[R] Overdispersion using repeated measures lmer
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Tue May 19 11:57:09 CEST 2009
Dear Christine,
(Month|Block) and (1|Block) + (1|Month) are completely different random effects. The first assumes that each Block exhibits a different linear trend along Month. The latter assumes that each block has a random effect, each month has a random effect and that the random effects of block and month are independent. So each month has a different effect, but within a given month that effect is the same on each block. It is up to you to see if that kind of assumption is valid in your design.
Missing values should not be a problem, as long as they are missing at random. I would not try to impute the missing values. How would you determine the imputed values? That requires a lot of assumptions and they could affect your model parameters.
HTH,
Thierry
----------------------------------------------------------------------------
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
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.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 data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Namens Christine Griffiths
Verzonden: dinsdag 19 mei 2009 11:01
Aan: r-help at r-project.org
Onderwerp: Re: [R] Overdispersion using repeated measures lmer
Thanks. I did try using quasipoisson and a negative binomial error but am unsure of the degree of overdispersion and whether it is simply due to missing values. I am investigating to see if I can replace these missing values so that I can have a balanced orthogonal design and use lme or aov instead which is easier to interpret. Any ideas on whether it is feasible to replace missing values for a small dataset with repeated measures? I have 6 blocks with 3 treatments sampled over 10 months. Two blocks are missing one treatment, albeit a different one. Also any suggestions about how I would go about this would be much appreciated.
I am also unsure of whether my random effects (Month|Block) for repeated measures with random slope and intercept is correct and whether (1|Month) +
(1|Block) represents repeated measures. Any confirmation would be great.
Cheers
Christine
Christine Griffiths-2 wrote:
>
> 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:
>
> Model<-lmer(Count~Treatment*Month+(Month|Block),data=dataset,family=po
> isson(link=sqrt))
>
> 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 appreciated.
>
> Many thanks in advance
> Christine
>
> ----------------------
> 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
> http://www.bio.bris.ac.uk/research/mammal/tortoises.html
>
> ______________________________________________
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