Update yli.turnbull for hpstat 2880fe8
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@ -224,7 +224,7 @@ def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, maxiter=
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return handle
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def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None, fail_prob_tolerance=None, se_method=None, zero_tolerance=None, transform_x=None, transform_y=None):
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def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None, ll_tolerance=None, se_method=None, zero_tolerance=None, transform_x=None, transform_y=None):
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# Estimate the survival function
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# Prepare arguments
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@ -232,9 +232,9 @@ def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None
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if maxiter:
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hpstat_args.append('--max-iterations')
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hpstat_args.append(str(maxiter))
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if fail_prob_tolerance:
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hpstat_args.append('--fail-prob-tolerance')
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hpstat_args.append(str(fail_prob_tolerance))
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if ll_tolerance:
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hpstat_args.append('--ll-tolerance')
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hpstat_args.append(str(ll_tolerance))
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if se_method:
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hpstat_args.append('--se-method')
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hpstat_args.append(se_method)
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@ -270,12 +270,17 @@ def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None
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ypoints = transform_y(ypoints)
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if ci:
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zstar = -stats.norm.ppf(config.alpha/2)
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survival_prob_se = np.array(raw_result['survival_prob_se'])
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# Get confidence intervals
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ci0 = survival_prob - zstar * survival_prob_se
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ci1 = survival_prob + zstar * survival_prob_se
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if raw_result['survival_prob_se']:
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zstar = -stats.norm.ppf(config.alpha/2)
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survival_prob_se = np.array(raw_result['survival_prob_se'])
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ci0 = survival_prob - zstar * survival_prob_se
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ci1 = survival_prob + zstar * survival_prob_se
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else:
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survival_prob_ci = np.array(raw_result['survival_prob_ci'])
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ci0 = survival_prob_ci.T[0]
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ci1 = survival_prob_ci.T[1]
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# Plot confidence intervals
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ypoints0 = np.concatenate([[1], ci0]).repeat(2)[:-1]
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