On Tue, 14 Jul 2009, Lars Bergemann wrote:
Hi. Thanks for your help with my previous question (comparing two lm()
models with a maximum likelihood ratio test)
I had a look at lrtest from the package lmtest as it has been suggested
to me, but I am not 100% sure if that is the right thing to do ...
That clearly depends on what you consider to be "right".
In any case, lrtest() computes (in an object-oriented way) -2 times the
loglikelihood difference and compares that with a chi-squared distribution
with the corresponding degrees of freedom (difference in df of the
models).
lrtest uses the same log likelihoods as you can extract by hand from
lm() with logLik - are this the maximum log likelihoods?
It's the maximized log-likelihood. Thus, it's the sum of (normal)
log-densities, evaluated at the observations and the estimated parameters.
How does R
calculate them? I tried google to find information about that, but I
could not find anything of interest.
I assume that you read help("logLik.lm")? If all else fails, looking at
the code of stats:::logLik.lm should also help. In essence, it is
0.5 * (sum(log(w)) - N * (log(2 * pi) + 1 - log(N) +
log(sum(w * res^2))))
where all variables have the obvious meaning.
Z
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