EstDimRMT: Estimates dimensionality of a data set using Random Matrix...

In isva: Independent Surrogate Variable Analysis

Description

Given the data matrix, it estimates the number of significant components of variation by comparing the observed distribution of spectral eigenvalues to the theoretical one under a Gaussian Orthogonal Ensemble (GOE). Specifically, a spectral decomposition of the data covariance matrix is performed and the number of eigenvalues larger than the theoretical maximum predicted by the GOE is taken as an estimate of the number of significant components.

Usage

EstDimRMT(data.m,plot=TRUE)

Arguments

data.m	Data matrix. Rows label features, Columns samples.
plot	Logical. Plots Eigenvalue densities if true.

Value

A list with following objects

cor	Data covariance matrix.
dim	Estimated intrinsic dimensionality of data.
estdens	Empirical density of eigenvalues.
thdens	Theoretical density of eigenvalues.

Author(s)

Andrew E Teschendorff

References

Random matrix approach to cross correlations in financial data. Plerou et al. Physical Review E (2002), Vol.65.

Examples

## see example for DoISVA

Example output

Loading required package: qvalue
Loading required package: fastICA
Loading required package: JADE

isva documentation built on May 1, 2019, 6:49 p.m.