I used vce(cluster) to account for clustering within 9 groups in a set of nested logistic regression models. Stata doesn't want to give me Wald chi2 stats because I have too many variables in the model in relation to # of clusters, and used up my df. Stata also said both Wald and lrtest would be misleading. So, what *wouldn't* be misleading to report to describe fit and compare fit among nested models? Are pseudo-R square, AIC, BIC, and log likelihood #s still meaningful to interpret? Or are there other stats I don't know about?
Thanks in advance!
Related Posts with What stats are appropriate to assess model fit for nested logit models if Wald is not possible for clustered data?
Please delete - double postingPosted twice by mistake, sorry …
running estimation1000 times with random assign treated groupsHi everyone, I applied a difference-in-difference approach to one of my research projects. Especial…
Running regression each country/year using GMM with xtabond2 Dear Statalist, I want to estimate using GMM predicted Y value for each year and each country in a…
how to split a var(string) into two vars hello, I have a question, how to sArray plit a into two as required: ----------------------- c…
Confidence Interval for Sensitivity and SpecificityI have the following data and would like to calculate the confidence interval for the sensitivity an…
Subscribe to:
Post Comments (Atom)
0 Response to What stats are appropriate to assess model fit for nested logit models if Wald is not possible for clustered data?
Post a Comment