[R-sig-ME] Specifying a (simple?) two level model

[Hans Ekbrand]

On Thu, Jun 30, 2011 at 11:30:18AM +0200, Reinhold Kliegl wrote:
> Your first model is asking too much. You are estimating 22 variance
> components and (22*21)/2 correlation parameters for them.
> Your second model is a good start for your question. Here is what the
> model returns for you:
> > ranef(my.fit)
> This values are what Douglas Bates prefers to call "the conditional
> modes of the random effects". To quote him: "If you want to be
> precise, these are the conditional modes of the random effects B given
> Y = y, evaluated at the parameter estimates."
> 
> Basically, they give you relative positions of countries and clusters
> to the intercept, taking into account the reliability (i.e., n of
> observations) you have for the other factors levels. So adding the
> terms yields a "prediction" on the basis of the two random factors.
>      This model does not give you country-specifc effects. One way to
> model the interaction is to assume  that there is a random effect for
> each county and a separate random effect for each combination of
> country and employment history.  If the random effects for these
> combinations are assumed to be independent with constant variance,
> then the following model is appropriate:
> 
> m2 <- glmer(poverty.third.year ~ 1 + cluster + (1 | country) + (1 |
> country:cluster), family = binomial("logit"), data = poverty.risks)
> This model still generates 499 conditional modes, but uses only 2
> variance components plus residual variance plus 21 fixed effects for
> clusters. This may be a good compromise or at least starting point.
> It completed in about 20 minutes on my machine.

Thank you Reinhold, for your hints and suggestions. Unless the first
model will converge after I take care of the separation-problem
spotted by Thierry, I'll use the suggestion you provde.