R help - Dec 2004 - erro in SVM (packsge "e1071")

Hello,

I am using SVM under e1071 package for nu-regression with 18 parameters. The 
variables are ordered factors, factors, date or numeric datatypes. I use the 
linear kernel.
It gives the following error that I cannot solve. I tryed debug, browser and 
all that stuff, but no way.
The error is:

Error in get(ctr, mode = "function", envir =
parent.frame())(levels(x),  :
        Orthogonal polynomials cannot be represented accurately enough for 236 
degrees of freedom

I use the nu parameter. However, reading ?svm help it says "parameter
needed
for 'nu-classification' and 'one-classification'". Does not
say anything about
nu-regression. It is an omission in the ?svm help page? Or am I 
notundestanding something?

I believe it has something to do with the calculus of the eigenvalues. Anyway 
how can I overpass this problem? Increasing the training data (is around 900 
records)?

Thanks for any help

Joao

Joao:

The reported error message is not from e1071.
How *exactly* did you call svm()?

As to the documentation of the nu parameter: yes, this is an omission,
of course, nu is used in nu-regression as well; thanks for pointing this
out.

best,
David

---------

Hello,

I am using SVM under e1071 package for nu-regression with 18 parameters.
The 
variables are ordered factors, factors, date or numeric datatypes. I use
the 
linear kernel.
It gives the following error that I cannot solve. I tryed debug, browser
and 
all that stuff, but no way.
The error is:

Error in get(ctr, mode = "function", envir =
parent.frame())(levels(x),
: 
        Orthogonal polynomials cannot be represented accurately enough
for 236 
degrees of freedom

I use the nu parameter. However, reading ?svm help it says "parameter
needed 
for 'nu-classification' and 'one-classification'". Does not
say anything
about 
nu-regression. It is an omission in the ?svm help page? Or am I 
notundestanding something?

I believe it has something to do with the calculus of the eigenvalues.
Anyway 
how can I overpass this problem? Increasing the training data (is around
900 
records)?

Thanks for any help

Joao




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