hdpca: Principal Component Analysis in High-Dimensional Data
Version 1.0.0

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
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("hdpca")

Popular man pages

Example: Example dataset
hdpc_est: High-dimensional PCA estimation
pc_adjust: Adjusting shrinkage in PC scores
select.nspike: Finding Distant Spikes
See all...

All man pages Function index File listing

Man pages

Example: Example dataset
hdpc_est: High-dimensional PCA estimation
pc_adjust: Adjusting shrinkage in PC scores
select.nspike: Finding Distant Spikes

Functions

Files

NAMESPACE
data
data/Example.RData
data/datalist
R
R/pca_functions.R
MD5
DESCRIPTION
man
man/select.nspike.Rd
man/pc_adjust.Rd
man/Example.rd
man/hdpc_est.Rd
hdpca documentation built on May 19, 2017, 10:45 a.m.

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