[R] Overdispersion using repeated measures lmer

[Christine Griffiths]

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=poisson(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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> 
> 

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