The predict() function returns a list with three elements. The first element, class, contains LDA’s predictions about the movement of the market. The second element, posterior, is a matrix whose kth column contains the posterior probability that the corresponding observation belongs to the kth class, computed from (4.10). Finally, x contains the linear discriminants, described earlier.

> lda.pred <- predict(lda.fit, Smarket.2005)
> names(lda.pred)
[1] "class"     "posterior" "x"      

The LDA and logistic regression predictions are almost identical.

> lda.class <- lda.pred$class
> table(lda.class, Direction.2005)
         Direction.2005
lda.class Down  Up
     Down   35  35
     Up     76 106
> mean(lda.class == Direction.2005)
[1] 0.5595238

Try creating a confusion matrix and calculating the accuracy of the following model (store the table and accuracy in lda.table3 and lda.acc3 respectively):

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Assume that:

• The ISLR2 and MASS libraries have been loaded
• The Smarket dataset has been loaded and attached