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Thank you Dan,

According to the new version of lme4 I refited my model as follows:

model <- glmer(Death ~ Year + Sex + Egg Volume + Hatch Order + (1|Nest 
ID), family = binomial, data = Data)
summary(model)

However, the same error message keeps showing up:

Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate

Interestingly, if I reduce the model to contain only one main effect 
(whichever), say Hatch_Order, things look better:

model2 <- glmer(Death 2 ~ Hatch Order + (1|Nest_ID), family = binomial, 
data = Data) summary(model2)

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial  ( logit )
Formula: Death_2 ~ Hatch_Order + (1 | Nest_ID)
    Data: surv.2

      AIC      BIC   logLik deviance df.resid
    118.5    131.8    -55.2    110.5      205

Scaled residuals:
     Min      1Q  Median      3Q     Max
-0.7390 -0.1714 -0.1682 -0.1506  3.7689

Random effects:
  Groups  Name        Variance Std.Dev.
  Nest_ID (Intercept) 1.586    1.259
Number of obs: 209, groups:  Nest ID, 115

Fixed effects:
                   Estimate Std. Error z value Pr(>|z|)
(Intercept)        -3.4824     1.1274  -3.089  0.00201 **
Hatch_OrderSecond  -0.1266     0.7576  -0.167  0.86729
Hatch_OrderThird    2.0486     0.7572   2.705  0.00682 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
             (Intr) Htc_OS
Htch_OrdrSc -0.111
Htch_OrdrTh -0.709  0.276

Any pointers please? Best. Luciano

El 10/22/2014 6:35 PM, Daniel Wright escribi?:

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