Package {InterconectaR}

Version:	1.0.1
Date:	2026-10-05
Title:	Psychological Network Analysis and Psychometric Insights
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Description:	Estimates psychological networks by group for research in the social sciences and complements them with psychometric modeling. It summarises centrality and bridge centrality indices, evaluates the stability of edges and centrality from bootstrap results, refines items by the stability of exploratory graph analysis (EGA), and plots networks with the variance explained of each node. It also estimates an a priori sample size for EGA through Monte Carlo simulation and includes an interactive application to plan the sample size of a network study. Methods follow Epskamp, Borsboom and Fried (2018) <doi:10.3758/s13428-017-0862-1> and Golino and Epskamp (2017) <doi:10.1371/journal.pone.0174035>.
License:	GPL (≥ 3)
URL:	https://github.com/jventural/InterconectaR
BugReports:	https://github.com/jventural/InterconectaR/issues
Imports:	bootnet, Cairo, correlation, cowplot, dplyr, EGAnet, forcats, future, future.apply, ggforce, ggplot2 (≥ 3.4.0), ggplotify, graphics, grDevices, grid, gridExtra, lavaan, magick, mgm, networktools, patchwork, png, progressr, purrr, qgraph, RColorBrewer, rlang, scales, semPlot, stats, tibble, tidyr, tidyselect, tools, utils, wesanderson
Suggests:	bslib, powerly, psychonetrics, shiny, viridis
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2026-10-05 11:15:51 UTC; PC
Author:	José Ventura-León [aut, cre]
Maintainer:	José Ventura-León <jventuraleon@gmail.com>
Repository:	CRAN
Date/Publication:	2026-10-15 17:00:17 UTC

Network Density Report

Description

Calculates and reports network density statistics.

Usage

Density_report(edge.matrix)

Arguments

Value

A character string reporting how many edges differ from zero out of the possible edges, and the resulting density as a percentage.

Examples

w <- matrix(c(0, 0.3, 0,
              0.3, 0, 0.2,
              0, 0.2, 0), nrow = 3,
            dimnames = list(c("A", "B", "C"), c("A", "B", "C")))
Density_report(w)

Bootstrap and Evaluate Network Analysis

Description

Performs bootstrap evaluation of network analysis algorithms.

Usage

boot_and_evaluate(
  data,
  true_network,
  algorithms,
  correlation_methods,
  sample_sizes = c(100, 250, 500, 1000),
  n_simulations = 100,
  seed = NULL,
  n_cores = 2
)

Arguments

Value

Data frame with performance metrics.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

true_net <- EGAnet::EGA(items, plot.EGA = FALSE)
res <- boot_and_evaluate(items, true_network = true_net,
                         algorithms = "walktrap",
                         correlation_methods = "pearson",
                         sample_sizes = c(100, 200),
                         n_simulations = 2, seed = 1, n_cores = 1)
head(res)

Centrality-SD Correlation

Description

Calculates correlation between centrality indices and standard deviations.

Usage

calculate_centrality_sd_correlation(Data, Centralitys)

Arguments

Value

A correlation object (from the correlation package) with the correlation of each of the two centrality indices with the standard deviation of the nodes.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
ct <- qgraph::centralityTable(net)
ct <- ct[ct$measure %in% c("Strength", "ExpectedInfluence"),
         c("graph", "node", "measure", "value")]
tab <- tidyr::pivot_wider(ct, names_from = "measure", values_from = "value")
calculate_centrality_sd_correlation(items, list(table = tab))

Bridge Centrality Plot

Description

Creates a comparative bridge centrality plot for two groups.

Usage

centrality_bridge_plot(
  networks_groups,
  group_names = c("Mujer", "Varón"),
  measure = "Bridge Expected Influence (1-step)",
  color_palette = c("#FF5733", "#33FFCE"),
  communities = NULL
)

Arguments

Value

A list with table (a data frame with the bridge centrality of each node in each group, raw and as z-scores) and plot (a ggplot object).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
items$sex <- rep(c("Female", "Male"), each = n / 2)

nets <- estimate_networks_by_group(items, group_var = "sex",
                                   columns = names(items)[1:8],
                                   default = "EBICglasso")
res <- centrality_bridge_plot(nets, group_names = c("Female", "Male"),
                              communities = rep(c("A", "B"), each = 4))
res$plot

Bullet Chart para Indices de Centralidad

Description

Crea un grafico tipo Bullet Chart para visualizar dos metricas de centralidad (por ejemplo, Strength y Bridge Strength) de forma compacta y comparativa.

