this is not an important question, but I wonder why lm returns an error, and whether this can be shut off. it would seem to me that returning NA's would make more sense in some cases---after all, the problem is clearly that coefficients cannot be computed.
I know that I can trap the lm.fit() error---although I have always found this to be quite inconvenient---and this is easy if I have only one regression in my lm() statement. but, let's presume I have a matrix with a few thousand dependent y variables (and the same independent X variables). Let's presume one of the y variables contains only NA's. I believe I now cannot use lm(y ~ X), because one of the regressions will throw the lm.fit exception. (all the other y vectors should have worked.) or is there a way to get lm() to work in such situations? /iaw ---- Ivo Welch (ivo.we...@brown.edu, ivo.we...@gmail.com) ______________________________________________ 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.
