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Thread: Replication in linear regression. General Estimating Equations (GEE) a good option?

  1. #1
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    Replication in linear regression. General Estimating Equations (GEE) a good option?




    or is it the only option?

    I've been collecting hosts in 3 different habitats, about 20 hosts every sampling trip. I've been recording prevalence of a parasite (1 or 0) and abundance (number of parasites per host) for each individual. I'd like to know:
    1. Abundance/prevalence (DV) are the same for the 3 habitats (IV).
    2. Some other variables (temperature, soil type, etc.)(IV) could affect the overall prevalence/abundance (DV) of the parasite.

    I'd say I need to use a linear regression, but I'm afraid that the hosts collected in each trip could be non independent.

    Since General Estimating Equations deals at a population level, I was thinking about applying it, but I'd like to confirm it with you.

    Should I use GEE ('geeglm' {geepack}) using each sampling trip as a clustering vector?


    More info:

    3 habitat; about 10 sampling trips to each; 20 hosts per trip. About 600 hosts.

    After applying the linear regression ('glm'), residuals are not normally distributed ('shapiro.test'). Durbin Watson test ('durbinWatsonTest' {car}) p-value > 0.05 and Breusch-Pagan Test ('bptest' {lmtest}) p-value > 0.05.

    I'm using R, so any answered tailored to it would be more than welcome.

  2. #2
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    Re: Replication in linear regression. General Estimating Equations (GEE) a good optio

    I'm curious about why nobody answer.

    Is it because the question is not clear enough? Because it is very specific? The format is incorrect?

    I tried to give as much info as possible, but feel free to ask me.

    Thanks.

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    Re: Replication in linear regression. General Estimating Equations (GEE) a good optio

    GEE could work, but given my understanding of your data, something like a zero-inflated poisson model with random effects might be the most appropriate choice. An example of this kind of model in R can be found here:
    https://groups.nceas.ucsb.edu/non-li...ITEUP/owls.pdf
    Or, for a book-length treatment, Zuur et al. have a book on zero-inflated models and GLMMs...I sort of assume they also look at models that combine the two (which is what the model I mentioned above is), but I don't know for sure as I don't have access to this book. I do have a different book by Zuur et al. and it's excellent, so I suspect this one is good as well:
    http://www.highstat.com/book4.htm
    “In God we trust. All others must bring data.”
    ~W. Edwards Deming

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    Re: Replication in linear regression. General Estimating Equations (GEE) a good optio


    Thanks Jake

    I'm not familiar with the zero-inflated poisson models, but I'll see if they could be valid for my data.

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