def _check_values(df, stop_times_events): # check_for_overlapping_intervals(df) # this is currenty too slow for production. check_low_var(df) check_complete_separation_low_variance(df, stop_times_events['event']) pass_for_numeric_dtypes_or_raise(df) check_for_immediate_deaths(stop_times_events) check_for_instantaneous_events(stop_times_events)
def _check_values(df, T, E): pass_for_numeric_dtypes_or_raise(df) check_nans_or_infs(T) check_nans_or_infs(E) check_nans_or_infs(df) check_low_var(df) check_complete_separation(df, E, T)
def _check_values(self, df, events, start, stop): # check_for_overlapping_intervals(df) # this is currently too slow for production. check_nans_or_infs(df) check_low_var(df) check_complete_separation_low_variance(df, events, self.event_col) check_for_numeric_dtypes_or_raise(df) check_for_immediate_deaths(events, start, stop) check_for_instantaneous_events(start, stop)
def _check_values(self, df, T, E, event_col): check_for_numeric_dtypes_or_raise(df) check_nans_or_infs(T) check_nans_or_infs(E) check_nans_or_infs(df) check_complete_separation(df, E, T, event_col) if self.fit_intercept: check_low_var(df)
def _check_values(df, E): # check_for_overlapping_intervals(df) # this is currenty too slow for production. check_low_var(df) check_complete_separation(df, E) pass_for_numeric_dtypes_or_raise(df)
def _check_values(df, E): # check_for_overlapping_intervals(df) # this is currenty too slow for production. check_low_var(df) check_complete_separation_low_variance(df, E) pass_for_numeric_dtypes_or_raise(df)
def _check_values(df, E): check_low_var(df) check_complete_separation(df, E) pass_for_numeric_dtypes_or_raise(df)
def _check_values(df, E): deaths = E == 1 check_low_var(df) check_low_var(df.loc[deaths], "Complete seperation possibly detected. ", " See https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/") check_low_var(df.loc[~deaths], "Complete seperation possibly detected. ", " See https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/") pass_for_numeric_dtypes_or_raise(df)