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.
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:
- Abundance/prevalence (DV) are the same for the 3 habitats (IV).
- 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.
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.
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
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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