[R] svm question

[Frank Gerrit Zoellner]

Hi!

I have a questionaccording to classifing new examples from an already trained svm. I have tarined a svm (e1071 package) with a training set of 1526 examples.
Now I have another data set with 2163 examples and I want to use the already trained svm for prediction:

 pred<-predict(a.svm,newdata);

Afterwards for further processessing I add further information to the results of teh classification from the original input (also 2163 x N matrix) 

result<-as.data.frame(cbind(newdata[81:84],pred))

But I receive an error:

Error in data.frame(..., check.names = FALSE) : 
        arguments imply differing number of rows: 2163, 1526

So I wonder why the svm is stick to a resultset of 1526 although I put in 2613 examples? To my mind I thought that the classification/predictin depends on the length of the feature vector not on the number of examples? Or am I doing something wrong?

Thanks 
-- 
Frank G. Zoellner
AG Angewandte Informatik
Technische Fakult"at
Universit"at Bielefeld
phone: +49(0)521-106-2951
fax:   +49(0)521-106-2992
email: fzoellne at techfak.uni-bielefeld.de