A dataframe or a statistical model.

Number of components to extract. If <code>n="all"</code>, then <code>n</code> is set as the number of variables minus 1 (<code>ncol(x)-1</code>). If <code>n="auto"</code> (default) or <code>n=NULL</code>, the number of components is selected through <code><a rd-options="" href="/link/n_factors?package=parameters&version=0.5.0" data-mini-rdoc="parameters::n_factors">n_factors</a></code>. In <code><a rd-options="" href="/link/reduce_parameters?package=parameters&version=0.5.0" data-mini-rdoc="parameters::reduce_parameters">reduce_parameters</a></code>, can also be <code>"max"</code>, in which case it will select all the components that are maximally pseudo-loaded (i.e., correlated) by at least one variable.

Arguments passed to or from other methods.

Performs an Independent Component Analysis using the FastICA algorithm. Contrary to PCA, that attempts to find uncorrelated sources (through least squares minimization), ICA attempts to find independent sources, i.e., the source space that maximizes the "non-gaussianity" of all sources. Contrary to PCA, ICA does not rank each source, which makes it a poor tool for dimensionality reduction. Requires the fastICA package to be installed.

Utilities for processing the parameters of various
statistical models. Beyond computing p values, CIs, and other indices
for a wide variety of models (see support list of insight; L<c3><bc>decke,
Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), this package
implements features like bootstrapping or simulating of parameters and
models, feature reduction (feature extraction and variable selection)
as well as functions to describe data and variable characteristics
(e.g. skewness, kurtosis, smoothness or distribution).

Daniel Lüdecke

parameters

Processing of Model Parameters

Daniel L<c3><bc>decke

Dominique Makowski

Mattan S. Ben-Shachar

Indrajeet Patil

S<c3><b8>ren H<c3><b8>jsgaard

Zen J. Lau

ICA function

Number of components to extract. If <code>n="all"</code>, then <code>n</code> is set as the number of variables minus 1 (<code>ncol(x)-1</code>). If <code>n="auto"</code> (default) or <code>n=NULL</code>, the number of components is selected through <code><a rd-options='' href='n_factors'>n_factors</a></code>. In <code><a rd-options='' href='reduce_parameters'>reduce_parameters</a></code>, can also be <code>"max"</code>, in which case it will select all the components that are maximally pseudo-loaded (i.e., correlated) by at least one variable.

ICA: Independent Component Analysis (ICA)

Description

Usage

Arguments

Examples