SVM plot decision function

In the paper I submitted, I have to deal with SVMs and I wanted to plot the decision function that my kernel made with a 2D dataset.

I stumbled upon a 2D problem that I am interested in visualizing (I already posted about it). The point is that I know now how to extract the decision function values for a grid so you are able to plot them. Again, I am surprised how difficult it is to find it on the Internet.

With e1071, you need the following code

im=predict(K.svm,  ... ,scale=F,decision.values=T)
im=matrix(attributes(im)$decision.values,nrow=100,byrow=F)
image(seq(0, 20, length.out=100), seq(0, 20, length.out=100), im,xlab="",ylab="")
points(y)
pdf.options(reset=T)

Notice that we are extracting the value of the decision function with the attribute decision.values from the prediction SVM object. This prediction SVM object, obviously, was created with a grid of points with a range larger than the input data.