solve_DAP_C {DAP} R Documentation

## Solves DAP optimization problem for a given lambda value

### Description

Uses block-coordinate descent algorithm to solve DAP problem.

### Usage

```solve_DAP_C(X1, X2, lambda, Vinit = NULL, eps = 1e-04, maxiter = 10000)
```

### Arguments

 `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 V. The default is 1e-4. `maxiter` Maximum number of iterations, the default is 10000.

### Value

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.

### Warnings

Please use scaled `X1` and `X2` for this function, they can be obtained using `standardizeData` to do so.

### Examples

```## 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)
```

[Package DAP version 1.0 Index]