Competitive Adaptive Reweighted Sampling (CARS) Algorithm

Implements Competitive Adaptive Reweighted Sampling (CARS) algorithm for variable selection from high-dimensional dataset using Partial Least Squares (PLS) regression models. CARS algorithm iteratively applies the Monte Carlo sub-sampling and exponential variable elimination techniques to identify/select the most informative variables/features subjected to minimal cross-validated RMSE score. The implementation of CARS algorithm is inspired from the work of Li et al. (2009) . This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), hyperspectral chemometrics areas, etc.

Competitive Adaptive Reweighted Sampling (CARS)

Competitive Adaptive Reweighted Sampling (CARS) Algorithm

Description

The carsAlgo implements Competitive Adaptive Reweighted Sampling (CARS) algorithm for variable selection from high-dimensional dataset using in Partial Least Squares (PLS) regression models. CARS algorithm iteratively applies the Monte Carlo sub-sampling and exponential variable elimination techniques to identify/select the most informative variables/features subjected to minimal cross-validated RMSE score. The implementation of CARS algorithm is inspired from the work of Li et al. (2009) doi:10.1016/j.aca.2009.06.046. This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), hyperspectral chemometrics areas, etc.

Installation

You can install the development version of cars like so:

install.packages(c("pls", "ggplot2", "stats"))

# Install cars from local source
install.packages("path/to/carsAlgo_0.5.0.tar.gz", repos = NULL, type = "source")
or 
remotes::install_github("mah-iasri/carsAlgo")

Example

This is a basic example of how cars can be used:

library(carsAlgo)
set.seed(1)
X <- matrix(rnorm(100 * 200), nrow = 100)
y <- X[, 5] * 2 + X[, 50] * -1.5 + rnorm(100, sd = 0.5)

cars_obj <- CARSAlgorithm(max_iter = 15, N = 30, cv_folds = 5)
result   <- fit(cars_obj, X, y, max_components = 8,
                plot_path = file.path(tempdir(), "cars_rmsecv_curve.jpg")))

cat("Best RMSECV      :", result$best_rmsecv, "\n")
cat("Selected features:", result$best_features, "\n")

Reference

Li, H., Liang, Y., Xu, Q., & Cao, D. (2009). Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648(1), 77–84. https://doi.org/10.1016/j.aca.2009.06.046