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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.