bar_impvar | R Documentation |
"bar_impvar"
returns a bar plot to visualize the ranking
of variables by importance in obtaining the terminal nodes of Pathmox.
bar_impvar(x, .cex.names = 1, .cex.axis = 1.2, .cex.main = 1, ...)
x |
An object of the class |
.cex.names |
Expansion factor for axis names (bar labels) |
.cex.axis |
Expansion factor for numeric axis labels |
.cex.main |
Allows fixing the size of the main. Equal to 1 to default |
... |
Further arguments are ignored |
The importance of each variable is determined by adding the F-statistic calculated for the variable in each split node of Pathmox.
Giuseppe Lamberti
Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path modeling segmentation. Applied Stochastic Models in Business and Industry, 32(4), 453-468. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/asmb.2168")}
Lamberti, G. (2015). Modeling with Heterogeneity, PhD Dissertation.
Sanchez, G. (2009). PATHMOX Approach: Segmentation Trees in Partial Least Squares Path Modeling, PhD Dissertation.
summary.plstree
, print.plstree
, pls.pathmox
,
bar_terminal
, and plot.plstree
## Not run:
# Example of PATHMOX approach in customer satisfaction analysis
# (Spanish financial company).
# Model with 5 LVs (4 common factor: Image (IMAG), Value (VAL),
# Satisfaction (SAT), and Loyalty (LOY); and 1 composite construct:
# Quality (QUAL)
# load library and dataset csibank
library(genpathmx)
data("csibank")
# Define the model using the lavaan syntax. Use a set of regression formulas to define
# first the structural model and then the measurement model
CSImodel <- "
# Structural model
VAL ~ QUAL
SAT ~ IMAG + QUAL + VAL
LOY ~ IMAG + SAT
# Measurement model
# Composite
QUAL <~ qual1 + qual2 + qual3 + qual4 + qual5 + qual6 + qual7
# Common factor
IMAG =~ imag1 + imag2 + imag3 + imag4 + imag5 + imag6
VAL =~ val1 + val2 + val3 + val4
SAT =~ sat1 + sat2 + sat3
LOY =~ loy1 + loy2 + loy3
"
# Identify the categorical variable to be used as input variables
in the split process
CSIcatvar = csibank[,1:5]
# Check if variables are well specified (they have to be factors
# and/or ordered factors)
str(CSIcatvar)
# Transform age and education into ordered factors
CSIcatvar$Age = factor(CSIcatvar$Age, levels = c("<=25",
"26-35", "36-45", "46-55",
"56-65", ">=66"),ordered = T)
CSIcatvar$Education = factor(CSIcatvar$Education,
levels = c("Unfinished","Elementary", "Highschool",
"Undergrad", "Graduated"),ordered = T)
# Run Pathmox analysis (Lamberti et al., 2016; 2017)
csi.pathmox = pls.pathmox(
.model = CSImodel ,
.data = csibank,
.catvar= CSIcatvar,
.alpha = 0.05,
.deep = 2
)
bar_impvar(csi.pathmox)
## End(Not run)
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