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?
Let me know if any other information would be useful in answering this
question.
Thank you in advance for any input.
This is the best model:
M17<-
glmmML(ldeli~bare+firefreq+canopy+treatment,cluster=season,family=poisson,data=ldeli)
Model-averaged coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -84.248439 30.376197 2.774 0.00555 **
bare -0.102111 0.023231 4.396 1.11e-05 ***
firefreq 3.370089 1.183091 2.849 0.00439 **
canopy -0.013598 0.007420 1.832 0.06688 .
treatmentLU 87.939276 30.376750 2.895 0.00379 **
treatmentSM01 67.595184 24.477350 2.762 0.00575 **
treatmentSMWF 64.612540 23.322285 2.770 0.00560 **
treatmentT01 80.142838 28.030787 2.859 0.00425 **
treatmentT03 77.088813 26.836430 2.873 0.00407 **
treatmentTB 56.163472 19.744217 2.845 0.00445 **
treatmentWF 84.313036 29.214505 2.886 0.00390 **
--
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Diana
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