genpathmox (version 0.2)

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 indicated.

Usage

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

Arguments

object

An object of class "xtree.pls".

...

Further arguments are ignored.

References

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

pls.pathmox

Examples

## 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)


 summary(fib.pathmox)