pathmox (version 0.2.0)

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

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.

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

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.

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)

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