Description Usage Arguments Details Value Author(s) Examples

For a given number of sub-Gaussian and super-Gaussian sources and Gaussian components, the function ica_pca will initialize the model multiple times. This function will compute the number of initializations that will be performed.

1 | ```
initializations(subgaussians, supergaussians, gaussians)
``` |

`subgaussians` |
the number of sub-Gaussian sources in the model |

`supergaussians` |
the number of super-Gaussian sources in the model |

`gaussians` |
the number of Gaussian sources in the model |

If the number of initializations is small (less than 50 to 100), the ica_pca function may fail to identify the optimal model; models with small numbers of initializations should be run several times using different values for seed and/or offset_random. As the number of sources and components gets large (e.g., with totals more than 10) the number of initializations grows quickly. To a first approximation, computation time is proportional to the number of initializations.

returns the number of initializations

Roger P. Woods, M.D.

1 | ```
initializations(4,1,2)
``` |

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