setup_ICA: setup_ICA

In rtemis: Schema-Driven Machine Learning and Visualization

setup_ICA

Description

Setup ICA config.

Usage

setup_ICA(
  k = 3L,
  type = "parallel",
  fun = "logcosh",
  alpha = 1,
  row_norm = TRUE,
  maxit = 100L,
  tol = 1e-04,
  features = NULL
)

Arguments

k	Integer [1, Inf): Number of components.
type	Character {"parallel", "deflation"}: Type of ICA.
fun	Character {"logcosh", "exp"}: ICA function.
alpha	Numeric [1, 2]: Used in approximation to neg-entropy with fun = "logcosh".
row_norm	Logical: If TRUE, standardize each case across features before ICA, i.e. subtract each case's mean and divide by its standard deviation. Appropriate when the features share units and each case's overall level and scale are a nuisance rather than signal, as for spectra or assays whose total intensity varies by sample – the usual situation for the wide, single-instrument matrices decomposition is run on, which is why it is the default. It spends two degrees of freedom per case, so its cost is roughly 2 / ncol(x): negligible for hundreds of features, but substantial for a handful, where each case's level and scale are more likely to be signal. Set FALSE for narrow or mixed-unit data.
maxit	Integer [1, Inf): Maximum number of iterations.
tol	Numeric [0, Inf): Tolerance.
features	Optional Character vector: Names of at least 2 distinct feature columns to decompose. NULL decomposes all numeric features.

Value

ICAConfig object.

Author(s)

EDG

Examples

ica_config <- setup_ICA(k = 3L)
ica_config

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.