R/scoreplot.R

In SCOUTer: Simulate Controlled Outliers

#'
#' scoreplot
#'
#' Returns the score plot providing a dataset and a pca model. Observations can 
#' be identified by the obstag input argument.
#'
#' @param X Matrix with the data to be displayed.
#' @param pcamodel List wiht the PCA model elements.
#' @param obstag Optional column vector of integers indicating the group of each
#' observation (\code{0} or \code{1}). Default value set to \code{matrix(0, nrow(X), 1)}.
#' @param pcx Optional integer with the number of the PC in the horizontal axis. Set to \code{1} by default.
#' @param pcy Optional integer with the number of the PC in the vertical axis. Set to \code{2} by default.
#' @param alpha Optional number between 0 and 1 expressing the type I risk assumed in the compuatation of the confidence ellipse,
#' set to \code{0.05} (5 %) by default.
#' @param plottitle Optional string with the plot title. Set to \code{"Score plot"} by default.
#' @return ggplot object with the generated score plot.
#' @examples 
#' X <- as.matrix(X)
#' pcamodel.ref <- pcamb_classic(X[1:40,], 3, 0.05, "cent")
#' scoreplot(X, pcamodel.ref)
#' tags <- dotag(X[1:40,], X[-c(1:40),])
#' scoreplot(X, pcamodel.ref, obstag = tags, pcx = 2, pcy = 3, alpha = 0.1, plottitle = "T-plot")
#' @export
scoreplot <- function(X, pcamodel, obstag = matrix(0, nrow(X), 1), pcx = 1, pcy = 2, alpha = 0.05, plottitle = "Score plot\n"){
  # Calcualte the scores according to the PCA model in pcamodel list
  pcavalues <- pcame(X, pcamodel)
  scplot <- scoreplotsimple(pcavalues$Tscores, pcx = pcx, pcy  = pcy, obstag = obstag,
                            alpha = alpha, varT = pcamodel$lambda, plottitle = plottitle)
  return(scplot)
}