Nothing
R/itemStability.R
In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics
Defines functions
itemStability_core
get_structure
hierEGA_structure
itemStability_deprecation
plot.itemStability
ggplot2_theme_defaults
item_stability_defaults
summary.itemStability
print.itemStability
itemStability_errors
#' @title Item Stability Statistics from \code{\link[EGAnet]{bootEGA}}
#'
#' @description Based on the \code{\link[EGAnet]{bootEGA}} results, this function
#' computes and plots the number of times an variable is estimated
#' in the same dimension as originally estimated by an empirical
#' \code{\link[EGAnet]{EGA}} structure or a theoretical/input structure.
#' The output also contains each variable's replication frequency (i.e., proportion of
#' bootstraps that a variable appeared in each dimension
#'
#' @param bootega.obj A \code{\link[EGAnet]{bootEGA}} object
#'
#' @param IS.plot Boolean (length = 1).
#' Should the plot be produced for \code{item.replication}?
#' Defaults to \code{TRUE}
#'
#' @param structure Numeric (length = number of variables).
#' A theoretical or pre-defined structure.
#' Defaults to \code{NULL} or the empirical \code{\link[EGAnet]{EGA}}
#' result in the \code{bootega.obj}
#'
#' @param ... Deprecated arguments from previous versions of \code{\link[EGAnet]{itemStability}}
#'
#' @return Returns a list containing:
#'
#' \item{membership}{A list containing:
#'
#' \itemize{
#'
#' \item \code{empirical} --- A vector of the empirical memberships from the
#' empirical \code{\link[EGAnet]{EGA}} result
#'
#' \item \code{bootstrap} --- A matrix of the homogenized memberships from the replicate samples in
#' the \code{\link[EGAnet]{bootEGA}} results
#'
#' \item \code{structure} --- A vector of the structure used in the analysis. If \code{structure = NULL}, then this output
#' will be the same as \code{empirical}
#'
#' }
#'
#' }
#'
#' \item{item.stability}{A list containing:
#'
#' \itemize{
#'
#' \item \code{empirical.dimensions} --- A vector of the proportion of times each item replicated
#' within the structure defined by \code{structure}
#'
#' \item \code{all.dimensions} --- A matrix of the proportion of times each item replicated
#' in each of the \code{structure} defined dimensions
#'
#' }
#'
#' }
#'
#' \item{plot}{Plot output if \code{IS.plot = TRUE}}
#'
#' @examples
#' # Load data
#' wmt <- wmt2[,7:24]
#'
#' \dontrun{
#' # Standard EGA example
#' boot.wmt <- bootEGA(
#' data = wmt, iter = 500,
#' type = "parametric", ncores = 2
#' )}
#'
#' # Standard item stability
#' wmt.is <- itemStability(boot.wmt)
#'
#' \dontrun{
#' # EGA fit example
#' boot.wmt.fit <- bootEGA(
#' data = wmt, iter = 500,
#' EGA.type = "EGA.fit",
#' type = "parametric", ncores = 2
#' )
#'
#' # EGA fit item stability
#' wmt.is.fit <- itemStability(boot.wmt.fit)
#'
#' # Hierarchical EGA example
#' boot.wmt.hier <- bootEGA(
#' data = wmt, iter = 500,
#' EGA.type = "hierEGA",
#' type = "parametric", ncores = 2
#' )
#'
#' # Hierarchical EGA item stability
#' wmt.is.hier <- itemStability(boot.wmt.hier)
#'
#' # Random-intercept EGA example
#' boot.wmt.ri <- bootEGA(
#' data = wmt, iter = 500,
#' EGA.type = "riEGA",
#' type = "parametric", ncores = 2
#' )
#'
#' # Random-intercept EGA item stability
#' wmt.is.ri <- itemStability(boot.wmt.ri)}
#'
#' @references
#' \strong{Original implementation of bootEGA} \cr
#' Christensen, A. P., & Golino, H. (2021).
#' Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
#' \emph{Psych}, \emph{3}(3), 479-500.
#'
#' \strong{Conceptual introduction} \cr
#' Christensen, A. P., Golino, H., & Silvia, P. J. (2020).
#' A psychometric network perspective on the validity and validation of personality trait questionnaires.
#' \emph{European Journal of Personality}, \emph{34}(6), 1095-1108.
#'
#' @author Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
#'
#' @seealso \code{\link[EGAnet]{plot.EGAnet}} for plot usage in \code{\link{EGAnet}}
#'
#' @export
#'
# Item Stability function ----
# Updated 18.11.2024
itemStability <- function (bootega.obj, IS.plot = TRUE, structure = NULL, ...){
# Set up ellipse arguments
ellipse <- list(...)
# Cover legacy arguments
ellipse <- itemStability_deprecation(ellipse)
# Check for 'structure' in 'ellipse'
if("structure" %in% names(ellipse)){
structure <- ellipse$structure
}
# Check for 'IS.plot' in 'ellipse'
if("IS.plot" %in% names(ellipse)){
IS.plot <- ellipse$IS.plot
}
# Argument errors
itemStability_errors(bootega.obj, IS.plot)
# Get empirical EGA
ega_object <- get_EGA_object(bootega.obj)
# Determine if hierarchical EGA
hierarchical <- is(ega_object, "hierEGA")
# Check for hierarchical EGA
if(hierarchical){
# Check for structure
structure <- hierEGA_structure(ega_object, structure)
# Set up results
results <- list()
# Get lower results
results$lower_order <- itemStability_core(
ega_object$lower_order, structure$lower_order,
bootega.obj$lower_order$boot.wc, bootega.obj$lower_order$iter
)
# Revalue higher order memberships
ega_object$higher_order$wc <- single_revalue_memberships(
ega_object$lower_order$wc, ega_object$higher_order$wc
)
# Get higher results
results$higher_order <- itemStability_core(
ega_object$higher_order, structure$higher_order,
bootega.obj$higher_order$boot.wc, bootega.obj$higher_order$iter
)
# Set class
class(results) <- "itemStability"
}else{
# Get regular results
results <- itemStability_core(
ega_object, structure, bootega.obj$boot.wc, bootega.obj$iter
)
}
# Add methods attributes from `bootEGA` object
attr(results, "methods") <- bootega.obj[c("EGA.type", "iter", "type")]
attr(results, "color") <- unique(
plot(bootega.obj$EGA, produce = FALSE, arguments = TRUE)$ARGS$node.color
)
# Check for hierarchical
if(hierarchical){
# Get number of legend columns
legend_rows <- digits(
max(results$lower_order$membership$structure, na.rm = TRUE)
) + 1
# Set up colors
colors <- attributes(results)$color
# Get number of higher order
higher_ndim <- seq_len(unique_length(structure$higher_order))
# Get colors
## Lower order
lower_colors <- colors[-higher_ndim]
lower_colors <- lower_colors[
structure$lower_order[order(structure$lower_order)]
]
## Higher order
higher_colors <- colors[higher_ndim]
higher_colors <- higher_colors[
structure$higher_order[order(structure$higher_order)]
]
# Get lower plot
results$lower_order$plot <- plot(results$lower_order, color = lower_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows))
# Get higher plot
results$higher_order$plot <- silent_call(
plot(results$higher_order, color = higher_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows)) +
ggplot2::scale_x_discrete(limits = rev(results$lower_order$plot$data$Node))
)
# Set up clean side-by-side
if(!"nrow" %in% names(ellipse) || ellipse$nrow == 1){
# Remove y-axis title from higher order
higher_order_plot <- results$higher_order$plot +
ggplot2::theme(axis.title.y = ggplot2::element_blank())
}
# Get final plot
results$plot <- ggpubr::ggarrange(
results$lower_order$plot, higher_order_plot,
labels = c("Lower Order", "Higher Order"),
...
