Synthetic

Generation of a synthetic dataset with <em>n</em>=10 observations (samples) and $p=200$ variables, 
    where $nvar=40$ 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.

datasets

MVR is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm), 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.
             Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

Mean-Variance Regularization

Synthetic function

A numeric matrix containing $n=10$ observations (samples) by rows 
    and $p=200$ 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.

format

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

Synthetic: Multi-Groups Synthetic Dataset

Description

Usage

Arguments

format

source

References