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")) 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 >> -- >> 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. >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> 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 > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/Generalized-linear-models-%28GLM%29-tp23279588p23283633.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
