genpathmox (version 0.6)

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

Description

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

Usage

pls.treemodel(
  xtree,
  terminal = TRUE,
  scaled = FALSE,
  label = FALSE,
  label.nodes = NULL,
  ...
)

Arguments

xtree

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

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:

weights

Matrix of outer weights for each terminal node

loadings

Matrix of loadings for each terminal node

path_coef

Matrix of path coefficients for each terminal node

path_sgnificance

Matrix of path coefficients the significance (p-value) for each terminal node

predictive_power_R2

Matrix of r-squared coefficients 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. (2021) Hybrid multigroup partial least squares structural equation modelling: an application to bank employee satisfaction and loyalty. Quality and Quantity; doi: 10.1007/s11135-021-01096-9;

Lamberti, G. et al. (2017) The Pathmox approach for PLS path modeling: Discovering which constructs differentiate segments.. Applied Stochastic Models in Business and Industry; doi: 10.1002/asmb.2270;

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;

Lamberti, G. (2015) Modeling with Heterogeneity. PhD Dissertation.

See Also

pls.pathmox

Examples

# NOT RUN {
 ## example of PLS-PM in bank customer satisfaction
 
data(csibank)

# select manifest variables
data.bank <-csibank[,6:32]

# define inner model matrix
Image 			  = rep(0,6)
Expectation	  = c(1,0,0,0,0,0)
Quality		    = c(0,1,0,0,0,0)
Value			    = c(0,1,1,0,0,0)
Satis			    = c(1,1,1,1,0,0)
Loyalty       = c(1,0,0,0,1,0)
inner.bank = rbind(Image,Expectation, Quality, Value, Satis,Loyalty)
colnames(inner.bank) = rownames(inner.bank)

# blocks of indicators (outer model)
outer.bank  = list(1:6,7:10,11:17,18:21,22:24,25:27)
modes.bank = rep("A", 6)


# re-ordering those segmentation variables with ordinal scale 
seg.bank= csibank[,1:5]

seg.bank$Age = factor(seg.bank$Age, ordered=TRUE)
seg.bank$Education = factor(seg.bank$Education, ordered=TRUE)


# Pathmox Analysis
bank.pathmox=pls.pathmox(data.bank, inner.bank, outer.bank, modes.bank,SVAR=seg.bank,signif=0.05,
                         deep=2,size=0.2,n.node=20)

nodes.models=pls.treemodel(bank.pathmox)
 
 
# }
# NOT RUN {
# }