| EstDimRMT {isva} | R Documentation |
Estimates dimensionality of a data set using Random Matrix Theory
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