[R-sig-ME] nAGQ = 0

[Jake Westfall]

...BTW, the syntax in question, for those not interested enough to locate
it in the previously attached R script, is:
 ~ (0+Trt)/x


On Thu, Sep 7, 2017 at 3:00 PM, Jake Westfall <jake.a.westfall at gmail.com>
wrote:

> To further clear things up for other readers, this formula syntax is a
> nice and very useful (if little known) trick for estimating separate
> intercepts and x slopes for each level of Trt. It creates a design matrix
> like:
>
> [1 0 x_1 0]
> [1 0 x_2 0]
> [0 1 0 x_3]
> [0 1 0 x_4]
>
> where the first two rows belong to Trt=1, the second two rows belong to
> Trt=2; columns 1 and 2 are the Trt1 and Trt2 intercepts, respectively; and
> columns 3 and 4 contain only the x values for Trt1 and Trt2, respectively,
> so their associated coefficients give the group-specific slopes for each
> level of Trt.
>
> Jake
>
> On Thu, Sep 7, 2017 at 2:41 PM, Phillip Alday <phillip.alday at mpi.nl>
> wrote:
>
>> 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
>> >
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>

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