Usage

centrality_bullet_plot(
  qgraph_obj,
  network,
  groups = NULL,
  measure0 = "Strength",
  measure1 = "Bridge Strength",
  color_bar = "#3498DB",
  color_marker = "#E74C3C",
  color_background = "#ECF0F1",
  bar_height = 0.6,
  marker_size = 4,
  use_abbrev = TRUE,
  labels = NULL,
  order_by = "measure0",
  show_values = TRUE,
  text_size = 11
)

Arguments

Value

Lista con dos elementos:

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
result <- centrality_bullet_plot(
  qgraph_obj = g,
  network = net,
  groups = rep(c("A", "B"), each = 4),
  measure0 = "Strength",
  measure1 = "Bridge Strength"
)
result$plot
result$table

Cleveland Dot Plot para Indices de Centralidad

Description

Crea un grafico tipo Cleveland Dot Plot para visualizar dos metricas de centralidad (por ejemplo, Strength y Bridge Strength). Estilo clasico y elegante para publicaciones.

Usage

centrality_cleveland_plot(
  qgraph_obj,
  network,
  groups = NULL,
  measure0 = "Strength",
  measure1 = "Bridge Strength",
  color0 = "#2C3E50",
  color1 = "#E74C3C",
  point_size = 4,
  segment_size = 0.8,
  segment_color = "grey60",
  use_abbrev = TRUE,
  labels = NULL,
  order_by = "measure0",
  show_zero_line = TRUE,
  text_size = 12
)

Arguments

Value

Lista con dos elementos:

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
result <- centrality_cleveland_plot(
  qgraph_obj = g,
  network = net,
  groups = rep(c("A", "B"), each = 4),
  measure0 = "Strength",
  measure1 = "Bridge Strength"
)
result$plot
result$table

Dual Centrality Plot

Description

Creates a plot comparing two centrality measures.

Usage

centrality_duo_plot(
  qgraph_obj,
  network,
  groups = NULL,
  measure0 = "ExpectedInfluence",
  measure1 = NULL,
  color_palette = c("#6C5CE7", "#00B894"),
  use_abbrev = TRUE,
  labels = NULL
)

Arguments

Value

A list with table (a data frame with the centrality values of each node) and plot (a ggplot object).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
res <- centrality_duo_plot(g, net,
                           groups = rep(c("A", "B"), each = 4),
                           measure1 = "Bridge Expected Influence (1-step)")
res$plot

Centrality Plot (Version 2)

Description

Creates centrality plots with optional bridge metrics.

Usage

centrality_plots2(
  qgraph_obj,
  network,
  groups = NULL,
  measure0 = "ExpectedInfluence",
  measure1 = NULL,
  color_palette = c("#FF0000", "#00A08A"),
  labels = NULL,
  legend_labels = NULL,
  use_abbrev = TRUE
)

Arguments

Value

A list with table (a data frame with the centrality values of each node) and plot (a ggplot object).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
res <- centrality_plots2(g, net,
                         groups = rep(c("A", "B"), each = 4),
                         measure1 = "Bridge Expected Influence (1-step)")
res$table
res$plot

Choose Best Method

Description

Selects the best correlation comparison method based on sample size and average inter-correlation.

Usage

choose_best_method(n, r_matrix)

Arguments

Value

A list with the best method name and details.

Examples

r <- matrix(c(1, 0.40, 0.35,
              0.40, 1, 0.30,
              0.35, 0.30, 1), nrow = 3)
choose_best_method(n = 250, r_matrix = r)

Combine Network and Centrality Graphs

Description

Combines a qgraph network plot with a centrality line plot.

Usage

combine_graphs_centrality(
  Figura1_Derecha,
  network,
  groups,
  error_Model,
  labels = NULL,
  abbreviate_labels = FALSE,
  ncol = 2,
  widths = c(0.5, 0.6),
  dpi = 600,
  legend.cex = 0.1,
  layoutScale = c(1, 1),
  vsize = 18,
  threshold = 0
)

Arguments

Value

A patchwork object with the network on the left and the centrality plot on the right.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
cent <- centrality_plots2(g, net,
                          groups = rep(c("A", "B"), each = 4),
                          measure1 = "Bridge Expected Influence (1-step)")
r2 <- mgm_error_metrics(items, type = rep("g", 8), level = rep(1, 8))$R2
combine_graphs_centrality(cent$plot, net, groups = list(A = 1:4, B = 5:8),
                          error_Model = r2, dpi = 100)

Combine Network and Centrality Graphs (Version 2)

Description

Combines a qgraph network plot with a centrality line plot.

