Yes, indeed! Problem solved! Thanks a lot!
Jean 2015-05-07 14:06 GMT-07:00 William Dunlap <[email protected]>: > Your nLL function returns 1e+308 in near-boundary cases. Since 1e+308 is so > close to machine infinity, it is easy to get into Inf-Inf (=NaN) or Inf/Inf > (=NaN) > situations when working with it. Try making that limiting value something > smaller, > like 1e+30, and you may have better luck. > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > On Thu, May 7, 2015 at 1:14 PM, Jean Marchal <[email protected]> > wrote: >> >> A follow-up to my yesterday's email. >> >> I was able to make a reproducible example. All you will have to do is >> load the .RData file that you can download here: >> >> https://drive.google.com/file/d/0B0DKwRjF11x4dG1uRWhwb1pfQ2s/view?usp=sharing >> >> and run this line of code: >> >> nlminb(start=sv, objective = nLL, lower = 0, upper = Inf, >> control=list(trace=TRUE)) >> >> which output the following: >> >> 0: 12523.401: 0.0328502 0.0744493 0.00205298 0.0248628 0.0881807 >> 0.0148887 0.0244485 0.0385922 0.0714495 0.0161784 0.0617551 0.0244901 >> 0.0784038 >> 1: 12421.888: 0.0282245 0.0697934 0.00000 0.0199076 0.0833634 >> 0.0101135 0.0189494 0.0336236 0.0712130 0.0160687 0.0616015 0.0244689 >> 0.0660129 >> 2: 12050.535: 0.00371847 0.0451786 0.00000 0.00000 0.0575667 >> 0.00000 0.00000 0.00697067 0.0697205 0.0156250 0.0608550 0.0243431 >> 0.0994355 >> 3: 12037.682: 0.00303460 0.0445012 0.00000 0.00000 0.0568530 >> 0.00000 0.00000 0.00636016 0.0696959 0.0156250 0.0608550 0.0243419 >> 0.0988824 >> 4: 12012.684: 0.00164710 0.0431313 0.00000 0.00000 0.0554032 >> 0.00000 0.00000 0.00515500 0.0696451 0.0156250 0.0608550 0.0243395 >> 0.0978328 >> 5: 12003.017: 0.00107848 0.0425739 0.00000 0.00000 0.0548073 >> 0.00000 0.00000 0.00469592 0.0696233 0.0156250 0.0608550 0.0243386 >> 0.0974616 >> 6: 11984.372: 0.00000 0.0414397 0.00000 0.00000 0.0535899 >> 0.00000 0.00000 0.00378996 0.0695782 0.0156250 0.0608550 0.0243370 >> 0.0967449 >> 7: 11978.154: 0.00000 0.0409106 0.00000 0.00000 0.0530158 >> 0.00000 0.00000 0.00340746 0.0695560 0.0156250 0.0608550 0.0243363 >> 0.0964537 >> 8: -0.0000000: 0.00000 nan 0.00000 0.00000 nan >> 0.00000 0.00000 nan nan nan nan nan nan >> >> Regards, >> >> Jean >> >> 2015-05-06 17:43 GMT-07:00 Jean Marchal <[email protected]>: >> > Dear list, >> > >> > I am doing some maximum likelihood estimation using nlminb() with >> > box-constraints to ensure that all parameters are positive. However, >> > nlminb() is behaving strangely and seems to supply NaN as parameters >> > to my objective function (confirmed using browser()) and output the >> > following: >> > >> > $par >> > [1] NaN NaN NaN 0 NaN 0 NaN NaN NaN NaN NaN NaN NaN >> > >> > $objective >> > [1] 0 >> > >> > $convergence >> > [1] 1 >> > >> > $iterations >> > [1] 19 >> > >> > $evaluations >> > function gradient >> > 87 542 >> > >> > $message >> > [1] "gr cannot be computed at initial par (65)" >> > >> > >> > When I use trace = TRUE, I can see the following: >> > >> > 0: 32495.488: 0.0917404 0.703453 1.89661 1.11022e-16 >> > 1.11022e-16 0.107479 1.11022e-16 1.11022e-16 1.11022e-16 0.472377 >> > 0.894128 1.86743 1.11022e-16 >> > 1: 4035.3900: 0.0917404 0.703453 1.89661 1.11022e-16 >> > 1.11022e-16 0.107479 1.11022e-16 1.11022e-16 1.11022e-16 0.472377 >> > 0.894128 1.86743 0.250000 >> > 2: 3955.8801: 0.0948452 0.704168 1.89651 0.000135456 0.0310485 >> > 0.107991 0.00138902 0.000427631 1.11022e-16 0.472331 0.894128 1.86743 >> > 0.250000 >> > 3: 3951.4141: 0.0948926 0.703906 1.89640 2.99167e-05 0.0315288 >> > 0.109692 0.00242572 0.00272185 7.96814e-05 0.472780 0.894130 1.86744 >> > 0.249998 >> > .... >> > 17: 3937.3923: 0.0947470 0.703030 1.89605 1.11022e-16 0.0300763 >> > 0.115081 0.00562496 0.00989997 0.000323268 0.474247 0.894142 1.86745 >> > 0.249737 >> > 18: 3937.3923: 0.0947470 0.703030 1.89605 1.11022e-16 0.0300763 >> > 0.115081 0.00562496 0.00989997 0.000323268 0.474247 0.894142 1.86745 >> > 0.249737 >> > 19: -0.0000000: -nan -nan -nan 1.11022e-16 -nan >> > -nan -nan -nan -nan -nan -nan -nan nan >> > >> > >> > my objective function looks like: >> > >> > nLL <- function(params){ >> > >> > mu <- drop(model.matrix(modelTermsObj) %*% params) >> > >> > if(any(mu < 0) || anyNA(mu) || any(is.infinite(mu))){ >> > return(.Machine$double.xmax) >> > } else { >> > return(-sum(dnbinom(x=args$data[,response], mu = mu, size = >> > params[length(params)], log = TRUE))) >> > } >> > } >> > >> > I tried different starting values, different bounds but without >> > success so far. Is this a bug? >> > >> > PS after trying to make a reproducible example that I gracefully >> > failed to do... I change my objective function so instead of using >> > model.matrix(), I did the maths (e.g. Y ~ A + B * C). Thus, mu is now >> > a bunch of NaN, and my objective function return .Machine$double.xmax >> > which is fine. Then nlminb stops and returns (like if nothing >> > happened): >> > >> > $par >> > [1] 1.11022e-16 1.11022e-16 2.69205e-04 1.11022e-16 1.68161e-03 >> > 1.06027e-03 1.16969e-05 1.11022e-16 8.51669e+01 7.31162e+01 >> > 5.04748e+00 5.28373e+00 1.23992e-01 >> > >> > $objective >> > [1] 3823.567 >> > >> > $convergence >> > [1] 0 >> > >> > $iterations >> > [1] 1 >> > >> > $evaluations >> > function gradient >> > 2 13 >> > >> > $message >> > [1] "X-convergence (3)" >> > >> > I can provide the data and model if necessary but cannot make them >> > publicly available (yet). >> > >> > Thank you, >> > >> > Jean >> >> ______________________________________________ >> [email protected] mailing list -- To UNSUBSCRIBE and more, see >> 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. > > ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see 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.
