class MyWeibullFitter(WeibullFitter):
@property
def median_confidence_interval_(self):
'''get the confidence interval of the median, must call after fit and plot'''
if self.median_ != np.inf:
self.timeline = np.linspace(self.median_, self.median_, 1)
return self.confidence_interval_survival_function_
else:
return None
if self.median_ != np.inf
check
cluster_col
is CoxPHFitter: https://lifelines.readthedocs.io/en/latest/Examples.html#correlations-between-subjects-in-a-cox-model. Another solution is to strata-ify per machine in the CoxPHFitter.
Disappointingly, 0.53 is a bit on the low end. Have you tried a LogNormalAFT - it can fit some models better.
What is the reference of the range of 0.55-0.7?
I think I saw it in Frank H. work, maybe his blog?
You can't compare CoxPH and WeibullAFT log likelihood values, no. Mostly because the CoxPH is a partial likelihood.