Usage

combine_graphs_centrality2(
  Figura1_Derecha,
  network,
  groups,
  error_Model,
  labels = NULL,
  abbreviate_labels = FALSE,
  ncol = 2,
  widths = c(0.5, 0.6),
  dpi = 600,
  legend.cex = 0.1
)

Arguments

Value

A patchwork object with the network on the left and the centrality plot on the right.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
cent <- centrality_plots2(g, net,
                          groups = rep(c("A", "B"), each = 4),
                          measure1 = "Bridge Expected Influence (1-step)")
r2 <- mgm_error_metrics(items, type = rep("g", 8), level = rep(1, 8))$R2
combine_graphs_centrality2(cent$plot, net, groups = list(A = 1:4, B = 5:8),
                           error_Model = r2, dpi = 100)

Combine Group Plots

Description

Combines network, centrality, and bridge plots into a labeled panel.

Usage

combine_groupBy(
  red_group,
  plot_centralidad_group,
  bridge_plot_group = NULL,
  width_a = 8,
  width_bc = 4.5,
  show_plot = TRUE
)

Arguments

Value

A ggplot object with the network (panel A), the centrality plot (panel B) and, when supplied, the bridge centrality plot (panel C).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
r2 <- mgm_error_metrics(items, type = rep("g", 8), level = rep(1, 8))$R2
net_plot <- plot_net(net$graph, r2 = r2, groups_list = list(A = 1:4, B = 5:8))
cent <- centrality_plots2(g, net)
bridge <- centrality_cleveland_plot(g, net, groups = rep(c("A", "B"), each = 4))
combine_groupBy(net_plot, cent$plot, bridge$plot, show_plot = FALSE)

Compute Network Scores

Description

Computes network scores with stability analysis using EGA.

Usage

compute_netScores(
  item_prefixes,
  data,
  rename_dims = TRUE,
  custom_names = NULL,
  add_sums = TRUE,
  stability_threshold = 0.7,
  stability_corr = "spearman",
  stability_model = "glasso",
  stability_algorithm = "louvain",
  stability_iter = 1000,
  stability_seed_start = NULL,
  stability_type = "resampling",
  stability_ncores = 2,
  stability_max_iter = 10,
  stability_plot = TRUE,
  ega_plot = TRUE,
  verbose = TRUE
)

Arguments

Value

A list of class ega_network_scores with data_complete (the data with the network scores and sums appended), net_scores, dimension_sums, dimensions_list, dimension_names, ega_objects, scale_columns, stability_results and removed_items.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("SC", 1:4), paste0("SC", 5:8))

scores <- compute_netScores("SC", items, rename_dims = FALSE,
                            stability_corr = "pearson",
                            stability_iter = 20, stability_seed_start = 1,
                            stability_ncores = 1, stability_plot = FALSE,
                            ega_plot = FALSE)
head(scores$net_scores)

Convert EGA List to Data Frame

Description

Converts a list of EGA results into a combined data frame.

Usage

convert_EGA_list_to_df(ega_list)

Arguments

Value

A data frame with one row per EGA result: the combination name, the correlation method and algorithm parsed from it, and the metrics returned by convert_EGA_to_df.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

combos <- run_EGA_combinations(items, corr = c("pearson", "spearman"),
                               algorithm = c("louvain", "walktrap"))
convert_EGA_list_to_df(combos)

Convert EGA Result to Data Frame

Description

Extracts network metrics from a single EGA result object.

Usage

convert_EGA_to_df(ega_result)

Arguments

Value

A one-row data frame with the network model, correlation method, community detection algorithm, lambda, number of nodes and edges, density, descriptive statistics of the non-zero edge weights, number of communities and TEFI.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

ega <- EGAnet::EGA(items, plot.EGA = FALSE)
convert_EGA_to_df(ega)

Estimate Networks by Group

Description

Estimates network models separately for each group in the data.

Usage

estimate_networks_by_group(
  data,
  group_var,
  columns,
  default = "ggmModSelect",
  stepwise = TRUE,
  corMethod = "spearman",
  abbreviate_vars = FALSE,
  abbr_minlength = 3,
  labels = NULL,
  set_labels_on_graph = TRUE
)

Arguments

Value

A named list with one estimated network (a bootnetResult object) per group. Each network carries the attribute var_mapping, a tibble that maps the original variable names to the labels used.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
items$sex <- rep(c("Female", "Male"), each = n / 2)

nets <- estimate_networks_by_group(items, group_var = "sex",
                                   columns = names(items)[1:8],
                                   default = "EBICglasso")
names(nets)

Filter Correlation Stability

Description

Extracts and filters correlation stability indices from bootstrap results.

Usage

filter_correlation_stability(caseDroppingBoot)

Arguments

Value

A data frame with the columns rowname (the centrality index) and Index (its correlation stability coefficient, CS, rounded to two decimals).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
case_boot <- bootnet::bootnet(net, nBoots = 20, type = "case",
                              statistics = c("strength", "expectedInfluence"),
                              nCores = 1, verbose = FALSE)
filter_correlation_stability(case_boot)

Edge Weights Summary

Description

Computes descriptive statistics for network edge weights.

Usage

get_edge_weights_summary(network, abs_weights = TRUE, round_digits = 2)

Arguments

Value

A one-row data frame with the number of non-zero edges (N) and the mean, standard deviation, minimum and maximum of their weights.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
get_edge_weights_summary(net)

Generate Likert-Style Threshold Patterns

Description

Convenience helper to build per-item thresholds for the latent normal scale used by sample_size_EGA_montecarlo() when data_type = "ordinal". Useful for simulating realistic Likert scenarios with floor or ceiling effects rather than the unrealistic equiprobable default.

Patterns are parameterized via a Beta distribution over the latent cumulative scale: shape1 = shape2 = 1 gives equiprobable categories, shape1 < shape2 concentrates mass on lower categories (floor), shape1 > shape2 concentrates mass on upper categories (ceiling).

Usage

make_likert_thresholds(
  n_items,
  categories,
  pattern = c("equiprobable", "floor", "ceiling", "mixed", "custom"),
  shape1 = NULL,
  shape2 = NULL,
  seed = NULL
)

Arguments

Value

A list of length n_items, each element a numeric vector of categories - 1 thresholds on the standard normal scale.

Examples

# 18 items, 5 categories, all with floor effect
tau_floor <- make_likert_thresholds(18, 5, "floor")
tau_floor[[1]]

# Half floor, half ceiling, randomly interleaved
tau_mixed <- make_likert_thresholds(18, 5, "mixed", seed = 1)

# Custom skew via Beta(1.5, 4)
tau_custom <- make_likert_thresholds(18, 5, "custom",
                                      shape1 = 1.5, shape2 = 4)

MGM Error Metrics

Description

Fits a Mixed Graphical Model and extracts prediction error metrics.

Usage

mgm_error_metrics(data, type, level, k = 2)

Arguments

Value

A list with the node predictability of the model: errorCon, a data frame with the RMSE and R2 of the continuous variables; errorCat, a data frame with the accuracy (CC) and normalized accuracy (nCC) of the categorical variables; and, when all variables are of the same kind, a named vector R2 or nCC ready to be used as the pie argument of qgraph.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

errors <- mgm_error_metrics(items, type = rep("g", 8), level = rep(1, 8))
errors$errorCon

MGM Errors by Group

Description

Fits Mixed Graphical Models and extracts errors for each group.

Usage

mgm_errors_groups(data, type, level, group, columns)

Arguments

Value

A named list with one tibble per group, holding the RMSE, R2, CC and nCC of each variable (in the order of columns).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
items$sex <- rep(c("Female", "Male"), each = n / 2)

mgm_errors_groups(items, type = rep("g", 8), level = rep(1, 8),
                  group = sex, columns = names(items)[1:8])

Network Reduction

Description

Reduces redundant node pairs using PCA or best goldbricker selection.

Usage

net_reduce2(
  data,
  badpairs,
  method = c("PCA", "best_goldbricker"),
  keep_unpaired = TRUE
)

Arguments

Value

A list with data (the reduced data frame), removed_items, added_variables, kept_from_original and pair_summary (a data frame describing what was done with each redundant pair).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
# a near-duplicate of A1
items$A1_copy <- items$A1 + rnorm(n, 0, 0.2)

redundant <- networktools::goldbricker(items, threshold = 0.5)
reduced <- net_reduce2(items, redundant, method = "PCA")
reduced$pair_summary

Optimize Leiden Resolution

Description

Optimizes the resolution parameter for Leiden community detection.

