plot.plstree: Plot function for the pathmox segmentation tree

In genpathmox: Pathmox Approach Segmentation Tree Analysis

Plot function for the pathmox segmentation tree

Description

The function plot.plstree allows to drow PATHMOX tree

Usage

## S3 method for class 'plstree'
plot(
  x,
  .root.col = "#CCFFFF",
  .node.col = "#99CCCC",
  .leaf.col = "#009999",
  .shadow.size = 0.003,
  .node.shadow = "#669999",
  .leaf.shadow = "#006666",
  .cex = 0.7,
  .seg.col = "#003333",
  .lwd = 1,
  .show.pval = TRUE,
  .pval.col = "#009999",
  .main = NULL,
  .cex.main = 1,
  ...
)

Arguments

x	An object of the class "plstree".
.root.col	Fill color of root node.
.node.col	Fill color of child nodes.
.leaf.col	Fill color of leaf.
.shadow.size	Relative size of shadows.
.node.shadow	Color of shadow of child nodes.
.leaf.shadow	Color of shadow of leaf nodes.
.cex	A numerical value indicating the magnification to be used for plotting text.
.seg.col	The color to be used for the labels of the segmentation variables.
.lwd	The line width, a positive number, defaulting to 1.
.show.pval	Logical value indicating whether the p-values should be plotted.
.pval.col	The color to be used for the labels of the p-values.
.main	A main title for the plot.
.cex.main	The magnification to be used for the main title.
...	Further arguments passed on to plot.plstree.

Author(s)

Giuseppe Lamberti

References

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. \Sexpr[results=rd]{tools:::Rd_expr_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, print.plstree, pls.pathmox, bar_terminal, and bar_impvar

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 lavaan 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           

"

# Identify the categorical variable to be used as input variables 
in the split process
CSIcatvar = csibank[,1:5]

# Check if variables are well specified (they have to be factors 
# and/or ordered factors)
str(CSIcatvar)

# Transform age and education into ordered factors
CSIcatvar$Age = factor(CSIcatvar$Age, levels = c("<=25", 
                                     "26-35", "36-45", "46-55", 
                                     "56-65", ">=66"),ordered = T)

CSIcatvar$Education = factor(CSIcatvar$Education, 
                            levels = c("Unfinished","Elementary", "Highschool",
                            "Undergrad", "Graduated"),ordered = T)
       
# Run Pathmox analysis (Lamberti et al., 2016; 2017)
csi.pathmox = pls.pathmox(
 .model = CSImodel ,
 .data  = csibank,
 .catvar= CSIcatvar,
 .alpha = 0.05,
 .deep = 2
)                     

Visualize the tree
plot(csi.pathmox)


## End(Not run)

genpathmox documentation built on Oct. 26, 2023, 5:08 p.m.