[R] Choosing how to vary slopes and intercepts in a hierarchical model

jjh21 jjharden at gmail.com
Wed Feb 25 21:31:48 CET 2009


Is there any set of rules for deciding exactly how to vary slopes and
intercepts in a HLM? I have NOMINATE scores from the senate over three
years. So I have multiple observations of senators, nested in states, nested
in years. Covariates include level-1 (senator in a specific year) variables
and state-level variables. I am having a hard time figuring out which
combination of random slopes and intercepts to use. What is the best way to
compare model specifications using the lmer() function? Should it just be a
likelihood ratio test? Also, how do I test whether the random effects for a
particular coefficient are significant? The model I fit first based on
theory fit reasonably well, but I have been able to get specifications with
higher likelihoods. How do I justify reporting those fits?

View this message in context: http://www.nabble.com/Choosing-how-to-vary-slopes-and-intercepts-in-a-hierarchical-model-tp22211111p22211111.html
Sent from the R help mailing list archive at Nabble.com.

More information about the R-help mailing list