Usage

optimize_leiden_resolution(
  data,
  corr = "cor_auto",
  gamma_values = c(0.01, 0.05, 0.1, 0.5, 1),
  objective_function = "CPM",
  verbose = TRUE
)

Arguments

Value

A list with best_gamma (the resolution with the lowest TEFI), max_valid_gamma (the largest resolution tested before TEFI became undefined), best_model (the EGA object for best_gamma), all_results (the EGA objects of the valid resolutions), comparison (a data frame with the TEFI and number of communities per resolution) and optimization_plot (a ggplot of TEFI against gamma).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

opt <- optimize_leiden_resolution(items, corr = "pearson",
                                  gamma_values = c(0.05, 0.1, 0.5),
                                  verbose = FALSE)
opt$comparison

Plot Average TEFI by Sample Size

Description

Plots median TEFI values across different sample sizes, algorithms, and correlation methods.

Usage

plot_avg_TEFI(results)

Arguments

Value

A ggplot object showing TEFI trends.

Examples

# A small table with the structure returned by boot_and_evaluate()
set.seed(1)
res <- expand.grid(Algorithm = c("louvain", "walktrap"),
                   Correlation_Method = c("pearson", "spearman"),
                   Sample_Size = c(100, 250, 500), Simulation = 1:5,
                   stringsAsFactors = FALSE)
res$TEFI <- rnorm(nrow(res), -5, 0.3)

plot_avg_TEFI(res)

Plot centrality measures by group

Description

Plot centrality measures by group

Usage

plot_centrality_by_group(
  networks_groups,
  replacements,
  measure_spec,
  color_palette = c("#FF5733", "#33FFCE")
)

Arguments

Value

A list with elements plot (a ggplot object) and table (a data frame).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
items$sex <- rep(c("Female", "Male"), each = n / 2)

nets <- estimate_networks_by_group(items, group_var = "sex",
                                   columns = names(items)[1:8],
                                   default = "EBICglasso")
res <- plot_centrality_by_group(nets, replacements = c("Female", "Male"),
                                measure_spec = "ExpectedInfluence")
res$plot

Plot centrality stability diagnostics

Description

Plot centrality stability diagnostics

Usage

plot_centrality_stability(
  caseDroppingBoot,
  nonParametricBoot,
  statistics = "bridgeStrength",
  labels = FALSE,
  color_palette = NULL
)

Arguments

Value

A combined patchwork plot object.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
case_boot <- bootnet::bootnet(net, nBoots = 20, type = "case",
                              statistics = c("bridgeStrength", "strength"),
                              communities = rep(c("A", "B"), each = 4),
                              nCores = 1, verbose = FALSE)
edge_boot <- bootnet::bootnet(net, nBoots = 20, nCores = 1, verbose = FALSE)
plot_centrality_stability(case_boot, edge_boot)

Plot Replication-Level Failure Decomposition Per Sample Size

Description

Stacked-bar visualization showing what fraction of Monte Carlo replications fails each constraint at every sample size. The bars decompose 1 - power_combined into mutually exclusive failure modes (in priority order): non-convergence, incorrect dimension recovery, and one bar per target metric whose threshold was not met. The complementary green segment shows the success rate (power_combined).

This is an original visualization (not present in powerly) intended to diagnose which constraint is the bottleneck at low n: e.g., is it convergence, dimension recovery, or a specific recovery target?

Usage

plot_failure_decomposition(x, order_targets = NULL, ...)

Arguments

Value

A ggplot object.

Examples

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4), within_edge = 0.20,
  sample_sizes = c(100, 200, 400), n_rep = 10,
  data_type = "continuous", seed = 1, verbose = FALSE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
plot_failure_decomposition(res)

Plot a latent network diagram (SEM path diagram)

Description

Plot a latent network diagram (SEM path diagram)

Usage

plot_latent_network_diagram(
  model_object,
  filename,
  output_path = NULL,
  width = 9.5,
  height = 6.5,
  resolution = 1500,
  node_labels = NULL,
  color_scheme = NULL,
  color_palette = "auto",
  plot_settings = NULL,
  device_settings = NULL,
  edge_settings = NULL,
  save_message = TRUE,
  show_manifest = TRUE
)

Arguments

Value

Invisibly, the path of the saved file. Called for its side effect: the diagram is written to file.path(output_path, filename).

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

fit <- lavaan::cfa("F1 =~ A1 + A2 + A3 + A4
                    F2 =~ B1 + B2 + B3 + B4", data = items)
plot_latent_network_diagram(fit, filename = "diagram.png",
                            output_path = tempdir(), resolution = 100)

Plot Latent Variable Models

Description

Creates visualizations for psychonetrics latent variable models.

