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 diffi
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))
}
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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(),
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