Construct conformal predictive interval based on distributional boosting
distBoost_based( x, c, alpha, data_fit, data_calib, weight_calib, weight_new, n.tree = 100 )
| x | a vector of the covariate of the test data. |
|---|---|
| c | the censoring time of the test data. |
| alpha | a number betweeo 0 and 1, specifying the miscaverage rate. |
| data_fit | a data frame, containing the training data. |
| data_calib | a data frame, containing the calibration data. |
| type | either "marginal" or "local". Determines the type of confidence interval. |
| dist | The distribution of T used in the cox model. |
low_ci a value of the lower bound for the survival time of the test point.
includeR 0 or 1, indicating if [r,inf) is included in the confidence interval.
Other model:
aft_based(),
gpr_based(),
np_based(),
ph_based(),
portnoy_based(),
pow_based(),
quantBoost_based(),
rf_based()