Hi, On Tue, Jan 5, 2010 at 7:01 PM, Amy Hessen <amy_4_5...@hotmail.com> wrote: > > Hi, > > I understand from help pages that in order to use a data set with svm, I have > to divide it into two files: one for the dataset without the class label and > the other file contains the class label as the following code:-
This isn't exactly correct ... look at the examples in the ?svm documentation a bit closer. > library(e1071) > x<- read.delim("mydataset_except-class-label.txt") > y<- read.delim("mydataset_class-labell.txt") > model <- svm(x, y, cross=5) > summary(model) > > but I couldn’t understand how I add “formula” parameter to it? Does formula > contain the class label too?? Using the first example in ?svm attach(iris) model <- svm(Species ~ ., data = iris) The first argument in the function call is the formula. The "Species" column is the class label. `iris` is a data.frame ... you can see that it has the label *in it*, look at the output of "head(iris) > and what I have to do to use testing set when I don’t use “cross” parameter. Just follow the example in ?svm some more, you'll see training a model and then testing it on data. The example happens to be the same data the model trained on. To use new data, you'll just need a data matrix/data.frame with as many columns as your original data, and as many rows as you have observations. The first step separates the labels from the data (you can do the same in your data -- you don't have to have separate test and train files that are different -- just remove the labels from it in R): attach(iris) x <- subset(iris, select = -Species) y <- Species model <- svm(x, y) # test with train data pred <- predict(model, x) Hope that helps, -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.