Usage

plot_lvm(
  x,
  plot = c("network", "loadings", "residcors", "residpcors", "latents"),
  ask,
  rotation = promax,
  vsize = c(5, 5),
  palette = "Set1",
  ...
)

Arguments

Value

Invisible list of qgraph objects.

Examples

if (requireNamespace("psychonetrics", quietly = TRUE)) {
  set.seed(123)
  n <- 300
  f1 <- rnorm(n)
  f2 <- 0.3 * f1 + rnorm(n)
  items <- data.frame(
    sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
    sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
  )
  names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

  lambda <- matrix(0, 8, 2)
  lambda[1:4, 1] <- 1
  lambda[5:8, 2] <- 1
  mod <- psychonetrics::lvm(items, lambda = lambda, verbose = FALSE)
  mod <- psychonetrics::runmodel(mod, verbose = FALSE)
  plot_lvm(mod, plot = c("loadings", "latents"))
}

Plot a single network with R2 progress rings

Description

Creates a ggplot of a single network with nodes colored by domain, R2 progress rings, gradient-colored edges, and optional abbreviated labels with a legend mapping.

Usage

plot_net(
  mat,
  r2,
  labels = colnames(mat),
  short_labels = NULL,
  full_labels = NULL,
  groups_list = NULL,
  group_cols = NULL,
  palette = "Darjeeling1",
  node_radius = 0.2,
  node_label_size = NULL,
  ring_offset = 0.045,
  ring_stroke = 2.4,
  ring_progress_col = "black",
  edge_min = 0,
  lwd_range = c(0.4, 4.5),
  alpha_range = c(0.15, 1),
  edge_color_pos = NULL,
  edge_color_neg = NULL,
  edge_curvature = 0.2,
  layout = "spring",
  layout_repulsion = 1,
  show_edge_values = TRUE,
  edge_label_digits = 2,
  edge_label_size = 3.2,
  edge_label_offset = 0.05,
  edge_label_bg = TRUE,
  edge_label_bg_fill = "white",
  edge_label_bg_alpha = 0.9,
  show_R2_text = TRUE,
  R2_text_size = NULL,
  R2_text_color = "grey30",
  R2_digits = 2,
  R2_position = "below",
  title = NULL,
  subtitle = NULL,
  caption = "default"
)

Arguments

Value

A ggplot object.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
r2 <- mgm_error_metrics(items, type = rep("g", 8), level = rep(1, 8))$R2
plot_net(
  mat = net$graph,
  r2  = r2,
  groups_list = list(Anxiety = 1:4, Depression = 5:8),
  short_labels = names(items),
  full_labels  = paste("Item", 1:8)
)

Plot two networks side by side with R2 progress rings

Description

Creates a faceted ggplot comparing two group networks (e.g., female vs male) with nodes colored by domain, R2 progress rings, gradient-colored edges, and optional abbreviated labels with a legend mapping.

Usage

plot_net_group(
  mat1,
  mat2,
  r2_1,
  r2_2,
  group_names = c("Femenino", "Masculino"),
  labels = colnames(mat1),
  short_labels = NULL,
  full_labels = NULL,
  groups_list = NULL,
  group_cols = NULL,
  palette = "Darjeeling1",
  node_radius = 0.2,
  node_label_size = NULL,
  ring_offset = 0.045,
  ring_stroke = 2.4,
  ring_progress_col = "black",
  edge_min = 0,
  lwd_range = c(0.4, 4.5),
  alpha_range = c(0.15, 1),
  edge_color_pos = NULL,
  edge_color_neg = NULL,
  edge_curvature = 0,
  show_edge_values = TRUE,
  edge_label_digits = 2,
  edge_label_size = 3.2,
  edge_label_offset = 0.05,
  edge_label_bg = TRUE,
  edge_label_bg_fill = "white",
  edge_label_bg_alpha = 0.9,
  show_R2_text = TRUE,
  R2_text_size = NULL,
  R2_text_color = "grey30",
  R2_digits = 2,
  R2_position = "below",
  title = NULL,
  subtitle = NULL,
  caption = "default"
)

Arguments

Value

A ggplot object.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
sex <- rep(c("Female", "Male"), each = n / 2)

net_f <- bootnet::estimateNetwork(items[sex == "Female", ], default = "EBICglasso")
net_m <- bootnet::estimateNetwork(items[sex == "Male", ], default = "EBICglasso")
r2_f <- mgm_error_metrics(items[sex == "Female", ], type = rep("g", 8),
                          level = rep(1, 8))$R2
r2_m <- mgm_error_metrics(items[sex == "Male", ], type = rep("g", 8),
                          level = rep(1, 8))$R2
plot_net_group(
  mat1 = net_f$graph, mat2 = net_m$graph,
  r2_1 = r2_f, r2_2 = r2_m,
  group_names = c("Female", "Male"),
  groups_list = list(Anxiety = 1:4, Depression = 5:8)
)

