Displaying 6 results from an estimated 6 matches for "dir_teacher".
2018 May 01
2
Specifying priors in a multi-response MCMCglmm
...ually exclusive, 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...
2018 May 01
0
Specifying priors in a multi-response MCMCglmm
...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...
2018 May 01
2
Specifying priors in a multi-response MCMCglmm
...> 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...
2018 Mar 22
2
MCMCglmm multinomial model results
...5 outcome variables
(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 = lis...
2018 Mar 23
0
MCMCglmm multinomial model results
...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(
&g...
2018 Mar 24
1
MCMCglmm multinomial model results
...wo 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),
> >...