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hdpca (version 1.1.3)

Principal Component Analysis in High-Dimensional Data

Description

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. Dey, R. and Lee, S. (2019) .

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Install

install.packages('hdpca')

Monthly Downloads

162

Version

1.1.3

License

GPL (>= 2)

Maintainer

Rounak Dey

Last Published

October 23rd, 2019

Functions in hdpca (1.1.3)

pc_adjust

Adjusting shrinkage in PC scores
hdpc_est

High-dimensional PCA estimation
hapmap

Example dataset - Hapmap Phase III
select.nspike

Finding Distant Spikes