These are the data used in Figures 2.1-2.3, and elsewhere through the
book.
The data are in the R data object:
ESL.mixture.rda
which you read in using load("ESL.mixture.rda")
The components are:
x 200 x 2 matrix of training predictors
y class variable; logical vector of TRUES and
FALSES - 100 of each
xnew matrix 6831 x 2 of lattice points in predictor space
prob vector of 6831 probabilities (of class TRUE) at each
lattice point
marginal marginal probability at each lattice point
px1 69 lattice coordinates for x.1
px2 99 lattice values for x.2 (69*99=6831)
means 20 x 2 matrix of the mixture centers, first ten for one
class, next ten for the other
So for example, the Bayes error rate is computed as
bayes.error<-sum(marginal*(prob*I(prob<0.5)+(1-prob)*I(prob>=.5)))
If pred is a vector of predictions (of the logit, say):
pred<-predict.logit(xnew)
then the test error is
test.error<-sum(marginal*(prob*I(pred <0)+(1-prob)*I(pred>=0)))