From r-help  Fri Nov 07 10:29:02 2003
From: "Pascal A. Niklaus" <Pascal.Niklaus () unibas ! ch>
Date: Fri, 07 Nov 2003 10:29:02 +0000
To: r-help
Subject: Re: [R] Hierarchical glm - Followup
X-MARC-Message: https://marc.info/?l=r-help&m=107151312420903

Pascal A. Niklaus wrote:

> Hi all,
>
> I'm not sure how to correctly analyse the following data with glm, and 
> hope for some advice from this list, ideally showing how to specify 
> the model in R and perform the tests, and also for suggestions of 
> literature.
>
> The data structure is like this:
>
>   - 20 plant populations were investigated (random factor pop), which 
> belong to different habitat types (factor ht)
>   - Within each plant population, individuals were grouped into 3 size 
> classes (factor sz)
>   - For each individual, some count data were recorded
>
> The dependent variables I'd like to analyse are either poission of 
> binomially distributed.
>
> For gaussian data, I would use the following model:
>
>   ht + pop %in% ht + sz + sz:ht + sz : pop %in %ht
>
> ht would basically be tested against pop (because the population is 
> the unit of replication for ht), and sz against sz:pop:ht. (the 
> hypotheses to test are that ht has an effect, and whether the effect 
> of sz on individuals of a population depends on ht)
>
> However, I do not know how to translate this to the deviance analysis 
> case. For example, when I fit the whole model, and then drop ht to 
> test for the effect of ht, the effect of ht shows up in pop (I 
> understand why, but don't know how to do this otherwise). If I compare 
> the null model to the model including ht only, do I then commit a 
> pseudoreplication?
>
> Thanks for your help
>
> Pascal 


I have seen F-tests being used to compare the mean deviance explained by 
a factor (deviance reduction/df) against the mean deviance explained by 
factor x random-effect... any comments on that approach?

Pascal