[R-sig-ME] Error message

Wed Nov 5 22:42:12 CET 2014

On 14-11-05 04:37 PM, Ken Beath wrote:
> 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.

  Ken, can you point me to heuristic and/or anecdotal and/or
(preferably) official or peer-reviewed discussions of when Laplace
approximation is most likely to fail?  (I know it fails when the
sampling distribution of the conditional modes is non-Normal, it makes
sense that that would occur esp. for binary data and small samples per
group, but I'm trying to get a more precise handle on it ...)

  cheers
    Ben Bolker

> 
> 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 escribió:
>>> 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
>>> 8701 N. MoPac Expressway, Suite 200, Austin, TX 78759
>>> (preferred method of communication is email, use cell if urgent)
>>> Office: 512.320.1827
>>> Cell: 786 342 4656
>>>
>>>
>>>
>>>
>>>
>>>
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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
>>>
>>>
>>>       [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org  mailing listhttps://
>> stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>> --
>> Luciano F. La Sala
>> Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
>> Cátedra de Epidemiología
>> Departamento de Biología, Bioquímica y Farmacia
>> Universidad Nacional del Sur
>> San Juan 670
>> Bahía Blanca (8000)
>> Argentina
>>
>>
>>         [[alternative HTML version deleted]]
>>
>>
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>>
>>
> 
>