search for: bcohort

...back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the past couple of days. Here are the models I tried: 1) Two level random intercept model with level-1 covariates only m1 <- lmer(.D ~ offset(log(.Y)) + (1|provn) +(1|bcohort) + x1 + x2 , data, poisson, method="Laplace") 2) Two-level random intercept model with both level-1 and level-2 covariates, but no cross-level interactions: m2 <- lmer(.D ~ offset(log(.Y)) + (1|provn) +(1|bcohort) + x1 + x2 + z1 + z2, data, poisson, method="Laplace") 3) Two...

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

...errors in the usual sense. > > Doran, Harold wrote: > > > These are the posterior variances of the random effects (I think > more > properly termed "empirical" posteriors). Your model > apparently includes > three levels of random variation (commu, > bcohort, residual). The first > are the variances associated with > your commu random effect and the > second are the variances associated with the bcohort random effect. > > > > Accessing either one would require > > > > fm at bVar$commu or fm at bVar$bcohort >...