R caret train glmnet final model lambda values not as specified

The final model is basically a refit with your whole dataset AFTER alpha and lambda were optimized using resampling techniques.

If you print model$finalModel$call you see the call being made (omitted x, y structure for shortness):

    Call:  glmnet(x, y, family = "binomial", alpha = 1)

Here, alpha is set (if you had set a sequence it would be the optimum alpha found), but no specified lambda is set to train, and therefore an automatic sequence is generated based on your data and the model is fitted. It then predicts with the same training set with lambdaOpt (and the rest of the sequence you gave). Take a look at glmnet vignette and how you can specify different lambda after training.

If you type:

    > names(model$modelInfo)     [1] "label" "library" "type" "parameters" "grid" "loop"            [7] "fit" "predict" "prob" "predictors" "varImp" "levels"         [13] "tags" "sort" "trim"

and then walk through each of those sections, you can take a look at what trainis doing. You can see in model$modelInfo$predict how it predicts on lambdaOpt and the rest of your sequence.

When you print model$results you actually get your list of lambda and the performance on the whole training set with each one:

 alpha       lambda  Accuracy      Kappa AccuracySD    KappaSD1      1 1.000000e-05 0.5698940 0.15166891 0.09061320 0.171335242      1 1.832981e-05 0.5698940 0.15166891 0.09061320 0.171335243      1 3.359818e-05 0.5698940 0.15166891 0.09061320 0.171335244      1 6.158482e-05 0.5698940 0.15166891 0.09061320 0.171335245      1 1.128838e-04 0.5698940 0.15166891 0.09061320 0.171335246      1 2.069138e-04 0.5698940 0.15166891 0.09061320 0.171335247      1 3.792690e-04 0.5698940 0.15166891 0.09061320 0.171335248      1 6.951928e-04 0.5698940 0.15166891 0.09061320 0.171335249      1 1.274275e-03 0.5675708 0.14690433 0.09071728 0.1708566510     1 2.335721e-03 0.5643334 0.14059590 0.09153010 0.1720403611     1 4.281332e-03 0.5629588 0.13822063 0.09403553 0.1771544112     1 7.847600e-03 0.5694974 0.15221600 0.08791315 0.1643392213     1 1.438450e-02 0.5700431 0.15448347 0.08864353 0.1650933214     1 2.636651e-02 0.5695053 0.15189752 0.08113581 0.1518461915     1 4.832930e-02 0.5635977 0.14112303 0.05833646 0.1161722616     1 8.858668e-02 0.5305835 0.08983718 0.08116759 0.1475230717     1 1.623777e-01 0.4800871 0.01124082 0.05827521 0.0571529818     1 2.976351e-01 0.4725241 0.00000000 0.04488500 0.0000000019     1 5.455595e-01 0.4725241 0.00000000 0.04488500 0.0000000020     1 1.000000e+00 0.4725241 0.00000000 0.04488500 0.00000000

To summarise what's happening in caret+glmnet:

1. optimizes alpha and lambdawithin the tuneGrid you provided using resampling techniques;

2. refits the model, now on the whole training set, with optimal alpha;

3. predicts on the whole training set with lambdaOpt found in 1. and on the rest of the sequence of lambdas in tuneGrid.