RunasSudo/scipy-yli
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Update yli.turnbull for hpstat 2880fe8

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This commit is contained in:
RunasSudo 2023-11-01 19:39:51 +11:00
parent 263f65f478
commit a48c59f780
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
1 changed files with 14 additions and 9 deletions
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yli/survival.py
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@ -224,7 +224,7 @@ def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, maxiter=
return handle return handle
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): 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):
# Estimate the survival function # Estimate the survival function
# Prepare arguments # Prepare arguments
@ -232,9 +232,9 @@ def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None
if maxiter: if maxiter:
hpstat_args.append('--max-iterations') hpstat_args.append('--max-iterations')
hpstat_args.append(str(maxiter)) hpstat_args.append(str(maxiter))
if fail_prob_tolerance: if ll_tolerance:
hpstat_args.append('--fail-prob-tolerance') hpstat_args.append('--ll-tolerance')
hpstat_args.append(str(fail_prob_tolerance)) hpstat_args.append(str(ll_tolerance))
if se_method: if se_method:
hpstat_args.append('--se-method') hpstat_args.append('--se-method')
hpstat_args.append(se_method) hpstat_args.append(se_method)
@ -270,12 +270,17 @@ def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, maxiter=None
ypoints = transform_y(ypoints) ypoints = transform_y(ypoints)
if ci: if ci:
zstar = -stats.norm.ppf(config.alpha/2)
survival_prob_se = np.array(raw_result['survival_prob_se'])
# Get confidence intervals # Get confidence intervals
ci0 = survival_prob - zstar * survival_prob_se if raw_result['survival_prob_se']:
ci1 = survival_prob + zstar * survival_prob_se zstar = -stats.norm.ppf(config.alpha/2)
survival_prob_se = np.array(raw_result['survival_prob_se'])
ci0 = survival_prob - zstar * survival_prob_se
ci1 = survival_prob + zstar * survival_prob_se
else:
survival_prob_ci = np.array(raw_result['survival_prob_ci'])
ci0 = survival_prob_ci.T[0]
ci1 = survival_prob_ci.T[1]
# Plot confidence intervals # Plot confidence intervals
ypoints0 = np.concatenate([[1], ci0]).repeat(2)[:-1] ypoints0 = np.concatenate([[1], ci0]).repeat(2)[:-1]