mboost.varimp: Importance statistics based on risk reduction in boosting

Description Usage Arguments Value References

Description

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.

Usage

1
mboost.varimp(Xaug, y, max.mstop = 100, baselearner = c("bbs", "bols", "btree"), cv.fold = 5, family = Gaussian())

Arguments

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.

Value

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

References

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")


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