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.