d_ICA: Independent Component Analysis

In egenn/rtemis: Machine Learning and Visualization

Independent Component Analysis

Description

Perform ICA decomposition using the fastICA algorithm in fastICA::fastICA or ica::fastica

Usage

d_ICA(
  x,
  k = 3,
  package = c("fastICA", "ica"),
  alg.type = "parallel",
  maxit = 100,
  scale = TRUE,
  center = TRUE,
  verbose = TRUE,
  trace = 0,
  ...
)

Arguments

x	Input data
k	Integer vector of length 1 or greater. Rank of decomposition
package	Character: Which package to use for ICA. "fastICA" will use fastICA::fastICA, "ica" will use ica::fastica. Default = "fastICA". Note: only fastICA works with k = 1
alg.type	Character: For package = "fastICA", "parallel" or "deflation".
maxit	Integer: Maximum N of iterations
scale	Logical: If TRUE, scale input data before decomposition.
center	Logical: If TRUE, also center input data if scale is TRUE.
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
trace	Integer: If > 0, print messages during ICA run. Default = 0
...	Additional parameters to be passed to fastICA::fastICA or ica::icafast

Details

Project scaled variables to ICA components. Input must be n by p, where n represents number of cases, and p represents number of features. fastICA will be applied to the transpose of the n x p matrix. fastICA will fail if there are any NA values or constant features: remove them using preprocess

Value

rtDecom object

Author(s)

E.D. Gennatas

See Also

Other Decomposition: d_H2OAE(), d_H2OGLRM(), d_Isomap(), d_KPCA(), d_LLE(), d_MDS(), d_NMF(), d_PCA(), d_SPCA(), d_SVD(), d_TSNE(), d_UMAP()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.