classify_DAP {DAP} | R Documentation |
Classify observations in the test set using the supplied matrix V and the training data.
classify_DAP(xtrain, ytrain, xtest, V, prior = TRUE)
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. |
V |
A p x 2 projection matrix. |
prior |
A logical indicating whether to put larger weights to the groups of larger size; the default value is |
Predicted class labels for the test data.
## This is an example for classify_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))
# Standardize the data
out_s = standardizeData(xtrain, ytrain, center = FALSE)
## Find V
out.proj = solve_DAP_C(X1 = out_s$X1, X2 = out_s$X2, lambda = 0.3)
V = cbind(diag(1/out_s$coef1)%*%out.proj$V[,1],diag(1/out_s$coef2)%*% out.proj$V[,2])
# Predict y using classify_DAP
ypred = classify_DAP(xtrain, ytrain, xtest, V = V)