Questions

1. Now fit a lasso model to the simulated data from the previous exercise, again using \(X,X^2,\cdots,X^{10}\) as predictors. Don’t forget to load the necessary package(s) and make use of the model.matrix function. Use cross-validation to select the optimal value of \(\lambda\), store this value in bestlam. Now build the model with the optimal lambda value and store the coefficient estimates in coef.lasso.
   
   

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Assume that:

• The glmnet library has been loaded
• The stats library has been loaded