I'm doing an analysis on disease in Chile. I'm using the rate as the
Dependent Variable and both remote sensing and housing-type data as the
Independent Variables. I have several questions regarding my methods:
- The data appears to follow the zero-inflated negative binomial distribution. I checked with the countfit tool in Stata, and the data best fits this distribution. Is it ok to fit a ZINB model to continuous data?
- I need to account for spatial autocorrelation in both the dependent, and the remote sensing data. I'm not sure exactly how to do this. After some reading, I thought that I needed to generate a spatial weights matrix (using ArcMap). If this is correct, how do I implement it? I generated one for the disease rate, and used it in Stata:
[pweight = weight], which greatly improved the model fit. However, I'm not sure if this is the correct usage.
Can anyone give me some pointers, or readings, or anything? I really want to complete this project!