Hi, On Sun, Jul 11, 2010 at 7:11 PM, Amy Hessen <[email protected]> wrote: > > Hi > Could you please explain the line that I got from the documentation of R? > does it mean that there is a difference between using and not using the > formula interface with SVM ?: > If the predictor variables include factors, the formula interface must be > used to get a correct model matrix.
It might have something to do with the fact that one would "expand" factors into a set of dummy variables (that can take either a 1, or 0 value) in order to encode all of the levels of the factors into different variables. You could do this yourself without using the formula, but you would then have to manually expand (column wise) your data to encode the vars yourself where as the formula interface does this for you? (I don't know that for sure, I'm just guessing from that sentence you mention -- look at the source code of the function in order verify this for yourself) Here's a ref: http://dss.princeton.edu/online_help/analysis/dummy_variables.htm You can google about using nominal/categorical variables to learn more. -- 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 ______________________________________________ [email protected] 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.
