hdpca v1.1.3

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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. Dey, R. and Lee, S. (2019) <doi.org/10.1016/j.jmva.2019.02.007>.

Functions in hdpca

Name Description
pc_adjust Adjusting shrinkage in PC scores
hdpc_est High-dimensional PCA estimation
hapmap Example dataset - Hapmap Phase III
select.nspike Finding Distant Spikes
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Details

Type Package
Date 2019-10-23
License GPL (>= 2)
Repository CRAN
NeedsCompilation no
Packaged 2019-10-23 12:48:00 UTC; Rounak
Date/Publication 2019-10-23 14:30:08 UTC

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