[R-sig-ME] Underdispersion in multilevel logistic regression
Steven J. Pierce
pierces1 at msu.edu
Mon Sep 10 05:35:42 CEST 2007
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)?
> model.17f <- lmer(formula = EventTV ~ Period + OR1pc + OR1flyer + OR2pctv
+
+ OR2flyertv + Spanish_Version + MUDwell + 0 +
+ (1 | ClusterID) + (1 | SurveyID),
+ data=RS05.Round1A, family = binomial(logit),
method="Laplace")
> summary(model.17f)
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/
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