Hey John,
Seems fair, and, I agree a more explicit or clear (ie, giving users
indications as to why/when the lm.influence is going to misfit the data)
warning makes sense in context.
Sincerely,
Eric
On Wed, Apr 3, 2019 at 10:18 AM Fox, John wrote:
> Dear Eric,
>
> I'm afraid that your argument
Hey guys,
I appreciate the replies.
I agree the issue is easy to catch; wouldn't it make sense to make a
warning given that these types of errors (I am sure there are other ways to
make the lm.influence have similar NaN performance, simply due to points
radically not fitting the data) are relativ
Second!
Bert Gunter
On Wed, Apr 3, 2019 at 9:35 AM Richard M. Heiberger wrote:
> fortune nomination.
>
>
> The lesson to me here is that if you fit a sufficiently unreasonable
> model to data, the computations may break down.
>
> On Wed, Apr 3, 2019 at 10:18 AM Fox, John wrote:
> >
> > Dear
fortune nomination.
The lesson to me here is that if you fit a sufficiently unreasonable
model to data, the computations may break down.
On Wed, Apr 3, 2019 at 10:18 AM Fox, John wrote:
>
> Dear Eric,
>
> I'm afraid that your argument doesn't make sense to me. As you saw when you
> tried
>
>
Dear Eric,
I'm afraid that your argument doesn't make sense to me. As you saw when you
tried
fit3 <- update(fit, subset = !(Name %in% c("Jupiter ", "Saturn ")))
glm.nb() effectively wasn't able to estimate the theta parameter of the
negative binomial model. So why would it be better to
Hi Peter,
Yes, that's another reflection of the degree to which Jupiter and Saturn are
out of line with the data for the other planet when you fit the very
unreasonable negative binomial model with Volume untransformed.
Best,
John
> On Apr 3, 2019, at 5:36 AM, peter dalgaard wrote:
>
> Yes,
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