Ranking-Based Variable Selection

Documentation for package ‘rbvs’ version 1.0.2

rbvs-package	Ranking-Based Variable Selection
distance.cor	Measure an impact of the covariates on the response using the distance correlation This function evaluates the distance correlation between the response 'y' and each column in the design matrix 'x' over subsamples in 'subsamples'.
factor.model.design	Generate factor model design matrix.
lasso.coef	Measure an impact of the covariates on the response using Lasso This function evaluates the Lasso coefficients regressing 'y' onto the design matrix 'x' over subsamples in 'subsamples'.
mcplus.coef	Measure an impact of the covariates on the response using MC+. This function evaluates the MC+ coefficients regressing 'y' onto the design matrix 'x' over subsamples in 'subsamples'.
pearson.cor	Measure an impact of the covariates on the response using Pearson correlatio. This function evaluates the Pearson correlation coefficient between the response 'y' and each column in the design matrix 'x' over subsamples in 'subsamples'.
rankings	Evaluate rankings
rbvs	Ranking-Based Variable Selection
rbvs.default	Ranking-Based Variable Selection
s.est.quotient	Estimate the size of the top-ranked set
standardise	Standardise data
subsample	Generates subsamples.
top.ranked.sets	Find k-top-ranked sets