Coefficients of Linear Discriminants in R

I've read the answers in What are "coefficients of linear discriminants" in LDA?, but I still don't understand what coefficients of linear discriminants on output of R means.

What is it? (How) Is it related to the decision boundary?

nb: my knowledge about LDA can be summed up in this slide.

library(ISLR, MASS)
train <- (Smarket$Year < 2005) lda.fit <- lda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train) Call: lda(Direction ~ Lag1 + Lag2, data = Smarket, subset = train) Prior probabilities of groups: Down Up 0.491984 0.508016 Group means: Lag1 Lag2 Down 0.04279022 0.03389409 Up -0.03954635 -0.03132544 Coefficients of linear discriminants: LD1 Lag1 -0.6420190 Lag2 -0.5135293  • stats.stackexchange.com/q/92109/3277 is similar question Commented Apr 11, 2014 at 7:59 • @ttnphns Your answer:'"Coefficients" are the regressional weights to compute the LDs by the Xs.' Linear discriminant function based on the slide I gave above is:$\delta_k (x) = x^T \Sigma ^{-1} \mu_k - \frac{1}{2} \mu^T_k \Sigma ^{-1} \mu_k + \log(\pi_k)$Do you mean the coefficients is$\Sigma ^{-1} \mu_k\$ in this case? Commented Apr 11, 2014 at 9:52
• I didn't examine the presentation under your link. And I'm not agile to decipher formulas. I meant there (by the coefficients) what I've outlined in a description of LDA algorithm here. You might want to take iris data and do LDA, and compare results with my output. Commented Apr 11, 2014 at 10:14
• I believe that pdf doc you base yourself on is sure correct (and is quite mathematical). But I suspect it is for 2-class case only. The algorithm outlined by me (extraction, classification) is general k-class LDA algorithm. Commented Apr 11, 2014 at 10:55