On Tue, 15 Jun 2004, Benjamin Esterni wrote: > I have a problem with the dr function: "dimension reduction".
It seems that you are using it inappropriately. > I give you my example, and i'll be pleased to read your comments. > > #let be X a matrix 50*100: > > library(dr); You should not be terminating lines with ;. This is R, not C. > X<- matrix(rnorm(50*100,5,1),50,100); > > #and let be Y a vector response: > Y<- sample(0:1,50,replace=T); > > #I choose (for the expérience, but in reality i don't have it) a few variables > #which are censed to explain Y: > > index<- sample(1:100,10); > X[Y==1,index]<-10*X[Y==1,index]; > > #so now I want to proceed to a logistic regression, but I don't know the vector > #"index". So I have to reduce the dimension of X, and that's why I use the function > #"dr" (dr package). > > model<- dr(Y~X,family="binomial",method="phdy"); > > edr<- dr.direction(model); > > #And now my problem: I hope that edr is a matrix constructed with linear > #combinaison of X, prinipally the "index" vectors of X. But in reality > it's not the #situation: Your variables have no predictive power at all. Look at pairs(cbind(Y, edr)) > library(nnet); > fit<-multinom(Y~.,data=data.frame(edr)); Take a look at this. The fit is useless, because your variables are. > pred<-predict(fit,data.frame(edr)); > table(Y,pred) > 0 21 > 0 19 Given that Y has 50 values and that table labels dims, how on earth did you get that? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html