12-04-2012 01:00 PM
I'm working on my dissertation and my data has missing data (like most data sets ). The missing data isn't horrible (6%-12% on a few variables), but I am imputing the data to restore some of the lost variance. After exploring the data and refining the model, I am satisfied with the output for proc MIANALYZE.
My question concerns the next steps in my analysis. I am performing geographically weighted regression (GWR) in ArcMap, so I can't analyze the pooled imputed data sets with a simple command like "By _imputation_". Unfortunately, this problem means I would have to perform X GWR's, where X is equivalent to the number of imputations, and pool the individual results. I could follow Little and Rubin (1987) methodology for manually combining data (i.e., averaging beta's and using matrix algebra to compute covariances). Does anyone have the SAS code, so that I could upload output from each GWR result (e.g. covariance matrix's) and automate this process to obtain variance, df, and p-values?
The real problem is creating a pooled estimate for local and global results across data sets. I would have no idea how to do that. Any suggestions?
General note: The data I'm imputing is at the client admission level and not the census tract level - or else I would use another technique (e.g., kriging) to estimate missing data.