Varying intercepts vs. varying slopes in MCMCglmm ordinal models
Hi, Quoting Jonathan Salerno <jdsalerno at ucdavis.edu> on Fri, 22 Feb 2013 13:05:51 -0800:
These are very simple questions which I think will be most easily answered conceptually and without any data. First, does MCMCglmm allow for specification of varying slopes vs. varying intercepts? In the call's most basic form, m<-MCMCglmm(outcome~fixed_effect, random=~cluster, family="ordinal", data=data, prior=prior), is the outcome 'intercept' or the slope of 'fixed_effect' varying by 'cluster'?
The intercept is varying by cluster. random=~us(1+fixed_effect):cluster gives a random intercept/slope model with estimated covariance, and random=~idh(1+fixed_effect):cluster is the same but with the covariance set to 0.
Second, as I understand it the model cannot be fit with a nested data structure (ie, varying at multiple levels e.g. modeling child test scores within schools within districts). However, can effects vary across two clustering levels if they are not nested (e.g., modeling tests within schools and by religion)? If so, how is the model specified?
MCMCglmm does not require effects to be nested: random=~school+religion fits two (cross-classified) sets of random effects. Jarrod
Thanks very much in advance. [[alternative HTML version deleted]]
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