Understanding/plotting fixed effects estimates &standard errors

hi dr Duffy,

many thanks for the attention and the reply.  I followed the instructions under "Predictions and/or confidence (or prediction) intervals on predictions" of the wiki FAQ; however, unless I missed something, the results were nearly identical (and therefore confidence intervals wide & overlapping) to what I had by removing the intercept from the model:

m1=lmer(cbind(success,fail) ~ factor + (1|spp/variable), bb, family='binomial')
summary(m1)

...
Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  1.43834    0.52289   2.751  0.00595 ** 
factor1       0.21081    0.04269   4.938 7.88e-07 ***

newdat=expand.grid(success=0,fail=0,factor=c('0',"1"))
mm=model.matrix(terms(m1),newdat)
estims=mm %*% fixef(m1)
estims

[,1]
1 1.438342
2 1.649149

pvar1=diag(mm %*% tcrossprod(vcov(m1),mm))
sqrt(pvar1)

[1] 0.5228946 0.5229200

#compare to

m3=lmer(cbind(success,fail) ~ factor -1 + (1|spp/variable), bb, family='binomial')
summary(m3)

...
Fixed effects:
      Estimate Std. Error z value Pr(>|z|)   
factor0    1.4383     0.5229   2.751  0.00595 **
factor1    1.6491     0.5229   3.154  0.00161 **

many thanks,

Abra?os,
Rafael Maia
---
webpage: http://gozips.uakron.edu/~rm72
"A little learning is a dangerous thing; drink deep, or taste not the Pierian spring." (A. Pope)
Graduate Student - Integrated Bioscience
University of Akron
http://gozips.uakron.edu/~shawkey/

This is nothing to with mixed modelling per se, but how your contrasts for the fixed effects are set up.  If this was fixed effects only, you could fit -1 + factor (no intercept) to give estimates for each level of factor with standard errors that you could correctly interpret as you are trying to.  The FAQ (http://glmm.wikidot.com/faq) shows plotting of confidence intervals for predictions.