)
}else{
# Get plot
results$plot <- plot(results, ...)
}
# Actually send plot
if(IS.plot){
silent_plot(results$plot)
}
# Return results
return(results)
}
#' @noRd
# itemStability errors
# Updated 04.08.2023
itemStability_errors <- function(bootega.obj, IS.plot)
{
# 'bootega.obj' errors
class_error(bootega.obj, "bootEGA", "itemStability")
# 'IS.plot' errors
length_error(IS.plot, 1, "itemStability")
typeof_error(IS.plot, "logical", "itemStability")
# 'structure' errors are handled in `get_structure`
}
#' @exportS3Method
# S3 Print Method ----
# Updated 23.07.2023
print.itemStability <- function(x, ...)
{
# Get attributes
bootega_attributes <- attr(x, "methods")
# Set proper EGA type name
ega_type <- switch(
bootega_attributes$EGA.type,
"ega" = "EGA",
"ega.fit" = "EGA.fit",
"hierega" = "hierEGA",
"riega" = "riEGA"
)
# Print EGA type
cat(paste0("EGA Type: ", ega_type), "\n")
# Set up methods
cat(
paste0(
"Bootstrap Samples: ", bootega_attributes$iter,
" (", totitle(bootega_attributes$type), ")"
)
)
# Add breakspace
cat("\n\n")
# Print replications
cat("Proportion Replicated in Dimensions:\n\n")
# Branch for hierarchical EGA
if(ega_type == "hierEGA"){
# Print results
print(
t(
data.frame(
Lower = x$lower_order$item.stability$empirical.dimensions,
Higher = x$higher_order$item.stability$empirical.dimensions
)
)
)
}else{
# Print results
print(x$item.stability$empirical.dimensions)
}
}
#' @exportS3Method
# S3 Summary Method ----
# Updated 05.07.2023
summary.itemStability <- function(object, ...)
{
print(object, ...) # same as print
}
#' @noRd
# Default plotting for `itemStability` ----
# Updated 23.07.2023
item_stability_defaults <- function(organize_df, ellipse)
{
# Set up default arguments
item_stability_defaults <- list(
data = organize_df,
x = "Node", y = "Replication",
group = "Community", color = "Community",
legend.title = "Communities",
add = "segments", rotate = TRUE, dot.size = 6,
label = round(organize_df$Replication, 2),
font.label = list(
color = "black", size = 8, vjust = 0.5
),
ggtheme = ggpubr::theme_pubr()
)
# Change arguments based on used input
return(overwrite_arguments(item_stability_defaults, ellipse))
}
#' @noRd
# Default for {ggplot2} `theme` ----
# Updated 31.03.2024
ggplot2_theme_defaults <- function(organize_df, ellipse)
{
# Get dimensions of the data frame
dimensions <- dim(organize_df)
# Set size defaults
size_default <- seq.int(6, 12, 0.25) # length = 25
# Adjust label sizes based on number of nodes
number_size <- min(
which(dimensions[1] > seq.int(200, 0, length.out = 25))
)
# Adjust label sizes based on characters in item name
max_characters <- max(nvapply(organize_df$Node, nchar))
character_size <- min(
which(max_characters > seq.int(100, 0, length.out = 25))
)
# Get text size
text_size <- size_default[min(number_size, character_size)]
# Set up defaults
theme_defaults <- list(
legend.title = ggplot2::element_text(face = "bold", hjust = 1),
axis.title = ggplot2::element_text(face = "bold"),
axis.text.y = ggplot2::element_text( # size based on above
size = size_default[min(number_size, character_size)]
)
)
# Get `theme` defaults and overwrite them as necessary
theme_ARGS <- obtain_arguments(ggplot2::theme, theme_defaults)
# Overwrite with user-supplied arguments
return(overwrite_arguments(theme_ARGS, ellipse))
}
#' @exportS3Method
# S3 Plot Method ----
# Updated 16.03.2025
plot.itemStability <- function(x, ...)# plot.type = c("all", "empirical"), ...)
{
# Check for missing
# plot.type <- swiftelse(missing(plot.type), "empirical", tolower(plot.type))
# Obtain ellipse arguments
ellipse <- list(...)
# Get attributes
x_attributes <- attributes(x)
# Check for hierarchical EGA
if(
"methods" %in% names(x_attributes) &&
x_attributes$methods$EGA.type == "hierega"
){
# Get number of legend columns
legend_rows <- digits(
max(x$lower_order$membership$structure, na.rm = TRUE)
) + 1
# Set up colors
colors <- attributes(x)$color
# Get number of higher order
higher_ndim <- seq_len(unique_length(x$higher_order$membership$structure))
# Get colors
## Lower order
lower_colors <- colors[-higher_ndim]
lower_colors <- lower_colors[
x$lower_order$membership$structure[order(x$lower_order$membership$structure)]
]
## Higher order
higher_colors <- colors[higher_ndim]
higher_colors <- higher_colors[
x$higher_order$membership$structure[order(x$higher_order$membership$structure)]
]
# Get lower plot
lower_order_plot <- plot(x$lower_order, color = lower_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows))
# Get higher plot
higher_order_plot <- silent_call(
plot(x$higher_order, color = higher_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows)) +
ggplot2::scale_x_discrete(limits = rev(lower_order_plot$data$Node))
)
# Return final plot
return(
silent_plot(
ggpubr::ggarrange(
lower_order_plot, higher_order_plot,
labels = c("Lower Order", "Higher Order"),
...
)
)
)
}else{
# # Check for print
# if(plot.type == "all"){
#
# # Obtain node names
# node_names <- names(x$membership$empirical)
#
# # Obtain dimensions
# dimensions <- dim(x$item.stability$all.dimensions)
# dim_sequence <- seq_len(dimensions[2])
#
# # Set up for heatmap
# heat_df <- data.frame(
# Node = factor(
# rep(node_names, each = dimensions[2]),
# levels = rev(
# node_names[
# order(
# x$membership$empirical,
# 1 - x$item.stability$empirical.dimensions,
# node_names
# )
# ]
# )
# ),
# Replication = as.vector(t(x$item.stability$all.dimensions)),
# Community = factor(rep(dim_sequence, dimensions[1]), levels = dim_sequence)
# )
#
# # Set alpha
# alpha <- swiftelse("alpha" %in% names(ellipse), ellipse$alpha, 0.70)
#
# # Remove "color" from `ellipse`
# if("color" %in% names(ellipse)){
# color <- ellipse$color
# ellipse <- ellipse[names(ellipse) != "color"]
# }
#
# # Set size defaults
# size_default <- seq.int(6, 12, 0.25) # length = 25
#
# # Adjust label sizes based on number of nodes
# number_size <- min(
# which(dimensions[1] > seq.int(200, 0, length.out = 25))
# )
#
# # Adjust label sizes based on characters in item name
# max_characters <- max(nvapply(as.character(heat_df$Node), nchar))
# character_size <- min(
# which(max_characters > seq.int(100, 0, length.out = 25))
# )
#
# # Get text size
# text_size <- size_default[min(number_size, character_size)]
#
# # Plot heatmap
# base_canvas <- ggplot2::ggplot(data = heat_df, ggplot2::aes(x = Community, y = Node)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Community, alpha = Replication * alpha)) +
# ggplot2::geom_text(
# label = swiftelse(heat_df$Replication == 0, "", heat_df$Replication),
# size = sqrt(number_size) - 1
# ) +
# ggplot2::scale_fill_manual(
# name = "Communities",
# values = c(
# ggplot2::alpha(attributes(x)$color, alpha = alpha),
# rep("grey", dimensions[2] - length(attributes(x)$color))
# )
# ) +
# ggplot2::scale_alpha_continuous(limits = c(0, 1), range = c(0, 1)) +
# ggplot2::guides(alpha = "none") +
# ggplot2::theme(
# panel.background = ggplot2::element_blank(),
# axis.ticks = ggplot2::element_blank(),
# axis.text = ggplot2::element_text(size = text_size),
# axis.title = ggplot2::element_text(size = text_size + 2, face = "bold"),
# axis.text.x = ggplot2::element_blank(),
# axis.title.x = ggplot2::element_blank(),
# legend.title = ggplot2::element_text(size = text_size, face = "bold", hjust = 0.5),
# legend.text = ggplot2::element_text(size = text_size - 2),
# legend.position = "top"
# )
#
# # Update colors
# if("scale_color_manual" %in% names(ellipse)){
# updated_canvas <- base_canvas +
# do.call(ggplot2::scale_color_manual, ellipse$scale_color_manual)
# }else if(exists("color", envir = environment())){
#
# # Use defined colors
# updated_canvas <- base_canvas +
# ggplot2::scale_color_manual(
# values = color,
# breaks = sort(x$membership$structure)
# )
#
# }else{
#
# # Use default of "polychrome"
# updated_canvas <- base_canvas +
# ggplot2::scale_color_manual(
# values = color_palette_EGA(
# "polychrome", x$membership$structure, sorted = TRUE
# ),
# breaks = sort(x$membership$structure)
# )
#
# }
#
# # Return plot
# return(updated_canvas)
#
# }else if(plot.type == "empirical"){
# Set up for plot
organize_df <- fast.data.frame(
c(
names(x$membership$empirical),
x$item.stability$empirical.dimensions,
x$membership$structure
), nrow = length(x$membership$structure), ncol = 3,
colnames = c("Node", "Replication", "Community")
)
# Set up data frame structure
organize_df$Replication <- as.numeric(organize_df$Replication)
organize_df$Community <- factor(
x$membership$structure,
levels = seq_len(unique_length(x$membership$structure))
)
# Remove "color" from `ellipse`
if("color" %in% names(ellipse)){
color <- ellipse$color
ellipse <- ellipse[names(ellipse) != "color"]
}
# Get base plot
base_canvas <- do.call(
ggpubr::ggdotchart,
item_stability_defaults(organize_df, ellipse)
)
# Add additional layer to plot with {ggplot2}'s `theme` updated
updated_canvas <- base_canvas +
ggplot2::ylim(c(0, 1)) + # non-negotiable
do.call( # flexibly allow user to adjust the `theme`
ggplot2::theme,
ggplot2_theme_defaults(organize_df, ellipse)
)
# Manually update alpha
updated_canvas$layers[[2]]$aes_params$alpha <- swiftelse(
"alpha" %in% names(ellipse), ellipse$alpha, 0.70
)
# Update colors
if("scale_color_manual" %in% names(ellipse)){
updated_canvas <- updated_canvas +
do.call(ggplot2::scale_color_manual, ellipse$scale_color_manual)
}else if(exists("color", envir = environment())){
# Use defined colors
updated_canvas <- updated_canvas +
ggplot2::scale_color_manual(
values = color,
breaks = sort(x$membership$structure)
)
}else{
# Use default of "polychrome"
updated_canvas <- updated_canvas +
ggplot2::scale_color_manual(
values = color_palette_EGA(
"polychrome", x$membership$structure, sorted = TRUE
),
breaks = sort(x$membership$structure)
)
}
# Lastly, get x-axis organization
if("scale_x_discrete" %in% names(ellipse)){
updated_canvas <- updated_canvas +
do.call(ggplot2::scale_x_discrete, ellipse$scale_x_discrete)
}else{ # Otherwise, apply default
updated_canvas <- updated_canvas +
ggplot2::scale_x_discrete(limits = rev(updated_canvas$data$Node))
}
# Return plot
return(updated_canvas)
# }
}
}
#' @noRd
# Argument Deprecation ----
# Updated 06.07.2023
itemStability_deprecation <- function(ellipse)
{
# Check if 'orig.wc' has been input as an argument
if("orig.wc" %in% names(ellipse)){
# Give deprecation warning
warning(
"The 'orig.wc' argument has been deprecated.\n\nInstead, use the 'structure'",
call. = FALSE
)
# Overwrite structure argument
ellipse$structure <- ellipse$orig.wc
# Remove 'orig.wc' argument
ellipse <- ellipse[names(ellipse) != "orig.wc"]
}
# Give warning for 'item.freq'
if("item.freq" %in% names(ellipse)){
warning("The 'item.freq' argument has been deprecated", call. = FALSE)
}
# Check if 'plot.item.rep' has been input as an argument
if("plot.item.rep" %in% names(ellipse)){
# Give deprecation warning
warning(
paste(
"The 'plot.item.rep' argument has been deprecated.\n\nInstead use: IS.plot =",
ellipse$plot.item.rep
), call. = FALSE
)
# Handle the plot appropriately
ellipse$IS.plot <- ellipse$plot.item.rep
}
# Return ellipse arguments
return(ellipse)
}
#' @noRd
