With optimx(c(30,50),ms=c(0.4,0.5), fn=LogLiketot)
where
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))
}
I think it will do something like this for the first call to LogLiketot:
LogLiketot(c(30,50), ms=c(0.4,0.5))
which is obviously not the usage you had in mind.
Also, I see you say the results for the bad usage above:
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
look very good but you do not comment about the results for the
correct usage of optimx:
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
Do you realize optimx is trying to _minimize_ your function? See that
the fvalues from the correct usage are much better (smaller) than you
first (bad) usage.
On Wed, Nov 30, 2011 at 4:16 AM, Diane Bailleul
<diane.baill...@u-psud.fr> wrote:
> Le 11/30/2011 2:09 AM, Florent D. a écrit :
>
> Thanks for your answer !
>
>> 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().
>
> I'm not dedicated to optim() fonction. I just want to optimise my two
> parameters and the loglikelihood result, and if there's a better fonction
> for that, I wish I could use it.
>
>
>> 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?
>
> My own function, otherwise it would be simplier to discuss about my
> problems.
>
>
>> 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.
>
> I'm not familiar, but I'll search about browser().
>
> If the function with par is correct, any idea of what I've made with this :
>
> optimx(c(30,50),ms=c(0.4,0.5), fn=LogLiketot)
>
>
> ?
>
>
>
>>
>>
>> On Tue, Nov 29, 2011 at 1:15 PM, Diane Bailleul
>> <diane.baill...@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
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list
>>> 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.
>>>
>
>
> --
> 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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