hdpca: Principal Component Analysis in High-Dimensional Data

In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores.

AuthorRounak Dey, Seunggeun Lee
Date of publication2016-08-02 09:13:22
MaintainerRounak Dey <deyrnk@umich.edu>
LicenseGPL (>= 2)
Version1.0.0

View on CRAN

Files

hdpca
hdpca/NAMESPACE
hdpca/data
hdpca/data/Example.RData
hdpca/data/datalist
hdpca/R
hdpca/R/pca_functions.R
hdpca/MD5
hdpca/DESCRIPTION
hdpca/man
hdpca/man/select.nspike.Rd hdpca/man/pc_adjust.Rd
hdpca/man/Example.rd
hdpca/man/hdpc_est.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.