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=1.20.0&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=1.20.0&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), 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.

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

Description

Usage

Arguments

Value

Details

References

See Also