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

Function to perform group Partial Least Squares to classify samples (supervised analysis) and select variables.

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`X` |
numeric matrix of predictors. |

`Y` |
a factor or a class vector for the discrete outcome. |

`ncomp` |
the number of components to include in the model (see Details). |

`keepX` |
numeric vector of length |

`max.iter` |
integer, the maximum number of iterations. |

`tol` |
a positive real, the tolerance used in the iterative algorithm. |

`ind.block.x` |
a vector of integers describing the grouping of the |

`gPLSda`

function fit gPLS models with *1, … ,*`ncomp`

components
to the factor or class vector `Y`

. The appropriate indicator (dummy)
matrix is created.

`ind.block.x <- c(3,10,15)`

means that *X* is structured into 4 groups: X1 to X3; X4 to X10, X11 to X15 and X16 to X*p* where *p* is the number of variables in the *X* matrix.

`sPLSda`

returns an object of class `"sPLSda"`

, a list
that contains the following components:

`X` |
the centered and standardized original predictor matrix. |

`Y` |
the centered and standardized indicator response vector or matrix. |

`ind.mat` |
the indicator matrix. |

`ncomp` |
the number of components included in the model. |

`keepX` |
number of |

`mat.c` |
matrix of coefficients to be used internally by |

`variates` |
list containing the variates. |

`loadings` |
list containing the estimated loadings for the |

`names` |
list containing the names to be used for individuals and variables. |

`tol` |
the tolerance used in the iterative algorithm, used for subsequent S3 methods |

`max.iter` |
the maximum number of iterations, used for subsequent S3 methods |

`iter` |
Number of iterations of the algorthm for each component |

`ind.block.x` |
a vector of integers describing the grouping of the X variables. |

Benoit Liquet and Pierre Lafaye de Micheaux.

Liquet Benoit, Lafaye de Micheaux Pierre , Hejblum Boris, Thiebaut Rodolphe (2016). A group and Sparse Group Partial Least Square approach applied in Genomics context. *Bioinformatics*.

On sPLS-DA:
Le Cao, K.-A., Boitard, S. and Besse, P. (2011). Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. *BMC Bioinformatics* **12**:253.

`sPLS`

, `summary`

,
`plotIndiv`

, `plotVar`

,
`cim`

, `network`

, `predict`

, `perf`

and http://www.mixOmics.org for more details.

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