auc: Calculate area under the curve statistics

In clusterCons: Consensus Clustering using Multiple Algorithms and Parameters

Calculate area under the curve statistics

Description

These functions calculate the area under the curve (AUC) for cumulative density functions of a consensus matrix. The function auc operates on an indvidual consensus matrix whereas aucs operates on an entire cluscomp analysis result as described below.

Usage

auc(x)
aucs(x)

Arguments

x	For auc(x), provide a numeric square data matrix such as an individual consensus matrix. For aucs(x) provide a list of "consmatrix" class objects (see consmatrix-class for details) such as those produced directly by the cluscomp function. The functions will not allow any missing values (NAs).

Value

auc(x) returns an individual AUC value.

aucs(x) returns a data.frame with the following variables.

k	cluster number as a factor
a	algorithm identifier as a factor
aucs	the AUC value

Author(s)

Dr. T. Ian Simpson [e-mail]

References

Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.

See Also

consmatrix-class

Examples

#load up a test cluscomp result
data('testcmr');

#look at the result structure
summary(testcmr);

#calculate an individual AUC value for a consensus matrix
ac <- auc(testcmr$e1_kmeans_k2@cm);

#calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans'
kmeanscmr <- testcmr[grep('kmeans',names(testcmr))];
acs <- aucs(kmeanscmr);

clusterCons documentation built on March 18, 2022, 5:16 p.m.