genpathmox (version 0.3)

plot.xtree.pls: Plot function for the Pathmox Segmentation Trees: PLS-PM

Description

The function plot.xtree.pls allows to drow PATHMOX tree for PLS-PM

Usage

# S3 method for xtree.pls
plot(x, root.col = "grey", node.col = "orange",
  leaf.col = "green2", shadow.size = 0.003, node.shadow = "red",
  leaf.shadow = "darkgreen", cex = 0.7, seg.col = "blue3", lwd = 1,
  show.pval = TRUE, pval.col = "blue", main = NULL, cex.main = 1, ...)

Arguments

x

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

root.col

Fill color of root node.

node.col

Fill color of child nodes.

leaf.col

Fill color of leaf.

shadow.size

Relative size of shadows.

node.shadow

Color of shadow of child nodes.

leaf.shadow

Color of shadow of leaf nodes.

cex

A numerical value indicating the magnification to be used for plotting text.

seg.col

The color to be used for the labels of the segmentation variables.

lwd

The line width, a positive number, defaulting to 1

show.pval

Logical value indicating whether the p-values should be plotted.

pval.col

The color to be used for the labels of the p-values.

main

A main title for the plot.

cex.main

The magnification to be used for the main title.

…

Further arguments passed on to plot.xtree.pls.

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.

Examples

# 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)

 # 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)

 # plot pathmox tree
 plot(pls.fib)
 
# }
# 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)

plot(fib.pathmox)
# }