[R-sig-ME] Repeated measure comparisons: should the identity be a random or a fixed variable?

[Jarrod Hadfield]

Hi Billy,

I think your models look reasonable, although in models 2 and 3 you  
may want  to treat month as a continuous variable in the fixed part of  
the model. Also, most count data are overdispersed with respect to the  
poisson and so a model that does not account for this will be  
anti-conservative in terms of standard errors etc. One way to deal  
with this is to fit an additional random effect at the level of each  
observation:

my.data$resid<-as.factor(1:dim(my.data)[2])

and fit (1|resid) in the model formula.

Cheers,

Jarrod




Quoting Billy <billy.requena at gmail.com>:

> Hello everybody!
>
> I'm relatively new at the mixed-models world and I'm facing a
> theoretical/philosophical problem.
> Let's go to my data collection.
>
> I wanna compare the number of eggs laid by females (different
> individuals or the same, I have no idea) at the time 1 and at the time
> 2 in the same location. Therefore, I have repeated measures by
> location and wanna compare time 1 versus time two. Given I have count
> data, to minimize the overdispersion I have considered the Poisson
> distribution for the errors.
> Furthermore, I have collected this data throughout one year and I'm
> also interested in temporal variation among months.
>
> model0 <- glmer ( y ~ 1 + (1|location) + (1|month), family="poisson")
> model1 <- glmer ( y ~ x + (1|location) + (1|month), family="poisson")
>
> where y = number of eggs laid,
>           x = factor concerning the first or the second oviposition
>           location = factor concerning the exactly position in the
> space (just an identity of the oviposition site and responsible for
> the repeated comparison)
>           month = factor concerning the month when I've collected the data
>
> Is that right? If I wanna repeated comparison regarding specific
> identity of oviposition sites, should this factor (location) be a
> random variable?
>
> Furthermore, in both examples above, I'm just considering a temporal
> variation (among months) as random a effect. But I'm also interested
> if there are significant seasonal variation in the comparison (the
> difference could be higher during warm season or not even existent
> during cold season). Then:
>
> model2 <- glmer ( y ~ x + month + (1|location), family="poisson")
> model3 <- glmer ( y ~ x * month + (1|location), family="poisson")
>
> Is that right too?
> Finally, I'll use a model selection approach to compare the different
> models and rank the most likely one to reproduce the data observed in
> the nature.
> Thanks to everyone
>
> --
> Gustavo Requena
> PhD student - Laboratory of Arthropod Behavior and Evolution
> Universidade de São Paulo
> Correspondence adress:
> a/c Glauco Machado
> Departamento de Ecologia - IBUSP
> Rua do Matão - Travessa 14 no 321 Cidade Universitária, São Paulo -  
> SP, Brasil
> CEP 05508-900
> Phone number: 55 11 3091-7488
>
> http://ecologia.ib.usp.br/opilio/gustavo.html
>
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>



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