is.valid

<code>vector</code> of bootstrap set of indices.

<code>list</code> of samples indices per sample group in original data.

Positive <code>integer</code> scalar of the number of sample groups.

Internal subroutine called by end-user <code><a href="/link/mvrt.test?package=MVR&version=1.00.0&to=MVR" rd-options="MVR" data-mini-rdoc="MVR::mvrt.test">mvrt.test</a></code> function to validate the bootstrap set of indices 
    if a bootstrap test is undertaken in <code><a href="/link/mvrt.test?package=MVR&version=1.00.0&to=MVR" rd-options="MVR" data-mini-rdoc="MVR::mvrt.test">mvrt.test</a></code>. Check that the bootstrapped indices of samples 
    (from all-groups samples) are unique per sample group in a multiple sample group situation.

bootstrap

resampling with replacement

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), 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. Key
        features include (i) Normalization and/or variance
        stabilization of the data, (ii) Computation of
        mean-variance-regularized t- and F-statistics, (iii) Generation
        of diverse diagnostic plots, (iv) Computationally efficiency
        implementation, using C++ interfacing, and an option for
        parallel computing to enjoy a fast and easy experience in the R
        environment.

is.valid: Validation Subroutine to Test the Bootstrap Set of Indices

Description

Usage

Arguments

Value

Details

References

See Also