,
R/ica_funs.R
fast_ICA.Rdfast_ICA() is a wrapper for fastICA::fastICA, with different defaults (runs in C, maximum iteration = 1000, tolerance = 1e-04, verbose), and that throws a warning in case of non-convergence. It returns an estimated unmixing matrix W (equivalent to the original K %*% W), and the mixing matrix A, consistent with the formulation X= S %*% A, and X %*% W = S where X is the matrix of data with N_samples by N_channels, and S is a matrix of sources with N_samples by N_sources. They are meant to be used with eeg_ica().
fast_ICA(
X,
n.comp = NULL,
alg.typ = "parallel",
fun = "logcosh",
alpha = 1,
method = "C",
row.norm = FALSE,
maxit = 1000,
tol = 1e-06,
w.init = NULL
)A matrix or data frame.
number of components to be extracted
if alg.typ == "parallel" the components are extracted
simultaneously (the default). if alg.typ == "deflation" the
components are extracted one at a time.
the functional form of the \(G\) function used in the approximation to neg-entropy (see ‘details’).
constant in range [1, 2] used in approximation to
neg-entropy when fun == "logcosh"
if method == "R" then computations are done
exclusively in R (default). The code allows the interested R user to
see exactly what the algorithm does.
if method == "C" then C code is used to perform most of the
computations, which makes the algorithm run faster. During
compilation the C code is linked to an optimized BLAS library if
present, otherwise stand-alone BLAS routines are compiled.
a logical value indicating whether rows of the data
matrix X should be standardized beforehand.
maximum number of iterations to perform.
a positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.
Initial un-mixing matrix of dimension
c(n.comp, n.comp). If NULL (default) then a matrix of
normal r.v.'s is used.
A list with the unmixing matrix W and the mixing matrix A.
Other ica methods:
fICA,
ica_matrix_lst()