Ah, so while re-creating my problem for copy-paste-debug
goodness on the listserv, I discovered what was confusing me.
Originally, when I ran the various models, I got these
log-likelihoods for results:
==
tf2ic2kzkr t
Hi Nick-
Are you are getting differences in relative AICs between models from simple
rescaling (multiplying by a constant)?
The actual values of the traits *might* matter for optimization, depending on
various parameters associated with optimization (and whatever algorithm is
being used - th
Doh! Really should have remembered that,
likelihoods-can-be-greater-than-1 is likelihood 101...
I am still a little puzzled by the dramatically different
results between rescaling and not, will try to post an
example in a sec...
On 3/7/11 12:37 PM, Nick Matzke wrote:
Hi all,
It seems to
Nick,
Log-likelihoods are calculated as the logarithm of the product of the
heights of the probability density function. Since the probability
density function must integrate to 1.0, it can have a height that is
much greater than 1.0 if all the probability density is concentrated on
a small
Hi all,
It seems to be a popular week for questions!
I am running fitContinuous on a variety of continuous trait
data. I am noticing that when the traits are in units where
the max is less than 1 (these are not ratio data, though),
many of the various models produce log-likelihoods that are
