HUM (version 2.0)

HUM-package: HUM calculation

Description

Functions to calculate AUC (area under curve) value for two classes and HUM (hypervolume under manifold) for more class labels in order to estimate the informativity of features to outcome. Tools for visualizing ROC curve in 2D- and 3D-space.

Functions

CalculateHUM_seq	Calculate a maximal HUM value amd the corresponding permutation of class labels
CalculateHUM_Ex	Calculate the HUM values with exaustive serach for specified number of class labels
CalculateHUM_ROC	Function to construct and plot the 2D- or 3d-ROC curve
CalcGene	Compute the HUM value for one feature
CalcROC	Compute the point coordinates to plot the 2D- or 3D-ROC curve
CalculateHUM_Plot	Plot the 2D-ROC curve
Calculate3D	Plot the 3D-ROC curve

Dataset

This package comes with one simulated dataset and a real dataset of 92 patients with 11 features with disease.

Installing and using

To install this package, make sure you are connected to the internet and issue the following command in the R prompt:

    install.packages("HUM")
  

To load the package in R:

    library(HUM)
  

Details

Package:	HUM
Type:	Package
Version:	1.0
Date:	2013-10-25
License:	GPL (>= 3)

The basic unit of the HUM package is the CalculateHUM_seq function. It will calculate the AUC in case of two class labels and HUM for more than two class labels for the input features. Function CalculateHUM_Ex is the extension of main function and provides the possibility to calculate all the combinations of amountL from all the class labels. Function CalculateHUM_ROC calculates the point coordinates in order to plot the 2D- and 3D-ROC curve, accuracy and the optimal threshold for the classifier (feature). The Functions CalcGene and CalcROC are the auxiliar function to perform the calculation. Function CalcROC calculates the point coordinates of a single feature for two-class or three-class problem, the optimal threshold for the 2-D and 3-D ROC curve and the corresponding feature values, the accuracy of the classifier (feature) for the optimal 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

CRAN packages pROC, or Bioconductor's roc for ROC curves.

CRAN packages Rcpp, gtools, rgl employed in this package.

Examples

# NOT RUN {
data(sim)

# Compute the HUM value with all possible class label permutation
indexF=c(3,4);
indexClass=2;
label=unique(sim[,indexClass])
indexLabel=label[1:3]
out=CalculateHUM_seq(sim,indexF,indexClass,indexLabel)
# Compute the HUM value with exaustive search of all class label combinations
# }
# NOT RUN {
data(sim)
indexF=c(3,4);
indexClass=2;
labels=unique(sim[,indexClass])
amountL=4;
out=CalculateHUM_Ex(sim,indexF,indexClass,labels,amountL)
# }
# NOT RUN {
# Calculate the coordinates for 2D- or 3D- ROC curve and the optimal threshold point
# }
# NOT RUN {
data(sim)
indexF=names(sim[,c(3),drop = FALSE])
indexClass=2
label=unique(sim[,indexClass])
indexLabel=label[1:3]
out=CalculateHUM_seq(sim,indexF,indexClass,indexLabel)
HUM<-out$HUM
seq<-out$seq
out=CalculateHUM_ROC(sim,indexF,indexClass,indexLabel,seq)
# }