# sksurv.linear_model.IPCRidge¶

class sksurv.linear_model.IPCRidge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto')[source]

Accelerated failure time model with inverse probability of censoring weights.

This model assumes a regression model of the form

$\log y = \beta_0 + \mathbf{X} \beta + \epsilon$

L2-shrinkage is applied to the coefficients $$\beta$$ and each sample is weighted by the inverse probability of censoring to account for right censoring (under the assumption that censoring is independent of the features, i.e., random censoring).

See 1 for further description.

Parameters

alpha (float, optional, default: 1.0) – Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates.

coef_

Weight vector.

Type

ndarray, shape = (n_features,)

References

1

W. Stute, “Consistent estimation under random censorship when covariables are present”, Journal of Multivariate Analysis, vol. 45, no. 1, pp. 89-103, 1993. doi:10.1006/jmva.1993.1028.

__init__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto')[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__([alpha, fit_intercept, normalize, …]) Initialize self. fit(X, y) Build an accelerated failure time model. Predict using the linear accelerated failure time model. score(X, y[, sample_weight]) Returns the concordance index of the prediction.
fit(X, y)[source]

Build an accelerated failure time model.

Parameters
• X (array-like, shape = (n_samples, n_features)) – Data matrix.

• 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

Return type

self

predict(X)[source]

Predict using the linear accelerated failure time model.

Parameters

X ({array-like, sparse matrix}, shape = (n_samples, n_features)) – Samples.

Returns

C – Returns predicted values on original scale (NOT log scale).

Return type

array, shape = (n_samples,)

score(X, y, sample_weight=None)[source]

Returns the concordance index of the prediction.

Parameters
• X (array-like, shape = (n_samples, n_features)) – Test samples.

• 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

cindex – Estimated concordance index.

Return type

float