sksurv.nonparametric.CensoringDistributionEstimator#
- class sksurv.nonparametric.CensoringDistributionEstimator(conf_level=0.95, conf_type=None)[source]#
Kaplan–Meier estimator for the censoring distribution.
Methods
__init__([conf_level, conf_type])fit(y)Estimate censoring distribution from training data.
get_params([deep])Get parameters for this estimator.
predict_ipcw(y)Return inverse probability of censoring weights at given time points.
predict_proba(time[, return_conf_int])Return probability of an event after given time point.
set_params(**params)Set the parameters of this estimator.
- fit(y)[source]#
Estimate censoring distribution from training data.
- Parameters
y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.
- Return type
self
- get_params(deep=True)#
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- predict_ipcw(y)[source]#
Return inverse probability of censoring weights at given time points.
\(\omega_i = \delta_i / \hat{G}(y_i)\)
- Parameters
y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.
- Returns
ipcw – Inverse probability of censoring weights.
- Return type
array, shape = (n_samples,)
- predict_proba(time, return_conf_int=False)[source]#
Return probability of an event after given time point.
\(\hat{S}(t) = P(T > t)\)
- Parameters
time (array, shape = (n_samples,)) – Time to estimate probability at.
return_conf_int (bool, optional, default: False) – Whether to return the pointwise confidence interval of the survival function. Only available if
fit()has been called with the conf_type parameter set.
- Returns
prob (array, shape = (n_samples,)) – Probability of an event at the passed time points.
conf_int (array, shape = (2, n_samples)) – Pointwise confidence interval at the passed time points. Only provided if return_conf_int is True.
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance