Nothing
R/get_plots.R
In genpathmox: Pathmox Approach Segmentation Tree Analysis
#' @title Plot function for the pathmox segmentation tree
#'
#' @description
#' The function \code{plot.plstree} allows to drow PATHMOX tree
#'
#' @param x An object of the class \code{"plstree"}.
#' @param .root.col Fill color of root node.
#' @param .node.col Fill color of child nodes.
#' @param .leaf.col Fill color of leaf.
#' @param .shadow.size Relative size of shadows.
#' @param .node.shadow Color of shadow of child nodes.
#' @param .leaf.shadow Color of shadow of leaf nodes.
#' @param .cex A numerical value indicating the magnification to be used for
#' plotting text.
#' @param .seg.col The color to be used for the labels of the segmentation
#' variables.
#' @param .lwd The line width, a positive number, defaulting to 1.
#' @param .show.pval Logical value indicating whether the p-values should be
#' plotted.
#' @param .pval.col The color to be used for the labels of the p-values.
#' @param .main A main title for the plot.
#' @param .cex.main The magnification to be used for the main title.
#' @param \dots Further arguments passed on to \code{\link{plot.plstree}}.
#'
#' @author Giuseppe Lamberti
#'
#' @references Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path
#' modeling segmentation. \emph{Applied Stochastic Models in Business and Industry}, \bold{32}(4), 453-468.
#' \doi{10.1002/asmb.2168}
#'
#' @references Lamberti, G. (2015). \emph{Modeling with Heterogeneity}, PhD Dissertation.
#'
#' @references Sanchez, G. (2009). \emph{PATHMOX Approach: Segmentation Trees in
#' Partial Least Squares Path Modeling}, PhD Dissertation.
#'
#' @seealso \code{\link{summary.plstree}}, \code{\link{print.plstree}}, \code{\link{pls.pathmox}},
#' \code{\link{bar_terminal}}, and \code{\link{bar_impvar}}
#'
#'@exportS3Method plot plstree
#' @examples
#' \dontrun{
#' # 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
#' )
#'
#' Visualize the tree
#' plot(csi.pathmox)
#'
#' }
#'
plot.plstree = function (x, .root.col = "#CCFFFF", .node.col = "#99CCCC", .leaf.col = "#009999",
.shadow.size = 0.003, .node.shadow = "#669999", .leaf.shadow = "#006666",
.cex = 0.7, .seg.col = "#003333", .lwd = 1, .show.pval = TRUE,
.pval.col = "#009999", .main = NULL, .cex.main = 1, ...)
{
.MOX = x$MOX
.last = nrow(.MOX)
.last.level = .MOX$depth[.last]
.num.levels = rep(1, .last.level + 1)
for (i in 1: .last.level) .num.levels[i + 1] <- 2^i
grDevices::dev.new()
graphics::par(mar = c(0.4, 0.4, 1, 1.5))
diagram::openplotmat()
.elpos = diagram::coordinates(.num.levels)
.fromto = cbind(.MOX[-1, 2], .MOX[-1, 1])
.nr = nrow(.fromto)
.arrpos = matrix(ncol = 2, nrow = .nr)
for (i in 1: .nr) .arrpos[i, ] = diagram::straightarrow(to = .elpos[.fromto[i,
2], ], from = .elpos[.fromto[i, 1], ], lwd = .lwd, arr.pos = 0.6,
arr.length = 0)
diagram::textellipse(.elpos[1, ], 0.045, 0.045, lab = c("Root", .MOX[1,
6]), box.col = .root.col, shadow.size = .shadow.size, cex = .cex,font=2)
for (i in 2: .last) {
.posi <- .MOX$node[i]
.nodlab <- c(paste("Node", .posi), .MOX$size[i])
if (.MOX$type[i] == "node") {
diagram::textellipse(.elpos[.posi, ], 0.05, 0.03, lab = .nodlab,
box.col = .node.col, shadow.col = .node.shadow,
shadow.size = .shadow.size, cex = .cex)
}
else {
