apply_DAP {DAP}

R Documentation

Apply DAP for binary classification

Description

Applies Discriminant Analysis via Projections to perform binary classification on the test dataset based on the training data.

Usage

apply_DAP(xtrain, ytrain, xtest, ytest = NULL, lambda_seq = NULL,
  n_lambda = 50, maxmin_ratio = 0.1, nfolds = 5, eps = 1e-04,
  maxiter = 10000, myseed = 1001, prior = TRUE)

Arguments

xtrain	A n x p training dataset; n observations on the rows and p features on the columns.
ytrain	A n vector of training group labels, either 1 or 2.
xtest	A m x p testing dataset; m observations on the rows and p features on the columns.
ytest	An optional m vector of testing group labels, either 1 or 2. If supplied, the function returns misclassification error rate; if NULL, the function returns predicted labels for xtest. Default is NULL.
lambda_seq	An optional sequence of tunning parameters lambda. Default is NULL, and the function generates its own sequence.
n_lambda	Number of lambda values, the default is 50.
maxmin_ratio	Smallest value for lambda, as a fraction of maximal value for which all coefficients are zero. The default is 0.1.
nfolds	Number of folds for cross-validation, the default is 5.
eps	Convergence threshold for the block-coordinate decent algorithm based on the maximum element-wise change in V. The default is 1e-4.
maxiter	Maximum number of iterations, the default is 10000.
myseed	Optional specification of random seed for generating the folds, the default value is 1001.
prior	A logical indicating whether to put larger weights to the groups of larger size; the default value is TRUE.

Details

If no feature is selected by DAP, the function will return error of 0.5 and no ypred, indicating that the classifier is no better than random guessing.

Value

A list of

error	Misclassification error rate (if ytest is provided).
ypred	Predicted labels on the test set (if ytest is NULL).
features	Number of selected features.
feature_id	Index of selected features.

Examples

## This is an example for apply_DAP

## Generate data
n_train = 50
n_test = 50
p = 100
mu1 = rep(0, p)
mu2 = rep(3, p)
Sigma1 = diag(p)
Sigma2 = 0.5* diag(p)

## Build training data and test data
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 will return a miclassification error rate.
ClassificationError = apply_DAP(xtrain, ytrain, xtest, ytest)

# Without ytest, the function will return predictions.
Ypredict = apply_DAP(xtrain, ytrain, xtest)

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