| Title: | Unsmoothed and Smoothed Penalized PCA using Nesterov Smoothing |
|---|---|
| Description: | We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns. |
| Authors: | Rebecca Hurwitz [aut, cre], Georg Hahn [ctb] |
| Maintainer: | Rebecca Hurwitz <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2025-03-10 03:04:40 UTC |
| Source: | https://github.com/cran/SPEV |
This function takes a matrix (m), a lambda value (lambda), the number of desired eigenvectors (k), and a mu value (mu) as input. It then computes eigenvectors 1 to k, penalized by the supplied lambda and smoothed by the Nesterov smoothing function.
smoothed_penalized_EV(m, lambda, k, mu)smoothed_penalized_EV(m, lambda, k, mu)
m |
A matrix generated from a large dataset. |
lambda |
A numeric vector of lambda values to use for the penalty. |
k |
The number of eigenvectors we consider in the analysis. |
mu |
A number assigned to mu; we are typically using 0.1. |
Returns smoothed eigenvectors 1 to k for the specified lambda value.
# Generate a small matrix for testing m <- matrix(rnorm(100), nrow = 10) # Call function (using matrix, lambda, mu, and k) smoothed_penalized_EV( m = m, lambda = 1, k = 2, mu = 0.1 )# Generate a small matrix for testing m <- matrix(rnorm(100), nrow = 10) # Call function (using matrix, lambda, mu, and k) smoothed_penalized_EV( m = m, lambda = 1, k = 2, mu = 0.1 )
This function takes a matrix (m), a lambda value (lambda), and the number of desired eigenvectors (k) as input. It then computes eigenvectors 1 to k, penalized by the supplied lambda.
unsmoothed_penalized_EV(m, lambda, k)unsmoothed_penalized_EV(m, lambda, k)
m |
A matrix generated from a large dataset. |
lambda |
A numeric vector of lambda values to use for the penalty. |
k |
The number of eigenvectors we consider in the analysis. |
Returns eigenvectors 1 to k for the specified lambda value.
# Generate a small matrix for testing m <- matrix(rnorm(100), nrow = 10) # Call function (using matrix, lambda, and k) unsmoothed_penalized_EV( m = m, lambda = 1, k = 2 )# Generate a small matrix for testing m <- matrix(rnorm(100), nrow = 10) # Call function (using matrix, lambda, and k) unsmoothed_penalized_EV( m = m, lambda = 1, k = 2 )