plot.hda: Plot transformed data
In hda: Heteroscedastic Discriminant Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/hda.r

Visualizes the scores on selected components of the discriminant space of reduced dimension.

1 2	## S3 method for class 'hda' plot(x, comps = 1:x$reduced.dimension, scores = TRUE, col = x$grouping, ...)

`x`	An object of class `hda`.
`comps`	A vector of component ids for which the data should be displayed.
`scores`	Logical indicating whether the scores in the projected space should be plotted. If FALSE estimated densities are plotted.
`col`	Color vector for the data to be displayed. Per default, different colors represent the classes.
`...`	Further arguments to be passed to the plot function.

Scatterplots of the scores or estimated densities.

No value is returned.

Gero Szepannek

Kumar, N. and Andreou, A. (1998): Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition. Speech Communication 25, pp.283-297.

Szepannek G., Harczos, T., Klefenz, F. and Weihs, C. (2009): Extending features for automatic speech recognition by means of auditory modelling. In: Proceedings of European Signal Processing Conference (EUSIPCO) 2009, Glasgow, pp.1235-1239.

hda, predict.hda, showloadings

library("mvtnorm")
library("MASS")

# simulate data for two classes
n           <- 50
meana       <- meanb <- c(0,0,0,0,0)
cova        <- diag(5)
cova[1,1]   <- 0.2
for(i in 3:4){
  for(j in (i+1):5){
    cova[i,j] <- cova[j,i] <- 0.75^(j-i)}
  }
covb       <- cova
diag(covb)[1:2]  <- c(1,0.2)

xa      <- rmvnorm(n, meana, cova)
xb      <- rmvnorm(n, meanb, covb)
x       <- rbind(xa,xb)
classes <- as.factor(c(rep(1,n), rep(2,n)))
## rotate simulated data
symmat <- matrix(runif(5^2),5)
symmat <- symmat + t(symmat)
even   <- eigen(symmat)$vectors
rotatedspace <- x %*% even
plot(as.data.frame(rotatedspace), col = classes)

# apply heteroscedastic discriminant analysis and plot data in discriminant space
hda.res <- hda(rotatedspace, classes)

# plot scores
plot(hda.res)