Using R, please simulate continuous data for y, x1, x2, x3, x4, x5, x6

Make sure code actually runs without error.

Augment the data by considering all two-way interactions (xixj for i=j). That is your set of predictors will consist of x1,x2,,x6,x1x2,x1x3,, x5x6. Using cv.glmnet () in R package glmnet, we will fit an elastic net by searching over 11 values (0,0.1,0.2,0.3,,0.9,1) and using the built-in values of cv. glmnet(), find combination of (,) that corresponds to minimum crossvalidation error. Fit a final model using this optimum value of (,) and report the predictors (including the interaction terms, if any) that are retained (i.e., have non-zero coefficients) in the final model