I have to fit a generalized linear model in R, and I have never done this before, so I'm in very much doubt.
I have a dataset (of 4036 observations) claims sum grp 1 3852 34570293 1 2 1194 7776468 1 3 3916 26343305 1 4 1258 5502915 1 5 11594 711453346 1 ... there are 4 groups (grp). The task is to determine the effect of sum and grp (and interactions between them) on the claims. I have to test using different link functions and distributions What I think I should do is (in R) > glm(claims~sum*grp, family=gaussian(link="log")) Call: glm(formula = claims ~ sum * grp, family = gaussian(link = "log")) Coefficients: (Intercept) sum grp sum:grp 1.215e+01 -4.466e-09 6.814e-02 5.294e-09 Degrees of Freedom: 4035 Total (i.e. Null); 4032 Residual Null Deviance: 3.371e+16 Residual Deviance: 3.355e+16 AIC: 131500 Is this right? And how can the output be interpreted? Did I even answer the question, and how can I plot a curve to the oberservations? /Thank you so much for helping -- View this message in context: http://www.nabble.com/Generalized-linear-models-tp23265349p23265349.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.