diagram::textrect(.elpos[.posi, ], 0.045, 0.025, lab = .nodlab,
box.col = .leaf.col, shadow.col = .leaf.shadow,
shadow.size = .shadow.size, cex = .cex)
}
}
aux = 1
for (i in seq(1, .nr, by = 2)) {
if (i == 1)
k = 1
else k = 1.15
.x1 = (.arrpos[i, 1] + .arrpos[i + 1, 1])/2
graphics::text(.x1, k * .arrpos[i, 2], .MOX$variable[i + 1], cex = .cex,
col = .seg.col,font=2)
if (.show.pval) {
if(x$Fg.r$`Pr(>F)`[aux]<0.001){
graphics::text(.x1, k * .arrpos[i, 2], paste("p.value <0.001"), cex = 0.9 * .cex, col = .pval.col,
pos = 1)
}
else
{
graphics::text(.x1, k * .arrpos[i, 2], paste("p.value =", round(x$Fg.r$`Pr(>F)`[aux],4),
sep = ""), cex = 0.9 * .cex, col = .pval.col,
pos = 1)
}
}
aux = aux + 1
}
for (i in 1: .nr) {
.posi <- .MOX$node[i + 1]
.seg.cat = as.character(.MOX$category[i + 1])
.seg.cat = unlist(strsplit(.seg.cat, "/"))
if (.posi%%2 == 0) {
for (h in 1:length(.seg.cat)) graphics::text(.arrpos[i, 1] -
0.03, .arrpos[i, 2] + h/55, .seg.cat[h], cex = .cex)
}
else {
for (h in 1:length(.seg.cat)) graphics::text(.arrpos[i, 1] +
0.03, .arrpos[i, 2] + h/55, .seg.cat[h], cex = .cex)
}
}
if (is.null(.main)) {
graphics::text(0.5, 0.95, c("PLS-SEM PATHMOX TREE"), cex = .cex.main)
}
else {
graphics::text(0.5, 0.95, .main, cex = .cex.main)
}
}
#' @title Comparative plot for the Pathmox terminal nodes
#'
#' @description
#' \code{bar_terminal} returns the path coefficient bar plots of the Pathmox terminal
#' nodes.
#'
#' @details
#' This function aims to visualize, using bar plots, the path coefficients of
#' the dependnet latent construct associated with the terminal nodes.
#' The user indicates the dependnet latent construct they want to visualize.
#' This is done using the same label as used in the structural model definition
#' (lavaan syntax). The comparison is done by analyzing the path coefficient
#' values for each node, or the values estimated in each node for each path coefficient.
#' In the former, the plot also returns the R^2. In the latter, the bar corresponding to the
#' node with the highest path coefficient value shows in a different color.
#' By default the comparison is done by analyzing the path coefficient values for each node.
#'
#' @param x An object of the class \code{"plstree"}.
#' @param .LV A string indicating the name of the dependent latent variable. The label
#' must be the same as used to define the structural model in the (lavaan syntax).
#' @param .bycoef Logical value indicating if the comparison is done by nodes or by path
#' coefficients. By default, \code{FALSE} means that the comparison is done
#' by nodes.
#' @param .cex.names Expansion factor for axis names (bar labels).
#' @param .cex.axis Expansion factor for numeric axis labels.
#' @param .cex.main Allows fixing the size of the main. It is equal to 1 to default.
#' @param \dots Further arguments are ignored.
#'
#' @author Giuseppe Lamberti
#'
#' @references Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path
#' modeling segmentation. \emph{Applied Stochastic Models in Business and Industry}, \bold{32}(4), 453-468.
#' \doi{10.1002/asmb.2168}
#'
#' @references Lamberti, G. (2015). \emph{Modeling with Heterogeneity}, PhD Dissertation.
#'
#' @references Sanchez, G. (2009). \emph{PATHMOX Approach: Segmentation Trees in
#' Partial Least Squares Path Modeling}, PhD Dissertation.
