genpathmox (version 0.6)

summary.xtree.pls: Summary function for the Pathmox Segmentation Trees: PLS-PM

Description

The function summary.xtree.pls returns the most important results obtained by the function pls.pathmox. In order, it provides the parameters algorithm ( threshold significance, node size limit", tree depth level, and the method used for the split partition), the essential characteristics of the tree (deep and number of terminals nodes), the basic characteristics of the nodes and the F-global and the F-coefficient results. For the test results, the significance level is also indicated.

Usage

# S3 method for xtree.pls
summary(x, ...)

Arguments

x

An object of class "xtree.pls".

…

Further arguments are ignored.

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.

pls.pathmox

Examples

# NOT RUN {
 
# }
# NOT RUN {
 ## example of PLS-PM in bank customer satisfaction
 
data(csibank)

# select manifest variables
data.bank <-csibank[,6:32]

# define inner model matrix
Image 			  = rep(0,6)
Expectation	  = c(1,0,0,0,0,0)
Quality		    = c(0,1,0,0,0,0)
Value			    = c(0,1,1,0,0,0)
Satis			    = c(1,1,1,1,0,0)
Loyalty       = c(1,0,0,0,1,0)
inner.bank = rbind(Image,Expectation, Quality, Value, Satis,Loyalty)
colnames(inner.bank) = rownames(inner.bank)

# blocks of indicators (outer model)
outer.bank  = list(1:6,7:10,11:17,18:21,22:24,25:27)
modes.bank = rep("A", 6)


# re-ordering those segmentation variables with ordinal scale 
seg.bank= csibank[,1:5]

seg.bank$Age = factor(seg.bank$Age, ordered=TRUE)
seg.bank$Education = factor(seg.bank$Education, ordered=TRUE)


# Pathmox Analysis
bank.pathmox=pls.pathmox(data.bank, inner.bank, outer.bank, modes.bank,SVAR=seg.bank,signif=0.05,
                         deep=2,size=0.2,n.node=20)
summary(bank.pathmox)
 
 
# }
# NOT RUN {
library(genpathmox)
data(csibank)

# select manifest variables
data.bank <-csibank[,6:32]

# define inner model matrix
Image 			  = rep(0,6)
Expectation	  = c(1,0,0,0,0,0)
Quality		    = c(0,1,0,0,0,0)
Value			    = c(0,1,1,0,0,0)
Satis			    = c(1,1,1,1,0,0)
Loyalty       = c(1,0,0,0,1,0)
inner.bank = rbind(Image,Expectation, Quality, Value, Satis,Loyalty)
colnames(inner.bank) = rownames(inner.bank)

# blocks of indicators (outer model)
outer.bank  = list(1:6,7:10,11:17,18:21,22:24,25:27)
modes.bank = rep("A", 6)


# re-ordering those segmentation variables with ordinal scale 
seg.bank= csibank[,1:5]

seg.bank$Age = factor(seg.bank$Age, ordered=TRUE)
seg.bank$Education = factor(seg.bank$Education, ordered=TRUE)


# Pathmox Analysis
bank.pathmox=pls.pathmox(data.bank, inner.bank, outer.bank, modes.bank,SVAR=seg.bank,signif=0.05,
                         deep=2,size=0.2,n.node=20)

summary(bank.pathmox)

# }