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I like to calculate model R-squares because it is an intuitive measure of
how well a model explains the data and, more importantly, because it is
something that the consumers of my analyses, natural resource managers,
understand.  For a spatially-explicit, hierarchical model I've been working
with in BUGS I calculate the deviance R-squared as:

1-(Model Deviance/(n-k-1)/(Null Deviance/(n-2)), where n is the sample size
and k is the number of model parameters.

Heretofore I was using pD, the number of estimable parameters, in place of
k, having derived pD from the DIC menu in WinBUGS1.4.  In some of the
models pD is very large, substantially reducing the model R-squared.

In one model, the "naive" Deviance R-squared is 1-(Model Deviance/Null
Deviance) or 1-(8078/20811) = 0.61.  Adjusting for sample size
considerations, n = 1840 and pD = 1077, the adjusted Deviance R-squared
becomes 0.06, an order smaller.

The number of covariates in this model is 7, so the inflation of pD is due
strictly to the spatial structuring of the model.  In another similar model
with a different response, pD is only 40, and thus has considerably less
influence on the calculation.

I haven't seen people use pD in place of k and so I don't really know if
this is a valid practice, so your comments would be appreciated if you have
any.  I'm wondering if I should stick strictly with the number of model
covariates rather than include all of the hyperparameters.  If you can
recommend another formulation of R-squared that might be more appropriate,
I'd be interested in that too.

Thanks much,

Wayne E. Thogmartin, PhD
Statistician (Biology) - Avian/Habitat Modeling
USGS Upper Midwest Environmental Sciences Center
575 Lester Avenue
Onalaska, WI 54650
608.783.7550 Ext 42
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