plot.hda {hda}

Plot transformed data

Description

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

Usage

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

Arguments

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.

Details

Scatterplots of the scores or estimated densities.

Value

No value is returned.

Author(s)

Gero Szepannek

References

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.

See Also

hda, predict.hda, showloadings

Examples

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)