genpathmox (version 0.9)

bar_impvar: Bar Plot of a ranking of categorical variables by importance

Description

"bar_impvar" returns a bar plot to visualize the ranking of variables by importance in obtaining the terminal nodes of Pathmox.

Usage

bar_impvar(x, cex.names = 1, cex.axis = 1.2, cex.main = 1, ...)

Arguments

x

An object of the class "plstree"

cex.names

Expansion factor for axis names (bar labels)

cex.axis

Expansion factor for numeric axis labels

cex.main

Allows fixing the size of the main. Equal to 1 to default

...

Further arguments are ignored

Author

Giuseppe Lamberti

Details

The importance of each variable is determined by adding the F-statistic calculated for the variable in each split node of Pathmox.

References

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, print.plstree, pls.pathmox, bar_terminal, 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 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
# 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          

"

# 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,
 .signif = 0.05,
 .deep=2
)                     
 
bar_impvar(csi.pathmox)

}