Description Usage Arguments Details See Also Examples
Plot method for objects of class "treemox.pls"
.
Barplots of path coefficients of terminal nodes with
respect to those of the global (root) model
1 2 3 4 5 6 |
x |
An object of class |
comp.by |
One of "nodes" or "latents". This argument indicates the type of barplots comparison. |
nodes.names |
Optional vector of names for the terminal nodes (must be a vector of length equal to the number of terminal nodes). |
ordered |
A logical value indicating whether the barplots are shown in increasing ordered. |
decreasing |
A logical value indicating if the sort order should be increasing or decreasing. |
color |
Optional vector of colors for the bars. When
|
show.box |
A logical value indicating whether a box is drawn around each barplot. |
border |
The color to be used for the border of the
bars. Use |
cex.names |
Expansion factor for axis names (bar labels). |
cex.axis |
Expansion factor for numeric axis labels. |
short.labs |
Logical value indicating if the labels
of the barplots should be abbreviated ( |
short.min |
Integer number indicating the minimum
length of the abbreviations for the labels. Only used
when |
... |
Arguments to be passed to/from other methods. |
This function aims to visualize the comparison between
path coefficients of the terminal nodes against the path
coefficients of the global model in the root node.
When comp.by="nodes"
a graphic window is displayed
for each endogenous latent variable of the PLS model, and
barplots of nodes are shown.
When
comp.by="latents"
a graphic window is displayed
for each endogenous relationship of the PLS model, and
barplots of independent latent variables are shown.
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 40 41 42 43 44 45 46 47 48 | ## 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)
# default plot
# comparative barplots of endogenous latent variables between nodes
plot(mob_nodes, comp.by="nodes")
# comparative barplots of nodes between latent regressors
plot(mob_nodes, comp.by="latents", decreasing=TRUE)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.