HUM-package | R Documentation |
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.
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.
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 |
This package comes with one simulated dataset and a real dataset of 92 patients with 11 features with disease.
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)
Natalia Novoselova, Frank Pessler
Maintainer: Natalia Novoselova <novos65@mail.ru>
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.
CRAN packages pROC, or Bioconductor's roc for ROC curves.
CRAN packages Rcpp, gtools, rgl employed in this package.
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) ## End(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) ## End(Not run)
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