comp.metr: Compute metrics

In netbenchmark: Benchmarking of several gene network inference methods

Description

A group of functions to plot precision-recall and ROC curves and to compute f-scores from the matrix returned by the evaluate function.

Usage

    fscore(table, beta=1)
    auroc(table,k=-1)
    aupr(table,k=-1)
    pr.plot(table,device=-1,...)
    roc.plot(table,device=-1,...)

Arguments

table	This is the matrix returned by the evaluate function where columns contain the confusion matrix TP,FP,TN,FN values. - see evaluate.
beta	Numeric used as the weight of the recall in the f-score formula - see details. The default value of this argument is -1, meaning precision as important as recall.
k	Numeric used as the index to compute the area under the curve until that point- see details. The default value of this argument is -1, meaning that the whole area under the curve is computed
device	The device to be used. This parameter allows the user to plot precision-recall and receiver operating characteristic curves for various inference algorithms on the same plotting window - see examples.
...	Arguments passed to plot.

Details

A confusion matrix contains FP,TP,FN,FP values.

• "true positive rate" tpr = TP/(TN+TP)

• "false positive rate" fpr = FP/(FN+FP)

• "precision" p = TP/(FP+TP)

• "recall" r = TP/(TP+FN)

• "f-beta-score" F_β = (1+β) \frac{p r} {r + β p} Fbeta = (1+beta) * p*r/(r + beta*p)

Value

The function roc.plot (pr.plot) plots the ROC-curve (PR-curve) and returns the device associated with the plotting window.

The function auroc (aupr) computes the area under the ROC-curve (PR-curve) using the trapezoidal approximation until point k.

The function fscore returns fscores according to the confusion matrices contained in the 'table' argument - see details.

See Also

evaluate, plot

Examples

    # Inference
    Net <- cor(syntren300.data)
    # Validation
    tbl <-  evaluate(Net,syntren300.net)
    # Plot PR-Curves
    max(fscore(tbl))
    dev <- pr.plot(tbl, col="green", type="l")
    aupr(tbl)
    idx <- which.max(fscore(tbl))

netbenchmark documentation built on May 2, 2019, 6:08 p.m.