[R-sig-ME] Error message

Ken Beath ken.beath at mq.edu.au
Wed Nov 5 22:37:59 CET 2014


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.

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
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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]]
> >
> > _______________________________________________
> > 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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>
>


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*Ken Beath*
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