Description Usage Arguments Value Author(s) References See Also Examples

Function to perform a clusterwise multiblock analyses (clusterwise multiblock Partial Least Squares, clusterwise multiblock Redundancy Analysis or clusterwise regularized multiblock regression) of several explanatory blocks *(X_1, …, X_K)* to explain a dependent dataset *Y*.

1 2 3 |

`Y` |
a matrix or data frame containing the dependent variable(s) |

`X` |
a matrix or data frame containing the explanatory variables |

`blo` |
a vector of the numbers of variables in each explanatory dataset |

`option` |
an option for the block weighting (by default, the first option is chosen): |

`G` |
an integer giving the expected number of clusters |

`H` |
an integer giving the expected number of dimensions of the component-based model |

`INIT` |
an integer giving the number of initializations required for the clusterwise analysis (20 by default) |

`method` |
an option for the multiblock method to be applied (by default, the first option is chosen): |

`Gamma` |
a numeric value of the regularization parameter for the multiblock regularized regression comprised between 0 and 1 (NULL by default). The value ( |

`parallel.level` |
Level of parallel computing, i.e. initializations are carried out simultaneously (high by default) |

A list containing the following components is returned:

`call` |
the matching call |

`error` |
a vector containing the value of the criterion to be minimized (overall prediction error) ; this error is performed on the centered and scaled data |

`beta.cr` |
a list of array that contain the intercept and the regression coefficients associated with the centered and scaled data for each of the |

`beta.raw` |
a list of array that contain the intercept and the regression coefficients associated with the raw data for each of the |

`hopt` |
the real number of dimensions of the component-based model ( |

`Ypred.cr` |
a list of matrices that contain the predicted dependent values associated with the centered and scaled data for each of the |

`Ypred.raw` |
a list of matrices that contain the predicted dependent values associated with the raw data for each of the |

`cluster` |
a vector containing the observation assignation to the |

Stephanie Bougeard (stephanie.bougeard@anses.fr)

Bougeard, S., Abdi, H., Saporta, G., Niang, N., Submitted, Clusterwise analysis for multiblock component methods.

1 2 3 4 5 6 7 8 9 | ```
data(simdata.red)
Data.X <- simdata.red[c(1:10, 21:30), 1:10]
Data.Y <- simdata.red[c(1:10, 21:30), 11:13]
## Note that the options (INIT=2) and (parallel.level = "low") are chosen to quickly
## illustrate the function.
## For real data, instead choose (INIT=20) to avoid local optima and (parallel.level = "high")
## to improve the computing speed.
res.cw <- cw.multiblock(Y = Data.Y, X = Data.X, blo = c(5, 5), option = "none", G = 2,
H = 1, INIT = 2, method = "mbpcaiv", Gamma = NULL, parallel.level = "low")
``` |

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