simulateScores: Generate linear discriminant scores from random data, after...

In hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots

Generate linear discriminant scores from random data, after selection

Description

Simulates the effect of generating scores from random data, possibly with predicted scores calculates also for additional 'observations'

Usage

simulateScores(nrows = 7129, cl = rep(1:3, c(19, 10, 2)), x = NULL, cl.other = NULL,
               x.other = NULL, nfeatures = 15, dimen=2, seed = NULL)

Arguments

nrows	number of rows of random data matrix
cl	classifying factor
x	data matrix, by default randomly generated
cl.other	classifying factor for additional observations
x.other	additional observations
nfeatures	number of features to select (by default uses aov F-statistic)
dimen	number of sets of discriminant scores to retain (at most one less than number of levels of cl)
seed	set, if required, so that calculations can be reproduced

Value

scores	matrix of scores
cl	classifying factor
other	matrix of 'other' scores
cl.other	classifying factor for scores.other
nfeatures	number of features used in generating the scores

Note

NB: Prior to 0.53, this function made (wrongly) a random selection of features.

Author(s)

John Maindonald

Examples

scorelist <- simulateScores(nrows=500, cl=rep(1:3, c(19,10,2)))
plot(scorelist$scores, col=unclass(scorelist$cl), pch=16)


## The function is currently defined as
simulateScores <-
  function (nrows = 7129, cl = rep(1:3, c(19, 10, 2)), x = NULL,
            cl.other = NULL, x.other = NULL, nfeatures = 15, dimen = 2,
            seed = NULL)
{
  if (!is.null(seed))
    set.seed(seed)
  m <- length(cl)
  m.other <- length(cl.other)
  if (is.null(x)) {
    x <- matrix(rnorm(nrows * m), nrow = nrows)
    rownames(x) <- paste(1:nrows)
  }
  else nrows <- dim(x)[1]
  if (is.null(x.other)) {
    x.other <- matrix(rnorm(nrows * m.other), nrow = nrows)
    rownames(x.other) <- paste(1:nrows)
  }
  if (is.numeric(cl))
    cl <- paste("Gp", cl, sep = "")
  if(!is.null(cl.other)){
    if (is.numeric(cl.other))
      cl.other <- paste("Gp", cl.other, sep = "")
    cl.other <- factor(cl.other)
  }
  cl <- factor(cl)
  if (dimen > length(levels(cl)) - 1)
    dimen <- length(levels(cl)) - 1
  ordfeatures <- orderFeatures(x, cl = cl, values = TRUE)
  stat <- ordfeatures$stat[1:nfeatures]
  ord.use <- ordfeatures$ord[1:nfeatures]
  xUse.ord <- data.frame(t(x[ord.use, ]))
  xUseOther.ord <- data.frame(t(x.other[ord.use, ]))
  ordUse.lda <- lda(xUse.ord, grouping = cl)
  scores <- predict(ordUse.lda, dimen = dimen)$x
  if(!is.null(cl.other))
    scores.other <- predict(ordUse.lda, newdata = xUseOther.ord,
                            dimen = dimen)$x else
  scores.other <- NULL
  invisible(list(scores = scores, cl = cl, other = scores.other,
                 cl.other = cl.other, nfeatures = nfeatures))
}

hddplot documentation built on Sept. 14, 2023, 5:07 p.m.