>From reviewing the first google page result for "Non-parametric regression R", I hope this link will prove useful:
http://socserv.mcmaster.ca/jfox/Courses/Oxford-2005/R-nonparametric-regression.html ----------------Contact Details:------------------------------------------------------- Contact me: tal.gal...@gmail.com | 972-52-7275845 Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | www.r-statistics.com (English) ---------------------------------------------------------------------------------------------- On Fri, Jul 9, 2010 at 11:01 AM, Ralf B <ralf.bie...@gmail.com> wrote: > I have two data sets, each a vector of 1000 numbers, each vector > representing a distribution (i.e. 1000 numbers each of which > representing a frequency at one point on a scale between 1 and 1000). > For similfication, here an short version with only 5 points. > > > a <- c(8,10,8,12,4) > b <- c(7,11,8,10,5) > > Leaving the obvious discussion about causality aside fro a moment, I > would like to see how well i can predict b from a using a regression. > Since I do not know anything about the distribution type and already > discovered non-normality I cannot use parametric regression or > anything GLM for that matter. > > How should I proceed in using non-parametric regression to model > vector a and see how well it predicts b? Perhaps you could extend the > given lines into a short example script to give me an idea? Are there > any other options? > > Best, > Ralf > > ______________________________________________ > 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. > [[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.
