HUM (version 2.0)

CalculateHUM_seq: Calculate HUM value

Description

This is the main function of the HUM package. It computes a HUM value and returns a “List” object, consisting of HUM value and the best permutation of class labels in “seq” vector. This “seq” vector can be passed to the function CalculateHUM_ROC.

Usage

CalculateHUM_seq(data,indexF,indexClass,indexLabel)

Arguments

data

a dataset, a matrix of feature values for several cases, the additional column with class labels is provided. Class labels could be numerical or character values. The maximal number of classes is ten. The indexClass determines the column with class labels.

indexF

a numeric or character vector, containing the column numbers or column names of the analyzed features.

indexClass

a numeric or character value, containing the column number or column name of the class labels.

indexLabel

a character vector, containing the column names of the class labels, selected for the analysis.

Value

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

HUM

a list of HUM values for the specified number of analyzed features

seq

a list of vectors, each containing the sequence of class labels

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 maximal HUM value between the all possible permutations of class labels, selected for analysis. See the “Value” section to this page for more details. Before returning, it will call the CalcGene function to calculate the HUM value for each feature (object).

Data can be provided in matrix form, where the rows correspond to cases with feature values and class label. The columns contain the values of individual features and the separate column contains class labels. The maximal number of class labels equals 10. The computational efficiency of the function descrease in the case of more than 1000 cases with more than 6 class labels..

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(sim)
# Basic example
indexF=names(sim[,c(3,4)])
indexClass=2
label=unique(sim[,indexClass])
indexLabel=label[1:3]
out=CalculateHUM_seq(sim,indexF,indexClass,indexLabel)
# }