MeanVarReg: Mean-Variance Regularization Core Subroutine

Description

Core subroutine of mvr and mvrt.test functions for mean-variance regularization and variance stabilization, and the computation of regularized test-statistics. Returns optimal cluster configuration. Internal function called by mvr and internally by mvrt.

Usage

MeanVarReg(data, nc.min, nc.max, probs, B, parallel, conf, verbose)

Arguments

data

numeric data matrix, where points to cluster are by rows (usually samples), or an object that can be coerced to such a matrix (such as a vector or a data.frame

nc.min

Positive integer scalar of the minimum number of clusters.

nc.max

Positive integer scalar of the maximum number of clusters.

Positive integer scalar of the number of Monte Carlo replicates of the inner loop of the sim statistic function.

probs

numeric vector of probabilities for quantile diagnostic plots.

parallel

logical scalar. Is parallel computing to be performed? Optional, defaults to FALSE.

conf

list of parameters for cluster configuration, passed from mvr or mvrt.test functions for parallel computing. In

verbose

logical scalar. Is the output to be verbose?

Value

membershipnumeric vector of cluster membership of each variable
ncPositive integer scalar of number of clusters found in optimal cluster configuration
gapnumeric vector of the similarity statistic values
sdenumeric vector of the standard errors of the similarity statistic values
mu.quantnumeric matrix (nc.max - nc.min + 1) x (length(probs)) of quantiles of means
sd.quantnumeric matrix (nc.max - nc.min + 1) x (length(probs)) of quantiles of standard deviations

Details

See details in mvr and mvrt.test.

References

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.