Description Usage Arguments Details Value See Also Examples

Runs the fastICA algorithm but does not perform any matrix preprocessing operations
first (this differs from the `fastICA`

function in the fastICA package.) Preprocessing steps should be carried out
by running other functions, e.g. `preprocessMatrix`

, on the input matrix prior to
using this function.

1 2 |

`X` |
a numeric matrix(-like object) |

`n.comp` |
an integer specifying the number of components to extract |

`alg.typ, fun, alpha, maxit, tol, verbose` |
See |

`w.init` |
integer value to reset random number generator with (see Details) |

`max.attempts` |
maximum number of attempts at convergence to make |

This function runs the fastICA algorithm on the input matrix but it does so without running
any preprocessing operations (such as centering and scaling on the rows and/or columns) on the input
matrix first. This allows alternative preprocessing methods (such as `scaleMatrix`

)
to be applied to the input matrix, which can improve results. The `preprocessMatrix`

function
may be run on the input matrix to carryout typical preprocessing steps, such as column centering.

Note: The default parameter values in `preprocessMatrix`

match those of `fastICA`

,
thus, running `preprocessMatrix`

followed by `dex.fastICA`

with default parameter values is equivalent to running `fastICA`

.

If `w.init`

is not NULL, its value will be used to reset the random number generator
(using `set.seed`

) prior to randomly generarting the initial W ('unmixing') matrix.
This differs from `fastICA`

, as w.init there can be either NULL or a matrix.
This modification to w.init was made in order to make reproducing results easier.

Another difference between this algorithm and `fastICA`

is that multiple attempts
at convergence can be made if convergence fails in the first attempt. The maximum number of attempts
that will be made is specified by the `max.attempts`

parameter. The maximum number of iterations in
each attempt, specified by `maxit`

, is increased by 1.5x in each subsequent attempt. If convergence
fails, the ICA solution found at the last iteration of the last attempt will be returned as an approximate
solution.

A list with the following elements:

- S
The estimated source matrix

- A
The estimated mixing matrix

- attempts
The number of attempts made at finding a solution

- iterations
The number of iterations made during the last attempt

- converged
A logical value indicating whether convergence was reached

`fastICA`

, `preprocessMatrix`

,
`predictModules`

1 2 3 4 | ```
x = matrix(rnorm(100), 10, 10)
x = preprocessMatrix(x)
m = dexFastICA(x, n.comp = 3)
m$converged
``` |

MPCary/DEXICA documentation built on June 26, 2017, 7:35 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.