Sorry, my mistake, I didn't get a notification or see it send. Thanks for clearing that up.
Best wishes Ed On 12 October 2012 16:58, David Winsemius <dwinsem...@comcast.net> wrote: > > On Oct 12, 2012, at 1:37 AM, Ed wrote: > >> Apologies for re-posting, my original message seems to have been >> overlooked by the moderators. >> > No. Your original post _was_ forwarded to the list. On my machine it appeared > at October 11, 2012 11:03:08 AM PDT. No one responded. It seems possible > that its lack of data or code is the reason for that state of affairs. > > -- > David. > >> ---------- Forwarded message ---------- >> From: Ed <icelus...@gmail.com> >> Date: 11 October 2012 19:03 >> Subject: party for prediction >> To: R-help@r-project.org >> >> >> Hi there >> >> I'm experiencing some problems using the party package (specifically >> mob) for prediction. I have a real scalar y I want to predict from a >> real valued vector x and an integral vector z. mob seemed the ideal >> choice from the documentation. >> >> The first problem I had was at some nodes in a partitioning tree, the >> components of x may be extremely highly correlated or effectively >> constant (that is x are not independent for all choices of components >> of z). When the resulting fit is fed into predict() the result is NA - >> this is not the same behaviour as models returned by say lm which >> ignore missing coefficients. I have fixed this by defining my own >> statsModel (myLinearModel - imaginative) which also ignores such >> coefficients when predicting. >> >> The second problem I have is that I get "Cholesky not positive >> definite" errors at some nodes. I guess this is because of numerical >> error and degeneracy in the covariance matrix? Any thoughts on how to >> avoid having this happen would be welcome; it is ignorable though for >> now. >> >> The third and really big problem I have is that when I apply mob to >> large datasets (say hundreds of thousands of elements) I get a >> "logical subscript too long" error inside mob_fit_fluctests. It's >> caught in a try(), and mob just gives up and treats the node as >> terminal. This is really hurting me though; with 1% of my data I can >> get a good fit and a worthwhile tree, but with the whole dataset I get >> a very stunted tree with a pretty useless prediction ability. >> >> I guess what I really want to know is: >> (a) has anyone else had this problem, and if so how did they overcome it? >> (b) is there any way to get a line or stack trace out of a try() >> without source modification? >> (c) failing all of that, does anyone know of an alternative to mob >> that does the same thing; for better or worse I'm now committed to >> recursive partitioning over linear models, as per mob? >> (d) failing all of this, does anyone have a link to a way to rebuild, >> or locally modify, an R package (preferably windows, but anything >> would do)? >> >> Sorry for the length of this post. If I should RTFM, please point me >> at any relevant manual by all means. I've spent a few days on this as >> you can maybe tell, but I'm far from being an R expert. >> >> Thanks for any help you can give. >> >> Best wishes, >> >> Ed > > David Winsemius, MD > Alameda, CA, USA > ______________________________________________ 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.
