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.infcheck
cluster_colis 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.