Dear Florent,
I know that I'm asking to optim to minimize my values, and that the
results with a lower fvalue are best supported than those with a higher
fvalue.
My comment was just from a data point of view. I'd like the lower ms
(second parameter) as possible, as well as the fvalue. So a ms o
optimx does allow you to use bounds. The default is using only methods from
optim(), but
even though I had a large hand in those methods, and they work quite well,
there are other
tools available within optimx that should be more appropriate for your problem.
For example, the current version of
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 l
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
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. wrote:
> I also think your last write-up for LogLiketot (using a single
> argument "par") is the
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 ow
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