adding .plot method to coxphfitter

lifelines/fitters/aalen_additive_fitter.py

@@ -32,15 +32,15 @@ class AalenAdditiveFitter(BaseFitter):
         For example, this shrinks the absolute value of c_{i,t}. Recommended, even if a small value.
       smoothing_penalizer: Attach a L2 penalizer to difference between adjacent (over time) coefficents. For
         example, this shrinks the absolute value of c_{i,t} - c_{i,t+1}.
-      nn_cumulative_hazard: If True, forces the negative values in cumulative hazards to be 0 instead. Default True.
+      nn_cumulative_hazard: If True, forces the negative values in predicted cumulative hazards to be 0 instead. Default True.
 
     """
 
     def __init__(self, fit_intercept=True, alpha=0.95, coef_penalizer=0.5, smoothing_penalizer=0., nn_cumulative_hazard=True):
         if not (0 < alpha <= 1.):
             raise ValueError('alpha parameter must be between 0 and 1.')
         if coef_penalizer < 0 or smoothing_penalizer < 0:
-            raise ValueError("penalizer parameter must be >= 0.")
+            raise ValueError("penalizer parameters must be >= 0.")
 
         self.fit_intercept = fit_intercept
         self.alpha = alpha

lifelines/fitters/coxph_fitter.py

@@ -381,7 +381,7 @@ def print_summary(self):
         print('n={}, number of events={}'.format(self.data.shape[0],
                                                  np.where(self.event_observed)[0].shape[0]),
               end='\n\n')
-        print(df.to_string(float_format=lambda f: '{:.3e}'.format(f)))
+        print(df.to_string(float_format=lambda f: '{:4.4f}'.format(f)))
         # Significance code explanation
         print('---')
         print("Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ",
@@ -540,3 +540,33 @@ def _compute_baseline_survival(self):
         if self.strata is None:
             survival_df.columns = ['baseline survival']
         return survival_df
+
+    def plot(self, standardized=False):
+        """
+        standardized: standardize each estimated coefficient and confidence interval endpoints by the standard error of the estimate.
+
+        """
+        from matplotlib import pyplot as plt
+
+        ax = plt.figure().add_subplot(111)
+        yaxis_locations = xrange(len(self.hazards_.columns))
+
+        lower_bound = self.confidence_intervals_.loc['lower-bound'].copy()
+        upper_bound = self.confidence_intervals_.loc['upper-bound'].copy()
+        hazards = self.hazards_.values[0].copy()
+
+        if standardized:
+            se = self._compute_standard_errors().loc['se']
+            lower_bound /= se
+            upper_bound /= se
+            hazards /= se
+
+        order = np.argsort(hazards)
+        ax.scatter(upper_bound.values[order], yaxis_locations, marker='|', c='k')
+        ax.scatter(lower_bound.values[order], yaxis_locations, marker='|', c='k')
+        ax.scatter(hazards[order], yaxis_locations, marker='o', c='k')
+        ax.hlines(yaxis_locations, lower_bound.values[order], upper_bound.values[order], color='k', lw=1)
+
+        plt.yticks(yaxis_locations, self.hazards_.columns[order])
+        plt.xlabel("standardized coef" if standardized else "coef")
+        return ax