Programming Homework Help

r1 = Carseats$Sales

p1 = data.matrix(Carseats[, c(‘CompPrice’, ‘Income’, ‘Price’,

‘Age’, ‘ShelveLoc’)])

p11=data.frame(p1)

##now apply ridge

Y <- p1

MM <- model.matrix(p11$CompPrice ~ ., data = p11) # the predictors as a datamatrix

ridge.mod <- glmnet(MM, p11$CompPrice, alpha = 1, lambda = 14)

# Apply cross validation (to pick the best value of lambda):

cv.out <- cv.glmnet(MM,p11$CompPrice , alpha = 1)

bestlam <- cv.out$lambda.1se

print(“ridge CV best value of lambda (one standard error)”)

print(bestlam)

ridge.coef <- predict(ridge.mod, type = “coefficients”, s = bestlam)

print(ridge.coef)