Message-ID: <AANLkTim+jfw35CxwxBoKOhtz45Hk7t7rSt5PZxsXeNc6@mail.gmail.com>
Date: 2011-01-21T18:24:55Z
From: Douglas Bates
Subject: nlminb doesn't converge and produce a warning
In-Reply-To: <AANLkTi=og=Ji530FVys1NJxHLGAx7xufAr3QSD82uBZF@mail.gmail.com>
On Fri, Jan 21, 2011 at 3:51 AM, kamel gaanoun <kamel.gaanoun at gmail.com> wrote:
> Hi Everybody,
>
> My problem is that nlminb doesn't converge, in minimising a logLikelihood
> function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6
> times).
Hmm, the length of the parameter vector shown below is 189, which is
neither 31*6 nor 2 + 29*6.
I suppose it is possible to do nonlinear optimization with box
constraints on such a large number of parameters but you should expect
it to take a long time and perhaps a lot of memory. Even if the
optimizer converges, it would be optimistic to expect that the
parameter value returned is necessarily the global optimum. I would
recommend trying to simplify the optimization problem. A method like
this is just using the computer as a blunt instrument with which to
bludgeon the problem to death (sometimes called the "SAS approach").
>
>
> I use nlminb like this :
> res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)),
> upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control =
> list(maxit=1000) )
>
> and that's the result :
>
> Message d'avis :
> In nlminb(vect, V, lower = c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, ?:
> ?unrecognized control element(s) named `maxit' ignored
>> res1
> $par
> ?[1] ? 2.48843979 ? 4.75209125 ? 2.57199837 ?16.80712783 ? 3.15211075
> 16.86606178 ?58.61925499 ?37.85793462 ?48.78215699
> ?[10] 151.64638501 ?43.60420299 ?15.14639541 ? 0.58754382 ? 0.76180935
> 0.66191763 ?-0.26802757 ?-0.96378197 ?-0.68369525
> ?[19] ? 0.37813096 ? 0.89778593 -10.26471908 ?-0.87265813 ? 6.43973968
> -1.74417166 ?12.00193419 ? 0.60638326 ?-1.66675589
> ?[28] ? 1.29312079 ? 1.39846863 ?-0.48449361 ?20.14470193 ?-0.50729841
> -2.15177967 ?-0.78155345 ? 0.41857810 ?-0.40863744
> ?[37] -17.18489562 ?-1.69140562 ? 1.45236861 ?-0.23738183 ? 5.47688642
> -0.71546576 ? 9.95015047 ?-2.16096138 ?-0.74503151
> ?[46] ?-0.66258461 ? 5.38871217 ? 2.53147752 -12.58827379 ?-0.45669589
> -0.37285088 ? 2.15116198 ?-2.50414066 ?-0.99752892
> ?[55] ? 4.83972450 ?-1.16496925 ?-3.53429528 ? 0.56083677 ?-9.87490932
> -1.75153657 ? 9.87912224 ?-0.75783517 ?-9.95423392
> ?[64] ?-0.07530469 ?-0.73466191 ?-0.27397382 ?15.15891548 ?-0.02489436
> 12.91493065 ?-4.65335356 ? 0.03524561 ? 0.00000000
> ?[73] ?-9.06720312 ?-0.25413758 ?-0.18578765 ? 0.53283198 ?-4.02688497
> -0.50581412 ?-0.31544940 ? 0.57450848 ? 6.15206152
> ?[82] ? 0.08178377 ? 0.82978606 ? 0.39337352 ?-3.65304712 ?-0.06833839
> 3.87790848 ?-1.08017043 ? 3.62779184 ?-0.14700541
> ?[91] -13.95610827 ?-1.50385432 ? 8.05851743 ?-1.24250013 ?-0.01249817
> 0.38085483 ?-4.97064573 ?-0.98852401 ?-3.00305183
> [100] ? 0.35053875 ?-4.26833889 ?-0.12463188 ?16.05828402 ? 0.41736764
> -0.94678922 ?-0.75813452 ? 2.15378348 ? 0.39586048
> [109] ? 1.41359441 ? 0.81603207 ?-4.43963958 ?-0.79438435 ? 0.49530882
> 0.11197484 ?-8.43196798 ? 1.00456535 -22.04423030
> [118] ?-0.11532887 ? 2.58085765 ? 1.41912515 ?-0.78120889 ?-1.23850824
> 12.39079062 ? 0.23567444 ? 1.39557879 ?-2.22993802
> [127] -12.58827379 ?-0.45669589 ?-0.37285088 ?-0.73563805 ? 3.40201735
> 0.58550247 ?-3.62769828 ? 0.21657740 ?-7.37785506
> [136] ?-0.68218180 ? 6.41876225 ? 0.38708385 ?-0.33009429 ?-0.25230736
> 3.53672719 ? 1.53676202 ? 3.65074513 ? 0.42623602
> [145] ?-7.26982010 ? 0.70597611 -23.15198788 ?-0.36822845 ?-2.29863267
> 0.70223129 -14.45665129 ?-0.54094864 ?-2.17858443
> [154] ?-0.56501734 ? 2.50032796 ?-0.45677181 ?12.04113439 ?-1.42294094
> -16.16874444 ?-0.49101846 ?-6.29724769 ?-1.38333722
> [163] -14.16552579 ? 1.57502968 ? 5.04329383 ? 0.24857745 ?-1.69885428
> -0.46757266 ? 4.41795651 ?-2.41006349 ? 4.61648610
> [172] ? 0.42235314 ?-3.22153895 ?-0.15443857 ? 1.07661101 ?-0.63653449
> -2.74034265 ? 0.20898466 ? 1.37927183 ? 0.26722477
> [181] -15.09685067 ? 0.87160467 -24.79722150 ? 1.48810684 ? 1.70068893
> -0.22538026 ? 7.63908028 ? 1.60431981 ?-7.52661064
>
> $objective
> [1] 1514.691
>
> $convergence
> [1] 1
>
> $message
> [1] "iteration limit reached without convergence (9)"
>
> $iterations
> [1] 150
>
> $evaluations
> function gradient
> ? ? 176 ? ?44935
>
> I tried many times to take the res1$par as initial values and retry againe
> but still doesn't converge.
>
>
> Any help will save me Thanks
>
> --
> Kamel Gaanoun
> (+33) (0)6.76.04.65.77
>
> ? ? ? ?[[alternative HTML version deleted]]
>
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