Pathmox Approach Segmentation Tree Analysis
Candidates to the bets partition for each of segmentation variables
Bar Plot of a ranking of categorical variables by importance
Comparative plot for the Pathmox terminal nodes
Labels of a categorical variable a binary partions
Bart matrix
Linear relations between latent variables.
Posibble partions for each node of the tree
Checks arguments
Check consistence
Combinations of a vector element
Path coefficient extraction
Path coefficient labels
Data preprocessing for F-tests
Vector minimum position
F-coefficient test
F-coefficients test results for each tree partition
F-global test
F-global test results for each tree partition
info class
General information about the pathmox algorithm
General information about the tree
moxtree class
node class
Observations belonging to the nodes
Binary partitions given a segmentation variable (factor).
Best partition given a set of segmentation variables
Calculating size (numeber of individual of a node)
Plot function for the pathmox segmentation tree
Pathmox Segmentation Trees in Partial Least Squares Structural Equatio...
create method plstree
Print function for Pathmox Segmentation Trees
printing the tree structure
Observations belonging to the root node
Calculating Deepth stop criterion
Best partition for a specific segmentation variable
Summary function for Pathmox Segmentation Trees
Observations belonging to the terminal nodes
Cheking F-tests results
Ranking of variables importance
It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) <doi:10.1007/s11135-021-01096-9>).