Biocomb (version 0.4)

CalcROC: Calculate ROC points

Description

This is the auxiliary function of the Biocomb package. It computes a point coordinates for plotting ROC curve and returns a “List” object, consisting of sensitivity and specificity values for 2D-ROC curve and 3D-points for 3D-ROC curve, the optimal threshold values with the corresponding feature values and the accuracy of the classifier (feature).

Usage

CalcROC(s_data, seq, thresholds)

Arguments

s_data

a list, which contains the vectors of sorted feature values for individual class labels.

seq

a numeric vector, containing the particular permutation of class labels.

thresholds

a numeric vector, containing the values of thresholds for calculating ROC curve coordinates.

Value

The data must be provided without missing values in order to process. A returned list consists of the following fields:

Sn

a specificity values for 2D-ROC construction and the first coordinate for 3D-ROC construction

Sp

a sensitivity values for 2D-ROC construction and the second coordinate for 3D-ROC construction

S3

the third coordinate for 3D-ROC construction

optSn

the optimal specificity value for 2D-ROC construction and the first coordinate of the op-timal threshold for 3D-ROC construction

optSp

the optimal sensitivity value for 2D-ROC construction and the second coordinate of the optimal threshold for 3D-ROC construction

optS3

the third coordinate of the optimal threshold for 3D-ROC construction

optThre

the feature value according to the optimal threshold (optSn,optSp) for two-class problem

optThre1

the first feature value according to the optimal threshold (optSn,optSp,optS3) for three-class problem

optThre2

the second feature value according to the optimal threshold (optSn,optSp,optS3) for three-class problem

accuracy

the accuracy of classifier (feature) for the optimal threshold

Threshold

The optimal threshold value is calculated for two-class problem as the pair “(optSn, optSp)” corresponding to the maximal value of “Sn+Sp”. According to “(optSn, optSp)” the corresponding feature threshold value “optThre” is calculated. The optimal threshold value is calculated for three-class problem as the pair “(optSn, optSp,optS3)” corresponding to the maximal value of “Sn+Sp+S3”.According to “(optSn, optSp,optS3)” the corresponding feature threshold values “optThre1,optThre2” are calculated. The accuracy of the classifier is the mean value of dQuote(optSn, optSp) for two-class problem and the mean value of “(optSn, optSp,optS3)” for three-class problem.

Errors

If there exists NA values for features or class labels no HUM value can be calculated and an error is triggered with message “Values are missing”.

Details

This function's main job is to compute the point coordinates to plot the 2D- or 3D-ROC curve, the optimal threshold values and the accuracy of classifier. See the “Value” section to this page for more details. The optimal threshold for two-class problem is the pair of sensitivity and specificity values for the selected feature. The optimal threshold for three-class problem is the 3D-point with the coordinates presenting the fraction of the correctly classified data objects for each class. The calculation of the optimal threshold is described in section “Threshold”.

References

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.

See Also

CalculateHUM_Ex, CalculateHUM_ROC

Examples

# NOT RUN {
data(leukemia72_2)
# Basic example
# class label must be factor
leukemia72_2[,ncol(leukemia72_2)]<-as.factor(leukemia72_2[,ncol(leukemia72_2)])

xdata=leukemia72_2
indexF=1:3
indexClass=ncol(xdata)

label=levels(xdata[,indexClass])
indexLabel=label

out=CalculateHUM_seq(xdata,indexF,indexClass,indexLabel)

HUM<-out$HUM
seq<-out$seq

indexL=NULL
for(i in 1:length(indexLabel))
{
  indexL=c(indexL,which(label==indexLabel[i]))
}

indexEach=NULL
indexUnion=NULL

for(i in 1:length(label))
{
  vrem=which(xdata[,indexClass]==label[i])
  indexEach=c(indexEach,list(vrem))
  if(length(intersect(label[i],indexLabel))==1)
    indexUnion=union(indexUnion,vrem)
}
s_data=NULL
dataV=xdata[,indexF[1]]  #single feature
prodValue=1
for (j in 1:length(indexLabel))
{
  vrem=sort(dataV[indexEach[[indexL[j]]]])

  s_data=c(s_data,list(vrem))
  prodValue = prodValue*length(vrem)
}
#calculate the threshold values for plot of 2D ROC and 3D ROC
thresholds <- sort(unique(dataV[indexUnion]))
thresholds=(c(-Inf, thresholds) + c(thresholds, +Inf))/2

out=CalcROC(s_data,seq[,indexF[1]], thresholds)
# }