Good afternoon;

I have recently come across a *general question* about linear mixed models that I can't seem to find an answer for on my own.  I am hoping that one of the braintrust on this list might be willing / able to help.

Specifically, I am analyzing a dataset that describes pond temperatures for a series (n = 25 ponds, 436 measurements).  Ponds were assigned to 1 of 4 treatments, and I want to find find out if temperature was affected by treatment.  

Pond-level inference, in general, is not of interest, so it seemed clear that POND should be included as a random effect.  However, I have good reason to believe that the change in temperature across the study was also variable by POND, and a model that includes a random slope term has good support, as determined by AIC.

However, as it turns out, the covariance parameter estimate for the intercept term in the random slope model is pretty small (i.e., I think most ponds start out at a pretty similar temperature).

I am just wondering if it makes good statistical sense to run a random slope model without the random intercept term?  

If so, does anyone know how one would one specify this in the model structure?

Many thanks in advance,
kbg

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Kirsty E. B. Gurney, Ph.D.
Alaska Cooperative Fish and Wildlife Research Unit
Institute of Arctic Biology
209 Irving 1 Building
University of Alaska Fairbanks
Fairbanks, AK 99775
T: (907) 474 - 7738
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