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. > [[alternative HTML version deleted]] ______________________________________________ [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.