# `hierEGA` check for structure input ----
# Updated 13.08.2023
hierEGA_structure <- function(ega_object, structure)
{
# Return NULL if NULL
if(is.null(structure)){
return(
list(
lower_order = ega_object$lower_order$wc,
higher_order = single_revalue_memberships(
ega_object$lower_order$wc, ega_object$higher_order$wc
)
)
)
}
# Get names of structure
structure_names <- names(structure)
# Not NULL, proceed with lower order
if(any(structure_names %in% c("lower_order", "higher_order"))){
# Set up result as NULL
result <- list(
lower_order = NULL,
higher_order = NULL
)
# First, check for lower order
if("lower_order" %in% structure_names){
# Perform checks
length_error(structure$lower_order, length(ega_object$lower_order$wc), "hierEGA_structure")
# If no error, then ensure names
names(structure$lower_order) <- names(ega_object$lower_order$wc)
# Send into result
result$lower_order <- structure$lower_order
}
# Then, check for higher order
if("higher_order" %in% structure_names){
# Get higher order length
higher_order_length <- length(ega_object$higher_order$wc)
# Check for lower order structure
if(!is.null(result$lower_order)){
lower_order_length <- unique_length(structure$lower_order)
}
# Perform checks
length_error(
structure$higher_order, c( # could be length of higher or lower order
lower_order_length,
higher_order_length,
length(ega_object$lower_order$wc)
), "hierEGA_structure"
)
# Check for higher order length
if(
length(structure$higher_order) %in%
c(lower_order_length, higher_order_length)
){
# Needs to be revalued
structure$higher_order <- single_revalue_memberships(
swiftelse( # If NULL, then base on empirical lower order result
is.null(result$lower_order),
ega_object$lower_order$wc,
result$lower_order
),
structure$higher_order
)
}
# If no error, then ensure names
names(structure$higher_order) <- names(ega_object$lower_order$wc)
# Send into result
result$higher_order <- structure$higher_order
}
# Return result
return(result)
}else{ # Send NULLs and warning
# Send warning
warning(
paste(
"Input to 'structure' was provided but did not match expected input.",
"For `hierEGA`, 'structure' should be a list with elements \"lower_order\",",
"\"higher_order\", or both.\n\nUsing default empirical EGA structure instead"
),
call. = FALSE
)
# Send back NULLs
return(
list(
lower_order = NULL,
higher_order = NULL
)
)
}
}
#' @noRd
# Get structure with error catching ----
# Updated 13.08.2023
get_structure <- function(bootega_wc, structure)
{
# Update target structure
if(is.null(structure)){
# No structure provided, then use empirical EGA
structure <- bootega_wc
}else{ # User provided structure... make sure it works
# Object type error
object_error(structure, c("vector", "factor", "matrix", "data.frame"), "get_structure")
# Get object type
object_type <- get_object_type(structure)
# Get length of bootstrap (empirical) membership
wc_length <- length(bootega_wc)
# Make adjustment for matrix or data frame
if(object_type %in% c("matrix", "data.frame")){
structure <- force_vector(structure)
}
# Make sure length is the same as bootstrap (empirical) membership
length_error(structure, wc_length, "get_structure")
# Finally, force values to be numeric
structure <- force_numeric(structure)
}
# Check that structure isn't missing completely
if(all(is.na(structure))){
stop(
paste0(
"The 'structure' provided contains all missing values. Check the empirical structure. If you did not provide an empirical structure, then check your `bootEGA` output:`your_output$EGA$wc`",
"\n\nIf all memberships are `NA`, then your network may be empty or different settings need to be applied in the community detection algorithm of `bootEGA`"
),
call. = FALSE
)
}
# Return the structure
return(structure)
}
#' @noRd
# Item stability core ----
# Main function -- separated to handle `hierEGA`
# Updated 23.07.2023
itemStability_core <- function(ega_object, structure, bootstrap_structure, iter)
{
# Get empirical memberships
empirical_memberships <- ega_object$wc
# Get node names
node_names <- names(empirical_memberships)
# Get structure (with error catching)
structure <- get_structure(empirical_memberships, structure)
# Get maximum number of communities
maximum_communities <- max(
max(bootstrap_structure, na.rm = TRUE),
unique_length(structure)
)
# Get homogenized memberships
homogenized_memberships <- community.homogenize(
target.membership = structure,
convert.membership = bootstrap_structure
)
# Tabulate for each variable and get proportions for each community
replicate_proportions <- matrix(
nvapply( # `matrix` prevents drop to vector when all are unidimensional
as.data.frame(homogenized_memberships),
tabulate, maximum_communities,
LENGTH = maximum_communities
), ncol = maximum_communities, byrow = TRUE
) / iter
# Assign names
dimnames(replicate_proportions) <- list(
node_names, # nodes
format_integer( # communities
numbers = seq_len(maximum_communities),
places = digits(maximum_communities) - 1
)
)
# Get empirical proportions
empirical_proportions <- nvapply(
nrow_sequence(replicate_proportions),
function(row){replicate_proportions[row, structure[row]]}
)
# Ensure proper names
names(empirical_proportions) <- node_names
# Initialize results
results <- list(
membership = list(
empirical = empirical_memberships,
bootstrap = homogenized_memberships,
structure = structure
),
item.stability = list(
empirical.dimensions = empirical_proportions,
all.dimensions = replicate_proportions
)
)
# Set class
class(results) <- "itemStability"
# Return results
return(results)
}
#' @noRd
# Global variables needed for CRAN checks ----
# Updated 30.01.2025
utils::globalVariables(c("Community", "Node", "Replication"))
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Defines functions itemStability_core get_structure hierEGA_structure itemStability_deprecation plot.itemStability ggplot2_theme_defaults item_stability_defaults summary.itemStability print.itemStability itemStability_errors
#' @title Item Stability Statistics from \code{\link[EGAnet]{bootEGA}}
#'
#' @description Based on the \code{\link[EGAnet]{bootEGA}} results, this function
#' computes and plots the number of times an variable is estimated
#' in the same dimension as originally estimated by an empirical
#' \code{\link[EGAnet]{EGA}} structure or a theoretical/input structure.
