hdpca: Principal Component Analysis in High-Dimensional Data

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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.

Author
Rounak Dey, Seunggeun Lee
Date of publication
2016-08-02 09:13:22
Maintainer
Rounak Dey <deyrnk@umich.edu>
License
GPL (>= 2)
Version
1.0.0

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Man pages

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

Files in this package

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