genpathmox (version 0.6)

treenode.pls: PLS-SEM results of a specific terminal node from the Pathmox Segmentation Trees

Description

Calculates basic PLS-SEM results for a specific terminal node of PATHMOX trees

Usage

treenode.pls(xtree, node, boot.val = TRUE, br = 500)

Arguments

xtree

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

node

is numeric value indicating the node that we want to aanalyze

boot.val

is string, if equal to TRUE, calculates the bootstrap intervals. By default is equal to TRUE.

br

is the numebr of boostrap resempling. By default is equal to 500.

Value

An object of class "treemodel.pls". Basically a list with the following results:

Reliability_indexes_and_unidimensionality

Classical reliabilitiy indices for PLS-SEM

Internal_consistency_and_R2

AVE and R2 for PLS-SEM

loadings

Outer model loadings

weights

Outer model weights

discriminant_validity

Discriminant validity - Fornell & Larcker criterion

path_coef

Coefficients of the inner model

total_effects

Total effects of the inner model

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)

treenode=treenode.pls(bank.pathmox,node=2,br=100)
 
 
# }