GLMM parameter estimates giving opposite trends
Diana Virkki <d.virkki <at> griffith.edu.au> writes:
I apologize if this is a simple question. I am running GLMM's using glmmML and model averaging with MuMIn. One of the parameter estimates for a parameter (firefreq) in the best model is giving a positive number, where in reality I know this to be a negative correlation. I have checked and double checked the data that has gone in and this is not the issue. This is occurring for numerous variables in my models. As far as I was aware the parameter estimate is indicative of the direction of the relationship? Is there any reason why this model would give me opposite trends?
It's a little hard to guess without a reproducible example (see http://tinyurl.com/reproducible-000), but one guess is that you have one or more confounding variables <http://en.wikipedia.org/wiki/Confounding> in your multivariate model; that is, the _marginal_ effect of fire frequency is to decrease the mean response, but the effect _conditional_ on all of the other variables in the model is to increase it. This phenomenon is most common when the predictors are strongly correlated. Do you get a sensible sign when you fit a model with just the focal parameter? Ben Bolker