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The response variable I'm dealing with is the proportion of a total area that is suitable habitat for a species of interest. So although the response variable is bounded between 0 and 1, my intuition is that it wouldn't be appropriate to call it binomial since the numerator and denominator of the proportion are non-integer. The beta distribution comes to mind, but I'm uncertain of the appropriate link function and whether there tools in R to deal with a beta.

Some background on my eventual goal: I'll likely be pursuing a conditional autoregressive model to account for spatial autocorrelation. I'll be treating space as 1-dimensional since I'm dealing with a river system, and so each observational unit only has two neighbors: one upstream and downstream.

I'll be working in R and JAGS/BUGS if I decide to go Bayesian.

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  • $\begingroup$ There are a variety of possible distributional models for compositional data. When considering two components, or when focusing on one of the components, some people use beta regression, for example. (With multiple components, there's Dirichlet models.) If you want to use a standard GLM package, you might consider a quasi-binomial, but it will restrict you to a particular form of relationship between variance and mean. A search on compositional data here should turn up a number of questions. $\endgroup$ – Glen_b May 15 '14 at 19:46

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