def test_information_criteria(self):
"""test get information criteria from a model."""
lf = self._makeLikelihoodFunction()
nfp = lf.getNumFreeParams()
lnL = lf.getLogLikelihood()
l = len(self.data)
self.assertFloatEqual(lf.getAic(), aic(lnL, nfp))
self.assertFloatEqual(lf.getAic(second_order=True), aic(lnL, nfp, l))
self.assertFloatEqual(lf.getBic(), bic(lnL, nfp, l))
def test_information_criteria(self):
"""test get information criteria from a model."""
lf = self._makeLikelihoodFunction()
nfp = lf.getNumFreeParams()
lnL = lf.getLogLikelihood()
l = len(self.data)
self.assertFloatEqual(lf.getAic(), aic(lnL, nfp))
self.assertFloatEqual(lf.getAic(second_order=True),
aic(lnL, nfp, l))
self.assertFloatEqual(lf.getBic(), bic(lnL, nfp, l))
def getAic(self, second_order=False):
"""returns Aikake Information Criteria
Arguments:
- second_order: if true, the second-order AIC is returned,
adjusted by the alignment length"""
if second_order:
sequence_length = sum(len(self.getParamValue('lht', locus=l).index)
for l in self.locus_names)
else:
sequence_length = None
lnL = self.getLogLikelihood()
nfp = self.getNumFreeParams()
return aic(lnL, nfp, sequence_length)
def test_aic_corrected(self):
"""correctly compute AIC corrected for small sample size"""
# from Burnham & Anderson 2002, p102
self.assertFloatEqual(aic(-9.7039, 4, sample_size=13), 32.4078)
def test_aic(self):
"""correctly compute AIC from Burnham & Anderson 2002, p102"""
self.assertFloatEqual(aic(-9.7039, 4), 27.4078)
def test_aic_corrected(self):
"""correctly compute AIC corrected for small sample size"""
# from Burnham & Anderson 2002, p102
self.assertFloatEqual(aic(-9.7039, 4, sample_size=13), 32.4078)
def test_aic(self):
"""correctly compute AIC from Burnham & Anderson 2002, p102"""
self.assertFloatEqual(aic(-9.7039, 4), 27.4078)
