summary.plstree {genpathmox}

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 class 'plstree'
summary(object, ...)

Arguments

object

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

print.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
summary(csi.pathmox.age)


## End(Not run)