Nothing
#' @title plotPCA
#'
#' @description
#' plotPCA returns a 2D plot of optimization data in it's own space using buildPCA.
#' It plots first two PCAs by default.
#'
#' @param x dataset of parameters to be transformed & plotted
#' @param control control list
#'
#' @importFrom stats biplot
#'
#' @examples
#' # define objective function
#' funGauss <- function (x) {
#' gauss <- function(par) {
#' y <- c(0.0009, 0.0044, 0.0175, 0.0540, 0.1295, 0.2420, 0.3521, 0.3989,
#' 0.3521, 0.2420, 0.1295, 0.0540, 0.0175, 0.0044, 0.0009)
#' m <- 15
#' x1 <- par[1]
#' x2 <- par[2]
#' x3 <- par[3]
#'
#' fsum <- 0
#' for (i in 1:m) {
#' ti <- (8 - i) * 0.5
#' f <- x1 * exp(-0.5 * x2 * (ti - x3) ^ 2) - y[i]
#' fsum <- fsum + f * f
#' }
#' return(fsum)
#' }
#' matrix(apply(x, # matrix
#' 1, # margin (apply over rows)
#' gauss),
#' , 1) # number of columns
#' }
#'
#' # define starting point
#' x1 <- matrix(c(1,1,1),1,)
#' funGauss(x1)
#'
#' # define boundaries
#' lower = c(-0.001,-0.007,-0.003)
#' upper = c(0.5,1.0,1.1)
#'
#' res <- spot(,funGauss, lower=lower, upper=upper, control=list(funEvals=15))
#'
#' control = list(scale=TRUE) #pca control list, # scale the variables
#'
#' plotPCA(res$x, control=control) # plot first two PCAs
#'
#' @return It returns a plot image.
#'
#' @seealso \code{\link{buildPCA}}, \code{\link{biplot}}
#'
#' @author Alpar Gür \email{alpar.guer@@smail.th-koeln.de}
#'
#' @export
plotPCA <- function(x, control = list()){
# default control list
con <- list(retx = TRUE,
center = TRUE,
scale = FALSE,
tol = NULL)
con[names(control)] <- control # update default control list with user defined controls
control <- con # use control as control list
results <- buildPCA(x, control)
pcaPlot <- biplot(results, scale = 0) #biplot plots first two components of PCs by default
return(pcaPlot)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.