Object to encapsulate numerical predictions together with the corresponding true class labels, optionally collecting predictions and labels for several cross-validation or bootstrapping runs.

`predictions`

A list, in which each element is a vector of predictions (the list has length > 1 for x-validation data.

`labels`

Analogously, a list in which each element is a vector of true class labels.

`cutoffs`

A list in which each element is a vector of all necessary cutoffs. Each cutoff vector consists of the predicted scores (duplicates removed), in descending order.

`fp`

A list in which each element is a vector of the number (not the rate!) of false positives induced by the cutoffs given in the corresponding 'cutoffs' list entry.

`tp`

As fp, but for true positives.

`tn`

As fp, but for true negatives.

`fn`

As fp, but for false negatives.

`n.pos`

A list in which each element contains the number of positive samples in the given x-validation run.

`n.neg`

As n.pos, but for negative samples.

`n.pos.pred`

A list in which each element is a vector of the number of samples predicted as positive at the cutoffs given in the corresponding 'cutoffs' entry.

`n.neg.pred`

As n.pos.pred, but for negatively predicted samples.

Every `prediction`

object contains information about the 2x2
contingency table consisting of tp,tn,fp, and fn, along with the
marginal sums n.pos,n.neg,n.pos.pred,n.neg.pred, because these form
the basis for many derived performance measures.

Objects can be created by using the `prediction`

function.

A detailed list of references can be found on the ROCR homepage at http://rocr.bioinf.mpi-sb.mpg.de.