This variable represents selection coefficients for individual animals (estimated in a separate analysis) and I'm hoping to test whether certain aspects of where they live affect how they select habitat (i.e., whether an animal that lives closer to a road selects habitat relative to roads differently than an animal further from a road). So I am hoping to use this variable as the dependent variable in a regression model, with a mixture of continuous and categorical predictor variables. Specifically, I'm hoping to use an information-theoretic approach to choose the best variables that predict selection behavior (the selection coefficients) and then plot predicted coefficients over the range of habitat variables. So I would plot estimated coefficients against distance to road to see if selection changes depending on how close an animal is to a road. However, I am unsure about the best way to formulate that model.
If I were to fit a simple linear regression, what sort of bias would I be introducing? Would this approach give reasonable predictions for most of the range of values (excluding the tails)?
Or does this suggest that there is some non-linearity in the data that should be dealt with in a different way?
Or is it possible and/or better to fit a regression model where the response is defined by a different distribution, such as the logistic distribution? In trying to find an answer to how to do this in R, I have only been able to find information on logistic regression, which, as far as I can tell, does not accommodate a continuous dependent variable (that isn't normally distributed) and so does not address my problem.
Any advice is much appreciated!