Pass further options to hpstat in yli.turnbull
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@ -135,7 +135,7 @@ def calc_survfunc_kaplanmeier(time, status, ci, transform_x=None, transform_y=No
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return xpoints, ypoints, None, None
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def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
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def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, maxiter=None, fail_prob_tolerance=None, se_method=None, zero_tolerance=None, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
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"""
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Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
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@ -157,6 +157,14 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, trans
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:type ci: bool
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:param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint
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:type step_loc: float
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:param maxiter: Maximum number of iterations to attempt
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:type maxiter: int
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:param fail_prob_tolerance: Terminate algorithm when the absolute change in failure probability in each interval is less than this tolerance
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:type fail_prob_tolerance: float
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:param se_method: Method for computing standard error or survival probabilities (see hpstat *turnbull* documentation)
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:type se_method: str
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:param zero_tolerance: Threshold for dropping failure probability when se_method is "oim-drop-zeros"
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:type zero_tolerance: float
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:param transform_x: Function to transform x axis by
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:type transform_x: callable
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:param transform_y: Function to transform y axis by
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@ -187,11 +195,11 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, trans
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for group in groups.groups:
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subset = groups.get_group(group)
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handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], ci, step_loc, transform_x, transform_y)
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handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], ci, step_loc, maxiter, fail_prob_tolerance, se_method, zero_tolerance, transform_x, transform_y)
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handle.set_label('{} = {}'.format(by, group))
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else:
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# No grouping
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plot_survfunc_turnbull(ax, df[time_left], df[time_right], ci, step_loc, transform_x, transform_y)
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plot_survfunc_turnbull(ax, df[time_left], df[time_right], ci, step_loc, maxiter, fail_prob_tolerance, se_method, zero_tolerance, transform_x, transform_y)
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if time_units:
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ax.set_xlabel('{} + {} ({})'.format(time_left, time_right, time_units))
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@ -206,8 +214,8 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, trans
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return fig, ax
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def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
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xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(time_left, time_right, ci, step_loc, transform_x, transform_y)
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def plot_survfunc_turnbull(ax, 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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xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(time_left, time_right, ci, step_loc, maxiter, fail_prob_tolerance, se_method, zero_tolerance, transform_x, transform_y)
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handle = ax.plot(xpoints, ypoints)[0]
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@ -216,12 +224,23 @@ def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, transfor
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return handle
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def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, 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, fail_prob_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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# TODO: Pass through other arguments
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hpstat_args = [config.hpstat_path, 'turnbull', '-', '--output', 'json']
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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 se_method:
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hpstat_args.append('--se-method')
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hpstat_args.append(se_method)
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if zero_tolerance:
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hpstat_args.append('--zero-tolerance')
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hpstat_args.append(str(zero_tolerance))
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# Export data to CSV
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csv_buf = io.StringIO()
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