Plot networks by group as a combined panel

Description

Plot networks by group as a combined panel

Usage

plot_networks_by_group(
  networks_by_group,
  width = 7,
  height = 7,
  units = "in",
  res = 300,
  groups = list(Factor1 = 1:6, Factor2 = 7:12),
  labels = NULL,
  pie = NULL,
  legend.cex = 0.5,
  GLratio = 1.5,
  ncol = NULL,
  plot_margin = 20,
  title_spacing = 6,
  show_plot = TRUE,
  layoutScale = c(1, 1),
  vsize = 12,
  esize = 20,
  node.width = 1,
  edge.labels = TRUE,
  edge.label.cex = 1.5,
  curveAll = 2,
  minimum = 0.1,
  border.width = 2.5,
  label.cex = 1,
  title.cex = 1.6,
  palette = "ggplot2",
  color = NULL
)

Arguments

Value

A combined ggplot (patchwork) object.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
items$sex <- rep(c("Female", "Male"), each = n / 2)

nets <- estimate_networks_by_group(items, group_var = "sex",
                                   columns = names(items)[1:8],
                                   default = "EBICglasso")
p <- plot_networks_by_group(nets, groups = list(A = 1:4, B = 5:8),
                            res = 100, show_plot = FALSE)
p

Plot Network Performance Metrics

Description

Creates faceted plots comparing performance metrics across algorithms and correlation methods.

Usage

plot_performance_metrics(averaged_results)

Arguments

Value

A ggplot object with performance comparison.

Examples

# A small table with the structure returned by boot_and_evaluate()
set.seed(1)
res <- expand.grid(Algorithm = c("louvain", "walktrap"),
                   Correlation_Method = c("pearson", "spearman"),
                   Sample_Size = c(100, 250, 500), Simulation = 1:5,
                   stringsAsFactors = FALSE)
k <- nrow(res)
res$sensitivity <- runif(k, 0.6, 0.9)
res$specificity <- runif(k, 0.7, 0.95)
res$precision <- runif(k, 0.6, 0.9)
res$correlation <- runif(k, 0.7, 0.95)
res$abs_cor <- runif(k, 0.7, 0.95)
res$bias <- runif(k, 0.01, 0.05)
res$TEFI <- rnorm(k, -5, 0.3)
res$FDR <- runif(k, 0.05, 0.2)

plot_performance_metrics(summary_metrics(res))

Plot the Power Curve with Bootstrap Confidence Band

Description

Visualizes the isotonic power curve produced by sample_size_EGA_montecarlo() together with a 95 percent bootstrap band, the recommended sample size and its confidence interval as a bracket on the x axis, and (optionally) the empirical validation point.

Conceptually related to the bootstrap-spline plot in the powerly package (Constantin et al., 2023), but distinct in styling, color palette, annotations (bracket on the x axis, validation marker) and underlying object (combined power, not an arbitrary statistic).

Usage

plot_power_curve_ci(
  x,
  show_validation = TRUE,
  show_observed = TRUE,
  ribbon_alpha = 0.25,
  palette = c(median = "#2A6F4D", ribbon = "#7FB99B", accent = "#C25E2A"),
  ...
)

Arguments

Value

A ggplot object.

Examples

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4), within_edge = 0.20,
  sample_sizes = c(100, 200, 400), n_rep = 10,
  data_type = "continuous", seed = 1, verbose = FALSE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
plot_power_curve_ci(res)

Plot Monte Carlo EGA Sample Size Results

Description

Creates a visual summary of the output from sample_size_EGA_montecarlo(). The plot shows selected performance metrics across candidate sample sizes, target criteria, and the recommended sample size when one is available. Optionally adds 95 percent Monte Carlo uncertainty ribbons for ARI and edge correlation.

Usage

plot_sample_size_EGA_montecarlo(
  x,
  metrics = c("p_dimensions", "median_ari", "median_edge_correlation",
    "median_sensitivity", "median_specificity", "median_precision", "median_fdr"),
  show_criteria = TRUE,
  show_recommended = TRUE,
  show_uncertainty = TRUE,
  point_size = 2.4,
  line_width = 0.8,
  ...
)

Arguments

Value

A ggplot object.