#' The output also contains each variable's replication frequency (i.e., proportion of
#' bootstraps that a variable appeared in each dimension
#'
#' @param bootega.obj A \code{\link[EGAnet]{bootEGA}} object
#'
#' @param IS.plot Boolean (length = 1).
#' Should the plot be produced for \code{item.replication}?
#' Defaults to \code{TRUE}
#'
#' @param structure Numeric (length = number of variables).
#' A theoretical or pre-defined structure.
#' Defaults to \code{NULL} or the empirical \code{\link[EGAnet]{EGA}}
#' result in the \code{bootega.obj}
#'
#' @param ... Deprecated arguments from previous versions of \code{\link[EGAnet]{itemStability}}
#'
#' @return Returns a list containing:
#'
#' \item{membership}{A list containing:
#'
#' \itemize{
#'
#' \item \code{empirical} --- A vector of the empirical memberships from the
#' empirical \code{\link[EGAnet]{EGA}} result
#'
#' \item \code{bootstrap} --- A matrix of the homogenized memberships from the replicate samples in
#' the \code{\link[EGAnet]{bootEGA}} results
#'
#' \item \code{structure} --- A vector of the structure used in the analysis. If \code{structure = NULL}, then this output
#' will be the same as \code{empirical}
#'
#' }
#'
#' }
#'
#' \item{item.stability}{A list containing:
#'
#' \itemize{
#'
#' \item \code{empirical.dimensions} --- A vector of the proportion of times each item replicated
#' within the structure defined by \code{structure}
#'
#' \item \code{all.dimensions} --- A matrix of the proportion of times each item replicated
#' in each of the \code{structure} defined dimensions
#'
#' }
#'
#' }
#'
#' \item{plot}{Plot output if \code{IS.plot = TRUE}}
#'
#' @examples
#' # Load data
#' wmt <- wmt2[,7:24]
#'
#' \dontrun{
#' # Standard EGA example
#' boot.wmt <- bootEGA(
#' data = wmt, iter = 500,
#' type = "parametric", ncores = 2
#' )}
#'
#' # Standard item stability
#' wmt.is <- itemStability(boot.wmt)
#'
#' \dontrun{
#' # EGA fit example
#' boot.wmt.fit <- bootEGA(
#' data = wmt, iter = 500,
#' EGA.type = "EGA.fit",
#' type = "parametric", ncores = 2
#' )
#'
#' # EGA fit item stability
#' wmt.is.fit <- itemStability(boot.wmt.fit)
#'
#' # Hierarchical EGA example
#' boot.wmt.hier <- bootEGA(
#' data = wmt, iter = 500,
#' EGA.type = "hierEGA",
#' type = "parametric", ncores = 2
#' )
#'
#' # Hierarchical EGA item stability
#' wmt.is.hier <- itemStability(boot.wmt.hier)
#'
#' # Random-intercept EGA example
#' boot.wmt.ri <- bootEGA(
#' data = wmt, iter = 500,
#' EGA.type = "riEGA",
#' type = "parametric", ncores = 2
#' )
#'
#' # Random-intercept EGA item stability
#' wmt.is.ri <- itemStability(boot.wmt.ri)}
#'
#' @references
#' \strong{Original implementation of bootEGA} \cr
#' Christensen, A. P., & Golino, H. (2021).
#' Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
#' \emph{Psych}, \emph{3}(3), 479-500.
#'
#' \strong{Conceptual introduction} \cr
#' Christensen, A. P., Golino, H., & Silvia, P. J. (2020).
#' A psychometric network perspective on the validity and validation of personality trait questionnaires.
#' \emph{European Journal of Personality}, \emph{34}(6), 1095-1108.
#'
#' @author Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
#'
#' @seealso \code{\link[EGAnet]{plot.EGAnet}} for plot usage in \code{\link{EGAnet}}
#'
#' @export
#'
# Item Stability function ----
# Updated 18.11.2024
itemStability <- function (bootega.obj, IS.plot = TRUE, structure = NULL, ...){
# Set up ellipse arguments
ellipse <- list(...)
# Cover legacy arguments
ellipse <- itemStability_deprecation(ellipse)
# Check for 'structure' in 'ellipse'
if("structure" %in% names(ellipse)){
structure <- ellipse$structure
}
# Check for 'IS.plot' in 'ellipse'
if("IS.plot" %in% names(ellipse)){
IS.plot <- ellipse$IS.plot
}
# Argument errors
itemStability_errors(bootega.obj, IS.plot)
# Get empirical EGA
ega_object <- get_EGA_object(bootega.obj)
# Determine if hierarchical EGA
hierarchical <- is(ega_object, "hierEGA")
# Check for hierarchical EGA
if(hierarchical){
# Check for structure
structure <- hierEGA_structure(ega_object, structure)
# Set up results
results <- list()
# Get lower results
results$lower_order <- itemStability_core(
ega_object$lower_order, structure$lower_order,
bootega.obj$lower_order$boot.wc, bootega.obj$lower_order$iter
)
# Revalue higher order memberships
ega_object$higher_order$wc <- single_revalue_memberships(
ega_object$lower_order$wc, ega_object$higher_order$wc
)
# Get higher results
results$higher_order <- itemStability_core(
ega_object$higher_order, structure$higher_order,
bootega.obj$higher_order$boot.wc, bootega.obj$higher_order$iter
)
# Set class
class(results) <- "itemStability"
}else{
# Get regular results
results <- itemStability_core(
ega_object, structure, bootega.obj$boot.wc, bootega.obj$iter
)
}
# Add methods attributes from `bootEGA` object
attr(results, "methods") <- bootega.obj[c("EGA.type", "iter", "type")]
attr(results, "color") <- unique(
plot(bootega.obj$EGA, produce = FALSE, arguments = TRUE)$ARGS$node.color
)
# Check for hierarchical
if(hierarchical){
# Get number of legend columns
legend_rows <- digits(
max(results$lower_order$membership$structure, na.rm = TRUE)
) + 1
# Set up colors
colors <- attributes(results)$color
# Get number of higher order
higher_ndim <- seq_len(unique_length(structure$higher_order))
# Get colors
## Lower order
lower_colors <- colors[-higher_ndim]
lower_colors <- lower_colors[
structure$lower_order[order(structure$lower_order)]
]
## Higher order
higher_colors <- colors[higher_ndim]
higher_colors <- higher_colors[
structure$higher_order[order(structure$higher_order)]
]
# Get lower plot
results$lower_order$plot <- plot(results$lower_order, color = lower_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows))
# Get higher plot
results$higher_order$plot <- silent_call(
plot(results$higher_order, color = higher_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows)) +
ggplot2::scale_x_discrete(limits = rev(results$lower_order$plot$data$Node))
)
# Set up clean side-by-side
if(!"nrow" %in% names(ellipse) || ellipse$nrow == 1){
# Remove y-axis title from higher order
higher_order_plot <- results$higher_order$plot +
ggplot2::theme(axis.title.y = ggplot2::element_blank())
}
# Get final plot
results$plot <- ggpubr::ggarrange(
results$lower_order$plot, higher_order_plot,
labels = c("Lower Order", "Higher Order"),
...
