Object to encapsulate numerical predictions together with the
corresponding true class labels, optionally collecting predictions and
labels for several cross-validation or bootstrapping runs.
Objects from the Class
Objects can be created by using the
prediction function.
Slots
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.
Note
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.