We are trying to fit a GLM that estimates the number of insurance claims made in a year using 6 independent variables.
The count variables are based on time frames (exposure) so to make it a fair comparison, we divide the count by exposure and call it frequency which gives an estimate of the counts you would expect in year.
The count variable follows a poisson distribution and exposure is a uniform distribution between 0 and 1.
The problem is that a GLM modeling frequency with a poisson family is returning warnings as it is modeling a count variable and not getting integer values (as count/exposure often returns float values). Rounding the frequency response variable is not an option as it leads to other conceptual problems.
Should I ignore the warnings and use the fitted model?