)
}else{
# Get plot
results$plot <- plot(results, ...)
}
# Actually send plot
if(IS.plot){
silent_plot(results$plot)
}
# Return results
return(results)
}
#' @noRd
# itemStability errors
# Updated 04.08.2023
itemStability_errors <- function(bootega.obj, IS.plot)
{
# 'bootega.obj' errors
class_error(bootega.obj, "bootEGA", "itemStability")
# 'IS.plot' errors
length_error(IS.plot, 1, "itemStability")
typeof_error(IS.plot, "logical", "itemStability")
# 'structure' errors are handled in `get_structure`
}
#' @exportS3Method
# S3 Print Method ----
# Updated 23.07.2023
print.itemStability <- function(x, ...)
{
# Get attributes
bootega_attributes <- attr(x, "methods")
# Set proper EGA type name
ega_type <- switch(
bootega_attributes$EGA.type,
"ega" = "EGA",
"ega.fit" = "EGA.fit",
"hierega" = "hierEGA",
"riega" = "riEGA"
)
# Print EGA type
cat(paste0("EGA Type: ", ega_type), "\n")
# Set up methods
cat(
paste0(
"Bootstrap Samples: ", bootega_attributes$iter,
" (", totitle(bootega_attributes$type), ")"
)
)
# Add breakspace
cat("\n\n")
# Print replications
cat("Proportion Replicated in Dimensions:\n\n")
# Branch for hierarchical EGA
if(ega_type == "hierEGA"){
# Print results
print(
t(
data.frame(
Lower = x$lower_order$item.stability$empirical.dimensions,
Higher = x$higher_order$item.stability$empirical.dimensions
)
)
)
}else{
# Print results
print(x$item.stability$empirical.dimensions)
}
}
#' @exportS3Method
# S3 Summary Method ----
# Updated 05.07.2023
summary.itemStability <- function(object, ...)
{
print(object, ...) # same as print
}
#' @noRd
# Default plotting for `itemStability` ----
# Updated 23.07.2023
item_stability_defaults <- function(organize_df, ellipse)
{
# Set up default arguments
item_stability_defaults <- list(
data = organize_df,
x = "Node", y = "Replication",
group = "Community", color = "Community",
legend.title = "Communities",
add = "segments", rotate = TRUE, dot.size = 6,
label = round(organize_df$Replication, 2),
font.label = list(
color = "black", size = 8, vjust = 0.5
),
ggtheme = ggpubr::theme_pubr()
)
# Change arguments based on used input
return(overwrite_arguments(item_stability_defaults, ellipse))
}
#' @noRd
# Default for {ggplot2} `theme` ----
# Updated 31.03.2024
ggplot2_theme_defaults <- function(organize_df, ellipse)
{
# Get dimensions of the data frame
dimensions <- dim(organize_df)
# Set size defaults
size_default <- seq.int(6, 12, 0.25) # length = 25
# Adjust label sizes based on number of nodes
number_size <- min(
which(dimensions[1] > seq.int(200, 0, length.out = 25))
)
# Adjust label sizes based on characters in item name
max_characters <- max(nvapply(organize_df$Node, nchar))
character_size <- min(
which(max_characters > seq.int(100, 0, length.out = 25))
)
# Get text size
text_size <- size_default[min(number_size, character_size)]
# Set up defaults
theme_defaults <- list(
legend.title = ggplot2::element_text(face = "bold", hjust = 1),
axis.title = ggplot2::element_text(face = "bold"),
axis.text.y = ggplot2::element_text( # size based on above
size = size_default[min(number_size, character_size)]
)
)
# Get `theme` defaults and overwrite them as necessary
theme_ARGS <- obtain_arguments(ggplot2::theme, theme_defaults)
# Overwrite with user-supplied arguments
return(overwrite_arguments(theme_ARGS, ellipse))
}
#' @exportS3Method
# S3 Plot Method ----
# Updated 16.03.2025
plot.itemStability <- function(x, ...)# plot.type = c("all", "empirical"), ...)
{
# Check for missing
# plot.type <- swiftelse(missing(plot.type), "empirical", tolower(plot.type))
# Obtain ellipse arguments
ellipse <- list(...)
# Get attributes
x_attributes <- attributes(x)
# Check for hierarchical EGA
if(
"methods" %in% names(x_attributes) &&
x_attributes$methods$EGA.type == "hierega"
){
# Get number of legend columns
legend_rows <- digits(
max(x$lower_order$membership$structure, na.rm = TRUE)
) + 1
# Set up colors
colors <- attributes(x)$color
# Get number of higher order
higher_ndim <- seq_len(unique_length(x$higher_order$membership$structure))
# Get colors
## Lower order
lower_colors <- colors[-higher_ndim]
lower_colors <- lower_colors[
x$lower_order$membership$structure[order(x$lower_order$membership$structure)]
]
## Higher order
higher_colors <- colors[higher_ndim]
higher_colors <- higher_colors[
x$higher_order$membership$structure[order(x$higher_order$membership$structure)]
]
# Get lower plot
lower_order_plot <- plot(x$lower_order, color = lower_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows))
# Get higher plot
higher_order_plot <- silent_call(
plot(x$higher_order, color = higher_colors, ...) +
ggplot2::guides(color = ggplot2::guide_legend(nrow = legend_rows)) +
ggplot2::scale_x_discrete(limits = rev(lower_order_plot$data$Node))
)
# Return final plot
return(
silent_plot(
ggpubr::ggarrange(
lower_order_plot, higher_order_plot,
labels = c("Lower Order", "Higher Order"),
...
