smoothed_penalized_EV {SPEV}

smoothed_penalized_EV

Description

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.

Usage

smoothed_penalized_EV(m, lambda, k, mu)

Arguments

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.

Value

Returns smoothed eigenvectors 1 to k for the specified lambda value.

Examples

# 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
)