysis z <- lda(Region ~ . , train) zn <- predict(z, newdata=test)$class mean(zn != test$Region) 2007/6/6, Uwe Ligges <[EMAIL PROTECTED]>: > > > So what about asking your teacher (who seems to be Peter Filzmoser) and > try to find out your homework yourself? > You might want to think about some assumptions that must hold for LDA > and look at the class of your explaining variables ... > > Uwe Ligges > > > > Soare Marcian-Alin wrote: > > Hello, > > > > I want to make a linear discriminant analysis for the dataset olive, and > I > > get always this error:# > > Warning message: > > variables are collinear in: lda.default(x, grouping, ...) > > > > ## Loading Data > > library(MASS) > > olive <- url(" > > > http://www.statistik.tuwien.ac.at/public/filz/students/multi/ss07/olive.R > ") > > print(load(olive)) > > > > y <- 1:572 > > x <- sample(y) > > y1 <- x[1:286] > > > > train <- olive[y1,-11] > > test <- olive[-y1,-11] > > > > summary(train) > > summary(test) > > > > table(train$Region) > > table(test$Region) > > > > # Linear Discriminant Analysis > > z <- lda(Region ~ . , train) > > predict(z, train) > > > > z <- lda(Region ~ . , test) > > predict(z, test) > > > > Thanks in advance! > > > > > > > > ------------------------------------------------------------------------ > > > > ______________________________________________ > > [email protected] 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. > -- Mit freundlichen Grüssen / Best Regards Soare Marcian-Alin [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
