Description Usage Arguments Value

View source: R/mboost_diff_Rsq.R

This function provides importance scores for variables (including the knockoffs) in order to compute statistics. The variable importance is measured by R-squares obtained from boosting.

1 | ```
stat.mboost_diff_Rsq(Xaug, y, max.mstop = 100, bl = c("bbs", "bols", "btree"), cv.fold = 5, family = Gaussian())
``` |

`Xaug` |
augmented design matrix combining original predictors and knockoff variables |

`y` |
response vector, or a survival object with two columns |

`max.mstop` |
maximum number of boosting iteration |

`bl` |
base-learners when fitting models using mboost. 'bols' means linear base-learners, 'bbs' penalized regression splines with a B-spline basis, and 'btree' boosts stumps. |

`cv.fold` |
number of folds in cross-validation to choose number of iteration |

`family` |
Binomial(), Binomial(link = “logit”, type=”glm”), Gaussian(), Poisson(), CoxPH(), Cindex(), GammaReg(), NBinomial(), Weibull(), Loglog(), Lognormal(), etc. See mboost documentation for details. |

2p vector containing varible importance for both orginal variables and knockoff variables

hanfu-bios/varsel documentation built on March 19, 2018, 10:08 a.m.

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