CalculateHUM_Plot | R Documentation |
This is the main function of the HUM package. It plots the 2D-ROC curve using the point coordinates, computed by the function CalculateHUM_ROC
.Optionally visualizes the optimal threshold point, which gives the maximal accuracy of the classifier(feature) (see CalcROC
).
CalculateHUM_Plot(sel,Sn,Sp,optSn,optSp,HUM,print.optim=TRUE)
sel |
a character value, which is the name of the selected feature. |
Sn |
a numeric vector of the x-coordinates of the ROC curve, which is the specificity values of the standard ROC analysis.. |
Sp |
a numeric vector of the y-coordinates of the ROC curve, which is the sensitivity values of the standard ROC analysis.. |
optSn |
the optimal specificity value for 2D-ROC construction |
optSp |
the optimal sensitivity value for 2D-ROC construction |
HUM |
a numeric vector of HUM values, calculated using function |
print.optim |
a boolean parameter to plot the optimal threshold point on the graph. The default value is TRUE. |
This function's main job is to plot the 2D-ROC curve according to the given point coordinates.
The function doesn't return any value.
If there exists NA values for specificity or sensitivity values, or HUM values the plotting fails and an error is triggered with message “Values are missing”.
Li, J. and Fine, J. P. (2008): ROC Analysis with Multiple Tests and Multiple Classes: methodology and its application in microarray studies.Biostatistics. 9 (3): 566-576.
CalculateHUM_seq
, CalculateHUM_ROC
data(sim) # Basic example indexF=names(sim[,c(3),drop = FALSE]) indexClass=2 label=unique(sim[,indexClass]) indexLabel=label[1:2] out=CalculateHUM_seq(sim,indexF,indexClass,indexLabel) HUM<-out$HUM seq<-out$seq out=CalculateHUM_ROC(sim,indexF,indexClass,indexLabel,seq) CalculateHUM_Plot(indexF,out$Sn,out$Sp,out$optSn,out$optSp,HUM)
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