[R-sig-ME] R-sig-mixed-models Digest, Vol 95, Issue 8
Luciano La Sala
lucianolasala at yahoo.com.ar
Fri Nov 7 20:06:47 CET 2014
Hi there,
There was a complete separation issue in my data. Sex was undetermined
for chicks that died (NAs), so that caused the problem when including
the variable as a predictor of Survival at day2. Removed Sex and things
look way better. Thank you guys!
L
El 11/6/2014 8:00 AM, r-sig-mixed-models-request at r-project.org escribió:
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> 1. Re: R-sig-mixed-models Digest, Vol 95, Issue 6 (Ken Beath)
> 2. Re: R-sig-mixed-models Digest, Vol 95, Issue 6 (Ben Bolker)
> 3. error message using glmmADMB (Nagata Mizuho)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Thu, 6 Nov 2014 13:38:38 +1100
> From: Ken Beath <ken.beath at mq.edu.au>
> To: Luciano La Sala <lucianolasala at yahoo.com.ar>
> Cc: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] R-sig-mixed-models Digest, Vol 95, Issue 6
> Message-ID:
> <CAF5_5czUsT9DFLtntAe5SOkfuqEmXhYQ7Xgfx5n4JQF_i2Or7g at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> nAGQ=0 uses an even more approximate method, so probably isn't advised.
> Looking at your output something has gone seriously wrong. The standard
> errors are all very large and the random effect variance is zero.
>
> Have you checked whether there is a collinearity problem between your fixed
> effects. Start with a model with all the fixed effects and no random and
> see how that works.
>
> On 6 November 2014 13:27, Luciano La Sala <lucianolasala at yahoo.com.ar>
> wrote:
>
>> 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 escribi?:
>>
>>> Send R-sig-mixed-models mailing list submissions to
>>> r-sig-mixed-models at r-project.org
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>>> To subscribe or unsubscribe via the World Wide Web, visit
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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>>> When replying, please edit your Subject line so it is more specific
>>> than "Re: Contents of R-sig-mixed-models digest..."
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>>> Today's Topics:
>>>
>>> 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
>>>> 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 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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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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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