PXR: Precision-Recall Measure
In gecko515/HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs

Description Usage Arguments Details Value Examples

Functions to compute the Precision-Recall (PXR) values through precrec package.

precision.at.all.recall.levels.single.class(labels, scores)

precision.at.given.recall.levels.over.classes(target, predicted,
  folds = NULL, seed = NULL, recall.levels = seq(from = 0.1, to = 1,
  by = 0.1))

`labels`	vector of the true labels (0 negative, 1 positive examples).
`scores`	a numeric vector of the values of the predicted labels (scores).
`target`	matrix with the target multilabel: rows correspond to examples and columns to classes. target[i,j]=1 if example i belongs to class j, target[i,j]=0 otherwise.
`predicted`	a numeric matrix with predicted values (scores): rows correspond to examples and columns to classes.
`folds`	number of folds on which computing the PXR. If `folds=NULL` (`def.`), the PXR is computed one-shot, otherwise the PXR is computed averaged across folds.
`seed`	initialization seed for the random generator to create folds. Set `seed` only if `folds`\neq`NULL`. If `seed=NULL` and `folds`\neq`NULL`, the PXR averaged across folds is computed without seed initialization.
`recall.levels`	a vector with the desired recall levels (`def:` `from:0.1`, `to:0.9`, `by:0.1`).

precision.at.all.recall.levels.single.class computes the precision at all recall levels just for a single class.

precision.at.given.recall.levels.over.classes computes the precision at fixed recall levels over classes.

precision.at.all.recall.levels.single.class returns a two-columns matrix, representing a pair of precision and recall values. The first column is the precision, the second the recall; precision.at.given.recall.levels.over.classes returns a list with two elements:

avgPXR: a vector with the average precision at different recall levels across classes;
PXR: a matrix with the precision at different recall levels: rows are classes, columns precision at different recall levels;

data(labels);
data(scores);
data(graph);
root <- root.node(g);
L <- L[,-which(colnames(L)==root)];
S <- S[,-which(colnames(S)==root)];
labels <- L[,1];
scores <- S[,1];
rec.levels <- seq(from=0.25, to=1, by=0.25);
PXR.single <- precision.at.all.recall.levels.single.class(labels, scores);
PXR <- precision.at.given.recall.levels.over.classes(L, S, folds=5, seed=23, 
          recall.levels=rec.levels);