Classification functions in linear discriminant analysis in R

Suppose x is your LDA object:

x$terms

You can have a peak at the object by looking at it's structure:

str(x)

Update:

Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),Sp = rep(c("s","c","v"), rep(50,3)))train <- sample(1:150, 75)table(Iris$Sp[train])z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)predict(z, Iris[-train, ])$classstr(z)List of 10 $ prior  : Named num [1:3] 0.333 0.333 0.333  ..- attr(*, "names")= chr [1:3] "c" "s" "v" $ counts : Named int [1:3] 30 25 20  ..- attr(*, "names")= chr [1:3] "c" "s" "v" $ means  : num [1:3, 1:4] 6.03 5.02 6.72 2.81 3.43 ...  ..- attr(*, "dimnames")=List of 2  .. ..$ : chr [1:3] "c" "s" "v"  .. ..$ : chr [1:4] "Sepal.L." "Sepal.W." "Petal.L." "Petal.W." $ scaling: num [1:4, 1:2] 0.545 1.655 -1.609 -3.682 -0.443 ...  ..- attr(*, "dimnames")=List of 2  .. ..$ : chr [1:4] "Sepal.L." "Sepal.W." "Petal.L." "Petal.W."  .. ..$ : chr [1:2] "LD1" "LD2" $ lev    : chr [1:3] "c" "s" "v" $ svd    : num [1:2] 33.66 2.93 $ N      : int 75 $ call   : language lda(formula = Sp ~ ., data = Iris, prior = c(1, 1, 1)/3, subset = train) $ terms  :Classes 'terms', 'formula' length 3 Sp ~ Sepal.L. + Sepal.W. + Petal.L. + Petal.W.  .. ..- attr(*, "variables")= language list(Sp, Sepal.L., Sepal.W., Petal.L., Petal.W.)  .. ..- attr(*, "factors")= int [1:5, 1:4] 0 1 0 0 0 0 0 1 0 0 ...  .. .. ..- attr(*, "dimnames")=List of 2  .. .. .. ..$ : chr [1:5] "Sp" "Sepal.L." "Sepal.W." "Petal.L." ...  .. .. .. ..$ : chr [1:4] "Sepal.L." "Sepal.W." "Petal.L." "Petal.W."  .. ..- attr(*, "term.labels")= chr [1:4] "Sepal.L." "Sepal.W." "Petal.L." "Petal.W."  .. ..- attr(*, "order")= int [1:4] 1 1 1 1  .. ..- attr(*, "intercept")= int 1  .. ..- attr(*, "response")= int 1  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv  .. ..- attr(*, "predvars")= language list(Sp, Sepal.L., Sepal.W., Petal.L., Petal.W.)  .. ..- attr(*, "dataClasses")= Named chr [1:5] "factor" "numeric" "numeric" "numeric" ...  .. .. ..- attr(*, "names")= chr [1:5] "Sp" "Sepal.L." "Sepal.W." "Petal.L." ... $ xlevels: Named list() - attr(*, "class")= chr "lda"

I think your question was flawed ... OK, maybe not flawed but somewhat misleading at the very least. The discriminant function(s) refers to distances between groups, so there is no function associated with a single group but rather a function that describes the distances between any two group centroids. I just answered a more recent question and placed an example of calculating a score function using the iris dataset and using it to label cases in a 2d plot of predictors. In the case of a 2 group analysis the function will be greater than zero for one group and less than zero for the other group.

There isn't a built-in way to get the information I needed, so I wrote a function to do it:

ty.lda <- function(x, groups){  x.lda <- lda(groups ~ ., as.data.frame(x))  gr <- length(unique(groups))   ## groups might be factors or numeric  v <- ncol(x) ## variables  m <- x.lda$means ## group means  w <- array(NA, dim = c(v, v, gr))  for(i in 1:gr){    tmp <- scale(subset(x, groups == unique(groups)[i]), scale = FALSE)    w[,,i] <- t(tmp) %*% tmp  }  W <- w[,,1]  for(i in 2:gr)    W <- W + w[,,i]  V <- W/(nrow(x) - gr)  iV <- solve(V)  class.funs <- matrix(NA, nrow = v + 1, ncol = gr)  colnames(class.funs) <- paste("group", 1:gr, sep=".")  rownames(class.funs) <- c("constant", paste("var", 1:v, sep = "."))  for(i in 1:gr) {    class.funs[1, i] <- -0.5 * t(m[i,]) %*% iV %*% (m[i,])    class.funs[2:(v+1) ,i] <- iV %*% (m[i,])  }  x.lda$class.funs <- class.funs  return(x.lda)}

This code follows the formulas in Legendre and Legendre's Numerical Ecology (1998), page 625, and matches the results of the worked example starting on page 626.