solve_DAP_C {DAP} | R Documentation |
Uses block-coordinate descent algorithm to solve DAP problem.
solve_DAP_C(X1, X2, lambda, Vinit = NULL, eps = 1e-04, maxiter = 10000)
X1 |
A n1 x p matrix of group 1 data (scaled). |
X2 |
A n2 x p matrix of group 2 data (scaled). |
lambda |
A value of the tuning parameter lambda. |
Vinit |
Optional starting point, the default is NULL, and the algorithm starts with the matrix of zeros. |
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. |
A list of
V |
A p x 2 projection matrix to be used in DAP classification algorithm. |
nfeature |
Number of nonzero features. |
iter |
Number of iterations until convergence. |
Please use scaled X1
and X2
for this function, they can be obtained using standardizeData
to do so.
## This is an example for solve_DAP_C
## 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))
## Standardize the data
out_s = standardizeData(xtrain, ytrain, center = FALSE)
## Apply solve_DAP_C
out = solve_DAP_C(X1 = out_s$X1, X2 = out_s$X2, lambda = 0.3)