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?
How to run gravity model PPML with constrained parameter on GDP?Dear professors, I have bilateral trade data for 119 countries and the year 2014. I want to run the …
How can we choose the appropriate significance level to interpret based on sample sizes?With the large sample size, we normally somehow interpret the coefficient significant at 10%, but in…
Linear-Log probit and linear probability modelsHi everyone. I am doing my thesis at the moment and I don't know how to interpret a key variable. T…
Importing .do fileHi all. I am trying to execute a .do file in Stata that I have downloaded and keep receiving this er…
Logistic regressionto whom it may concern, i am working on a topic "risk factors of child mortality" by using logistic …
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