The R package
DAP provides tools for high-dimensional binary classification in the case of unequal covariance matrices. It implements methods from the following paper: * Sparse quadratic classification rules via linear dimension reduction by Gaynanova and Wang (2017).
To install the latest version from Github, use
library(DAP) library(MASS) # Example ## Specify model parameters p = 100 mu1 = rep(0, p) mu2 = c(rep(3, 10), rep(0, p-10)) Sigma1 = diag(p) Sigma2 = 0.5*diag(p) ## Build training data and test data n_train = 50 n_test = 50 x1 = MASS::mvrnorm(n = n_train, mu = mu1, Sigma = Sigma1) x2 = MASS::mvrnorm(n = n_train, mu = mu2, Sigma = Sigma2) xtrain = rbind(x1, x2) x1_test = MASS::mvrnorm(n = n_test, mu = mu1, Sigma = Sigma1) x2_test = MASS::mvrnorm(n = n_test, mu = mu2, Sigma = Sigma2) xtest = rbind(x1_test, x2_test) ytrain = c(rep(1, n_train), rep(2, n_train)) ytest = c(rep(1, n_test), rep(2, n_test)) ## Apply DAP # Given ytest, the function returns the miclassification error rate. ClassificationError = apply_DAP(xtrain, ytrain, xtest, ytest) # Without ytest, the function returns predicted labels. Ypredict = apply_DAP(xtrain, ytrain, xtest)
This package is free and open source software, licensed under GPL (>=2).