RefFreeEWAS (version 2.2)

EstDimRMT: Dimension estimation by Random Matrix Theory

Description

Method for estimating latent dimension by Random Matrix Theory.

Usage

EstDimRMT(Rmat)

Arguments

Rmat

Residual matrix for which to estimate latent dimension.

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.

Details

Method for estimating latent dimension by Random Matrix Theory. This function originated in the package isva, authored by A. Teschendorff. Previous versions of RefFreeEWAS used the isva version of the function. However, because of dependency issues in that package, the present version of RefFreeEWAS simply reproduces the function found in version 1.9 of isva and removes the dependency on the isva package. Documentation from isva: Given a 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.

References

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

2. Independent Surrogate Variable Analysis to deconvolve confounding factors in large-scale microarray profiling studies. Teschendorff AE, Zhuang JJ, Widschwendter M. Bioinformatics. 2011 Jun 1;27(11):1496-505.

Examples

# NOT RUN {
data(RefFreeEWAS)

# }
# NOT RUN {
  tmpDesign <- cbind(1, rfEwasExampleCovariate)
  tmpBstar <- rfEwasExampleBetaValues <!-- %*% tmpDesign %*% solve(t(tmpDesign)%*%tmpDesign) -->
  EstDimRMT(rfEwasExampleBetaValues-tmpBstar <!-- %*% t(tmpDesign)) -->
# }