R/Glass.R

In ltsspca: Sparse Principal Component Based on Least Trimmed Squares

#' @title Glass data
#' @description Glass data of Lemberge et al. (2000) containing Electron Probe X-ray Microanalysis (EPXMA)
#' intensities for different wavelengths of 16–17th century archaeological glass vessels. This dataset was also used in Hubert et al. (2005) and Hubert et al. (2016).
#' @source Lemberge, P., De Raedt, I., Janssens, K. H., Wei, F., and Van Espen, P. J. (2000), ``Quantitative Z-Analysis of the 16--17th Century Archaelogical Glass Vessels using PLS Regression of EPXMA and \eqn{\mu}-XRF Data," \emph{Journal of Chemometrics}, 14, 751--763.
#' @references Hubert, M., Rousseeuw, P. J., and Vanden Branden, K. (2005), ``ROBPCA: A New Approach to Robust Principal Component Analysis,'' \emph{Technometrics}, 47, 64--79.
#' @references Hubert, M., Reynkens, T., Schmitt, E. and Verdonck, T. (2016), ``Sparse PCA for High-Dimensional Data With Outliers,'' \emph{Technometrics}, 58, 424--434.
#' @format A data frame with columns:
#' \describe{
#' A data frame with 180 observations and 750 variables. These variables correspond to
#' EPXMA intensities for different wavelengths and are indicated by \code{V1}, \code{V2}, ..., \code{V750}.
#' }
#' @examples
#' \dontrun{
#'  data(Glass)
#' }
"Glass"