standardizeData {DAP} | R Documentation |
Given matrix X
with corresponding class labels in Y
, the function column-centers X
, divides it into two submatrices corresponding to each class, and scales the columns of each submatrix to have eucledean norm equal to one.
standardizeData(X, Y, center = 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. |
center |
A logical indicating whether |
A list of
X1 |
A n1 x p standardized matrix with observations from group 1. |
X2 |
A n2 x p standardized matrix with observations from group 2. |
coef1 |
Back-scaling coefficients for |
coef2 |
Back-scaling coefficients for |
Xmean |
Column means of the matrix |
# An example for the function standardizeData ## 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 data out_s = standardizeData(xtrain, ytrain, center = FALSE)