RunasSudo/scipy-yli
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Bring yli.turnbull up to date with hpstat 82f4a54

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RunasSudo 2023-12-31 18:08:19 +11:00
parent a48c59f780
commit f2987bfd2d
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
1 changed files with 22 additions and 8 deletions
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30
yli/survival.py
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@ -135,7 +135,7 @@ def calc_survfunc_kaplanmeier(time, status, ci, transform_x=None, transform_y=No
return xpoints, ypoints, None, None
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):
def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, maxiter=None, ll_tolerance=None, se_method=None, zero_tolerance=None, ci_precision=None, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
"""
Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
@ -159,12 +159,14 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, maxit
:type step_loc: float
:param maxiter: Maximum number of iterations to attempt
:type maxiter: int
:param fail_prob_tolerance: Terminate algorithm when the absolute change in failure probability in each interval is less than this tolerance
:type fail_prob_tolerance: float
:param ll_tolerance: Terminate algorithm when the absolute change in log-likelihood is less than this tolerance
:type ll_tolerance: float
:param se_method: Method for computing standard error or survival probabilities (see hpstat *turnbull* documentation)
:type se_method: str
:param zero_tolerance: Threshold for dropping failure probability when se_method is "oim-drop-zeros"
:type zero_tolerance: float
:param ci_precision: Desired precision of confidence limits when se-method is "likelihood-ratio"
:type ci_precision: float
:param transform_x: Function to transform x axis by
:type transform_x: callable
:param transform_y: Function to transform y axis by
@ -195,11 +197,17 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, maxit
for group in groups.groups:
subset = groups.get_group(group)
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)
handle = plot_survfunc_turnbull(
ax, subset[time_left], subset[time_right],
ci=ci, step_loc=step_loc, maxiter=maxiter, ll_tolerance=ll_tolerance, se_method=se_method, zero_tolerance=zero_tolerance, ci_precision=ci_precision, transform_x=transform_x, transform_y=transform_y
)
handle.set_label('{} = {}'.format(by, group))
else:
# No grouping
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)
plot_survfunc_turnbull(
ax, df[time_left], df[time_right],
ci=ci, step_loc=step_loc, maxiter=maxiter, ll_tolerance=ll_tolerance, se_method=se_method, zero_tolerance=zero_tolerance, ci_precision=ci_precision, transform_x=transform_x, transform_y=transform_y
)
if time_units:
ax.set_xlabel('{} + {} ({})'.format(time_left, time_right, time_units))
@ -214,8 +222,11 @@ def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, maxit
return fig, ax
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):
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)
def plot_survfunc_turnbull(ax, time_left, time_right, *, ci=True, step_loc=0.5, maxiter=None, ll_tolerance=None, se_method=None, zero_tolerance=None, ci_precision=None, transform_x=None, transform_y=None):
xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(
time_left, time_right,
ci=ci, step_loc=step_loc, maxiter=maxiter, ll_tolerance=ll_tolerance, se_method=se_method, zero_tolerance=zero_tolerance, ci_precision=ci_precision, transform_x=transform_x, transform_y=transform_y
)
handle = ax.plot(xpoints, ypoints)[0]
@ -224,7 +235,7 @@ def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, maxiter=
return handle
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):
def calc_survfunc_turnbull(time_left, time_right, *, ci=True, step_loc=0.5, maxiter=None, ll_tolerance=None, se_method=None, zero_tolerance=None, ci_precision=None, transform_x=None, transform_y=None):
# Estimate the survival function
# Prepare arguments
@ -241,6 +252,9 @@ def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None
if zero_tolerance:
hpstat_args.append('--zero-tolerance')
hpstat_args.append(str(zero_tolerance))
if ci_precision:
hpstat_args.append('--ci-precision')
hpstat_args.append(str(ci_precision))
# Export data to CSV
csv_buf = io.StringIO()