Description Usage Arguments Details Value Author(s) Examples

This function runs the fastICA algorithm several times with random initializations. The obtained components are clustered and the medoids of these clusters are used as the final estimates. The returned estimates are ordered by decreasing Iq values which measure the compactness of the clusters (see details).

1 2 3 4 5 |

`X` |
A data matrix with n rows representing observations (e.g genes) and p columns representing variables (e.g samples). |

`nbComp` |
The number of components to be extracted. |

`nbIt` |
The number of iterations of FastICA |

`alg.type` |
If |

`fun` |
The functional form of the G function used in
the approximation to neg-entropy (see 'details' of the
help of function |

`row.norm` |
a logical value indicating whether rows
of the data matrix |

`maxit` |
The maximum number of iterations to perform. |

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

`funClus` |
The clustering function to be used to cluster the estimates |

`bootstrap` |
if TRUE the data is bootstraped before each fastICA iteration, else (default) only random initializations are done |

`...` |
Additional parameters for codefunClus |

This function implements in R fastICA iterations followed by a clustering step, as defined in the matlab package 'icasso'. Among the indices computed by icasso, only the Iq index is currently computed. As defined in 'icasso', the Iq index measures the difference between the intra-cluster similarity and the extra-cluster similiarity. No visualization of the clusters is yet available.

If `bootstrap=TRUE`

a bootstrap (applied to the
observations) is used to perturb the data before each
iteration, then function `fastICA`

is applied with
random initializations.

By default, in 'icasso', agglomerative hierarchical
clustering with average linkage is performed. To use the
same clustering, please use `funClus="hclust"`

and
`method="average"`

. But this function also allows
you to apply the clustering of your choice among
`kmeans, pam, hclust, agnes`

by specifying
`funClus`

and adding the adequat additional
parameters.

See details of the functions
`fastICA`

.

A list consisting of:

- A
the estimated mixing matrix

- S
the estimated source matrix

, itemWthe estimated unmixing matrix,

- Iq
Iq indices.

Anne Biton

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## generate a data
set.seed(2004);
M <- matrix(rnorm(5000*6,sd=0.3),ncol=10)
M[1:100,1:3] <- M[1:100,1:3] + 2
M[1:200,1:3] <- M[1:200,4:6] +1
## Random initializations are used for each iteration of FastICA
## Estimates are clustered using hierarchical clustering with average linkage
res <- clusterFastICARuns(X=M, nbComp=2, alg.type="deflation",
nbIt=3, funClus="hclust", method="average")
## Data are boostraped before each iteration and random initializations
## are used for each iteration of FastICA
## Estimates are clustered using hierarchical clustering with ward
res <- clusterFastICARuns(X=M, nbComp=2, alg.type="deflation",
nbIt=3, funClus="hclust", method="ward")
``` |

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