Although both are known now, there is a time element involved in
which one, max.loss was fixed at the time of underwriting and loss was
unknown at that time. This *is* an insurance question is it not?
Wouldn't the question be: Can one use the group variable to estimate
the proportion of max.loss actually lost following underwriting?
I would vote "no" on all of those offered solutions. If you believe
otherwise, you should write an explanation to a hypothetical professor
what the output of any of the models actually means.
--
David Winsemius
On Apr 28, 2009, at 2:38 PM, mathallan wrote:
Actually both max.loss and loss are known values (in dollars). I'm
very much
doubt, what to choose.
glm(max.loss~loss,family=gaussian(link="identity")
or
glm(formula = sum ~ claims * as.factor(grp), family = gaussian(link =
"identity"))
There are no variables named claims or sum. "sum" would be a bad
choice in variable names because of the potential confusion with the
function of the same name.
or
glm(loss~max.loss,family=gaussian(link="identity")
we have to look at gaussian and gamma, with link identity and log.
But my problem is what is going to be between the ~
David Winsemius wrote:
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
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______________________________________________
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide
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--
View this message in context:
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______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
