search for: enh_teach

...ive, I need to use a multi-response model that is *not* multinomial. I'm now trying to figure out how to specify the priors on the multi-response model. Any help would be much appreciated. My data look like this: X other focal village present r teaching Opp_teacher Dir_teacher Enh_teacher SocTol_teacher Eval_teacher Total_teacher f_Age f_Ed Age Ed1 61 10202 10213 0 15 0.250000000 2 0 0 0 0 2 2 1 0 48 82 63 10203 10213 0 19 0.500000000 6 0 0 4 0...

...se model that is *not* > multinomial. I'm now trying to figure out how to specify the priors on the > multi-response model. Any help would be much appreciated. > > My data look like this: > > X other focal village present r teaching Opp_teacher > Dir_teacher Enh_teacher SocTol_teacher Eval_teacher Total_teacher > f_Age f_Ed Age Ed1 61 10202 10213 0 15 0.250000000 > 2 0 0 0 0 2 > 2 1 0 48 82 63 10203 10213 0 19 > 0.500000000 6 0 0...

...mial. I'm now trying to figure out how to specify the priors on > the > > multi-response model. Any help would be much appreciated. > > > > My data look like this: > > > > X other focal village present r teaching Opp_teacher > > Dir_teacher Enh_teacher SocTol_teacher Eval_teacher Total_teacher > > f_Age f_Ed Age Ed1 61 10202 10213 0 15 0.250000000 > > 2 0 0 0 0 2 > > 2 1 0 48 82 63 10203 10213 0 19 > > 0.500000000 6...

...iables (each with count data), and an additional two random effects built into the models. The issue is that when I use the following code, the summary only gives me results for four of the outcome variables. Here is the code for my model: m3.random <- MCMCglmm(cbind(Opp_teacher , Dir_teacher, Enh_teacher, SocTol_teacher, Eval_teacher) ~ trait -1, random = ~ us(trait):other + us(trait):focal, rcov = ~ us(trait):units, prior = list( R = list(fix=1, V=0.5 * (I + J), n = 4), G = list( G1 = list(V = diag(...

...and an additional two random effects built into the > models. The issue is that when I use the following code, the summary only > gives me results for four of the outcome variables. > > Here is the code for my model: > > m3.random <- MCMCglmm(cbind(Opp_teacher , Dir_teacher, Enh_teacher, > SocTol_teacher, Eval_teacher) ~ trait -1, > random = ~ us(trait):other + us(trait):focal, > rcov = ~ us(trait):units, > prior = list( > R = list(fix=1, V=0.5 * (I + J), n = 4), > G = list( >...

...ects built into > the > > models. The issue is that when I use the following code, the summary only > > gives me results for four of the outcome variables. > > > > Here is the code for my model: > > > > m3.random <- MCMCglmm(cbind(Opp_teacher , Dir_teacher, Enh_teacher, > > SocTol_teacher, Eval_teacher) ~ trait -1, > > random = ~ us(trait):other + us(trait):focal, > > rcov = ~ us(trait):units, > > prior = list( > > R = list(fix=1, V=0.5 * (I + J), n = 4), > >...