R help - May 2009 - Least-square support vector machines regression!

Dear R-community,

I was using SVM regression (svm {e1071}) for predictions of single  
soil properties of a huge data set (3000 samples). There are for the  
eps-regression using the radial basis kernel three optimization  
parameters needed.

To make things easier (using only two optimization parameters and not  
loosing performance) I wanted to use LS SVM regression  
(lssvm{kernlab}). But it looks to me that it is not yet implemented.  
At least I got error messages, which I could not find a solution for  
(Error in if (n !_dim(y)[1] stop ("Labels y and data x dont match").

Otherwise I could not find another LSSVM regression implementation in  
R, or is there?

Regards,

Thomas

Dr. Thomas Terhoeven-Urselmans
Post-Doc Fellow
Soil infrared spectroscopy
World Agroforestry Center (ICRAF)
United Nations Avenue, Gigiri
PO Box 30677-00100 Nairobi, Kenya
Ph: 254 20 722 4113 or via USA 1 650 833 6654 ext. 4113
Fax 254 20 722 4001 or via USA 1 650 833 6646
Email: t.urselmans at cgiar.org
Internet: http://worldagroforestrycentre.org

> To make things easier (using only two optimization parameters and not
> loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}).
But
> it looks to me that it is not yet implemented. At least I got error
> messages, which I could not find a solution for (Error in if (n !_dim(y)[1]
> stop ("Labels y and data x dont match").
I've used the lssvm function in kernlab without issue.

You should follow the posting guide and provide a reproducible example
so that there is a possibility of answering your question. Plus, what
versions etc.

Max