#'
#' @seealso \code{\link{summary.plstree}}, \code{\link{print.plstree}}, \code{\link{pls.pathmox}},
#' \code{\link{plot.plstree}}, and \code{\link{bar_impvar}}
#'
#' @export
#' @examples
#' \dontrun{
#' # 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
#'
#' "
#'
#' # Run pathmox on one single variable
#' age = csibank[,2]
#'
#' # Transform age into an ordered factor
#' age = factor(age, levels = c("<=25", "26-35", "36-45", "46-55",
#' "56-65", ">=66"),ordered = T)
#'
#' csi.pathmox.age = pls.pathmox(
#' .model = CSImodel ,
#' .data = csibank,
#' .catvar= age,
#' .alpha = 0.05,
#' .deep = 1
#' )
#'
#' # Visualize the bar plot by comparing the nodes
#' bar_terminal(csi.pathmox.age, .LV = "SAT")
#'
#' # Visualize the bar plot by comparing path coefficients
#' bar_terminal(csi.pathmox.age, .LV = "SAT", .bycoef = TRUE)
#'
#' }
#'
bar_terminal = function (x,.LV,.bycoef = FALSE,.cex.names = 1,.cex.axis = 1.2,.cex.main = 1,...)
{
.nn = rownames(x$terminal_path)
.chars = paste("->",.LV,sep="")
if(.bycoef)
{
.nodes.name = colnames(.end_eq)
.chars2 = paste("R^2",.LV,sep=" ")
.rq = NA
}
else{
.end_eq = (x$terminal_paths[grepl(.chars, .nn, fixed = TRUE),])
.nodes.name = colnames(.end_eq)
.chars2 = paste("R^2",.LV,sep=" ")
.rq = (x$terminal_paths[grepl(.chars2, .nn, fixed = TRUE),])
}
.rs = c(1, 1, 1, 2, 2, 2, 2, 2, 3, 2, 3, 3, 4, 4)
.cs = c(1, 2, 3, 2, 3, 3, 4, 4, 3, 5, 4, 4, 4, 4)
.index.mat = cbind(1:14, .rs, .cs)
.lvs = nrow(.end_eq)
.colors = hcl.colors(.lvs, palette = "Teal")
.nn = ncol(.end_eq)
par(mfrow = .index.mat[.nn, 2:3])
par(mar = c(3, 3, 3, 3))
abline(h = 0)
ylim <- 1.15 * c(min(.end_eq[1, ]), max(.end_eq[1, ]))
for (.n in 1: .nn)
{
if(.bycoef){
.maxfac = 1+(.end_eq[,.n]==max(.end_eq[,.n]))
barplot(.end_eq[,.n], main = paste(.nodes.name[.n]),col=c("#99CCCC","#009999")[.maxfac],
cex.names =.cex.names, cex.axis = .cex.axis, cex.main = .cex.main, ylim=
c(0,max(.end_eq[,.n])+0.1),...)
}
else{
barplot(.end_eq[,.n], main = paste(.nodes.name[.n],"\n","R^2:",.rq[.n]),col = .colors,
cex.names = .cex.names, cex.axis = .cex.axis, cex.main = .cex.main,ylim=
c(0,max(.end_eq[,.n])+0.1),...)
}
}
}
#' @title Bar Plot of a ranking of categorical variables by importance
#'
#' @description
#' \code{"bar_impvar"} returns a bar plot to visualize the ranking
#' of variables by importance in obtaining the terminal nodes of Pathmox.
#'
#' @details
#' The importance of each variable is determined by adding the F-statistic
#' calculated for the variable in each split node of Pathmox.
#'
#' @param x An object of the class \code{"plstree"}
#' @param .cex.names Expansion factor for axis names (bar labels)
#' @param .cex.axis Expansion factor for numeric axis labels
#' @param .cex.main Allows fixing the size of the main. Equal to 1 to default
#' @param \dots Further arguments are ignored
#'
#' @author Giuseppe Lamberti
#'
#' @reference Lamberti, G., et al. (2021). University image, hard skills or soft skills:
#' Which matters most for which graduate students? \emph{Quality and Quantity}.
