The output is the summary of significance tests for binary splits, where the cut-off values are optimized for each covariate.
uni.logrank(t.vec, d.vec, X.mat)
:Vector of survival times (time to either death or censoring)
:Vector of censoring indicators (1=death, 0=censoring)
: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
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