cv.boss: Cross-validation for Best Orthogonalized Subset Selection... In BOSSreg: Best Orthogonalized Subset Selection (BOSS)

Description

Cross-validation for Best Orthogonalized Subset Selection (BOSS) and Forward Stepwise Selection (FS).

Usage

 1 2 3 4 5 6 7 8 9 10 cv.boss( x, y, maxstep = min(nrow(x) - intercept - 1, ncol(x)), intercept = TRUE, n.folds = 10, n.rep = 1, show.warning = TRUE, ... )

Arguments

 x A matrix of predictors, see boss. y A vector of response variable, see boss. maxstep Maximum number of steps performed. Default is min(n-1,p) if intercept=FALSE, and it is min(n-2, p) otherwise. intercept Logical, whether to fit an intercept term. Default is TRUE. n.folds The number of cross validation folds. Default is 10. n.rep The number of replications of cross validation. Default is 1. show.warning Whether to display a warning if CV is only performed for a subset of candidates. e.g. when n

Details

This function fits BOSS and FS (boss) on the full dataset, and performs n.folds cross-validation. The cross-validation process can be repeated n.rep times to evaluate the out-of-sample (OOS) performance for the candidate subsets given by both methods.

Value

• boss: An object boss that fits on the full dataset.

• n.folds: The number of cross validation folds.

• cvm.fs: Mean OOS deviance for each candidate given by FS.

• cvm.boss: Mean OSS deviance for each candidate given by BOSS.

• i.min.fs: The index of minimum cvm.fs.

• i.min.boss: The index of minimum cvm.boss.

Sen Tian

References

• Tian, S., Hurvich, C. and Simonoff, J. (2021), On the Use of Information Criteria for Subset Selection in Least Squares Regression. https://arxiv.org/abs/1911.10191

• BOSSreg Vignette https://github.com/sentian/BOSSreg/blob/master/r-package/vignettes/BOSSreg.pdf