Re: [R] Only one class shown in SVM plot?

Thanks a lot! After thinking about it it definitely makes sense ! Greetings, Matthias -- View this message in context: http://r.789695.n4.nabble.com/Only-one-class-shown-in-SVM-plot-tp4637782p4638629.html Sent from the R help mailing list archive at Nabble.com.

Re: [R] Only one class shown in SVM plot?

Ok, here a simple example. The file http://r.789695.n4.nabble.com/file/n4637924/test.csv test.csv has 400 lines containing 20 columns (1. column is class label, the other 19 are the features). So what I'm doing is / data - read.csv(file=test.csv, head=F, sep=,) names(data) - c(Class,V1, V2,

[R] Plotting LDA results

Dear Users! I think I still have some problems in understanding LDA and the methods of plotting the results. The case is the following: I'm having a dataset containing two classes where each datapoint has 19 dimensions. Training with lda(...) works fine, and I'm getting 19 LD coefficients. So far

[R] Only one class shown in SVM plot?

Hello users! I'm calculating a simple model using svm(...) from the e1071 package. So far so good, with a linear kernel I'm getting 5 SVs. When plotting the result I see very well separated data clouds, but the underlying color is constantly pink, so as far as I understand no class separation is

Re: [R] Plotting LDA results

Thank you! Still not clear...I can plot two of my data dimensions against each other where I see two separated clouds. So it must be possible to determine the coefficients of the dividing line from the model I get from lda(...), or am I completely wrong now...? -- View this message in context:

Re: [R] Scaling in SVM

Thanks! For testing purposes this rescaling works! But unfortunately due to timing constraints I'm not able to do the rescaling of the data, so as I mentioned I have to work on with unscaled data. So I have to calculate $f(\vec x) = sum_{i \in sv} coefs_i \langle \vec x_i \cdot \vec x \rangle -

[R] Scaling in SVM

Dear Community! I'm using the svm method of package e1071 for classifying my data. This really works fine, but however I have to work after creating the support vectors and the parameters with unscaled data. So the problem is when I try to train the classifier with the option scale=F the result is