cv_DAP: Cross-validation for DAP
In DAP: Discriminant Analysis via Projections

Description Usage Arguments Value Examples

Chooses optimal tuning parameter lambda for DAP based on the k-fold cross-validation to minimize the misclassification error rate

1 2	cv_DAP(X, Y, lambda_seq, nfolds = 5, eps = 1e-04, maxiter = 1000, myseed = 1001, prior = TRUE)

`X`	A n x p training dataset; n observations on the rows and p features on the columns.
`Y`	A n vector of training group labels, either 1 or 2.
`lambda_seq`	A sequence of tuning parameters to choose from.
`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`.

A list of

`lambda_seq`	The sequence of tuning parameters used.
`cvm`	The mean cross-validated error rate - a vector of length `length(lambda_seq)`
`cvse`	The estimated standard error vector corresponding to `cvm`.
`lambda_min`	Value of tuning parameter corresponding to the minimal error in `cvm`.
`lambda_1se`	The largest value of tuning parameter such that the correspondig error is within 1 standard error of the minimal error in `cvm`.
`nfeature_mat`	A `nfolds` x `length(lambda_seq)` matrix of the number of selected features.
`error_mat`	A `nfolds` x `length(lambda_seq)` matrix of the error rates.

## This is an example for cv_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
x1 = MASS::mvrnorm(n = n_train, mu = mu1, Sigma = Sigma1)
x2 = MASS::mvrnorm(n = n_train, mu = mu2, Sigma = Sigma2)
xtrain = rbind(x1, x2)
ytrain = c(rep(1, n_train), rep(2, n_train))

## Apply cv_DAP
fit = cv_DAP(X = xtrain, Y = ytrain, lambda_seq = c(0.2, 0.3, 0.5, 0.7, 0.9))