An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018)
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:
To install the latest version from Github, use
devtools::install_github("irinagain/DAP")library(DAP)library(MASS) # Example ## Specify model parametersp = 100mu1 = 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 datan_train = 50n_test = 50x1 = 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).