[R] Predict function for 'newdata' of different dimension in svm

[mcbride at duke.edu]

I am using the "predict" function on a support vector machine (svm)
object, and I don't understand why I can't predict on a dataset with more
observations than the training dataset.

I think this problem is a generic "predict" problem, but I'm not sure.

The original svm was fit on 50 observations.

cd1.svm<-svm(boot.dist.dat$Acode~boot.dist.dat$EXT+boot.dist.dat$TOF,cost=100,gamma=20)

## for these training data,
> names(boot.dist.dat)
[1] "TOF"   "EXT"   "Acode"
> dim(boot.dist.dat)
[1] 50  3

Now I want to use the svm classifier on a new dataset with 175
observations:

new.dat<-data.frame(TOF=Cd1[cand.adult,]$TOF,EXT=Cd1[cand.adult,]$EXT,Acode=rep(0,175),row.names=NULL)

## for the new dataset,
> names(new.dat)
[1] "TOF"   "EXT"   "Acode"
> dim(new.dat)
[1] 175   3

Now try to predict:

> predict(cd1.svm,newdata=new.dat)

Error in "names<-.default"(`*tmp*`, value = c("1", "2", "3", "4", "5",  :
        'names' attribute [175] must be the same length as the vector [50]

What am I missing?  Why would the row names have to be the same?

Thanks so much,
Sandra McBride

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sandra McBride
Research Scientist
Nicholas School of the Environment and
                Earth Sciences (NSEES)
Box 90328 Duke University
Levine Science Research Center
Durham, NC 27708-0328
(919) 622 3663