[R] nlminb supplying NaN parameters to objective function
Jean Marchal
jean.d.marchal at gmail.com
Fri May 8 01:46:50 CEST 2015
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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>
>
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