genpathmox (version 0.5)

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(
  xtree,
  alpha = 0.05,
  terminal = TRUE,
  scaled = FALSE,
  label = FALSE,
  label.nodes = NULL,
  ...
)

Arguments

xtree

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

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

total_effects

list of matrix with the terminal effects for each terminal node

Details

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

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.

See Also

pls.pathmox

Examples

# NOT RUN {
 
# }
# 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)
 
                 
 # 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)

 fib.comp=pls.treemodel(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)

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

# }