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 |
| imports | boot , lpSolve |
| depends | R (>= 3.0.0) |
| Contributors | Seunggeun Lee, Rounak Dey |
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