I did a negative binomial regression on a data set with 4 covariables. The count outcome has values up to 600. I did a mixture model with 2 components, also called a latent class model.
However, I am not sure if the results make sense. These are the parameter estimates for the first component:
intercept 2.1 v1 3.6 v2 -0.5 v3 1.1 v4 2.2
and for the second component:
intercept 100.1 v1 -0.7 v2 2.5 v3 -2.0 v4 0.5
I know that exponentiating the coefficients gives incidence rate ratios. These would be very high here. I am usually used to having coefficients in the range of -1 to +1. But I never had a data set with count values that high. Is this the reason for the high parameter estimates or is something wrong here?