On 13-May-10 20:04:50, efree...@berkeley.edu wrote: > I stumbled across this and I am wondering if this is unexpected > behavior or if I am missing something. > >> pnorm(-1.0e+307, log.p=TRUE) > [1] -Inf >> pnorm(-1.0e+308, log.p=TRUE) > [1] NaN > Warning message: > In pnorm(q, mean, sd, lower.tail, log.p) : NaNs produced >> pnorm(-1.0e+309, log.p=TRUE) > [1] -Inf > > I don't know C and am not that skilled with R, so it would be hard > for me to look into the code for pnorm. If I'm not just missing > something, I thought it may be of interest. > > Details: > I am using Mac OS X 10.5.8. I installed a precompiled binary version. > Here is the output from sessionInfo(), requested in the posting guide: > R version 2.11.0 (2010-04-22) > i386-apple-darwin9.8.0 > > locale: > [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > loaded via a namespace (and not attached): > [1] tools_2.11.0 > > Thank you very much, > > Eric Freeman > UC Berkeley
This is probably platform-independent. I get the same results with R on Linux. More to the point: You are clearly "pushing the envelope" here. First, have a look at what R makes of your inputs to pnorm(): -1.0e+307 # [1] -1e+307 -1.0e+308 # [1] -1e+308 -1.0e+309 # [1] -Inf So, somewhere beteen -1e+308 and -1.0e+309. the envelope burst! Given -1.0e+309, R returns -Inf (i.e. R can no longer represent this internally as a finite number). Now look at pnorm(-Inf,log.p=TRUE) # [1] -Inf So, R knows how to give the correct answer (an exact 0, or -Inf on the log scale) if you feed pnorm() with -Inf. So you're OK with -1e+N where N >= 309. For smaller powers, e.g. -1e+(200:306), these give pnorm() much less than -1.0e+309, and presumably R's algorithm (which I haven't studied either ... ) returns 0 for pnorm(), as it should to the available internal accuracy. So, up to pnorm(-1.0e+307, log.p=TRUE) = -Inf. All is as it should be. However, at -1e+308, "the envelope is about to burst", and something may occur within the algorithm which results in a NaN. So there is nothing anomalous about your results except at -1e+308, which is where R is at a critical point. That's how I see it, anway! Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861 Date: 14-May-10 Time: 00:01:27 ------------------------------ XFMail ------------------------------ ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
