genpathmox (version 0.9)

print.plstree: Print function for Pathmox Segmentation Trees

Description

The function print.plstree returns the pls.pathmox results.

Usage

# S3 method for plstree
print(x, ...)

Arguments

x

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.

See Also

summary.plstree, pls.pathmox, bar_terminal, bar_impvar and plot.plstree

Examples

 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
print(csi.pathmox.age)

}