You have to save your CV predictions via the "savePred" option in your trainControl
object. I'm not sure what package your "cadets" data is from, but here is a trivial example using iris:
> library(caret)
> ctrl <- trainControl(method = "cv", savePred=T, classProb=T)
> mod <- train(Species~., data=iris, method = "svmLinear", trControl = ctrl)
> head(mod$pred)
pred obs setosa versicolor virginica rowIndex .C Resample
1 setosa setosa 0.982533940 0.009013592 0.008452468 11 0.25 Fold01
2 setosa setosa 0.955755054 0.032289120 0.011955826 35 0.25 Fold01
3 setosa setosa 0.941292675 0.044903583 0.013803742 46 0.25 Fold01
4 setosa setosa 0.983559919 0.008310323 0.008129757 49 0.25 Fold01
5 setosa setosa 0.972285699 0.018109218 0.009605083 50 0.25 Fold01
6 versicolor versicolor 0.007223973 0.971168170 0.021607858 59 0.25 Fold01
EDIT: The "C" is one of tuning parameters for your SVM. Check out the help for the ksvm
function in the kernlab package for more details.
EDIT2: Trivial regression example
> library(caret)
> ctrl <- trainControl(method = "cv", savePred=T)
> mod <- train(Sepal.Length~., data=iris, method = "svmLinear", trControl = ctrl)
> head(mod$pred)
pred obs rowIndex .C Resample
1 4.756119 4.8 13 0.25 Fold01
2 4.910948 4.8 31 0.25 Fold01
3 5.094275 4.9 38 0.25 Fold01
4 4.728503 4.8 46 0.25 Fold01
5 5.192965 5.3 49 0.25 Fold01
6 5.969479 5.9 62 0.25 Fold01