highDimensionARI: Calculate ARI for high-dimensional data via data splits
In HTSCluster: Clustering High-Throughput Transcriptome Sequencing (HTS) Data

Description Usage Arguments Value Author(s) References

This function is used to calculate Adjusted Rand Index (ARI) values for high-dimensional data.

1	highDimensionARI(x, y, splits = 2, verbose = FALSE)

`x`	Vector of classification labels
`y`	Vector of classification labels
`splits`	Number of subsets data should be split into
`verbose`	`TRUE` if verbose output is desired

Value of Adjusted Rand Index for samples x and y

Andrea Rau <andrea.rau@jouy.inra.fr>

Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux G. (2015). Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31(9):1420-1427.

Rau, A., Celeux, G., Martin-Magniette, M.-L., Maugis-Rabusseau, C. (2011). Clustering high-throughput sequencing data with Poisson mixture models. Inria Research Report 7786. Available at http://hal.inria.fr/inria-00638082.

HTSCluster documentation built on May 2, 2019, 4:59 p.m.