Univariate-Guided Sparse Regression
simulate Gaussian data
simulate two class data
Simulate data for use in uniLasso and uniReg
Compare the nonzero coefficients and univariate counterparts
simulate counterexample data
Fit a cross-validated univariate guided lasso model.
plot the cross-validation curve produced by cv.uniReg
Fit a cross-validated univariate guided lasso model, followed by a las...
make predictions from a "cv.uniReg" object.
print a cross-validated uniReg object
Create the univariate info for use in uniLasso
Compute bootstrap confidence intervals for a univariate guided regress...
Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class 'glmnet', so that all of the methods for 'glmnet' are available. See Chatterjee, Hastie and Tibshirani (2025) <doi:10.1162/99608f92.c79ff6db> for details.