Comments (4)
- I would expect log-likelihood values to be negative.
- The likelihood for a model a greater number of parameters should be tend to be higher, thus the log-likelihood should be a smaller negative value (closer to 0).
from clmp.
Using the examples/test.nwk
tree, I get the following:
> clmp(t1)
log likelihood for 2 state model is 2238.543290
rates: 495.368077 1305.115690
Q: [ * 2.526692 ]
[ 23.309447 * ]
> res0 <- clmp(t1, nrates=1)
log likelihood for 1 state model is 2227.311795
rates: 531.823491
Q: [ * ]
from clmp.
Log-likelihood calculation is being rescaled in mmpp.c:likelihood()
:
new_scale = get_scale(&w->L[i * nrates], nrates);
for (pstate = 0; pstate < nrates; ++pstate) {
w->L[i * nrates + pstate] /= pow(10, new_scale);
}
w->scale[i] += new_scale;
}
// from utils.c
lik = (reconstruct ? max_doubles : sum_doubles)(&w->L[rt * nrates], nrates);
return log10(lik) + w->scale[rt];
Modify to return scaling constant?
from clmp.
It looks like scaling constant is always set to 0
if nrates=1
, so I'm not sure the above is a satisfactory answer...
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Related Issues (20)
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