[R-sig-ME] rare binary outcome, MCMCglmm, and priors (related to separation)

[David Atkins]

On 8/30/10 2:58 PM, David Duffy wrote:
[snip]

>
> Hi. why are you using a mixed model here: dispersion, or are there
> multiple reports per individual?

Ack; seems like there is always something that I miss in a post - apologies!

Yes, this was a daily diary study with data collected over 60 days per 
individual (with some variability in compliance).  Thus, this is why I 
was thinking of glmer/MCMCglmm.

cheers, Dave

Another approach for separated/sparse
> data implemented in R is the penalized likelihood approach in the brlr,
> logistf, brglm (and Design) packages:
>
> brglm(formula = cbind(ipv.yes, ipv.no) ~ (ang.cut + prov.cut +
> alc.cut)^2, family = binomial(), data = ipv)
>
> Coefficients: (1 not defined because of singularities)
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -8.9666 1.4145 -6.339 2.31e-10 ***
> ang.cut 2.8959 1.4775 1.960 0.05000 .
> prov.cut 2.3740 0.4587 5.175 2.27e-07 ***
> alc.cut 7.8680 2.7082 2.905 0.00367 **
> ang.cut:prov.cut NA NA NA NA
> ang.cut:alc.cut -7.0703 2.8616 -2.471 0.01348 *
> prov.cut:alc.cut -0.4007 0.9962 -0.402 0.68747
>
> Model 1: cbind(ipv.yes, ipv.no) ~ (ang.cut + prov.cut + alc.cut)
> Model 2: cbind(ipv.yes, ipv.no) ~ (ang.cut + prov.cut + alc.cut)^2
> Resid. Df Resid. Dev Df Deviance P(>|Chi|)
> 1 2 1.0875
> 2 0 1.8387 2 -0.75117
>
> Cheers, David Duffy.