R/xshift.R

In SCOUTer: Simulate Controlled Outliers

#'
#' xshift
#'
#' Shift of an observation. The performed operation results as a combination of two main 
#' directions: the direction of maximum gradient for the SPE (weighted by the parameter b)
#'  and the direction of the projection of the observation on the model (weighted by the 
#'  parameter a).
#'
#' @param X Matrix with observations that will be shifted.
#' @param P Loading matrix of the PCA model according to which the shift will be performed.
#' @param a A number or vector tuning the shift in the direction of its projection.
#' @param b A number or vector tuning the shift in the direction of its residual.
#' @return Matrix with shifted observation as rows, keeping the order of the input matrix 
#' \code{X}.
#' @examples 
#' X <- as.matrix(X)
#' pcamodel.ref <- pcamb_classic(X, 3, 0.1, "autosc") # PCA-MB with all observations
#' # Shift observation #10 increasing by a factor of 2 and 4 its T^2 and its SPE respectively
#' x.new <- xshift(X[10,], pcamodel.ref$P, sqrt(2) - 1, sqrt(4) - 1) 
#' @export
xshift <- function(X, P, a, b){
  if (is.null(dim(X)) == TRUE){
    X <- t(as.matrix(X))
  }
  n <- nrow(X)
  I <- diag(nrow(P))
  Xout <- matrix(NA, nrow = 0, ncol = ncol(X))
  for (ni in 1:nrow(X)){
    x2 <- (X[ni,] %*% (I + b[ni] * (I - (P %*% t(P)))) %*% (I + a[ni] * (P %*% t(P))))
    Xout <- rbind(Xout, x2)
  }
  return(Xout)
}