R Package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization

• Activity Number: 335
• Type: Topic Contributed
• Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section for Statistical Programmers and Analysts
• Author(s): Jean-Eudes J. Dazard*+ and Hua Xu and J. Sunil Rao
• Companies: Case Western Reserve University and Case Western Reserve University and University of Miami
• Keywords: R package ; Parallel Programming ; Mean-Variance Estimation ; Regularization and Variance Stabilization ; Regularized Test-statistics ; High-Dimensional Data

Abstract

We present an implementation in the R language for statistical computing of our recent non-parametric joint adaptive mean-variance regularization and variance stabilization procedure. The method is specifically suited for handling difficult problems posed by high-dimensional multivariate datasets, such as in 'omics'-type data, among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. The implementation offers a complete set of features including: (i) normalization and/or variance stabilization function, (ii) computation of mean-variance-regularized t- and F-statistics, (iii) generation of diverse diagnostic plots, (iv) synthetic and real 'omics' test datasets, (v) computationally efficient implementation, using C interfacing, and an option for parallel computing, (vi) manual and documentation on how to setup a cluster. To make each feature as user-friendly as possible, only one subroutine per functionality is to be handled by the end-user. It is available as an R package, called MVR (Mean-Variance Regularization), downloadable from the CRAN.