#' doi: \doi{10.1007/s11135-021-01149-z}
#'
#' @references Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path
#' modeling segmentation. \emph{Applied Stochastic Models in Business and Industry}, \bold{32}(4), 453-468.
#' \doi{10.1002/asmb.2168}
#'
#' @references Lamberti, G. (2015). \emph{Modeling with Heterogeneity}, PhD Dissertation.
#'
#' @references Sanchez, G. (2009). \emph{PATHMOX Approach: Segmentation Trees in
#' Partial Least Squares Path Modeling}, PhD Dissertation.
#'
#' @seealso \code{\link{summary.plstree}}, \code{\link{print.plstree}}, \code{\link{pls.pathmox}},
#' \code{\link{bar_terminal}}, and \code{\link{plot.plstree}}
#'
#' @export
#' @examples
#' \dontrun{
#' # 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$Education,
#' levels = c("Unfinished","Elementary", "Highschool",
#' "Undergrad", "Graduated"),ordered = T)
#'
#' # Run Pathmox analysis (Lamberti et al., 2016; 2017)
#' csi.pathmox = pls.pathmox(
#' .model = CSImodel ,
#' .data = csibank,
#' .catvar= CSIcatvar,
#' .alpha = 0.05,
#' .deep = 2
#' )
#'
#' bar_impvar(csi.pathmox)
#'
#' }
#'
bar_impvar = function (x,.cex.names = 1,.cex.axis = 1.2, .cex.main = 1,...)
{
grDevices::dev.new()
graphics::par(mai=c(1.4,0.82,0.82,0.42))
diagram::openplotmat()
.imp = x$var_imp[,2]/sum(x$var_imp[,2])
names(.imp) = x$var_imp[,1]
.maxfac = 1+(.imp==max(.imp))
barplot(.imp, main = "Ranking variable importance",col=c("#99CCCC","#009999")[.maxfac],
cex.names = .cex.names, cex.axis = .cex.axis, cex.main = .cex.main,
ylim = c(0,max(.imp)+0.1),...)
}
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#' @title Plot function for the pathmox segmentation tree
#'
#' @description
#' The function \code{plot.plstree} allows to drow PATHMOX tree
#'
#' @param x An object of the class \code{"plstree"}.
#' @param .root.col Fill color of root node.
#' @param .node.col Fill color of child nodes.
#' @param .leaf.col Fill color of leaf.
#' @param .shadow.size Relative size of shadows.
#' @param .node.shadow Color of shadow of child nodes.
#' @param .leaf.shadow Color of shadow of leaf nodes.
#' @param .cex A numerical value indicating the magnification to be used for
#' plotting text.
#' @param .seg.col The color to be used for the labels of the segmentation
#' variables.
#' @param .lwd The line width, a positive number, defaulting to 1.
#' @param .show.pval Logical value indicating whether the p-values should be
#' plotted.
#' @param .pval.col The color to be used for the labels of the p-values.
#' @param .main A main title for the plot.
#' @param .cex.main The magnification to be used for the main title.
#' @param \dots Further arguments passed on to \code{\link{plot.plstree}}.
#'
#' @author Giuseppe Lamberti
#'
#' @references Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path
#' modeling segmentation. \emph{Applied Stochastic Models in Business and Industry}, \bold{32}(4), 453-468.
#' \doi{10.1002/asmb.2168}
#'
#' @references Lamberti, G. (2015). \emph{Modeling with Heterogeneity}, PhD Dissertation.
#'
#' @references Sanchez, G. (2009). \emph{PATHMOX Approach: Segmentation Trees in
#' Partial Least Squares Path Modeling}, PhD Dissertation.
#'
#' @seealso \code{\link{summary.plstree}}, \code{\link{print.plstree}}, \code{\link{pls.pathmox}},
#' \code{\link{bar_terminal}}, and \code{\link{bar_impvar}}
#'
#'@exportS3Method plot plstree
#' @examples
#' \dontrun{
#' # 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
#' )
#'
#' Visualize the tree
#' plot(csi.pathmox)
#'
#' }
#'
plot.plstree = function (x, .root.col = "#CCFFFF", .node.col = "#99CCCC", .leaf.col = "#009999",
.shadow.size = 0.003, .node.shadow = "#669999", .leaf.shadow = "#006666",
.cex = 0.7, .seg.col = "#003333", .lwd = 1, .show.pval = TRUE,
.pval.col = "#009999", .main = NULL, .cex.main = 1, ...)
{
.MOX = x$MOX
.last = nrow(.MOX)
.last.level = .MOX$depth[.last]
.num.levels = rep(1, .last.level + 1)
for (i in 1: .last.level) .num.levels[i + 1] <- 2^i
grDevices::dev.new()
graphics::par(mar = c(0.4, 0.4, 1, 1.5))
diagram::openplotmat()
.elpos = diagram::coordinates(.num.levels)
.fromto = cbind(.MOX[-1, 2], .MOX[-1, 1])
.nr = nrow(.fromto)
.arrpos = matrix(ncol = 2, nrow = .nr)
for (i in 1: .nr) .arrpos[i, ] = diagram::straightarrow(to = .elpos[.fromto[i,
2], ], from = .elpos[.fromto[i, 1], ], lwd = .lwd, arr.pos = 0.6,
arr.length = 0)
diagram::textellipse(.elpos[1, ], 0.045, 0.045, lab = c("Root", .MOX[1,
6]), box.col = .root.col, shadow.size = .shadow.size, cex = .cex,font=2)
for (i in 2: .last) {
.posi <- .MOX$node[i]
.nodlab <- c(paste("Node", .posi), .MOX$size[i])
if (.MOX$type[i] == "node") {
diagram::textellipse(.elpos[.posi, ], 0.05, 0.03, lab = .nodlab,
box.col = .node.col, shadow.col = .node.shadow,
shadow.size = .shadow.size, cex = .cex)
}
else {
diagram::textrect(.elpos[.posi, ], 0.045, 0.025, lab = .nodlab,
box.col = .leaf.col, shadow.col = .leaf.shadow,
shadow.size = .shadow.size, cex = .cex)
}
}
aux = 1
for (i in seq(1, .nr, by = 2)) {
if (i == 1)
k = 1
else k = 1.15
.x1 = (.arrpos[i, 1] + .arrpos[i + 1, 1])/2
graphics::text(.x1, k * .arrpos[i, 2], .MOX$variable[i + 1], cex = .cex,
col = .seg.col,font=2)
if (.show.pval) {
if(x$Fg.r$`Pr(>F)`[aux]<0.001){
graphics::text(.x1, k * .arrpos[i, 2], paste("p.value <0.001"), cex = 0.9 * .cex, col = .pval.col,
pos = 1)
}
else
{
graphics::text(.x1, k * .arrpos[i, 2], paste("p.value =", round(x$Fg.r$`Pr(>F)`[aux],4),
sep = ""), cex = 0.9 * .cex, col = .pval.col,
pos = 1)
}
}
aux = aux + 1
}
for (i in 1: .nr) {
.posi <- .MOX$node[i + 1]
.seg.cat = as.character(.MOX$category[i + 1])
.seg.cat = unlist(strsplit(.seg.cat, "/"))
if (.posi%%2 == 0) {
for (h in 1:length(.seg.cat)) graphics::text(.arrpos[i, 1] -
0.03, .arrpos[i, 2] + h/55, .seg.cat[h], cex = .cex)
}
else {
for (h in 1:length(.seg.cat)) graphics::text(.arrpos[i, 1] +
0.03, .arrpos[i, 2] + h/55, .seg.cat[h], cex = .cex)
}
}
if (is.null(.main)) {
graphics::text(0.5, 0.95, c("PLS-SEM PATHMOX TREE"), cex = .cex.main)
}
else {
graphics::text(0.5, 0.95, .main, cex = .cex.main)
}
}
#' @title Comparative plot for the Pathmox terminal nodes
#'
#' @description
#' \code{bar_terminal} returns the path coefficient bar plots of the Pathmox terminal
#' nodes.
#'
#' @details
#' This function aims to visualize, using bar plots, the path coefficients of
#' the dependnet latent construct associated with the terminal nodes.
#' The user indicates the dependnet latent construct they want to visualize.
#' This is done using the same label as used in the structural model definition
#' (lavaan syntax). The comparison is done by analyzing the path coefficient
#' values for each node, or the values estimated in each node for each path coefficient.
#' In the former, the plot also returns the R^2. In the latter, the bar corresponding to the
#' node with the highest path coefficient value shows in a different color.
#' By default the comparison is done by analyzing the path coefficient values for each node.
#'
#' @param x An object of the class \code{"plstree"}.
#' @param .LV A string indicating the name of the dependent latent variable. The label
#' must be the same as used to define the structural model in the (lavaan syntax).
#' @param .bycoef Logical value indicating if the comparison is done by nodes or by path
#' coefficients. By default, \code{FALSE} means that the comparison is done
#' by nodes.
#' @param .cex.names Expansion factor for axis names (bar labels).
#' @param .cex.axis Expansion factor for numeric axis labels.
#' @param .cex.main Allows fixing the size of the main. It is equal to 1 to default.
#' @param \dots Further arguments are ignored.
#'
#' @author Giuseppe Lamberti
#'
#' @references Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path
#' modeling segmentation. \emph{Applied Stochastic Models in Business and Industry}, \bold{32}(4), 453-468.
#' \doi{10.1002/asmb.2168}
#'
#' @references Lamberti, G. (2015). \emph{Modeling with Heterogeneity}, PhD Dissertation.
#'
#' @references Sanchez, G. (2009). \emph{PATHMOX Approach: Segmentation Trees in
#' Partial Least Squares Path Modeling}, PhD Dissertation.
#'
#' @seealso \code{\link{summary.plstree}}, \code{\link{print.plstree}}, \code{\link{pls.pathmox}},
#' \code{\link{plot.plstree}}, and \code{\link{bar_impvar}}
#'
#' @export
#' @examples
#' \dontrun{
#' # 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
#'
#' "
#'
#' # Run pathmox on one single variable
#' age = csibank[,2]
#'
#' # Transform age into an ordered factor
#' age = factor(age, levels = c("<=25", "26-35", "36-45", "46-55",
#' "56-65", ">=66"),ordered = T)
#'
#' csi.pathmox.age = pls.pathmox(
#' .model = CSImodel ,
#' .data = csibank,
#' .catvar= age,
#' .alpha = 0.05,
#' .deep = 1
#' )
#'
#' # Visualize the bar plot by comparing the nodes
#' bar_terminal(csi.pathmox.age, .LV = "SAT")
#'
#' # Visualize the bar plot by comparing path coefficients
#' bar_terminal(csi.pathmox.age, .LV = "SAT", .bycoef = TRUE)
#'
#' }
#'
bar_terminal = function (x,.LV,.bycoef = FALSE,.cex.names = 1,.cex.axis = 1.2,.cex.main = 1,...)
{
.nn = rownames(x$terminal_path)
.chars = paste("->",.LV,sep="")
if(.bycoef)
{
.nodes.name = colnames(.end_eq)
.chars2 = paste("R^2",.LV,sep=" ")
.rq = NA
}
else{
.end_eq = (x$terminal_paths[grepl(.chars, .nn, fixed = TRUE),])
.nodes.name = colnames(.end_eq)
.chars2 = paste("R^2",.LV,sep=" ")
.rq = (x$terminal_paths[grepl(.chars2, .nn, fixed = TRUE),])
}
.rs = c(1, 1, 1, 2, 2, 2, 2, 2, 3, 2, 3, 3, 4, 4)
.cs = c(1, 2, 3, 2, 3, 3, 4, 4, 3, 5, 4, 4, 4, 4)
.index.mat = cbind(1:14, .rs, .cs)
.lvs = nrow(.end_eq)
.colors = hcl.colors(.lvs, palette = "Teal")
.nn = ncol(.end_eq)
par(mfrow = .index.mat[.nn, 2:3])
par(mar = c(3, 3, 3, 3))
abline(h = 0)
ylim <- 1.15 * c(min(.end_eq[1, ]), max(.end_eq[1, ]))
for (.n in 1: .nn)
{
if(.bycoef){
.maxfac = 1+(.end_eq[,.n]==max(.end_eq[,.n]))
barplot(.end_eq[,.n], main = paste(.nodes.name[.n]),col=c("#99CCCC","#009999")[.maxfac],
cex.names =.cex.names, cex.axis = .cex.axis, cex.main = .cex.main, ylim=
c(0,max(.end_eq[,.n])+0.1),...)
}
else{
barplot(.end_eq[,.n], main = paste(.nodes.name[.n],"\n","R^2:",.rq[.n]),col = .colors,
cex.names = .cex.names, cex.axis = .cex.axis, cex.main = .cex.main,ylim=
c(0,max(.end_eq[,.n])+0.1),...)
}
}
}
#' @title Bar Plot of a ranking of categorical variables by importance
#'
#' @description
#' \code{"bar_impvar"} returns a bar plot to visualize the ranking
#' of variables by importance in obtaining the terminal nodes of Pathmox.
#'
#' @details
#' The importance of each variable is determined by adding the F-statistic
#' calculated for the variable in each split node of Pathmox.
#'
#' @param x An object of the class \code{"plstree"}
#' @param .cex.names Expansion factor for axis names (bar labels)
#' @param .cex.axis Expansion factor for numeric axis labels
#' @param .cex.main Allows fixing the size of the main. Equal to 1 to default
#' @param \dots Further arguments are ignored
#'
#' @author Giuseppe Lamberti
#'
#' @reference Lamberti, G., et al. (2021). University image, hard skills or soft skills:
#' Which matters most for which graduate students? \emph{Quality and Quantity}.
#' doi: \doi{10.1007/s11135-021-01149-z}
#'
#' @references Lamberti, G., Aluja, T. B., and Sanchez, G. (2016). The Pathmox approach for PLS path
#' modeling segmentation. \emph{Applied Stochastic Models in Business and Industry}, \bold{32}(4), 453-468.
#' \doi{10.1002/asmb.2168}
#'
#' @references Lamberti, G. (2015). \emph{Modeling with Heterogeneity}, PhD Dissertation.
#'
#' @references Sanchez, G. (2009). \emph{PATHMOX Approach: Segmentation Trees in
#' Partial Least Squares Path Modeling}, PhD Dissertation.
#'
#' @seealso \code{\link{summary.plstree}}, \code{\link{print.plstree}}, \code{\link{pls.pathmox}},
#' \code{\link{bar_terminal}}, and \code{\link{plot.plstree}}
#'
#' @export
#' @examples
#' \dontrun{
#' # 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$Education,
#' levels = c("Unfinished","Elementary", "Highschool",
#' "Undergrad", "Graduated"),ordered = T)
#'
#' # Run Pathmox analysis (Lamberti et al., 2016; 2017)
#' csi.pathmox = pls.pathmox(
#' .model = CSImodel ,
#' .data = csibank,
#' .catvar= CSIcatvar,
#' .alpha = 0.05,
#' .deep = 2
#' )
#'
#' bar_impvar(csi.pathmox)
#'
#' }
#'
bar_impvar = function (x,.cex.names = 1,.cex.axis = 1.2, .cex.main = 1,...)
{
grDevices::dev.new()
graphics::par(mai=c(1.4,0.82,0.82,0.42))
diagram::openplotmat()
.imp = x$var_imp[,2]/sum(x$var_imp[,2])
names(.imp) = x$var_imp[,1]
.maxfac = 1+(.imp==max(.imp))
barplot(.imp, main = "Ranking variable importance",col=c("#99CCCC","#009999")[.maxfac],
cex.names = .cex.names, cex.axis = .cex.axis, cex.main = .cex.main,
ylim = c(0,max(.imp)+0.1),...)
}
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