Description Usage Arguments Value References

This function provides importance scores for variables (including the knockoffs) in order to compute statistics. The variable importance is measured by in-bag risk reduction per boosting step.

1 | ```
mboost.varimp(Xaug, y, max.mstop = 100, baselearner = 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 |

`baselearner` |
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

Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost. Computational Statistics, 29, 3–35. http://dx.doi.org/10.1007/s00180-012-0382-5 Available as vignette via: vignette(package = "mboost", "mboost_tutorial")

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