We can use the predict() function for a number of purposes. For instance, we can obtain the ridge regression coefficients for a new value of \(\lambda\), say 50:

> predict(ridge.mod, s = 50, type = "coefficients")[1:20,]
  (Intercept)         AtBat          Hits         HmRun 
 4.876610e+01 -3.580999e-01  1.969359e+00 -1.278248e+00 
         Runs           RBI         Walks         Years 
 1.145892e+00  8.038292e-01  2.716186e+00 -6.218319e+00 
       CAtBat         CHits        CHmRun         CRuns 
 5.447837e-03  1.064895e-01  6.244860e-01  2.214985e-01 
         CRBI        CWalks       LeagueN     DivisionW 
 2.186914e-01 -1.500245e-01  4.592589e+01 -1.182011e+02 
      PutOuts       Assists        Errors    NewLeagueN 
 2.502322e-01  1.215665e-01 -3.278600e+00 -9.496680e+00 

Try predicting the coefficients for \(\lambda = 60\) and the output in ridge.pred:

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Assume that:

• The ISLR2 and glmnet libraries have been loaded
• The Hitters dataset has been loaded and attached