Synthetic {MVR}

Multi-Groups Synthetic Dataset

Description

Generation of a synthetic dataset with n=10 observations (samples) and p=100 variables, where nvar=20 of them are significantly different between the two sample groups.

This is a balanced design with two sample groups (G=2), under unequal sample group variance.

Usage

Synthetic

Format

A numeric matrix containing n=10 observations (samples) by rows and p=100 variables by columns, named v_{1},...,v_{p}. Samples are balanced (n_{1}=5,n_{2}=5) between the two groups (G_{1}, G_{2}). Compressed Rda data file.

Acknowledgments

This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. This project was partially funded by the National Institutes of Health (P30-CA043703).

Author(s)

• "Jean-Eudes Dazard, Ph.D." [e-mail]

• "Hua Xu, Ph.D." [e-mail]

• "Alberto Santana, MBA." [e-mail]

Maintainer: "Jean-Eudes Dazard, Ph.D." [e-mail]

Source

See model #2 in Dazard et al., 2011, 2012.

References

• Dazard J-E. and J. S. Rao (2010). "Regularized Variance Estimation and Variance Stabilization of High-Dimensional Data." In JSM Proceedings, Section for High-Dimensional Data Analysis and Variable Selection. Vancouver, BC, Canada: American Statistical Association IMS - JSM, 5295-5309.

• Dazard J-E., Hua Xu and J. S. Rao (2011). "R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization." In JSM Proceedings, Section for Statistical Programmers and Analysts. Miami Beach, FL, USA: American Statistical Association IMS - JSM, 3849-3863.

• Dazard J-E. and J. S. Rao (2012). "Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data." Comput. Statist. Data Anal. 56(7):2317-2333.