Dear all,
I'm running a self-written numerical optimization routine (hazard model) which includes computing the inverse of the outer product of the score. I have been getting the above error message ("System is computationally singular"), and after some tweaking, I realized that these variables have some high numbers and the problem could be circumvented by scaling them down (i.e. dividing them by 100 or taking log). Since this is obviously not the best procedure, and since I have to estimate more complex models down the rode, I would like to understand better the reason which causes this problem. It is not a multicollinearity issue (I get the error even when using one single variable), and I think my code is clean (better be paranoid though). My sense is that the outer product just becomes large, and these are hard to invert. Maybe there are restrictions concering R in the size of the numbers? If that is the case, I think I would fare better scaling down the outer product rather than the variable itself, but I first wanted to ask the community to get and understanding of what could be the problem. Thanks a lot, Stephan Lindner -- ----------------------- Stephan Lindner University of Michigan ______________________________________________ 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.