On Dec 24, 2009, at 11:42 PM, Vishal Thapar wrote:
Hi useR's,
I am resending this request since I got no response for my last post
and I
am new to the list so pardon me if I am violating the protocol.
I am trying to use the "Kernlab" package for training and prediction
using
SVM's. I am getting the following error when I am trying to use the
predict
function:
I'm guessing that the package is really "kernlab".
predictSvm = predict(modelforSVM, testSeq);
Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") :
contrasts can
be applied only to factors with 2 or more levels
Sounds like R does not like the structure of your testSeq argument.
Perhaps it was expecting a factor argument with levels that matched
those used in the training set?
The training file is a data frame with 501 columns: Col 1 is "Class"
which
is "+" or "-" and Cols V1 to V500 are "A/C/G/T" . There are 200
seq's for
training (100 + and - each). this is very similar to the
"promotergene" data
set included as example with the package.
The model that I have generated is as follows:
modelforSVM <- ksvm(Class ~ ., data = train500, kernel = "rbfdot",
kpar =
"automatic", C = 60, cross = 3, prob.model = TRUE)
The testSeq is a vector of 500 characters casted as a data.frame. I
tried
adding the Class column as well later to the testSeq data frame but
got the
same error.
Why not offer the results of dput() on that object. Or you could offer
the output of str(testSeq) , even if you aren't going to create a
smaller test object that could be used for testing.
I am using R with windows, 32 bit, version 2.9.0
Any help that I can get is really appreciated.
Thanks,
Vishal
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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