print.plstree {genpathmox}

Print function for Pathmox Segmentation Trees

Description

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

Usage

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

Arguments

x

An object of class "plstree".

...

Further arguments are ignored.

Author(s)

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., Aluja, T. B., and Sanchez, G. (2017). The Pathmox approach for PLS path modeling: Discovering which constructs differentiate segments. Applied Stochastic Models in Business and Industry, 33(6), 674-689. doi: 10.1007/s11135-021-01096-9

Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path modeling segmentation. Applied Stochastic Models in Business and Industry, 32(4), 453-468. doi: 10.1002/asmb.2168

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

Sanchez, G. (2009). PATHMOX Approach: Segmentation Trees in Partial Least Squares Path Modeling, PhD Dissertation.

See Also

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

Examples

 ## Not run: 
# Example of PATHMOX approach in customer satisfaction analysis 
# (Spanish financial company).
# Model with 5 LVs (4 common factor: Image (IMAG), Value (VAL), 
# Satisfaction (SAT), and Loyalty (LOY); and 1 composite 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
# Composite
QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5 + qual6 + qual7 
     
# Common factor
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,
 .alpha = 0.05,
 .deep = 1
)  

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


## End(Not run)