st: marginal vs. conditional log likelihood in GLLAMM

[Dan MacNulty <macn0007@umn.edu>]

Dear all,
I'm using GLLAMM to examine the effects of various factors on the binary 
outcome hunting success (kill/no kill) of individually-known 
free-ranging wolves in Yellowstone National Park, USA. I'm using the 
Akaike information criteria (AIC) to select the best approximating model 
among a set of candidate models. AIC = -2log likelihood + 2K, where K is 
the 'degrees of freedom' correction, or number of parameters in the model.

However, Vaida & Blanchard (2005) point out that AIC in current use is 
not appropriate for subject-specific inferences with mixed effects 
models because it is based on the marginal log likelihood, which they 
say should be reserved for population-level inferences. Thus, where the 
focus of research is on the subjects rather than the population, Vaida & 
Blanchard propose a conditional AIC in which the likelihood is the 
conditional likelihood, and K=p+1, where p is the 'effective number of 
parameters' for the mean model defined by Hodges & Sargent (2001).

My understanding is that the log likelihood displayed in the output for 
GLLAMM is the marginal log likelihood. If that's the case, does anyone 
know how to request a conditional log likelihood in GLLAMM? I'd also be 
grateful to learn how others have calculated the 'effective number of 
parameters', p, following Hodges & Sargent (2001).

thanks,
Dan MacNulty
University of Minnesota

References:

Vaida, F. & Blanchard, S. 2005. Conditional Akaike information for mixed 
effects models. Biometrika 92:351-370.

Hodges, J.S. & Sargent, D.J. 2001. Counting degrees of freedom in 
hierarchical and other richly parameterized models. Biometrika 88:367-379.


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