minet (version 3.30.0)

vis.res: Visualize Results

Description

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

Usage

pr(table) rates(table) fscores(table, beta=1) show.pr(table,device=-1,...) show.roc(table,device=-1,...) auc.roc(table) auc.pr(table)

Arguments

table

This is the data.frame returned by the validate function where columns contain TP,FP,TN,FN values (confusion matrix) as well as the threshold value used - see validate.

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.

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

Value

The function show.roc (show.pr) plots the ROC-curve (PR-curve) and returns the device associated with the plotting window.The function auc.roc (auc.pr) computes the area under the ROC-curve (PR-curve) using the trapezoidal approximation.The function pr returns a data.frame where steps is the number of thresholds used in the validation process. The first column contains precisions and the second recalls - see details.The function rates also returns a data.frame where the first column contains true positive rates and the second column false positive rates - see details.The function fscores returns fscores according to the confusion matrices contained in the 'table' argument - see details.

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" $Fbeta = (1+beta) * p*r/(r + beta*p)$

References

Patrick E. Meyer, Frederic Lafitte, and Gianluca Bontempi. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics, Vol 9, 2008.

See Also

validate, plot

Examples

data(syn.data)
data(syn.net)
# Inference
mr <- minet( syn.data, method="mrnet", estimator="spearman" )
ar <- minet( syn.data, method="aracne", estimator="spearman"  )
clr<- minet( syn.data, method="clr", estimator="spearman"  )
# Validation
mr.tbl <- validate(mr,syn.net)
ar.tbl <- validate(ar,syn.net)
clr.tbl<- validate(clr,syn.net)
# Plot PR-Curves
max(fscores(mr.tbl))
dev <- show.pr(mr.tbl, col="green", type="b")
dev <- show.pr(ar.tbl, device=dev, col="blue", type="b")
show.pr(clr.tbl, device=dev, col="red",type="b")
auc.pr(clr.tbl)