[R] GLMM parameter estimates giving opposite trends

[Ben Bolker]

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