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

The function `pathmox`

calculates a binary
segmentation tree for PLS Path Models following the
PATHMOX algorithm. In contrast, `fix.pathmox`

obtains a supervised PATHMOX tree in the sense of
allowing the user to interactively fix the partitions
along the construction process of the tree.

1 2 |

`pls` |
An object of class |

`EXEV` |
A data frame of factors contaning the segmentation variables. |

`X` |
Optional dataset (matrix or data frame) used
when argument |

`signif` |
A numeric value indicating the significance threshold of the F-statistic. Must be a decimal number between 0 and 1. |

`size` |
A numeric value indicating the minimum size of elements inside a node. |

`deep` |
An integer indicating the depth level of the tree. Must be an integer greater than 1. |

`tree` |
A logical value indicating if the tree should
be displayed ( |

The argument `EXEV`

must be a data frame containing
segmentation variables as factors (see
`factor`

). The number of rows in `EXEV`

must be the same as the number of rows in the data used
in `pls`

.

The argument `size`

can be defined as a decimal
value (i.e. proportion of elements inside a node), or as
an integer (i.e. number of elements inside a node).

When the object `pls`

does not contain a data matrix
(i.e. `pls$data=NULL`

), the user must provide the
data matrix or data frame in `X`

.

An object of class `"treemox"`

. Basically a list
with the following results:

`MOX` |
Data frame with the results of the segmentation tree |

`FT` |
Data frame containing the results of the F-test for each node partition |

`candidates` |
List of data frames containing the candidate splits of each node partition |

`list.nodes` |
List of elements for each node |

Gaston Sanchez

Sanchez, G. (2009) *PATHMOX Approach: Segmentation
Trees in Partial Least Squares Path Modeling.* PhD
Dissertation.

http://www.gastonsanchez.com/Pathmox_Approach_Thesis_Gaston_Sanchez.pdf

`techmox`

, `plot.treemox`

,
`treemox.pls`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
## Not run:
## example of PLS-PM in customer satisfaction analysis
## model with seven LVs and reflective indicators
data(csimobile)
# select manifest variables
data_mobile = csimobile[,8:33]
# define path matrix (inner model)
IMAG = c(0, 0, 0, 0, 0, 0, 0)
EXPE = c(1, 0, 0, 0, 0, 0, 0)
QUAL = c(0, 1, 0, 0, 0, 0, 0)
VAL = c(0, 1, 1, 0, 0, 0, 0)
SAT = c(1, 1, 1, 1, 0, 0, 0)
COM = c(0, 0, 0, 0, 1, 0, 0)
LOY = c(1, 0, 0, 0, 1, 1, 0)
mob_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, COM, LOY)
# blocks of indicators (outer model)
mob_blocks = list(1:5, 6:9, 10:15, 16:18, 19:21, 22:24, 25:26)
mob_modes = rep("A", 7)
# apply plspm
mob_pls = plspm(data_mobile, mob_path, mob_blocks, modes = mob_modes,
scheme="factor", scaled=FALSE)
# re-ordering those segmentation variables with ordinal scale
# (Age and Education)
csimobile$Education = factor(csimobile$Education,
levels=c("basic","highschool","university"),
ordered=TRUE)
# select the segmentation variables
seg_vars = csimobile[,1:7]
# Pathmox Analysis
mob_pathmox = pathmox(mob_pls, seg_vars, signif=.10, size=.10, deep=2)
## End(Not run)
``` |

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