I'm working with over-dispersed count data, which is zero inflated (~2/3 zeros). I've fit a hurdle model using
I have three related questions:
1) When fitting a hurdle model such as:
hurdle(count ~ factor1*factor2, data=data, dist="negbin")
I'm receiving a warning message that I'm having a hard time interpreting:
Warning message: In sqrt(diag(vc_count)[kx + 1]) : NaNs produced
I also tried using
dist="poisson", which does not return a warning.
Any idea what the warning message means?
2) When fitting both the Poisson and negative binomial GLMs to the counts, I wanted to analyze the model. I did a
waldtest() and a
lrtest() (Wald Test and Likelihood Ratio test respectively), comparing the fitted model to a null model with only an intercept. The comparison showed that the model with a negative binomial distribution for counts was not significant (potentially due to the warning?) and that the model with a Poisson distribution was significant. Is this an appropriate way to assess the model significance?
3) I wasn't sure how to assess the fits of the models (usually looking at
plot(glm.object), so I subsetted the count>0 values and fit the GLM and looked at the residual-fitted values plot as well as the Q-Q plot, to assess the fit. Is that an acceptable method? Note that objects of which are class
hurdle cannot be used in
Any help or insights are much appreciated!