Bert Gunter <gunter.berton <at> gene.com> writes:
>
> I certainly second all Jeff's comments.
>
> **HOWEVER** :
> http://www.tandfonline.com/doi/pdf/10.1080/00401706.1978.10489610
>
> IIRC, DUD's provenance is old, being originally a BMDP feature.
>
If you want to do derivative-free nonlinear least-squares fitting
you could do something like:
library("bbmle")
dnorm2 <- function(x,mean,log=FALSE) {
ssq <- sum((x-mean)^2)
n <- length(x)
dnorm(x,mean,sd=ssq/n,log=log)
}
mle2(y~dnorm2(mean=a*x^b),data=...,method="Nelder-Mead")
This is not necessarily the most efficient/highly tuned possibility,
but it is one reasonably quick way to get going (you can substitute
other derivative-free optimizers, e.g.
library("optimx")
mle2(...,optimizer="optimx",method="bobyqa")
(I think).
This isn't the exact algorithm you asked for, but it might serve
your purpose.
Ben Bolker
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
