Update coxph_fitter.py
update check_assumptions to match printed output with fitter parameters (specifically changed `strata_col` to `strata`)
@@ -1571,13 +1571,13 @@ the following on the original dataset, df: `df.groupby(%s).size()`. Expected is
# This should capture dichotomous / low cardinality values.
if n_uniques <= 10 and value_counts.min() >= 4:
print(
- " Advice: with so few unique values (only {0}), you can try `strata_col=['{1}']` in the call in `.fit`. See documentation here: https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html".format(
+ " Advice: with so few unique values (only {0}), you can try `strata=['{1}']` in the call in `.fit`. See documentation here: https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html".format(
n_uniques, variable
)
)
else:
print(
- """ Advice: try binning the variable '{var}' using pd.cut, and then specify it in `strata_col=['{var}']` in the call in `.fit`. See more documentation here: https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html""".format(
+ """ Advice: try binning the variable '{var}' using pd.cut, and then specify it in `strata=['{var}']` in the call in `.fit`. See more documentation here: https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html""".format(
var=variable
)
)
