A general question that came to me after reading many of the enlightening
threads on this list regarding the nuancess of a step-wise approach to
modeling. How does one make decisions in a case where the same model needs
to be applied ten-of-thousands of times, as in genomics datasets where a
model is iterated over many, many, genes? For some genes, a complex model
may be of benefit, where for others, a simpler model would suffice. On a
related note, I recently applied mcmcsample to a genomics dataset and found
that the eDF had a range of ~3-4 depending on the gene. Am I to infer that
the degress of freedom varies by gene using the same model? Should I use an
"average" DF or individualize?

Is there a way to summarize the thousands of graphs that would be generated
on a per gene basis?

Confused but very interested in mixed-models approaches to genomics data,
Mark

-- 
Mark W. Kimpel MD  ** Neuroinformatics ** Dept. of Psychiatry
Indiana University School of Medicine

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