Dear all, is there a way to extract individual likelihoods from a glm/lrm object? By individual likelihoods, I mean the likelihoods whose product give the overall likelihood of the model. I guess the code in the base package, used to compute the Akaike Information Criterion may help me. However, I couldn't figure it out, probably because I'm rather new to likelihood theory and ML estimation ;-)
The aic function just sums the corresponding density ("d") function
with log=T (except for the normal and inverse Gauss, where it is written out
explicitly). Note that mle are not available for the dispersion
parameter of the gamma and inverse Gauss, although this makes vary
little difference in almost all cases. Thus, you just need to feed the
fitted values (and, if appropriate, the dispersion estimate) into the
corresponding density function without summing.
Cheers, Jim
Thanks for any help/suggestion/tip,
Bruno
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