Hi,
I would like to know if some of you have a solution for this problem:
I use optimx (from package optimx) to fit the parameters of a model
(complex model based on several imbricated exponential functions).
I use the two methods : method = c("Nelder-Mead", "BFGS") with the options:
control=list(dowarn=FALSE, follow.on=TRUE, kkt=FALSE, trace=1,
REPORT=100, maxit=1000)
For some situations, it works as expected, but not for others.
The problem occurs at the transition between the two methods:
For example at the end of the Nelder-Mead method the value is 47.55839
but at the beginning of the BFGS it drops again at 47.62xxx and at the
end of the BFGS it is "only" 47.56198, so a local minimum (see below a
result).
DHA DHH T12H value fevals gevals niter
convcode kkt1 kkt2 xtimes
Nelder-Mead 46.93154 39.94028 318.4949 47.55839 409 NA NA
10 NA NA 156.896
BFGS 45.29744 36.80026 321.5996 47.56198 54 5 NA
0 NA NA 32.604
After investigations, it seems that when parameters are transmitted from
one method to the next, the values is truncated at the 5th digit. And as
my model has several exponential functions imbricated, it is very
sensitive to the precision of the parameters. It does not change the
main conclusion, but I would prefer not have such a problem.
Does someone has a solution ?
I would prefer continue to use optimx.
Thanks a lot.
Marc
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