)
)
)
}else{
# # Check for print
# if(plot.type == "all"){
#
# # Obtain node names
# node_names <- names(x$membership$empirical)
#
# # Obtain dimensions
# dimensions <- dim(x$item.stability$all.dimensions)
# dim_sequence <- seq_len(dimensions[2])
#
# # Set up for heatmap
# heat_df <- data.frame(
# Node = factor(
# rep(node_names, each = dimensions[2]),
# levels = rev(
# node_names[
# order(
# x$membership$empirical,
# 1 - x$item.stability$empirical.dimensions,
# node_names
# )
# ]
# )
# ),
# Replication = as.vector(t(x$item.stability$all.dimensions)),
# Community = factor(rep(dim_sequence, dimensions[1]), levels = dim_sequence)
# )
#
# # Set alpha
# alpha <- swiftelse("alpha" %in% names(ellipse), ellipse$alpha, 0.70)
#
# # Remove "color" from `ellipse`
# if("color" %in% names(ellipse)){
# color <- ellipse$color
# ellipse <- ellipse[names(ellipse) != "color"]
# }
#
# # Set size defaults
# size_default <- seq.int(6, 12, 0.25) # length = 25
#
# # Adjust label sizes based on number of nodes
# number_size <- min(
# which(dimensions[1] > seq.int(200, 0, length.out = 25))
# )
#
# # Adjust label sizes based on characters in item name
# max_characters <- max(nvapply(as.character(heat_df$Node), nchar))
# character_size <- min(
# which(max_characters > seq.int(100, 0, length.out = 25))
# )
#
# # Get text size
# text_size <- size_default[min(number_size, character_size)]
#
# # Plot heatmap
# base_canvas <- ggplot2::ggplot(data = heat_df, ggplot2::aes(x = Community, y = Node)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Community, alpha = Replication * alpha)) +
# ggplot2::geom_text(
# label = swiftelse(heat_df$Replication == 0, "", heat_df$Replication),
# size = sqrt(number_size) - 1
# ) +
# ggplot2::scale_fill_manual(
# name = "Communities",
# values = c(
# ggplot2::alpha(attributes(x)$color, alpha = alpha),
# rep("grey", dimensions[2] - length(attributes(x)$color))
# )
# ) +
# ggplot2::scale_alpha_continuous(limits = c(0, 1), range = c(0, 1)) +
# ggplot2::guides(alpha = "none") +
# ggplot2::theme(
# panel.background = ggplot2::element_blank(),
# axis.ticks = ggplot2::element_blank(),
# axis.text = ggplot2::element_text(size = text_size),
# axis.title = ggplot2::element_text(size = text_size + 2, face = "bold"),
# axis.text.x = ggplot2::element_blank(),
# axis.title.x = ggplot2::element_blank(),
# legend.title = ggplot2::element_text(size = text_size, face = "bold", hjust = 0.5),
# legend.text = ggplot2::element_text(size = text_size - 2),
# legend.position = "top"
# )
#
# # Update colors
# if("scale_color_manual" %in% names(ellipse)){
# updated_canvas <- base_canvas +
# do.call(ggplot2::scale_color_manual, ellipse$scale_color_manual)
# }else if(exists("color", envir = environment())){
#
# # Use defined colors
# updated_canvas <- base_canvas +
# ggplot2::scale_color_manual(
# values = color,
# breaks = sort(x$membership$structure)
# )
#
# }else{
#
# # Use default of "polychrome"
# updated_canvas <- base_canvas +
# ggplot2::scale_color_manual(
# values = color_palette_EGA(
# "polychrome", x$membership$structure, sorted = TRUE
# ),
# breaks = sort(x$membership$structure)
# )
#
# }
#
# # Return plot
# return(updated_canvas)
#
# }else if(plot.type == "empirical"){
# Set up for plot
organize_df <- fast.data.frame(
c(
names(x$membership$empirical),
x$item.stability$empirical.dimensions,
x$membership$structure
), nrow = length(x$membership$structure), ncol = 3,
colnames = c("Node", "Replication", "Community")
)
# Set up data frame structure
organize_df$Replication <- as.numeric(organize_df$Replication)
organize_df$Community <- factor(
x$membership$structure,
levels = seq_len(unique_length(x$membership$structure))
)
# Remove "color" from `ellipse`
if("color" %in% names(ellipse)){
color <- ellipse$color
ellipse <- ellipse[names(ellipse) != "color"]
}
# Get base plot
base_canvas <- do.call(
ggpubr::ggdotchart,
item_stability_defaults(organize_df, ellipse)
)
# Add additional layer to plot with {ggplot2}'s `theme` updated
updated_canvas <- base_canvas +
ggplot2::ylim(c(0, 1)) + # non-negotiable
do.call( # flexibly allow user to adjust the `theme`
ggplot2::theme,
ggplot2_theme_defaults(organize_df, ellipse)
)
# Manually update alpha
updated_canvas$layers[[2]]$aes_params$alpha <- swiftelse(
"alpha" %in% names(ellipse), ellipse$alpha, 0.70
)
# Update colors
if("scale_color_manual" %in% names(ellipse)){
updated_canvas <- updated_canvas +
do.call(ggplot2::scale_color_manual, ellipse$scale_color_manual)
}else if(exists("color", envir = environment())){
# Use defined colors
updated_canvas <- updated_canvas +
ggplot2::scale_color_manual(
values = color,
breaks = sort(x$membership$structure)
)
}else{
# Use default of "polychrome"
updated_canvas <- updated_canvas +
ggplot2::scale_color_manual(
values = color_palette_EGA(
"polychrome", x$membership$structure, sorted = TRUE
),
breaks = sort(x$membership$structure)
)
}
# Lastly, get x-axis organization
if("scale_x_discrete" %in% names(ellipse)){
updated_canvas <- updated_canvas +
do.call(ggplot2::scale_x_discrete, ellipse$scale_x_discrete)
}else{ # Otherwise, apply default
updated_canvas <- updated_canvas +
ggplot2::scale_x_discrete(limits = rev(updated_canvas$data$Node))
}
# Return plot
return(updated_canvas)
# }
}
}
#' @noRd
# Argument Deprecation ----
# Updated 06.07.2023
itemStability_deprecation <- function(ellipse)
{
# Check if 'orig.wc' has been input as an argument
if("orig.wc" %in% names(ellipse)){
# Give deprecation warning
warning(
"The 'orig.wc' argument has been deprecated.\n\nInstead, use the 'structure'",
call. = FALSE
)
# Overwrite structure argument
ellipse$structure <- ellipse$orig.wc
# Remove 'orig.wc' argument
ellipse <- ellipse[names(ellipse) != "orig.wc"]
}
# Give warning for 'item.freq'
if("item.freq" %in% names(ellipse)){
warning("The 'item.freq' argument has been deprecated", call. = FALSE)
}
# Check if 'plot.item.rep' has been input as an argument
if("plot.item.rep" %in% names(ellipse)){
# Give deprecation warning
warning(
