Error message

I would try it using adaptive Gauss-Hermite, by setting nAgQ=3 or more and
seeing how that works. It really should be your first option when fitting a
GLMM, and something that should be checked anyway. In your case with binary
data and approx 2 per group the Laplace approximation is almost certainly
poor.

On 5 November 2014 22:55, Luciano La Sala <lucianolasala at yahoo.com.ar>
wrote:

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?:

The lme4 package has changed some. Details are inhttp://

arxiv.org/pdf/1406.5823.pdf

For your problem, the first thing to note is glmer is now used instead

of lmer for generalized linear models.  Glancing at your model the other
bits look like they should work.

Dan

Daniel B. Wright, Ph.D.
Statistical Research Division
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-----Original Message-----
From:r-sig-mixed-models-bounces at r-project.org  [mailto:

r-sig-mixed-models-bounces at r-project.org] On Behalf Of Luciano La Sala

Sent: Wednesday, October 22, 2014 4:20 PM
Cc:r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Error message

Hello,

A few years back I used to fit GLMM (binomial response) using lmer

function in lme4. Back then I had to specify the family of response
variable  (dead /alive) as binomial. Now I have to refit those models using
quite newer versions of both R (R x64 3.1.1) and lme4 (lme4_1.1-7), but
things seem to have changed quite a bit.

My response variable is death (yes/no), and independent variables are

Year (2006 / 2007), Sex (M / F), Egg volume (continuous), and Hatching
Order (ordered factor variable, namely first, second, third). I need to
control autocorrelation among siblings, so I use "Nest ID" to fit random
intercepts for different nests.

My model is:

model.1 <- lmer(Death_2 ~ Year + Sex + Egg_Volume + Hatch_Order +

(1|Nest_ID), family = binomial, data = Data)

summary(model.1)

But I get the error and warning messages below:

Error in eval(expr, envir, enclos) :
    (maxstephalfit) PIRLS step-halvings failed to reduce deviance in

pwrssUpdate In addition:Warning message:

In lmer(Death_2 ~ Year + Sex + Egg_Volume + Hatch_Order + (1 |

Nest_ID),  :

    calling lmer with 'family' is deprecated; please use glmer() instead


Question: how can I circumvent these two issues?

Thanks in advance.

Luciano


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--
Luciano F. La Sala
Consejo Nacional de Investigaciones Cient?ficas y T?cnicas (CONICET)
C?tedra de Epidemiolog?a
Departamento de Biolog?a, Bioqu?mica y Farmacia
Universidad Nacional del Sur
San Juan 670
Bah?a Blanca (8000)
Argentina


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