genpathmox (version 0.3)

print.xtree.pls: Print function for the Pathmox Segmentation Trees: PLS-PM

Description

The function print.xtree.pls print the pls.pathmox tree

Usage

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

Arguments

x

An object of class "xtree.pls".

…

Further arguments are ignored.

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;

Aluja, T., Lamberti, G., Sanchez, G. (2013). Extending the PATHMOX approach to detect which constructs differentiate segments. In H., Abdi, W. W., Chin, V., Esposito Vinzi, G., Russolillo, and L., Trinchera (Eds.), Book title: New Perspectives in Partial Least Squares and Related Methods (pp.269-280). Springer.

Lamberti, G. (2014) Modeling with Heterogeneity. PhD Dissertation.

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

Tenenhaus M., Esposito Vinzi V., Chatelin Y.M., and Lauro C. (2005) PLS path modeling. Computational Statistics & Data Analysis, 48, pp. 159-205.

summary.xtree.pls.

Examples

# NOT RUN {
 ## example of PLS-PM in alumni satisfaction

 data(fibtele)

 # select manifest variables
 data.fib <-fibtele[,12:35]

 # define inner model matrix
 Image   		= rep(0,5)
	Qual.spec	  = rep(0,5)
	Qual.gen		= rep(0,5)
	Value			  = c(1,1,1,0,0)
	Satis			  = c(1,1,1,1,0)
 inner.fib = rbind(Image,Qual.spec, Qual.gen, Value, Satis)
 colnames(inner.fib) = rownames(inner.fib)

 # blocks of indicators (outer model)
 outer.fib  = list(1:8,9:11,12:16,17:20,21:24)
 modes.fib  = rep("A", 5)

 # apply plspm
 pls.fib = plspm(data.fib, inner.fib, outer.fib, modes.fib)

 # re-ordering those segmentation variables with ordinal scale
  seg.fib= fibtele[,2:11]

	 seg.fib$Age = factor(seg.fib$Age, ordered=T)
	 seg.fib$Salary = factor(seg.fib$Salary,
			levels=c("<18k","25k","35k","45k",">45k"), ordered=T)
	 seg.fib$Accgrade = factor(seg.fib$Accgrade,
			levels=c("accnote<7","7-8accnote","accnote>8"), ordered=T)
	 seg.fib$Grade = factor(seg.fib$Grade,
	    levels=c("<6.5note","6.5-7note","7-7.5note",">7.5note"), ordered=T)

 # Pathmox Analysis
 fib.pathmox=pls.pathmox(pls.fib,seg.fib,signif=0.05,
					deep=2,size=0.2,n.node=20)

 print(fib.pathmox)
 
# }
# NOT RUN {
 library(genpathmox)
 data(fibtele)

 # select manifest variables
 data.fib <-fibtele[1:50,12:35]

 # define inner model matrix
 Image       = rep(0,5)
	Qual.spec		= rep(0,5)
	Qual.gen		= rep(0,5)
	Value			  = c(1,1,1,0,0)
	Satis			  = c(1,1,1,1,0)
 inner.fib = rbind(Image,Qual.spec, Qual.gen, Value, Satis)
 colnames(inner.fib) = rownames(inner.fib)

 # blocks of indicators (outer model)
 outer.fib = list(1:8,9:11,12:16,17:20,21:24)
 modes.fib = rep("A", 5)

 # apply plspm
 pls.fib = plspm(data.fib, inner.fib, outer.fib, modes.fib)


 # re-ordering those segmentation variables with ordinal scale
 seg.fib = fibtele[1:50,c(2,7)]
	seg.fib$Salary = factor(seg.fib$Salary,
			levels=c("<18k","25k","35k","45k",">45k"), ordered=TRUE)

 # Pathmox Analysis
fib.pathmox = pls.pathmox(pls.fib,seg.fib,signif=0.5,
					deep=1,size=0.01,n.node=10)

print(fib.pathmox)
# }