On Nov 08, 2011 at 11:16am Colin Aitken wrote:
> An unresolved problem is: what does R do when the explanatory factors
> are not defined as factors when it obtains a different value for the
> intercept but the correct value for the fitted value?
Colin,
I don't think that happens (that the fit
Sorry about that. However I have solved the problem by declaring the
explanatory variables as factors.
An unresolved problem is: what does R do when the explanatory factors
are not defined as factors when it obtains a different value for the
intercept but the correct value for the fitted val
Nov 08, 2011; 4:58am Rolf Turner wrote:
>(in response to
>>> Professor Colin Aitken,
>>> Professor of Forensic Statistics,
>!!!)
>
>>
>> Do you suppose you could provide a data-corpse for us to dissect?
>Fortune nomination!!!
I think Sherlock would have said, "But it's elementary, my dea
On 08/11/11 07:11, David Winsemius wrote:
(in response to
Professor Colin Aitken,
Professor of Forensic Statistics,
!!!)
Do you suppose you could provide a data-corpse for us to dissect?
Fortune nomination!!!
cheers,
Rolf Turner
On Nov 07, 2011 at 7:59pm Colin Aitken wrote:
> How does R estimate the intercept term \alpha in a loglinear
> model with Poisson model and log link for a contingency table of counts?
Colin,
If you fitted this using a GLM then the default in R is to use so-called
treatment contrasts (i.e. Dunne
On Nov 07, 2011 at 9:04pm Mark Difford wrote:
> So here the intercept represents the estimated counts...
Perhaps I should have added (though surely unnecessary in your case) that
exponentiation gives the predicted/estimated counts, viz 21 (compared to 18
for the saturated model).
##
> exp(3.0445
On Nov 7, 2011, at 12:59 PM, Colin Aitken wrote:
How does R estimate the intercept term \alpha in a loglinear
model with Poisson model and log link for a contingency table of
counts?
(E.g., for a 2-by-2 table {n_{ij}) with \log(\mu) = \alpha +
\beta_{i} + \gamma_{j})
I fitted such a mod
How does R estimate the intercept term \alpha in a loglinear
model with Poisson model and log link for a contingency table of counts?
(E.g., for a 2-by-2 table {n_{ij}) with \log(\mu) = \alpha + \beta_{i} +
\gamma_{j})
I fitted such a model and checked the calculations by hand. I agreed
wit
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