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.
