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Today's Topics:
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?:
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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
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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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