On Jul 9, 2010, at 4:01 AM, Ralf B 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.
You can use density estimation,. There was a recent thread that included worked examples using MASS::kde2d and locfit::locfit for graphical display of joint distributions.
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
David Winsemius, MD West Hartford, CT ______________________________________________ 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.
