Your immediate problem may be solved, but the exact value of that limiting value affects the parameter estimates a fair bit. I have not really looked at your function, but the ledge around it puts a kink (discontinuous first derivative) into it, which can mess up optimizers.
Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, May 7, 2015 at 4:46 PM, Jean Marchal <jean.d.marc...@gmail.com> wrote: > Yes, indeed! Problem solved! > > Thanks a lot! > > Jean > > 2015-05-07 14:06 GMT-07:00 William Dunlap <wdun...@tibco.com>: > > 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 <jean.d.marc...@gmail.com> > > 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 <jean.d.marc...@gmail.com>: > >> > 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 > >> > >> ______________________________________________ > >> R-help@r-project.org 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. > > > > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.
