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Jean Marchal

2015-May-07 00:43 UTC

[R] nlminb supplying NaN parameters to objective function

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

Jean Marchal

2015-May-07 20:14 UTC

head link

[R] nlminb supplying NaN parameters to objective function

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

William Dunlap

2015-May-07 21:06 UTC

head link

[R] nlminb supplying NaN parameters to objective function

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
>
> ______________________________________________
> R-help at 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.
>
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R help - May 2015 - nlminb supplying NaN parameters to objective function

[R] nlminb supplying NaN parameters to objective function

[R] nlminb supplying NaN parameters to objective function

[R] nlminb supplying NaN parameters to objective function