Dear Statalisters:
I am analyzing a logistic regression with svy command. As you
know, with “svy” many statistics, i.e. dbeta, dx2, ddeviance, ROC,
etc. etc. for assessment of model are not calculated in logit
postestimation, except “svylogitgof”. This may be a stupid
question.. but my question is how I can assess the fit of svy logit
regression model?
Here are two approaches that I can think of:
1. According to Hosmer and Lemeshow, one approach is to compare
design-based analysis with model-based analysis.
2. The other approach would be to estimate those statistics only with
cluster (without pweight) and evaluate them.
Is there any better approach to evaluate survey logit model? If no,
which approach is better?
Thank very much for you help in advance.
Best regards,
Hisako
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
