genpathmox (version 0.2)

pls.treemodel: PLS-PM results of terminal nodes from the Pathmox Segmentation Trees

Description

Calculates basic PLS-PM results for the terminal nodes of PATHMOX trees

Usage

pls.treemodel(xpls, xtree, X = NULL, alpha = 0.05, terminal = TRUE,
  scaled = FALSE, label = FALSE, label.nodes = NULL, ...)

Arguments

xpls

An object of class "plspm" returned by plspm.

xtree

An object of class "xtree.pls" returned by pls.pathmox.

X

Optional dataset (matrix or data frame) used when argument dataset=NULL inside xpls.

alpha

is numeric value indicating the significance threshold of the invariance test

terminal

is string, if equal to TRUE, just the terminal nodes are considered for the output reults. when it is equal to FALSE,the PLS-PM results are generated for all nodes of the tree

scaled

to standardize the latent variables or not

label

is a string. It is false for defect. If it is TRUE, label.nodes has to be fix.

label.nodes

is a vector with the name of the nodes. It is null for defect.

...

Further arguments passed on to pls.treemodel.

Value

• An object of class "treemodel.pls". Basically a list with the following results:
• inner
  Matrix of the inner relationship between latent variables of the PLS-PM model
• invariance.test
  A data frame containing the results of the invariance test
• weights
  Matrix of outer weights for each terminal node
• loadings
  Matrix of loadings for each terminal node
• paths
  Matrix of path coefficients for each terminal node
• r2
  Matrix of r-squared coefficients for each terminal node
• sign
  list of matrix with the significance for each terminal node

Details

The argument xpls must be the same used for calculating the xtree object. When the object xpls does not contain a data matrix (i.e. pls$data=NULL), the user must provide the data matrix or data frame in X.

The argument xtree is an object of class "xtree.pls" returned by pls.pathmox.

References

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

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

See Also

pls.pathmox,plot.xtree.pls

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)

 fib.comp=pls.treemodel(pls.fib,fib.pathmox)