Description Usage Arguments Details Value Author(s) References Examples

View source: R/uni.selection.R

This function perform univariate feature selection using univariate significance tests, where the Wald statistics or score statistics are used to measure significance. Features are selected according to whether their P-values are less than a given threshold by user. The cross-validated likelihood (CVL) value is computed for selected features (Matsui 2006; Emura et al. 2018).

1 2 | ```
uni.selection(t.vec, d.vec, X.mat, P.value = 0.001, K = 10,
score=FALSE,d0=0,randomize=FALSE,c.plot=TRUE,permutation=FALSE)
``` |

`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 |

`P.value` |
A threshold for selecting features |

`K` |
The number of cross-validation folds |

`score` |
If TRUE, the score test is performed instead of the Wald test |

`d0` |
A positive constant to stabilize the variance (Witten & Tibshirani 2010) |

`randomize` |
If TRUE, randomize patient ID's before cross-validation |

`c.plot` |
If TRUE, the plot of c-index is displayed |

`permutation` |
If TRUE, the FDR is computed by randomly permutating the gene expressions |

Predictive ability of the selected genes are evaluated throught cross-validated log-likelihood (CVL) and c-index are computed.

`gene ` |
Gene symbols |

`beta ` |
Estimated regression coefficients |

`Z ` |
Z-value for testing H_0: beta=0 (Wald test) |

`P ` |
P-value for testing H_0: beta=0 (Wald test) |

`c_index ` |
c-index |

`CVL ` |
Cross-validated partial likelihood |

`Genes ` |
The number of genes, the number of selected genes, and the number of falsely selected genes |

`FDR ` |
False Discovery Rate |

Takeshi Emura

Matsui S (2006). Predicting Survival Outcomes Using Subsets of Significant Genes in Prognostic Marker Studies with Microarrays. BMC Bioinformatics: 7:156.

Emura T, Chen YH (2016). Gene Selection for Survival Data Under Dependent Censoring: a Copula-based Approach, Stat Methods Med Res 25(No.6): 2840-57

Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Stat Method Med Res 19:29-51

1 2 3 4 5 6 |

compound.Cox documentation built on May 24, 2018, 5:03 p.m.

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