Ben Bolker <bbolker <at> gmail.com> writes:
>
> Paul Hudson <paulhudson028 <at> gmail.com> writes:
>
[snip]
> library("tweedie")
> set.seed(1001)
> r <- rtweedie(1000,1.5,mu=2,phi=2)
> library("bbmle")
> dtweedie2 <- function(x,power,mu,phi,log=FALSE,debug=FALSE) {
> if (debug) cat(power,mu,phi,"\n")
> res <- dtweedie(y=x,xi=power,mu=mu,phi=phi)
> if (log) log(res) else res
> }
> m <- mle2(r~dtweedie2(power=exp(logpower),
> mu=exp(logmu),
> phi=exp(logphi)),
> ## don't start with logpower=0 (power=1)
> start=list(logpower=0.1,logmu=0,logphi=0),
> data=data.frame(r),
> method="Nelder-Mead")
>
> dtweedie2(r,xi=exp(0.1),mu=1,phi=1)
>
> In principle MASS::fitdistr could be made to work too.
PS in hindsight, you're better off with the built-in tweedie.power()
recommended by another poster. Estimating the power parameter for
Tweedie distributions is known to be difficult, and the naive approach I show
above may only work in best-case scenarios.
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