EstDimRMT: Dimension estimation by Random Matrix Theory
In RefFreeEWAS: EWAS using Reference-Free DNA Methylation Mixture Deconvolution

Description Usage Arguments Details Value Author(s) References Examples

Method for estimating latent dimension by Random Matrix Theory.

1	EstDimRMT(Rmat)

Rmat

Residual matrix for which to estimate latent dimension.

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.

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.

E. Andres Houseman

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

data(RefFreeEWAS)

## Not run: 
  tmpDesign <- cbind(1, rfEwasExampleCovariate)
  tmpBstar <- rfEwasExampleBetaValues 
  EstDimRMT(rfEwasExampleBetaValues-tmpBstar 

## End(Not run)