Description Usage Arguments Details Value Examples

The output is the summary of significance tests for binary splits, where the cut-off values are optimized for each covariate.

1 | ```
uni.logrank(t.vec, d.vec, X.mat)
``` |

`t.vec` |
:Vector of survival times (time to either death or censoring) |

`d.vec` |
:Vector of censoring indicators (1=death, 0=censoring) |

`X.mat` |
:n by p matrix of covariates, where n is the sample size and p is the number of covariates |

The output can be used to construct a logrank tree.

A dataframe containing:

Pvalue: the P-value of the two-sample logrank test, where the cut-off value is optimized

cut_off_point: the optimal cutt-off values of the binary splits given a feature

left.sample.size: the sample size of a left child node

right.sample.size: the sample size of a right child node

1 2 3 4 5 6 |

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