[R] Overdispersion using repeated measures lmer

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Tue May 19 09:54:38 CEST 2009

Dear Christine,

The poisson family does not allow for overdispersion (nor
underdispersion). Try using the quasipoisson family instead.




ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Christine Griffiths
Verzonden: maandag 18 mei 2009 13:26
Aan: r-help at r-project.org
Onderwerp: [R] Overdispersion using repeated measures lmer

Dear All

I am trying to do a repeated measures analysis using lmer and have a
number of issues. I have non-orthogonal, unbalanced data.  Count data
was obtained over 10 months for three treatments, which were arranged
into 6 blocks. 
Treatment is not nested in Block but crossed, as I originally designed
an orthogonal, balanced experiment but subsequently lost a treatment
from 2 blocks. My fixed effects are treatment and Month, and my random
effects are Block which was repeated sampled.  My model is:


Is this the only way in which I can specify my random effects? I.e. can
I specify them as: (1|Block)+(1|Month)?

When I run this model, I do not get any residuals in the error term or
estimated scale parameters and so do not know how to check if I have
overdispersion. Below is the output I obtained.

Generalized linear mixed model fit by the Laplace approximation
Formula: Count ~ Treatment * Month + (Month | Block)
   Data: dataset
   AIC   BIC logLik deviance
 310.9 338.5 -146.4    292.9
Random effects:
 Groups Name        Variance   Std.Dev. Corr
 Block  (Intercept) 0.06882396 0.262343
        Month       0.00011693 0.010813 1.000
Number of obs: 160, groups: Block, 6

Fixed effects:
                          Estimate Std. Error z value Pr(>|z|)
(Intercept)               1.624030   0.175827   9.237  < 2e-16 ***
Treatment2.Radiata        0.150957   0.207435   0.728 0.466777
Treatment3.Aldabra       -0.005458   0.207435  -0.026 0.979009
Month                    -0.079955   0.022903  -3.491 0.000481 ***
Treatment2.Radiata:Month  0.048868   0.033340   1.466 0.142717
Treatment3.Aldabra:Month  0.077697   0.033340   2.330 0.019781 *
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) Trt2.R Trt3.A Month  T2.R:M Trtmnt2.Rdt -0.533
Trtmnt3.Ald -0.533  0.450
Month       -0.572  0.585  0.585
Trtmnt2.R:M  0.474 -0.882 -0.402 -0.661
Trtmnt3.A:M  0.474 -0.402 -0.882 -0.661  0.454

Any advice on how to account for overdispersion would be much

Many thanks in advance

Christine Griffiths
School of Biological Sciences
University of Bristol
Woodland Road
Bristol BS8 1UG
Tel: 0117 9287593
Fax 0117 925 7374
Christine.Griffiths at bristol.ac.uk

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