pathmox (version 0.2.0)

pathmox: PATHMOX Approach: 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

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).

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

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.

References

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

See Also

techmox, plot.treemox, treemox.pls.

Examples

## 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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