Sure, I am creating a partial dependence plot (reference Friedman's stochastic gradient paper from, I want to say, 2001). The idea is to find the relationship between one of the predictors, say x1, and y by creating the following plot: take a random sample of actual data points, hold other predictors fixed (x2-xp), vary x1 across its range, create a string of predictions for each value of x1, repeat for all observations in sample, and finally average all the predictions for each value of x1. If you think about it, this plot solves Simpson's paradox under fairly mild conditions.
The code I wrote does this using predict() which is useful for modeling approaches like GAMs. Mike On Wed, Apr 23, 2008 at 8:47 PM, hadley wickham <[EMAIL PROTECTED]> wrote: > On Wed, Apr 23, 2008 at 7:31 PM, Mike Dugas <[EMAIL PROTECTED]> wrote: > > Thanks for the help. That explains why my time testing showed no > > difference. Is there any way to speed up the program? It is unbearably > > slow if I increase the number of loops. > > Could you explain exactly what you're trying to do with your code? > It's a little hard to understand. > > Hadley > > > -- > http://had.co.nz/ > [[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.
