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R-sig-mixed-models Digest, Vol 9, Issue 9

4 messages · Iasonas Lamprianou, Steven J. Pierce, Douglas Bates

#
Dear friends,
first of all I would like to thank everybody, and especially Dr Bates, for all the support on using lmer. I now have another question. When using lmer on binary responses using family=binomial, we do not get the residual variance. In the default option (normal model) we do get the variance for every rndom effect, as well as the residual variance, so that we can compute the % of variance accounted by each effect. How can we find the residual variance in the binomial case, so that we compute the% of variance explained by each random effect? Thank you for your help once again
jason
 
Dr. Iasonas Lamprianou
Department of Education
The University of Manchester
Oxford Road, Manchester M13 9PL, UK
Tel. 0044 161 275 3485
iasonas.lamprianou at manchester.ac.uk


----- Original Message ----
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Sent: Friday, 7 September, 2007 1:00:09 PM
Subject: R-sig-mixed-models Digest, Vol 9, Issue 9


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Today's Topics:

   1. Re: lmer and autocorrelation structures (Douglas Bates)


----------------------------------------------------------------------

Message: 1
Date: Thu, 6 Sep 2007 19:35:34 -0500
From: "Douglas Bates" <bates at stat.wisc.edu>
Subject: Re: [R-sig-ME] lmer and autocorrelation structures
To: "Chris O'Brien" <obrienc at email.arizona.edu>
Cc: r-sig-mixed-models at r-project.org
Message-ID:
    <40e66e0b0709061735m73a8a30cu6be832f30ef630b2 at mail.gmail.com>
Content-Type: text/plain; charset=ISO-8859-1
On 9/6/07, Douglas Bates <bates at stat.wisc.edu> wrote:
P.P.S.  I have always felt that to qualify as a Yodaism, Simon's first
sentence should have been "R this is.", not "This is R."



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End of R-sig-mixed-models Digest, Vol 9, Issue 9
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      ___________________________________________________________

now.
#
Hi folks,

I'm doing some multilevel logistic models with lmer() and I noticed that the
estimated scale in my model (see code & results below) suggests the presence
of under-dispersion. Are there any guidelines on when the scale is
sufficiently far from 1 that one should conclude that underdispersion (or
overdispersion) is serious enough to warrant switching from family =
binomial(logit) to family = quasibinomial(logit)?
+ 
+                             OR2flyertv + Spanish_Version + MUDwell + 0 +
+                             (1 | ClusterID) + (1 | SurveyID), 
+                 data=RS05.Round1A, family = binomial(logit),
method="Laplace")
Generalized linear mixed model fit using Laplace 
Formula: EventTV ~ Period + OR1pc + OR1flyer + OR2pctv + OR2flyertv +
Spanish_Version + MUDwell + 0 + (1 | ClusterID) + (1 | SurveyID) 
   Data: RS05.Round1A 
 Family: binomial(logit link)
  AIC  BIC logLik deviance
 5324 5578  -2631     5262
Random effects:
 Groups    Name Variance   Std.Dev.  
 SurveyID       1.4455e+00 1.2023e+00
 ClusterID      5.0000e-10 2.2361e-05
number of obs: 27460, groups: SurveyID, 1787; ClusterID, 52

Estimated scale (compare to  1 )  0.6785013 
 


Steven J. Pierce, M.S.
Doctoral Student in Ecological/Community Psychology
Department of Psychology
Michigan State University
240B Psychology Building
East Lansing, MI 48824-1116

E-mail: pierces1 at msu.edu
Web: http://www.psychology.msu.edu/eco/
#
On 9/9/07, Iasonas Lamprianou <lamprianou at yahoo.com> wrote:
The binomial distribution is determined by the mean (i.e. the
probability of success on each trial) and the number of trials.
Unlike the normal distribution there is no scale parameter in addition
to the parameters determining the mean.  If the distribution of the
response in a mixed model, conditional on the value of the linear
predictor, is binomial then there is no separate calculation of the
residual variance and you cannot meaningfully talk about % of variance
accounted for by each effect.
#
You have a variance component for ClusterID that is being estimated as
"effectively zero".  You should refit the model with that random
effects term removed and check the estimated scale for the modified
model.

I don't have good guidelines for under- or over-dispersion.  When the
estimated scale is < 0.75 I get concerned and when it is < 0.5 I get
very concerned but I don't know if those are reasonable comparison
values or not.
On 9/9/07, Steven J. Pierce <pierces1 at msu.edu> wrote: