# pathmox: PATHMOX Approach: Segmentation Trees in Partial Least Squares... In pathmox: Pathmox Approach of Segmentation Trees in Partial Least Squares Path Modeling

## Description

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.

## Usage

 ```1 2``` ``` pathmox(pls, EXEV, X = NULL, signif = 0.05, size = 0.1, deep = 2, tree = TRUE) ```

## Arguments

 `pls` An object of class `"plspm"` returned by `plspm`. `EXEV` A data frame of factors contaning the segmentation variables. `X` Optional dataset (matrix or data frame) used when argument `dataset=NULL` inside `pls`. `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 (`TRUE` by default).

## Details

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`.

## Value

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

## References

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

`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) ```