# plot LDA fit using R function plot()

I am doing the lab section: classifying the stock data using LDA in the book "Introduction to Statistical Learning with Applications in R", here is the lab video. Basically, this lab uses LDA to predict the stock Up or Down from Lag1 and Lag2 as following,

 lda.fit = lda(Direction~Lag1+Lag2, data=Smarket, subset=Year<2005)


The coefficients are

Coefficients of linear discriminants:
LD1
Lag1 -0.6420190
Lag2 -0.5135293


And following the lab steps, plot the LDA fit,

plot(lda.fit)


the plot is like below

I am having difficulties interpreting the plots. In the book it says that The plot() function produces plots of the linear discriminants, obtained by computing −0.642 × Lag1 − 0.514 × Lag2 for each of the training observations.  I don't understand what this sentence exactly is meaning here,

1. What's the x and y axis of this plot? And what does the axis mean here?
2. Why the plot is a bar plot?
3. How the computing −0.642 × Lag1 − 0.514 × Lag2 is related to the x and y axis?
• Please add the [self-study] tag & read its wiki. (You will have to delete a tag, [discriminant] is probably the best choice.) Commented Jun 14, 2015 at 20:31
• Although the context is R, these questions seem on topic to me. Commented Jun 14, 2015 at 20:32
• Do you have any idea about plotting this lda.fit in Python? Commented Feb 22, 2022 at 14:03