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#11




I did find This Stackexchange on discrete models useful. It goes over various metrics to evaluate your model.
There's also the DHARMa package that scales the QQ plot to work for poisson. 
#13




You might also look at 50 and 75 buckets.
Looks better regarding the crunched residuals. Seeing a trend in the residual graph usually means that something could be done better or that one of your assumptions (e.g., Poisson distributed) is off. I agree with the suggestion to use the "quasiPoisson" option instead since it's very likely that your (true) variance is larger than the mean. That could "fix" the observed trend you're seeing with the crunched residuals with 100 buckets.
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#14




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and Pure Premium, the sort order is just predicted since predictions are already pure premium? 
#16




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I found a formula that tests for overdispersion and it does show that my data is slightly overdispersed. I also found a function influencePlot from the car package that shows what points are highly leveraging the model. Can investigate removing some of those. I also tested a zeroinfl model and it is a better fit according to AIC. As moralHazard pointed out, I'm probably calculating frequency gini incorrectly cause I forgot to divide by exposure 
#17




gini is good for making a decision regarding if one model "does a better job" than another wrt discriminating differences in results (e.g., identifying higher premium/freq/sev obs from lower ones).
Residuals are good for making a determination of whether or not the underlying assumptions of the model are appropriate. I've seen situations where "odd" results from a residual plot actually indicated that there the assumption that "all data belong to the same population" was incorrect and we needed to either segregate the data or introduce another variable to recognize the differences. You won't get that from the gini.
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#19




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glm, peril, pure premium 
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