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Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection (BS) upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit.

This repository contains an R packageBOSSreg that provides a highly optimized implementation of BOSS and an heuristic degrees of freedom, which can be further plugged into an information criterion such as AICc in order to selection the subset from candidates. Various choices of information criteria are provided including AICc, Cp, GCV, AIC and BIC. The R package also implements forward stepwise regression (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS.

It also contains the code to reproduce the results in the paper,Tian, S., Hurvich, C. and Simonoff, J. (2021): "On the Use of Information Criteria for Subset Selection in Least Squares Regression".

Note that the implementation of FS inBOSSreg is built upon the implementation of FS in the R packagebestsubset. We simplify the expressions and write all the main functions in C++ using the efficient Armadillo library. We expect a slightly faster implementation of FS compared to thebestsubset package. It's also worth pointing out that thebestsubset package offers an efficient implementation of BS that can fit on high dimensional data (breaks the ad-hoc limit of dimension being around 30 given by the traditionalleaps algorithm). Some of the results in the paper utilize this package.

To install the latest version of the package (v0.2.0, see r-package/NEWS.md for updates in each release), run the following:

library(devtools)install_github(repo="sentian/BOSSreg", subdir="r-package")

Alternatively, a stable version (v0.2.0) can be installed from CRAN

install.packages("BOSSreg", repos = "http://cran.rstudio.com")

For a simple guide of the functionalities of the package, please refer to theBOSSreg's Vignette.

For a complete documentation of the package, please refer to theBOSSreg's Documentation.

The structure of the 'code' directory is shown below.

'plots.R' and 'tables.R' generate the figures and tables that can be found in thepaper/figures andpaper/tables directories, respectively. Note that 'plots.R' is self-contained and does not rely on the simulation or real data results. 'tables.R' requires simulation and real data results as inputs, which can be found in 'code/run_model/simulation/results' and 'code/run_model/realdata/results' directories, respectively.

'.sh' are bash files that submit the corresponding R code to a Linux server for running in parallels.

code├── plots_tables## generate figures and tables in the paper│   ├── plots.R# code that reproduces all the figures│   └── tables.R# code that reproduces all the tables├── run_model## fit the models│   ├── realdata│   │   ├── results# realdata results│   │   │   └──*.rds│   │   ├── forestfires.csv# forest fire dataset│   │   ├── run.R# code that fits and evaluates methods│   │   ├── run_forestfire.R# code that fits MIO BS on forest fire dataset│   │   └── run_forestfire.sh│   └── simulation│   │   ├── para_forhpc# parameters for each configuration│   │   │   └──*.txt│   │   ├── results# simulation results│   │   │   ├── generalx# results for a general X│   │   │       └──*.rds│   │   │   └── orthx# results for an orthogonal X│   │   │       └──*.rds│   │   ├── run.R# code to fit and evaluate all methods│   │   ├── run_srlasso.R# code to fit and evaluate the simplifed relaxed lasso│   │   ├── run_generalx.sh│   │   └── run_orthx.sh└── utils.R## code that contains functions shared by other R codes