Message-ID: <CAJPTfnmfAedSx=YQD+BoD6V70Qa1duCd9V+qZg7=qzB3gjx2fg@mail.gmail.com>
Date: 2011-11-30T01:09:16Z
From: Florent D.
Subject: Parameters setting in functions optimization
In-Reply-To: <4ED5214F.7030504@u-psud.fr>
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
> Universit? Paris-Sud 11 - Facult? des Sciences d'Orsay
> Unit? Ecologie, Syst?matique et Evolution
> D?partement Biodiversit?, Syst?matique et Evolution
> UMR 8079 - UPS CNRS AgroParisTech
> Porte 320, premier ?tage, B?timent 360
> 91405 ORSAY CEDEX FRANCE
> (0033) 01.69.15.56.64
>
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