ROCcurve {a4Classif} | R Documentation |
A ROC curve plots the fraction of true positives (TPR = true positive rate) versus the fraction of false positives (FPR = false positive rate) for a binary classifier when the discrimination threshold is varied. Equivalently, one can also plot sensitivity versus (1 - specificity).
ROCcurve( object, groups, probesetId = NULL, geneSymbol = NULL, main = NULL, probe2gene = TRUE, ... )
object |
ExpressionSet object for the experiment |
groups |
String containing the name of the grouping variable. This should be a
the name of a column in the |
probesetId |
The probeset ID. These should be stored in the |
geneSymbol |
The gene symbol. These should be stored in the column |
main |
Main title on top of the graph |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot) |
... |
Possibility to add extra plot options. See |
a plot is drawn in the current device. prediction object is returned invisibly.
Willem Talloen
Some explanation about ROC can be found on http://en.wikipedia.org/wiki/ROC_curve and http://www.anaesthetist.com/mnm/stats/roc/Findex.htm. The latter has at the bottom a nice interactive tool to scroll the cut-off and to see how it affects the FP/TP table and the ROC curve.
# simulated data set esSim <- simulateData() ROCcurve(probesetId = 'Gene.1', object = esSim, groups = 'type', addLegend = FALSE)