[R-sig-ME] Error message

[Luciano La Sala]

Hello everyone, 

I am running R V.2.13.1 on Windows 7. I have a dataset which includes
"Hatching success" (0,1) as dependent variable and "Year" (two factors:
2006, 2007), "Sex" (two factors), "Clutch size" (three factors: 1 egg, 2
eggs, 3 eggs), "Egg volume" (continuous), and interaction term
"EggVolume*Year" as independent variables. The model includes NestID as
random term (intercepts). 

Following, model specification, warning message and output: 

model <- lmer(HatchFailure ~ Year + Sex + Egg_Volume + Clutch_Size +
Egg_Volume*Year + (1|NestID), family = binomial, data = Data)

Mensajes de aviso perdidos       
glm.fit: algorithm did not converge

summary(model)

Generalized linear mixed model fit by the Laplace approximation 
Formula: HatchFailure ~ Year + Sex + EggVolume + ClutchSize + EggVolume *
Year + (1 | NestID) 
   Data: Data 
 AIC   BIC     logLik  deviance
  16 41.72 -9.437e-10 1.887e-09

Random effects:
 Groups Name        Variance Std.Dev.
 NestID (Intercept) 1.5362   1.2394  
Number of obs: 184, groups: NestID, 106

Fixed effects:
                       Estimate Std. Error z value Pr(>|z|)
(Intercept)          -2.657e+01  7.859e+05       0        1
Year2007             -1.457e-14  1.211e+06       0        1
SexMale               4.487e-16  8.953e+04       0        1
EggVolume            -1.751e-17  1.022e+04       0        1
ClutchSizeTwo-eggs   -1.670e-15  1.395e+05       0        1
ClutchSizeThree-eggs -1.503e-15  1.283e+05       0        1
Year2007:EggVolume    1.490e-08  1.540e+04       0        1

Correlation of Fixed Effects:
            (Intr) Yr2007 SexMal EggVlm CltchSzTw- CltchSzTh-
Year2007    -0.628                                           
SexMale     -0.087 -0.025                                    
EggVolume   -0.986  0.619  0.011                             
CltchSzTw-g -0.184  0.118  0.132  0.061                      
CltchSzThr-  0.005  0.050  0.151 -0.138  0.692               
Yr2007:EggV  0.640 -0.997  0.033 -0.635 -0.112     -0.047

So at this point my main questions are 

(1) Why the non convergence warning and how can I fix this. 

(2) I'm getting huge Std. Errors and very odd z and P-values. I imagine this
has to do with the non-convergence issue but I am not sure. It's important
to note that whenever "sex" is included in my model, I get these odd results
with (z = 0, p = 1) regardless of which and how many vars I include or drop
from the model. Simplifying my model doesn't seem to solve the problem :( 
The variable "sex" seems to be the problem here but I have no clue as to
what I should do to overcome this and still be able to assess the role of
"sex" as independent variable!  
	
Thank you so very much in advance for any help. 

Luciano La Sala