plotPCAvariance: plotPCAvariance

In SPOT: Sequential Parameter Optimization Toolbox

plotPCAvariance

Description

plotPCAvariance illustrates the total variance within the dataset. It plots the effectiveness of each principal component and can be used to decide how many and which prinicpal components to plot. In order to create this plot, users don't need to build PCA beforehand since it handles this process automatically.

Usage

plotPCAvariance(x)

Arguments

x	dataset of parameters to be transformed & plotted

Value

It returns a plot image.

Author(s)

Alpar Gür [e-mail]

See Also

buildPCA

Examples

# objective function
funBard <- function (x) {
  bard <- function(par) {
    y <- c(0.14, 0.18, 0.22, 0.25, 0.29, 0.32, 0.35, 0.39, 0.37, 0.58,
           0.73, 0.96, 1.34, 2.10, 4.39)
    m <- 15
    x1 <- par[1]
    x2 <- par[2]
    x3 <- par[3]
    
    fsum <- 0
    for (u in 1:m) {
      v <- 16 - u
      w <- min(u, v)
      f <- y[u] - (x1 + u / (v * x2 + w * x3))
      fsum <- fsum + f * f
    }
    return(fsum)
  }
  matrix(apply(x, # matrix
               1, # margin (apply over rows)
               bard),
         , 1) # number of columns
}

# starting point
x1 <- matrix(c(1,1),1,)
funBard(x1)

#boundaries
lower = c(-0.001,-0.007,-0.003)
upper = c(0.5,1.0,1.1)

res <- spot(,funBard, lower=lower, upper=upper, control=list(funEvals=15))

plotPCAvariance(res$x) # plot variance within the dataset

SPOT documentation built on June 26, 2022, 1:06 a.m.