On Wed, Oct 10, 2012 at 3:35 PM, Baoqiang Cao <bqcaom...@gmail.com> wrote: > Hi, > > I have a question about using lm on matrix, have to admit it is very > trivial but I just couldn't find the answer after searched the mailing > list and other online tutorial. It would be great if you could help. > > I have a matrix "trainx" of 492(rows) by 220(columns) that is my x, > and trainy is 492 by 1. Also, I have the newdata testx which is 240 > (rows) by 220 (columns). Here is what I got: > > py <- predict(lm(trainy ~ trainx ), data.frame(testx)) > Warning message: > 'newdata' had 240 rows but variable(s) found have 492 rows > > The fitting formula I intended is: trainy ~ trainx[,1] + trainx[,2] + > .. +trainx[,220]. >
I think you want a formula like trainy ~ . meaning "trainy" explained by everything else. (Admittedly, I think any model with 220 regressors is going to be absolutely terrible, but that's a different email) What I think is happening here is that lm() looks for "trainx" as a column name in the data set you provide, can't find it, and then finds the "trainx" dataset as a whole, which doesn't fit the dimensionality you need. Take a look at ?formula for more on how to use formula notation properly. Cheers, Michael ______________________________________________ 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.
