predict.hda {hda}

Heteroscedastic discriminant analysis

Description

Computes linear transformation of new data into lower dimensional discriminative space using some model produced by hda.

Usage

## S3 method for class 'hda'
predict(object, newdata, alldims = FALSE, task = c("dr", "c"), ...)

Arguments

object

Model resulting from a call of hda.

newdata

A matrix or data frame to be transformed into lower dimensional space of the same dimension as the data used for building the model.

alldims

Logical flag specifying whether the result should contain only the reduced space (default) or should also include the redundant dimensions and thus be of the same dimension as the input data. In this case the reduced space is given by the first newdim columns.

task

"dr" for standard application of the hda model to newdata. Choose "c" for classification of new data. This is an interface to predict function of naiveBayes. The option can be chosen if crule = TRUE has been specified in the hda() call.

...

Further arguments to be passed to the naiveBayes predict function.

Value

If option type = "dr" the transformed data are returned. For type = "c" both the transformed data as well as the resulting object of the naive Bayes prediction are 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, showloadings, 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

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

# simulate new data
xanew      <- rmvnorm(n,meana,cova)
xbnew      <- rmvnorm(n,meanb,covb)
xnew       <- rbind(xanew,xbnew)
classes <- as.factor(c(rep(1,n),rep(2,n)))
newrotateddata <- x %*% even
plot(as.data.frame(newrotateddata), col = classes)

# transform new data 
prediction <- predict(hda.res, newrotateddata)
plot(as.data.frame(prediction), col = classes)

# predict classes for new data on automatically computed naive Bayes classification rule 
# this requires package e1071
hda.res2 <- hda(rotatedspace, classes, crule = TRUE)
prediction2 <- predict(hda.res2, newrotateddata, task = "c")
prediction2