paste(
"The 'plot.item.rep' argument has been deprecated.\n\nInstead use: IS.plot =",
ellipse$plot.item.rep
), call. = FALSE
)
# Handle the plot appropriately
ellipse$IS.plot <- ellipse$plot.item.rep
}
# Return ellipse arguments
return(ellipse)
}
#' @noRd
# `hierEGA` check for structure input ----
# Updated 13.08.2023
hierEGA_structure <- function(ega_object, structure)
{
# Return NULL if NULL
if(is.null(structure)){
return(
list(
lower_order = ega_object$lower_order$wc,
higher_order = single_revalue_memberships(
ega_object$lower_order$wc, ega_object$higher_order$wc
)
)
)
}
# Get names of structure
structure_names <- names(structure)
# Not NULL, proceed with lower order
if(any(structure_names %in% c("lower_order", "higher_order"))){
# Set up result as NULL
result <- list(
lower_order = NULL,
higher_order = NULL
)
# First, check for lower order
if("lower_order" %in% structure_names){
# Perform checks
length_error(structure$lower_order, length(ega_object$lower_order$wc), "hierEGA_structure")
# If no error, then ensure names
names(structure$lower_order) <- names(ega_object$lower_order$wc)
# Send into result
result$lower_order <- structure$lower_order
}
# Then, check for higher order
if("higher_order" %in% structure_names){
# Get higher order length
higher_order_length <- length(ega_object$higher_order$wc)
# Check for lower order structure
if(!is.null(result$lower_order)){
lower_order_length <- unique_length(structure$lower_order)
}
# Perform checks
length_error(
structure$higher_order, c( # could be length of higher or lower order
lower_order_length,
higher_order_length,
length(ega_object$lower_order$wc)
), "hierEGA_structure"
)
# Check for higher order length
if(
length(structure$higher_order) %in%
c(lower_order_length, higher_order_length)
){
# Needs to be revalued
structure$higher_order <- single_revalue_memberships(
swiftelse( # If NULL, then base on empirical lower order result
is.null(result$lower_order),
ega_object$lower_order$wc,
result$lower_order
),
structure$higher_order
)
}
# If no error, then ensure names
names(structure$higher_order) <- names(ega_object$lower_order$wc)
# Send into result
result$higher_order <- structure$higher_order
}
# Return result
return(result)
}else{ # Send NULLs and warning
# Send warning
warning(
paste(
"Input to 'structure' was provided but did not match expected input.",
"For `hierEGA`, 'structure' should be a list with elements \"lower_order\",",
"\"higher_order\", or both.\n\nUsing default empirical EGA structure instead"
),
call. = FALSE
)
# Send back NULLs
return(
list(
lower_order = NULL,
higher_order = NULL
)
)
}
}
#' @noRd
# Get structure with error catching ----
# Updated 13.08.2023
get_structure <- function(bootega_wc, structure)
{
# Update target structure
if(is.null(structure)){
# No structure provided, then use empirical EGA
structure <- bootega_wc
}else{ # User provided structure... make sure it works
# Object type error
object_error(structure, c("vector", "factor", "matrix", "data.frame"), "get_structure")
# Get object type
object_type <- get_object_type(structure)
# Get length of bootstrap (empirical) membership
wc_length <- length(bootega_wc)
# Make adjustment for matrix or data frame
if(object_type %in% c("matrix", "data.frame")){
structure <- force_vector(structure)
}
# Make sure length is the same as bootstrap (empirical) membership
length_error(structure, wc_length, "get_structure")
# Finally, force values to be numeric
structure <- force_numeric(structure)
}
# Check that structure isn't missing completely
if(all(is.na(structure))){
stop(
paste0(
"The 'structure' provided contains all missing values. Check the empirical structure. If you did not provide an empirical structure, then check your `bootEGA` output:`your_output$EGA$wc`",
"\n\nIf all memberships are `NA`, then your network may be empty or different settings need to be applied in the community detection algorithm of `bootEGA`"
),
call. = FALSE
)
}
# Return the structure
return(structure)
}
#' @noRd
# Item stability core ----
# Main function -- separated to handle `hierEGA`
# Updated 23.07.2023
itemStability_core <- function(ega_object, structure, bootstrap_structure, iter)
{
# Get empirical memberships
empirical_memberships <- ega_object$wc
# Get node names
node_names <- names(empirical_memberships)
# Get structure (with error catching)
structure <- get_structure(empirical_memberships, structure)
# Get maximum number of communities
maximum_communities <- max(
max(bootstrap_structure, na.rm = TRUE),
unique_length(structure)
)
# Get homogenized memberships
homogenized_memberships <- community.homogenize(
target.membership = structure,
convert.membership = bootstrap_structure
)
# Tabulate for each variable and get proportions for each community
replicate_proportions <- matrix(
nvapply( # `matrix` prevents drop to vector when all are unidimensional
as.data.frame(homogenized_memberships),
tabulate, maximum_communities,
LENGTH = maximum_communities
), ncol = maximum_communities, byrow = TRUE
) / iter
# Assign names
dimnames(replicate_proportions) <- list(
node_names, # nodes
format_integer( # communities
numbers = seq_len(maximum_communities),
places = digits(maximum_communities) - 1
)
)
# Get empirical proportions
empirical_proportions <- nvapply(
nrow_sequence(replicate_proportions),
function(row){replicate_proportions[row, structure[row]]}
)
# Ensure proper names
names(empirical_proportions) <- node_names
# Initialize results
results <- list(
membership = list(
empirical = empirical_memberships,
bootstrap = homogenized_memberships,
structure = structure
),
item.stability = list(
empirical.dimensions = empirical_proportions,
all.dimensions = replicate_proportions
)
)
# Set class
class(results) <- "itemStability"
# Return results
return(results)
}
#' @noRd
# Global variables needed for CRAN checks ----
# Updated 30.01.2025
utils::globalVariables(c("Community", "Node", "Replication"))
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