keep current limits
@@ -113,7 +113,7 @@ class CoxTimeVaryingFitter(BaseFitter):
df['upper %.2f' % self.alpha] = self.confidence_intervals_.loc['upper-bound'].values
return df
- def _newton_rhaphson(self, df, stop_times_events, show_progress=False, step_size=None, precision=10e-5):
+ def _newton_rhaphson(self, df, stop_times_events, show_progress=False, step_size=None, precision=10e-6):
"""
Newton Rhaphson algorithm for fitting CPH model.
@@ -162,7 +162,7 @@ class CoxTimeVaryingFitter(BaseFitter):
hessian, gradient = h, g
if show_progress:
- print("Iteration %d: norm_delta = %.5f, step_size = %.5f, ll = %.5f, seconds_since_start = %.1f" % (i, norm(delta), step_size, ll, time.time() - start))
+ print("Iteration %d: norm_delta = %.6f, step_size = %.6f, ll = %.6f, seconds_since_start = %.1f" % (i, norm(delta), step_size, ll, time.time() - start))
# convergence criteria
if norm(delta) < precision:
