def standardized_mean_differences(self, iptw_only=True):
"""Calculates the standardized mean differences for all variables. Default calculates the standardized mean
difference for all variables included in the IPTW denominator
Parameters
----------
iptw_only : bool, optional
Whether the diagnostic should be run on IPTW only or include weights from the missing_model. Default is
True, which returns IPTW only
Returns
-------
DataFrame
Returns pandas DataFrame of calculated standardized mean differences. Columns are labels (variables labels),
smd_u (unweighted standardized difference), and smd_w (weighted standardized difference)
"""
if iptw_only:
df = self.df
df['_ipfw_'] = self.iptw
else:
df = self.df
df['_ipfw_'] = self.iptw * self.ipmw
s = standardized_mean_differences(df=df,
treatment=self.treatment,
weight='_ipfw_',
formula=self.__mdenom)
return s
def standardized_mean_differences(self):
"""Calculates the standardized mean differences for all variables based on the inverse probability weights.
Returns
-------
DataFrame
Returns pandas DataFrame of calculated standardized mean differences. Columns are labels (variables labels),
smd_u (unweighted standardized difference), and smd_w (weighted standardized difference)
"""
df = self.df.copy()
df['_ipw_'] = np.where(df[self.exposure] == 1, 1 / df['_g1_'], 1 / (1 - df['_g1_']))
s = standardized_mean_differences(df=df, treatment=self.exposure,
weight='_ipw_', formula=self.__mweight)
return s