[R] Parameters setting in functions optimization

[Florent D.]

Oh, and your message:

In log(LikeGi(l, i, par[1], par[2])) : NaNs produced

means your LikeGi is returning something negative. Can't take the log of it...


On Tue, Nov 29, 2011 at 8:09 PM, Florent D. <flodel at gmail.com> wrote:
> I also think your last write-up for LogLiketot (using a single
> argument "par") is the correct approach if you want to feed it to
> optim().
>
> So now you have a problem with  log(LikeGi(l, i, par[1], par[2])) for
> some values of par[1] and par[2].
>
> Where is LikeGi coming from? a package or is it your own function?
>
> You could add some print statements (if you are familiar with
> "browser()" it is even better) so you may see what values of "par" are
> causing trouble.
>
>
> On Tue, Nov 29, 2011 at 1:15 PM, Diane Bailleul
> <diane.bailleul at u-psud.fr> wrote:
>> Good afternoon everybody,
>> I'm quite new in functions optimization on R and, whereas I've read lot's of
>> function descriptions, I'm not sure of the correct settings for function
>> like "optimx" and "nlminb".
>> I'd like to minimize my parameters and the loglikelihood result of the
>> function.
>> My parameters are a mean distance of dispersion and a proportion of
>> individuals not assigned, coming from very far away.
>> The function LikeGi reads external tables and it's working as I want (I've
>> got a similar model on Mathematica).
>>
>> My "final" function is LogLiketot :
>> LogLiketot<- function(dist,ms)
>> {
>> res <- NULL
>> for(i in 1:nrow(pop5)){
>>    for(l in 1:nrow(freqvar)){
>> res <- c(res, pop5[i,l]*log(LikeGi(l,i,dist,ms)))
>>    }
>>        }
>> return(-sum(res))
>>            }
>>
>> dist is the mean dispersal distance (0, lots of meters) and ms the
>> proportion of individuals (0-1).
>> Of course, I want them to be as low as possible.
>>
>> I'd tried to enter the initials parameters as indicated in the tutorials :
>> optim(c(40,0.5), fn=LogLiketot)
>>>Error in 1 - ms : 'ms' is missing
>> But ms is 0.5 ...
>>
>> So I've tried this form :
>> optimx(c(30,50),ms=c(0.4,0.5), fn=LogLiketot)
>> with different values for the two parameters :
>>                    par  fvalues      method fns grs itns conv KKT1 KKT2
>> xtimes
>>>2    19.27583, 25.37964 2249.698        BFGS  12   8 NULL    0 TRUE TRUE
>>> 57.5
>>>1 29.6787861, 0.1580298 2248.972 Nelder-Mead  51  NA NULL    0 TRUE TRUE
>>> 66.3
>>
>> The first line is not possible but as I've not constrained the optimization
>> ... but the second line would be a very good result !
>>
>> Then, searching for another similar cases, I've tried to change my function
>> form:
>>
>> LogLiketot<- function(par)
>> {
>> res <- NULL
>> for(i in 1:nrow(pop5)){
>>    for(l in 1:nrow(freqvar)){
>> res <- c(res, pop5[i,l]*log(LikeGi(l,i,par[1],par[2])))
>>    }
>>        }
>> return(-sum(res))
>>            }
>>
>> where dist=par[1] and ms=par[2]
>>
>> And I've got :
>> optimx(c(40,0.5), fn=LogLiketot)
>>                    par  fvalues      method fns grs itns conv KKT1 KKT2
>> xtimes
>>>2 39.9969607, 0.9777634 1064.083        BFGS  29  10 NULL    0 TRUE   NA
>>>  92.03
>>>1 39.7372199, 0.9778101 1064.083 Nelder-Mead  53  NA NULL    0 TRUE   NA
>>>  70.83
>> And I've got now a warning message :
>>>In log(LikeGi(l, i, par[1], par[2])) : NaNs produced
>> (which are very bad results in that case)
>>
>>
>> Anyone with previous experiences in optimization of several parameters could
>> indicate me the right way to enter the initial parameters in this kind of
>> functions ?
>>
>> Thanks a lot for helping me !
>>
>> Diane
>>
>> --
>> Diane Bailleul
>> Doctorante
>> Université Paris-Sud 11 - Faculté des Sciences d'Orsay
>> Unité Ecologie, Systématique et Evolution
>> Département Biodiversité, Systématique et Evolution
>> UMR 8079 - UPS CNRS AgroParisTech
>> Porte 320, premier étage, Bâtiment 360
>> 91405 ORSAY CEDEX FRANCE
>> (0033) 01.69.15.56.64
>>
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>>
>