[R-sig-ME] R-sig-mixed-models Digest, Vol 95, Issue 6
Luciano La Sala
lucianolasala at yahoo.com.ar
Thu Nov 6 03:27:22 CET 2014
Dear Ken and Ben,
Thank you so much for your prompt responses. This is more frustrating
than interesting to me. Weird, but the model runs "smoothly" if I use
nAGQ=0 (output below). Any value other than that yields the mentioned
error. I have no idea how this Gauss-Hermite Quadrature stuff works, or
if setting nAGQ to 0 makes my model building strategy (AIC criterion) a
poor choice. Should I stick with nAGQ=0 then?
> model.1 <- glmer(Death_2 ~ Year + Sex + Egg_Volume + Hatch_Order +
(1|Nest_ID), nAGQ=0, family = binomial, data = surv.2)
> summary(model.1)
Generalized linear mixed model fit by maximum likelihood (Adaptive
Gauss-Hermite Quadrature, nAGQ = 0)
[glmerMod]
Family: binomial ( logit )
Formula: Death_2 ~ Year + Sex + Egg_Volume + Hatch_Order + (1 | Nest_ID)
Data: surv.2
AIC BIC logLik deviance df.resid
22.0 44.7 -4.0 8.0 182
Scaled residuals:
Min 1Q Median 3Q Max
-0.2291 0.0000 0.0000 0.0000 4.4713
Random effects:
Groups Name Variance Std.Dev.
Nest_ID (Intercept) 0 0
Number of obs: 189, groups: Nest_ID, 111
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.185e+01 1.538e+04 -0.003 0.998
Year2007 1.933e+01 1.096e+04 0.002 0.999
Sex -1.878e+01 1.139e+04 -0.002 0.999
Egg_Volume -5.620e-03 2.077e-01 -0.027 0.978
Hatch_OrderSecond 1.997e+01 1.079e+04 0.002 0.999
Hatch_OrderThird -3.482e-01 2.544e+04 0.000 1.000
Correlation of Fixed Effects:
(Intr) Yr2007 Sex Egg_Vl Htc_OS
Year2007 -0.713
Sex 0.000 0.000
Egg_Volume -0.001 0.000 0.000
Htch_OrdrSc -0.701 0.000 0.000 0.000
Htch_OrdrTh -0.298 0.000 0.000 0.000 0.424
El 11/5/2014 6:38 PM, r-sig-mixed-models-request at r-project.org escribió:
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> Today's Topics:
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> 1. Re: Error message (Luciano La Sala)
> 2. Re: Error message (Ben Bolker)
> 3. Re: subject level predictions with lme4 from incomplete
> longitudinal profile (Tarca, Adi)
> 4. Re: Error message (Ken Beath)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 05 Nov 2014 08:55:27 -0300
> From: Luciano La Sala <lucianolasala at yahoo.com.ar>
> To: Daniel Wright <Daniel.Wright at act.org>
> Cc: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] Error message
> Message-ID: <545A102F.3030407 at yahoo.com.ar>
> Content-Type: text/plain; charset="UTF-8"
>
> 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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>>
>>
>>
>>
>>
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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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>
--
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
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