R/cv.SeSDA.R

In TULIP: A Toolbox for Linear Discriminant Analysis with Penalties

cv.SeSDA <- function(x, y, nfolds = 5,lambda=NULL, lambda.opt="min", standardize=FALSE, alpha=1, eps=1e-7){
  
  n1 <- sum(y==1)
  n2 <- sum(y==2)
  
  n <- nrow(x)
  p <- ncol(x)
  x.norm <- matrix(0,n,p)
  transform <- array(list(),p)
  for(i in 1:p){
    obj.norm <- getnorm(x[,i],y)
    x.norm[,i] <- obj.norm$x.norm
    transform[[i]] <- obj.norm$transform
  }
  
  
  obj <- cv.dsda(x.norm, y=y, nfolds=nfolds, lambda.opt=lambda.opt, standardize = standardize, lambda = lambda, alpha = alpha, eps = eps)
  
  outlist <- list(transform=transform, objdsda = obj)
  class(outlist) <- c('cv.SeSDA')
  outlist
  
}