How can I plot a biplot for LDA in r? - r

Question

I did a linear discriminant analysis using the function lda() from the package MASS. Now I would try to plot a biplot like in ade4 package (forLDA). Do you know how can I do this?
If I try to use the biplot() function it doesn't work. For example, if I use the Iris data and make LDA:
dis2 <- lda(as.matrix(iris[, 1:4]), iris$Species)
then I can plot it using the function plot(), but if I use the function biplot() it doesn't work:
biplot(dis2)
Error in nrow(y) : argument "y" is missing, with no default
How can I plot the arrows of variables?

Answer 1

I wrote the following function to do this:
lda.arrows <- function(x, myscale = 1, tex = 0.75, choices = c(1,2), ...){
## adds `biplot` arrows to an lda using the discriminant function values
heads <- coef(x)
arrows(x0 = 0, y0 = 0,
x1 = myscale * heads[,choices[1]],
y1 = myscale * heads[,choices[2]], ...)
text(myscale * heads[,choices], labels = row.names(heads),
cex = tex)
}
For your example:
dis2 <- lda(as.matrix(iris[, 1:4]), iris$Species)
plot(dis2, asp = 1)
lda.arrows(dis2, col = 2, myscale = 2)
The length of the arrows is arbitrary relative to the lda plot (but not to each other, of course!). If you want longer or shorter arrows, change the value of myscale accordingly. By default, this plots arrows for the first and second axes. If you want to plot other axes, change choices to reflect this.

Answer 2

My understanding is that biplots of linear discriminant analyses can be done, it is implemented in fact also in R package ggbiplot, see https://github.com/vqv/ggbiplot/tree/experimental and package ggord, see https://github.com/fawda123/ggord, for your example:
install.packages("devtools")
library(devtools)
install_github("fawda123/ggord")
library(ggord)
ord <- lda(Species ~ ., iris, prior = rep(1, 3)/3)
ggord(ord, iris$Species)
Also the book "Biplots in practice" by M. Greenacre has one chapter (chapter 11) on it and in Figure 11.5 it shows a biplot of a linear discriminant analysis of the iris dataset:

Answer 3

You can achieve this using the ggord package from github. The dataset used is IRIS dataset
# --- data partition -- #
set.seed(555)
IRSam <- sample.int(n = nrow(IR), size = floor(.60*nrow(IR)), replace = FALSE, prob = NULL)
IRTrain <- IR[IRSam,]
IRTest <- IR[-IRSam,]
# --- Prediction --- #
p<- predict(IR.lda, IRTrain)
# --- plotting a biplot --- #
library(devtools)
# install_github('fawda123/ggord') --- Used to install ggord from github we need to run devtools to achieve this.
library(ggord)
ggord(IR.lda, IRTrain$Species, ylim=c(-5,5), xlim=c(-10,10))