[R-sig-ME] Error message

Ben Bolker bbolker at gmail.com
Wed Nov 5 13:09:08 CET 2014


  I don't remember if I responded to this before or not, but this looks
interesting/annoying: could you send or post the data?  Just to confirm,
any single-fixed-effect model works?  How about two-effect combinations
(I know, that's a bit of a nuisance)?

On 14-11-05 06:55 AM, Luciano La Sala 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
>> 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]]
>>
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> 
> 
> 
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