I think you are off-track because max.loss does not sound like a
proper Y variable. Because max.loss is an amount that is known, in the
insurance applications I have seen it would have been modeled within
an offset term. Many of the examples have used number of ships or
buildings or the person years of exposure but I do not see that the
general strategy is limited to only such considerations.
I would also suggest that you consider links other than Gaussian,
perhaps negative binomial.
The task for the analyst is then to translate output from the chosen
model into interpretable meaning on the scale of interest, but I
assume your course instructor will help with that.
--
David Winsemius
On Apr 28, 2009, at 11:34 AM, mathallan wrote:
>
> Hi
>
> I got a dataset
>
> loss max.loss grp
> 1 10 50 2
> 2 15 33 1
> 3 18 49 2
> 4 33 38 1
> 5 8 50 3
> 6 19 29 1
> 7 22 51 4
> 8 50 50 2
> 9 16 38 1
> 10 24 30 3
>
> were loss and max.loss are monetary values (in dollar). Grp is group
> number.
>
> By use of GLM, I have to determine the effect of max.loss and grp (and
> interactions between them) on loss. My question is how to do this.
>
> Is it something like
>
> glm(max.loss~loss,family=gaussian(link="identity")
>
> were ofcourse I can change gaussian with Gamma,... and link with
> log,...
>
> But am I on right track, or what should I change?
>
>
> Thanks
> --
> View this message in context:
http://www.nabble.com/Generalized-linear-models-%28GLM%29-tp23279588p23279588.html
> Sent from the R help mailing list archive at Nabble.com.
>
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT