Description Usage Arguments Details Value Author(s) References See Also Examples
The function pathmox
calculates a binary
segmentation tree for PLS Path Models following the
PATHMOX algorithm. In contrast, fix.pathmox
obtains a supervised PATHMOX tree in the sense of
allowing the user to interactively fix the partitions
along the construction process of the tree.
1 2 |
pls |
An object of class |
EXEV |
A data frame of factors contaning the segmentation variables. |
X |
Optional dataset (matrix or data frame) used
when argument |
signif |
A numeric value indicating the significance threshold of the F-statistic. Must be a decimal number between 0 and 1. |
size |
A numeric value indicating the minimum size of elements inside a node. |
deep |
An integer indicating the depth level of the tree. Must be an integer greater than 1. |
tree |
A logical value indicating if the tree should
be displayed ( |
The argument EXEV
must be a data frame containing
segmentation variables as factors (see
factor
). The number of rows in EXEV
must be the same as the number of rows in the data used
in pls
.
The argument size
can be defined as a decimal
value (i.e. proportion of elements inside a node), or as
an integer (i.e. number of elements inside a node).
When the object pls
does not contain a data matrix
(i.e. pls$data=NULL
), the user must provide the
data matrix or data frame in X
.
An object of class "treemox"
. Basically a list
with the following results:
MOX |
Data frame with the results of the segmentation tree |
FT |
Data frame containing the results of the F-test for each node partition |
candidates |
List of data frames containing the candidate splits of each node partition |
list.nodes |
List of elements for each node |
Gaston Sanchez
Sanchez, G. (2009) PATHMOX Approach: Segmentation Trees in Partial Least Squares Path Modeling. PhD Dissertation.
http://www.gastonsanchez.com/Pathmox_Approach_Thesis_Gaston_Sanchez.pdf
techmox
, plot.treemox
,
treemox.pls
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ## Not run:
## example of PLS-PM in customer satisfaction analysis
## model with seven LVs and reflective indicators
data(csimobile)
# select manifest variables
data_mobile = csimobile[,8:33]
# define path matrix (inner model)
IMAG = c(0, 0, 0, 0, 0, 0, 0)
EXPE = c(1, 0, 0, 0, 0, 0, 0)
QUAL = c(0, 1, 0, 0, 0, 0, 0)
VAL = c(0, 1, 1, 0, 0, 0, 0)
SAT = c(1, 1, 1, 1, 0, 0, 0)
COM = c(0, 0, 0, 0, 1, 0, 0)
LOY = c(1, 0, 0, 0, 1, 1, 0)
mob_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, COM, LOY)
# blocks of indicators (outer model)
mob_blocks = list(1:5, 6:9, 10:15, 16:18, 19:21, 22:24, 25:26)
mob_modes = rep("A", 7)
# apply plspm
mob_pls = plspm(data_mobile, mob_path, mob_blocks, modes = mob_modes,
scheme="factor", scaled=FALSE)
# re-ordering those segmentation variables with ordinal scale
# (Age and Education)
csimobile$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)
## End(Not run)
|
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