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.
Hello. I'm reading the docs on coxph regression (https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html).
a) the badfit image appears to be broken
b) in the
rossi dataset example provided, plotting the KM curve against the baseline hazards appears not to have a good spread. is there an included dataset that could be used for this example that would show a bigger spread, like the one in the goodfit picture?
kmf = lifelines.KaplanMeierFitter() kmf.fit(rossi_dataset['week'],rossi_dataset['arrest']) fig, ax = plt.subplots() ax.plot(cox_prop_hazards.baseline_survival_,color='b') ax.plot(kmf.survival_function_,color='r')