# Plot transformed data Visualizes the scores on selected components of the discriminant space of reduced dimension. ```r ## 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. ## 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`, `showloadings` ## Examples ```r 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) ```

plot.hda.rd function