vis.res {minet} | R Documentation |
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.
pr(table) rates(table) fscores(table, beta=1) show.pr(table,device=-1,...) show.roc(table,device=-1,...)
table |
This is the (steps x 5) data.frame returned by the validate function where
steps is the number of thresholds used in the validation process and 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 |
A confusion matrix contains FP,TP,FN,FP values.
The function show.roc
(show.pr
) plots the ROC-curve (PR-curve) and returns the device associated with the plotting window.
The function pr
returns a (steps
x 2) 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 (steps
x 2) data.frame where the first column contains true
positive rates and the second column false positive rates - see details.
The function fscores
returns steps
fscores according to the steps
confusion matrices
contained in the 'table' argument - see details.
Patrick E. Meyer, Kevin Kontos, Frederic Lafitte, and Gianluca Bontempi. Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007.
data(syn.data) data(syn.net) # Inference mr <- minet( syn.data, method="mrnet", estimator="mi.empirical" ) ar <- minet( syn.data, method="aracne", estimator="mi.empirical" ) clr<- minet( syn.data, method="clr", estimator="mi.empirical" ) # 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")