summary.plstree: Summary function for Pathmox Segmentation Trees

Description

The function summary.plstrees returns the most important results obtained by the function pls.pathmox. In order, it provides the parameters of the algorithm (threshold significance, node size limit, tree depth level and the method used for the split partition), the basic characteristics of the tree (depth and number of terminal nodes), the split results (F-global and F-coefficient). It also returns a ranking of the categorical variables by importance and the terminal node results (path coefficients and R^2).

Usage

# S3 method for plstree
summary(object, ...)

Arguments

object: An object of class "plstree".
...: Further arguments are ignored.

Author

Giuseppe Lamberti

References

Lamberti, G. (2021). Hybrid multigroup partial least squares structural equation modelling: an application to bank employee satisfaction and loyalty. Quality and Quantity, doi: 10.1007/s11135-021-01096-9.

Lamberti, G. et al. (2017). The Pathmox approach for PLS path modeling: discovering which constructs differentiate segments.Applied Stochastic Models in Business and Industry, doi: 10.1002/asmb.2270.

Lamberti, G. et al. (2016). The Pathmox approach for PLS path modeling segmentation. Applied Stochastic Models in Business and Industry, doi: 10.1002/asmb.2168.

Lamberti, G. (2015). Modeling with Heterogeneity, PhD Dissertation.

Examples

Run this code

 if (FALSE) {
# Example of PATHMOX approach in customer satisfaction analysis 
# (Spanish financial company).
# Model with 5 LVs (4 reflective: Image (IMAG), Value (VAL), 
# Satisfaction (SAT), and Loyalty (LOY); and 1 formative construct: 
# Quality (QUAL))

# load library and dataset csibank
library(genpathmx)
data("csibank")

# Define the model using the laavan syntax. Use a set of regression formulas to define 
# first the structural model and then the measurement model

CSImodel <- "
# Structural model
VAL  ~ QUAL
SAT  ~ IMAG  + QUAL + VAL
LOY  ~ IMAG + SAT

# Measurement model
# Formative
QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5 + qual6 + qual7 
     
# Reflective
IMAG <~ imag1 + imag2 + imag3 + imag4 + imag5 + imag6 
VAL  <~ val1  + val2  + val3  + val4
SAT  =~ sat1  + sat2  + sat3           
LOY  =~ loy1  + loy2  + loy3          

"

# Run pathmox on one single variable
age = csibank[,2]

# Transform age into an ordered factor
age = factor(age, levels=c("<=25", "26-35", "36-45", "46-55",
                                      "56-65", ">=66"),ordered=T)
                                      
csi.pathmox.age = pls.pathmox(
 .model = CSImodel ,
 .data  = csibank,
 .catvar= age,
 .signif = 0.05,
 .deep=1
)  

# Visualize the Pathmox results
summary(csi.pathmox.age)

}