Hi Jessica, On Thu, Apr 26, 2012 at 11:59 AM, Jessica Streicher <j.streic...@micromata.de> wrote: > Hi! > > how do i get to the source code of kpca or even better predict.kpca(which it > tells me doesn't exist but should) ?
Probably you have to do kernlab:::predict.kpca from your R workspace, but why not just download the source package and have at it? http://cran.r-project.org/src/contrib/kernlab_0.9-14.tar.gz HTH, -steve > > (And if anyone has too much time: > Now if i got that right, the @pcv attribute consists of the principal > components, and for kpca, these are defined as projections of some random > point x, which was transformed into the other feature space -> f(x), > projected onto the actual PC (eigenvector of Covariance). This can be > computed as the sum of the (eigenvectors of the Kernel matrix * the kernel > function(sample_i,x)) > > Now assume i have some new points and want to project them, how can i do that > with only having @pcv? > Wouldn't i rather need the eigenvectors of K? > ) > > > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. -- 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.
