[R] nlminb doesn't converge and produce a warning

[Douglas Bates]

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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