showloadings function

Loadings plot for heteroscedastic discriminant analysis

Visualizes the loadings of the original variables on the components of the transformed discriminant space of reduced dimension.

showloadings(object, comps = 1:object$reduced.dimension, loadings = TRUE, ...)

Arguments

• object: An object of class hda.
• comps: A vector of component ids for which the loadings should be displayed.
• loadings: Logical indicating whether loadings or variable importance lifts should be plotted.
• ...: Further arguments to be passed to the plot functions.

Details

Scatterplots of loadings (or lifts) of any variable on any hda component to give an idea of what variables do mainly contribute to the different discriminant components (see corresponding values of object). Note that as opposed to linear discriminant analysis not only location but also scale differences contribute to class discrimination of the hda components.

Returns

No value is returned.

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.

Author(s)

Gero Szepannek

See Also

hda, predict.hda, plot.hda

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)

# visualize loadings
showloadings(hda.res)