cv_DAP {DAP} | R Documentation |
Chooses optimal tuning parameter lambda for DAP based on the k-fold cross-validation to minimize the misclassification error rate
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 |
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 |
A list of
lambda_seq |
The sequence of tuning parameters used. |
cvm |
The mean cross-validated error rate - a vector of length |
cvse |
The estimated standard error vector corresponding to |
lambda_min |
Value of tuning parameter corresponding to the minimal error in |
lambda_1se |
The largest value of tuning parameter such that the correspondig error is within 1 standard error of the minimal error in |
nfeature_mat |
A |
error_mat |
A |
## 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))