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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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