Defining the optimum partition given a set of segmentation variables

1 2 | ```
partopt.pls(x, y, inner, outer, mode, scheme, scaling, scaled, method, n.node,
...)
``` |

`x` |
matrix or data frame containing the data. |

`y` |
matrix or data.frame of the segmentation variables. |

`inner` |
A square (lower triangular) boolean matrix representing the inner model (i.e. the path relationships between latent variables). |

`outer` |
list of vectors with column indices or column names from Data indicating the sets of manifest variables forming each block (i.e. which manifest variables correspond to each block). |

`mode` |
character vector indicating the type of measurement for each
block. Possible values are: |

`scheme` |
string indicating the type of inner weighting
scheme. Possible values are |

`scaling` |
optional list of string vectors indicating the type of
measurement scale for each manifest variable specified in |

`scaled` |
whether manifest variables should be standardized.
Only used when |

`method` |
string indicating the method: LM or LAD |

`n.node` |
number indicating a stop condition |

`...` |
Further arguments passed on to |

Internal function. `partopt.pls`

is called by `pls.pathmox`

.

list containing information of the optimum partition given a set of segmentation variables

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.