def run_diagnostics(self, decimal=3):
"""Run all currently implemented diagnostics for the exposure and outcome models. Each
`run_diagnostics` provides results for all implemented diagnostics for ease of the user. For publication
quality presentations, I recommend calling each diagnostic function individually and utilizing the optional
parameters
Note
----
The plot presented cannot be edited. To edit the plots, call `plot_kde` or `plot_love` directly. Those
functions return an axes object
Parameters
----------
decimal : int, optional
Number of decimal places to display. Default is 3
Returns
-------
None
"""
if not self._fit_outcome_ or not self._fit_exposure_:
raise ValueError("The exposure_model and outcome_model function must be ran before any diagnostics")
# Weight diagnostics
print('\tExposure Model Diagnostics')
self.positivity(decimal=decimal)
print('\n======================================================================')
print(' Standardized Mean Differences')
print('======================================================================')
print(self.standardized_mean_differences().set_index(keys='labels'))
print('======================================================================\n')
# Outcome accuracy diagnostics
print('\tOutcome Model Diagnostics')
v = self._predicted_y_ - self.df[self.outcome]
outcome_accuracy(true=self.df[self.outcome], predicted=self._predicted_y_, decimal=decimal)
df = self.df.copy()
df['_ipw_'] = np.where(df[self.exposure] == 1, 1 / df['_g1_'], 1 / (1 - df['_g1_']))
plt.figure(figsize=[8, 6])
plt.subplot(221)
plot_love(df=df, treatment=self.exposure, weight='_ipw_', formula=self.__mweight)
plt.title("Love Plot")
plt.subplot(223)
plot_kde(df=df, treatment=self.exposure, probability='_g1_')
plt.title("Kernel Density of Propensity Scores")
plt.subplot(222)
plot_kde_accuracy(values=v.dropna(), color='green')
plt.title("Kernel Density of Accuracy")
plt.tight_layout()
plt.show()
def run_diagnostics(self, decimal=3):
"""Runs diagnostics for the g-formula regression model used. Diagnostics include summary statistics and a
Kernel Density plot for the predictive accuracy of the model. The model compares the model predicted value to
the observed outcome value.
"""
# Summary statistics of prediction accuracy
outcome_accuracy(true=self.gf[self.outcome],
predicted=self._predicted_y_,
decimal=decimal)
# Distribution plot of accuracy
self.plot_kde()
plt.title("Kernel Density of Accuracy")
plt.tight_layout()
plt.show()