Error message
There is nothing I know of, which I find surprising. If you look at Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2005). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. Journal of Econometrics, 128(2), 301?323 they compare different number of quadrature points starting at 5, and find that they need more in situations where the clusters are small and random effects variance high. In Rabe-Hesketh and Skrondal's book "Multilevel and Longitudinal Modeling Using Stata" they recommend a minimum of 5 adaptive quadrature points for binary data. Obviously not peer reviewed but this entry from Andrew Gelmans blog is interesting http://andrewgelman.com/2010/09/10/r_vs_stata_or_d/ The best strategy is what I was taught with non-adaptive Gauss_Hermite and that is to increase the quadrature points and see if it makes a difference. If it doesn't then you have the right number of quadrature points. One of my experience with this was fitting some unusual random effects models to binary data using adaptive Gauss-Hermite, using routines I wrote. I wondered why I needed 21 points, so I plotted the log-likelihood for a cluster and one side was very steep (almost cliff-like) compared to the other, so it just wasn't going to work.
On 6 November 2014 08:42, Ben Bolker <bbolker at gmail.com> wrote:
On 14-11-05 04:37 PM, Ken Beath wrote:
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.
Ken, can you point me to heuristic and/or anecdotal and/or
(preferably) official or peer-reviewed discussions of when Laplace
approximation is most likely to fail? (I know it fails when the
sampling distribution of the conditional modes is non-Normal, it makes
sense that that would occur esp. for binary data and small samples per
group, but I'm trying to get a more precise handle on it ...)
cheers
Ben Bolker
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 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.
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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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*Ken Beath* Lecturer Statistics Department MACQUARIE UNIVERSITY NSW 2109, Australia Phone: +61 (0)2 9850 8516 Building E4A, room 526 http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/ CRICOS Provider No 00002J This message is intended for the addressee named and may...{{dropped:9}}