[R] nlminb supplying NaN parameters to objective function
Jean Marchal
jean.d.marchal at gmail.com
Fri May 8 02:37:59 CEST 2015
Thanks for the advice! I will continue to monitor the optimizer behaviour.
Jean
2015-05-07 17:03 GMT-07:00 William Dunlap <wdunlap at tibco.com>:
> 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.marchal at gmail.com>
> wrote:
>>
>> Yes, indeed! Problem solved!
>>
>> Thanks a lot!
>>
>> Jean
>>
>> 2015-05-07 14:06 GMT-07:00 William Dunlap <wdunlap at 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.marchal at 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.marchal at 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
>> >>
>> >> ______________________________________________
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>> >> and provide commented, minimal, self-contained, reproducible code.
>> >
>> >
>
>
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