gpower_var_plot: Plots explained variance and sparsity of the components

In plofknaapje/gpowerpca: Sparse PCA using the GPower method

Description

This plot shows what happens to the proportion of explained variance and proportion of sparsity if rho increases. The sparsity is non-decreasing, but the explained variance is not. This is also the case in the original MatLab code of the inventors of the method.

Usage

gpower_var_plot(
  data,
  k,
  intervals,
  reg = "l1",
  center = TRUE,
  block = FALSE,
  mu = 1
)

Arguments

data	Input matrix of size (p x n) with p < n.
k	Number of components, 0 < k < p.
intervals	The amount of intervals in the range of values of rho for which gpower is run. For l1, the range of values is [0, 1], for l0, the range is [0, 0.33]. The block algorithms are unable to run at 0.4 and above and will only cover the range where GPower is able to run.
reg	regularisation type to use in the optimization. Either 'l0' or 'l1'. The default is 'l1' since it performed best in experiments.
center	Centers the data. Either TRUE or FALSE. The default is TRUE.
block	Optimization method. If FALSE, the components are calculated individually. If TRUE, all components are calculated at the same time. The default is FALSE.
mu	Mean to be applied to each component in the block. Either a vector of float of size k or a float which will be repeated k times. Only used if block is TRUE. The default is 1.

Examples

set.seed(360)
p <- 20
n <- 50
k <- 5
data <- scale(matrix(stats::rnorm(p * n), nrow = p, ncol = n), scale = FALSE)

gpower_var_plot(
  data = data,
  k = k,
  intervals = 40,
  reg = 'l1',
  center = TRUE
)

plofknaapje/gpowerpca documentation built on July 27, 2021, 4:17 a.m.