R/standardNormalVariate.R

In l-ramirez-lopez/prospectr: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data

#' @title Standard normal variate transformation
#'
#' @description
#' \loadmathjax
#' This function normalizes each row of an input matrix by
#' subtracting each row by its mean and dividing it by its standard deviation
#' @usage
#' standardNormalVariate(X)
#' @param X a numeric matrix of spectral data (optionally a data frame that can
#' be coerced to a numerical matrix).
#' @author Antoine Stevens
#' @examples
#' data(NIRsoil)
#' NIRsoil$spc_snv <- standardNormalVariate(X = NIRsoil$spc)
#' # 10 first snv spectra
#' matplot(
#'   x = as.numeric(colnames(NIRsoil$spc_snv)),
#'   y = t(NIRsoil$spc_snv[1:10, ]),
#'   type = "l",
#'   xlab = "wavelength, nm",
#'   ylab = "snv"
#' )
#' \dontrun{
#' apply(NIRsoil$spc_snv, 1, sd) # check
#' }
#'
#' @return a matrix of normalized spectral data.
#' @details
#' SNV is simple way for normalizing spectral data that intends to correct for
#' light scatter.
#' It operates row-wise:
#'
#' \mjdeqn{SNV_i = \frac{x_i - \bar{x}_i}{s_i}}{SNV_i = \frac{x_i - \bar{x}_i}{s_i}}
#'
#' where \mjeqn{x_i}{x_i} is the signal of the \mjeqn{i}{i}th observation,
#' \mjeqn{\bar{x}_i}{\bar{x}_i} is its mean and \mjeqn{s_i}{s_i} its standard
#' deviation.
#' @seealso \code{\link{msc}}, \code{\link{detrend}}, \code{\link{blockScale}},
#' \code{\link{blockNorm}}
#' @references Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate
#' transformation and de-trending of near-infrared diffuse reflectance spectra.
#' Applied spectroscopy, 43(5): 772-777.
#' @export
#'
standardNormalVariate <- function(X) {
  if (!any(class(X) %in% c("matrix", "data.frame"))) {
    stop("X must be a matrix or optionally a data.frame")
  }
  X <- sweep(X, 1, rowMeans(X, na.rm = TRUE), "-")
  X <- sweep(X, 1, apply(X, 1, sd, na.rm = TRUE), "/")
  as.matrix(X)
}