A more important point that want to make is that I find few people taking
advantage of the "comparative evaluation" or benchmarking ability of optimx.
There is no "uniformly best" optimizer for all problems. Different ones turn
out to perform better for different problems and it is quite difficult to know
a priori which one would be the best for "my" problem. Thus, the benchmarking
capability provided by optimx is a powerful feature.
Ravi
________________________________________
From: Ben Bolker <bbol...@gmail.com>
Sent: Thursday, January 15, 2015 9:29 AM
To: Ravi Varadhan; R-Help
Cc: malqura...@ksu.edu.sa
Subject: Re: R 3.1.2 mle2() function on Windows 7 Error and multiple solutions
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For what it's worth, you can use either nlminb (directly) or optimx
within the mle2 wrapper by specifying the 'optimizer' parameter ...
this gives you flexibility in optimization along with the convenience
of mle2 (likelihood ratio tests via anova(), likelihood profiling, etc.)
On 15-01-15 09:26 AM, Ravi Varadhan wrote:
> Hi,
>
>
>
> I tried your problem with optimx package. I found a better
> solution than that found by mle2.
>
>
>
> ?library(optimx)
>
>
>
> # the objective function needs to be re-written
>
> LL2 <- function(par,y) {
>
> lambda <- par[1] alpha <- par[2] beta <- par[3] R =
> Nweibull(y,lambda,alpha,beta)
>
> -sum(log(R)) }
>
>
>
> optimx(fn=LL2, par=c(.01,325,.8),y=y,
> lower=c(.00001,.00001,.00001),upper = c(Inf,
> Inf,Inf),control=list(all.methods=TRUE))
>
>
>
> # Look at the solution found by `nlminb' and `nmkb'. This is the
> optimal one. This log-likelihood is larger than that of mle2 and
> other optimizers in optimx.
>
>
>
> If this solution is not what you are looking for, your problem may
> be poorly scaled. First, make sure that the likelihood is coded
> correctly. If it is correct, then you may need to improve the
> scaling of the problem.
>
>
>
>
>
> Hope this is helpful,
>
> Ravi
>
>
>
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