Mixed effects model with binomial errors - problem

ok... the model now runs properly (say, without errors). Now about the
result.
These are the averages per treatments

tapply(VecesArbolCo.VecesCo.C1,T2,mean)
  a     b      c     d 
0.49 0.56 0.45 0.58 


I run this very simple model

summary(model1<-lmer(cbind(VisitsExpTree,TotalVisits-VisitsExpTree)~
treatment +(1|Individual), family=binomial, data=r))

Generalized linear mixed model fit by the Laplace approximation 
Formula: cbind(VisitsExpTree,TotalVisits-VisitsExpTree)~ treatment
+(1|Individual)  
   Data: r 
   AIC   BIC logLik deviance
 242.3 255.9 -116.2    232.3
Random effects:
 Groups    Name        Variance Std.Dev.
 Individuo (Intercept) 0.14075  0.37517 
Number of obs: 112, groups: Individuo, 37

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)          0.37228    0.19031  1.9562  0.05044 . 
treatmentb          0.03367    0.24520  0.1373  0.89079   
treatmentc         -0.60606    0.23330 -2.5978  0.00938 **
treatmentd         -0.25504    0.22790 -1.1191  0.26311   
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 

Correlation of Fixed Effects:
    (Intr) T2b    T2c   
T2b -0.675              
T2c -0.697  0.543       
T2d -0.720  0.544  0.581


wouldnt we expect the intercept to be roughtly the mean of treatment a?
and thus the estimate of treatmentb to be +0.07, c: -0.04 and d: +0.09
roughly?

Is this model just completely not estimating well, or are the estimates
not the 'real values'. 

I tried to get teh predict function to give me the 4 predicted values
based on the model, but i havent succeeded in doing so. maybe someone can
help me on that one too (predict(model1,type="response") doesnt work)

thnx