,

`R/ica_funs.R`

`fast_ICA.Rd`

`fast_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
)
```

- X
A matrix or data frame.

- n.comp
number of components to be extracted

- alg.typ
if

`alg.typ == "parallel"`

the components are extracted simultaneously (the default). if`alg.typ == "deflation"`

the components are extracted one at a time.- fun
the functional form of the \(G\) function used in the approximation to neg-entropy (see ‘details’).

- alpha
constant in range [1, 2] used in approximation to neg-entropy when

`fun == "logcosh"`

- method
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.- row.norm
a logical value indicating whether rows of the data matrix

`X`

should be standardized beforehand.- maxit
maximum number of iterations to perform.

- tol
a positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.

- w.init
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()`