[R-sig-ME] nAGQ = 0

[Phillip Alday]

As mentioned previously, the formula syntax for this one is a bit
confusing. The left-hand side was easy enough to figure out from the
brms docs, but the division operator on the RHS was a trick I had never
used before -- it took me looking at the output from the
glmer(...,nAGQ=0) model to get that it's equivalent to	

~ 0 + Trt + Trt:x + (x | Rep)

Phillip



On 09/07/2017 06:12 AM, Rolf Turner wrote:
> 
> On 07/09/17 10:57, Poe, John wrote:
> 
>> This is where my being a political scientist on a listserv full of
>> definitely not political scientists is going to make me look dumb.
>> I've never actually seen a model specification like that before for a
>> multilevel model. Is the outcome supposed to be the proportion dead
>> out of the total population for each row? I'm missing something about
>> this that is probably very obvious.
>>
>> After fiddling with it I was able to get it to converge for one chain
>> but I wouldn't trust it at all right now.
> 
> Well, I hadn't seen a model specification quite like that either.  It's
> brm() syntax, which is a bit different from glm() or glmer() syntax.
> 
> The model being fitted is a binomial model, with success probability p
> modelled by
> 
>     g(p) = beta_0 + beta_1 * x + Z_0 + Z_1 * x
> 
> where the Z_k are the random effects, and where g() is the link
> function, e.g. cloglog(). (I *think* I've got that right.)
> 
> Of course beta_0 and beta_1 depend on treatment group and Z_0 and Z_1
> depend on "Rep", which is nested within treatment group.
> 
> Note that we are talking *generalized* linear mixed models here; the
> responses are binomial success counts.  "Success" = Dead, since one is
> trying to kill the bugs.
> 
> For what it's worth the glmer() syntax is
> 
>    glmer(cbind(Dead,Alive) ~ (0 + Trt)/x + (x | Rep),
>          family=binomial(link="cloglog"),data=X)
> 
> I got the brm() syntax from the vignette; as I said, I'd never seen it
> before.
> 
> cheers,
> 
> Rolf
>