>From Ben Bolker:
> I would trust the gamma.dispersion() result more, although I
>agree that the difference is worrisome. The way to look at this
>further would be to profile the dispersion parameter. As I recall
>there isn't such a built in option in MASS (profile.glm only
>profiles the coefficients), but you may be able to do it
>*approximately* like this:
>
>library(bbmle)
>m1 <- mle2(precip_sbi ~ dgamma(shape=a,scale=mu/a),
> parameters=list(mu~precip_oxx+precip_oxx_sq),
> data=w.combo, start=list(mu=0.1,a=2))
>p1 <- profile(m1)
>plot(p1)
Ben: Thanks for the suggestion. I ran the code you sent, and according to
the graphical profile, the 99% confidence interval for shape is [.4, .7],
corresponding to a 99% interval for the dispersion parameter of [1.4, 2.5].
So your profile tool agrees with gamma.dispersion().
I now feel more somewhat more comfortable about gamma.dispersion(), but I'm
still worried by the difference between summary() and gamma.dispersion().
While I have a basic understanding of GLM's, I don't understand why the
reported value for dispersion would be 'crude'. Note that the word 'crude'
comes from help(gamma.shape) {MASS}.
If you or anyone else could help me further understand this issue, I'd
greatly appreciate it.
Tim Handley
Research Assistant
Channel Islands National Park
(Will be working from both CHIS and SAMO)
CHIS Phone: 805-658-5759 (Tue, Wed, Thu)
SAMO Phone: 805-370-2300 x2412(Mon, Fri)
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