stat.mboost_diff_Rsq: Importance statistics based on R-square via boosting

Description Usage Arguments Value

View source: R/mboost_diff_Rsq.R

Description

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.

Usage

1
stat.mboost_diff_Rsq(Xaug, y, max.mstop = 100, bl = 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

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.

Value

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.