genpathmox (version 0.5)

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 basic characteristics of the tree (deep and number of terminal nodes), the basic characteristics of the nodes and the F-global the F-block and F-coefficients results. For the test results the significance level is also indicated.

Usage

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

Arguments

object

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.

pls.pathmox

Examples

# NOT RUN {
 
 
# }
# NOT RUN {
 ## example of PLS-PM in alumni satisfaction
 
 # 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)
 
                 
 # 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(data.fib, inner.fib, outer.fib, modes.fib,SVAR=seg.fib,signif=0.05,
				deep=2,size=0.2,n.node=20)
 				
 summary(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)
 
                 

 # 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(data.fib, inner.fib, outer.fib, modes.fib,SVAR=seg.fib,signif=0.5,
				deep=1,size=0.01,n.node=10)

summary(fib.pathmox)

# }