Parameters setting in functions optimization

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