withinsumsq

Positive <code>integer</code> scalar of the number of rows in the data matrix (where points to cluster are by rows (usually samples)).

Positive <code>integer</code> scalar of the number of columns in the data matrix (where variables are by columns).

Positive <code>integer</code> scalar of the fixed number of clusters.

Positive <code>integer</code> scalar of the number of Monte Carlo replicates of the inner loop of the gap statistic function.

Wrapping subroutine around internal C subroutine for returning Monte-Carlo replicates 
    of within-cluster sum of squares distances for a given number of clusters and
    under a standard Gaussian reference distribution.

cluster

distance

dispersion

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.

withinsumsq: Within-Cluster Sum of Squares Distances Subroutine

Description

Usage

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References

See Also