treemox.pls: PLS-PM results of terminal nodes from a PATHMOX or TECHMOX...

In pathmox: Pathmox Approach of Segmentation Trees in Partial Least Squares Path Modeling

Description

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

Usage

  treemox.pls(pls, treemox, X = NULL)

Arguments

pls	An object of class "plspm" returned by plspm.
treemox	An object of class "treemox" returned by pathmox or techmox.
X	Optional dataset (matrix or data frame) used when argument dataset=NULL inside pls.

Details

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

Value

An object of class "treemox.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
paths	Matrix of path coefficients for each terminal node
r2	Matrix of r-squared coefficients for each terminal node

Author(s)

Gaston Sanchez

See Also

pathmox, techmox, plot.treemox.

Examples

## Not run: 
 ## example of PLS-PM in customer satisfaction analysis
 ## model with seven LVs and reflective indicators
 data(csimobile)

 # select manifest variables
 data_mobile = csimobile[,8:33]

 # define path matrix (inner model)
 IMAG = c(0, 0, 0, 0, 0, 0, 0)
 EXPE = c(1, 0, 0, 0, 0, 0, 0)
 QUAL = c(0, 1, 0, 0, 0, 0, 0)
 VAL = c(0, 1, 1, 0, 0, 0, 0)
 SAT = c(1, 1, 1, 1, 0, 0, 0)
 COM = c(0, 0, 0, 0, 1, 0, 0)
 LOY = c(1, 0, 0, 0, 1, 1, 0)
 mob_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, COM, LOY)

 # blocks of indicators (outer model)
 mob_blocks = list(1:5, 6:9, 10:15, 16:18, 19:21, 22:24, 25:26)
 mob_modes = rep("A", 7)

 # apply plspm
 mob_pls = plspm(data_mobile, mob_path, mob_blocks, modes = mob_modes,
                 scheme = "factor", scaled = FALSE)

 # re-ordering those segmentation variables with ordinal scale (Age and Education)
 csimobile$Education = factor(csimobile$Education,
     levels=c("basic","highschool","university"),
     ordered=TRUE)

 # select the segmentation variables
 seg_vars = csimobile[,1:7]

 # Pathmox Analysis
 mob_pathmox = pathmox(mob_pls, seg_vars, signif=.10, size=.10, deep=2)

 # applying function treemox.pls
 mob_nodes = treemox.pls(mob_pls, mob_pathmox)

 # comparative barplots
 plot(mob_nodes)
 
## End(Not run)

pathmox documentation built on May 1, 2019, 11:31 p.m.