R help - Oct 2011 - How to use gev.fit (package ismev) under box constraints?

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Hallo,

it seems as if something did not work with my first email

I would like to estimate parameters of a general extreme value (GEV)
distribution using maximum likelihood as implemented in the gev.fit function of
package ismev. If I do the follwing:

y.training<- c(22, 22, 18, 19, 18, 18, 22, 27, 25, 19, 18, 21, 18, 20, 18,
19, 18, 21, 29, 18, 22, 19, 19, 24, 18, 21, 22, 20, 20, 27, 18, 20, 20, 18, 18,
18, 21, 18, 18, 21, 26, 19, 18, 19, 19, 18, 19, 18, 20, 20, 25, 21, 26, 22, 20,
19, 22, 21, 21, 20, 20, 19, 18, 22, 22, 27, 19, 20, 26, 29, 18, 20, 19, 22, 23,
18, 20, 20, 22, 18, 23, 18, 20, 19, 27, 21, 22, 18, 18, 19, 18, 21, 18, 23, 18,
18, 20, 20, 24, 19, 18, 19, 19, 23, 19, 18, 25, 18, 24, 19)
fit<-gev.fit(xdat=y.training,show=F)
round(fit$mle,2) # 18.00 , 0.00 , 3.96

# The estimated shape parameter is 3.96. I would like to perform the estimation
under the constraint that the shape parameter is smaller than 1, but the
following does not work:

fit<-gev.fit(xdat=y.training,show=F,method="L-BFGS-B",lower=c(0,0,-2),upper=c(50,10,1))
round(fit$mle,2) # 18.09 , 0.27 , 3.05

It seems as is the "lower" and "upper" values are not passed
to the optim function in the way they should be. A warning says that they are
only passed to the "control" part of optim. Therefore my question:
(How) is it possible to use the gev.fit-function to perform the ML estimation
under the constraint that the shape parameter is smaller than 1? Or more
general: Is it possible to use the gev.fit function under box constraints as it
should be possible for optim?

Thanks in advance.

You've identified a problem with the ismev package and it really is the
package
maintainers who are in the best position to fix it. As noted, box constraints
(lower and
upper) go OUTSIDE the control=list(). (Only L-BFGS-B has bounds in optim().)

This is also the case for many routines called by the optimx package (nlm,
nlminb, bobyqa,
Rvmmin, Rcgmin, etc.) Soon there'll be a Nelder-Mead variant and a Hooke
& Jeeves with
bounds/box constraints. A couple of routines also have masks, which are
parameters that
can be temporarily fixed for a run. These can be very useful for problems where
one or two
parameters are "usually" fixed or set to one of only a few values.

This information may be of use for other package maintainers. For those wanting
to use
optimx, get in touch with me off-list. Which reminds me that I have to get in
touch with
someone who already mentioned they wanted some advice on just this matter.

JN


On 10/22/2011 06:00 AM, r-help-request at r-project.org
wrote:
> Message: 61
> Date: Fri, 21 Oct 2011 17:08:25 +0200 (CEST)
> From: "NoSkill ButStyle" <NoSkillButStyle at web.de>
> To: r-help at r-project.org
> Subject: Re: [R] How to use gev.fit (package ismev) under box
> 	constraints?
> Message-ID: <594607686.882100.1319209705292.JavaMail.fmail at
mwmweb023>
> Content-Type: text/plain; charset="UTF-8"
> 
> Hallo,
> 
> it seems as if something did not work with my first email
> 
> I would like to estimate parameters of a general extreme value (GEV)
distribution using maximum likelihood as implemented in the gev.fit function of
package ismev. If I do the follwing:
> 
> y.training<- c(22, 22, 18, 19, 18, 18, 22, 27, 25, 19, 18, 21, 18, 20,
18, 19, 18, 21, 29, 18, 22, 19, 19, 24, 18, 21, 22, 20, 20, 27, 18, 20, 20, 18,
18, 18, 21, 18, 18, 21, 26, 19, 18, 19, 19, 18, 19, 18, 20, 20, 25, 21, 26, 22,
20, 19, 22, 21, 21, 20, 20, 19, 18, 22, 22, 27, 19, 20, 26, 29, 18, 20, 19, 22,
23, 18, 20, 20, 22, 18, 23, 18, 20, 19, 27, 21, 22, 18, 18, 19, 18, 21, 18, 23,
18, 18, 20, 20, 24, 19, 18, 19, 19, 23, 19, 18, 25, 18, 24, 19)
> fit<-gev.fit(xdat=y.training,show=F)
> round(fit$mle,2) # 18.00 , 0.00 , 3.96
> 
> # The estimated shape parameter is 3.96. I would like to perform the
estimation under the constraint that the shape parameter is smaller than 1, but
the following does not work:
> 
>
fit<-gev.fit(xdat=y.training,show=F,method="L-BFGS-B",lower=c(0,0,-2),upper=c(50,10,1))
> round(fit$mle,2) # 18.09 , 0.27 , 3.05
> 
> It seems as is the "lower" and "upper" values are not
passed to the optim function in the way they should be. A warning says that they
are only passed to the "control" part of optim. Therefore my question:
(How) is it possible to use the gev.fit-function to perform the ML estimation
under the constraint that the shape parameter is smaller than 1? Or more
general: Is it possible to use the gev.fit function under box constraints as it
should be possible for optim?
> 
> Thanks in advance.
> 
> 
> 
> ------------------------------