Examples

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4), within_edge = 0.20,
  sample_sizes = c(100, 200, 400), n_rep = 10,
  data_type = "continuous", seed = 1, verbose = FALSE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
plot_sample_size_EGA_montecarlo(res)

Narrative summary of a sample-size simulation

Description

Prints a one-paragraph dynamic summary of the recommended sample size together with the structural assumptions that led to it (number of items, number of dimensions, factor sizes, replications, power target, validation result and sensitivity sweep).

Usage

## S3 method for class 'sample_size_EGA_montecarlo'
print(x, digits = 3, show_plots = FALSE, ...)

Arguments

Value

Invisibly returns x.

Examples

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4), within_edge = 0.20,
  sample_sizes = c(100, 200, 400), n_rep = 10,
  data_type = "continuous", seed = 1, verbose = FALSE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
print(res)

Print method for scale_network_analysis objects

Description

Print method for scale_network_analysis objects

Usage

## S3 method for class 'scale_network_analysis'
print(x, ...)

Arguments

Value

Invisibly returns the input object.

Process LCT (Loadings Comparison Test) results

Description

Process LCT (Loadings Comparison Test) results

Usage

process_LCT(...)

Arguments

Value

A tibble summarizing the LCT results.

Examples

# Two objects with the structure returned by EGAnet::LCT()
scale_a <- list(empirical = "Factor", bootstrap = "Factor",
                proportion = c(Factor = 0.86, Network = 0.14))
scale_b <- list(empirical = "Network", bootstrap = "Network",
                proportion = c(Factor = 0.22, Network = 0.78))
process_LCT(scale_a, scale_b)

Create a combined panel with qgraph network and centrality plot

Description

Create a combined panel with qgraph network and centrality plot

Usage

qgraph_centrality_panel(
  Figura1_Derecha,
  network,
  groups,
  error_Model,
  labels = NULL,
  abbreviate_labels = FALSE,
  ncol = 2,
  widths = c(0.5, 0.6),
  dpi = 600,
  legend.cex = 0.1,
  legend_title = "Métrica",
  keep_centrality_scale = TRUE,
  layoutScale = c(0.9, 0.9),
  GLratio = 2,
  vsize = 18,
  esize = 18,
  node.width = 0.8,
  curveAll = 2,
  palette = "pastel",
  layout = "spring",
  edge.labels = TRUE,
  edge.label.cex = 1
)

Arguments

Value

A combined patchwork plot object.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
cent <- centrality_plots2(g, net,
                          groups = rep(c("A", "B"), each = 4),
                          measure1 = "Bridge Expected Influence (1-step)")
r2 <- mgm_error_metrics(items, type = rep("g", 8), level = rep(1, 8))$R2
qgraph_centrality_panel(cent$plot, net, groups = list(A = 1:4, B = 5:8),
                        error_Model = r2, dpi = 100)

Iteratively refine items by bootstrap stability

Description

Iteratively refine items by bootstrap stability

Usage

refine_items_by_stability(
  data,
  threshold = 0.8,
  corr = "spearman",
  model = "glasso",
  algorithm = "louvain",
  iter = 1000,
  seed_start = NULL,
  type = "resampling",
  ncores = 2,
  max_iter = 10,
  plot.itemStability = FALSE
)

Arguments

Value

A list containing the final bootstrap result, stability data, removed items, plots, and dimension consistency tables.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

refined <- refine_items_by_stability(items, corr = "pearson", iter = 20,
                                     seed_start = 1, ncores = 1)
refined$stability_df
refined$removed_items

Run EGA Combinations

Description

Runs EGA with multiple combinations of correlation methods and algorithms.

Usage

run_EGA_combinations(
  data,
  corr = c("cor_auto", "pearson", "spearman"),
  algorithm = c("leiden", "louvain", "walktrap"),
  leiden_args = list(objective_function = "CPM", resolution_parameter = 1)
)

Arguments

Value

A list of EGA results with class "EGA_combinations".

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

combos <- run_EGA_combinations(items, corr = c("pearson", "spearman"),
                               algorithm = c("louvain", "walktrap"))
names(combos)

Launch the NetPowerLab Shiny Application

Description

Opens an interactive application to plan the sample size of a psychological network study (Gaussian graphical model). The reference network is declared block by block, one block per instrument, and acts as the effect size of the design. The application then wraps the two simulation approaches: bootnet::netSimulator(), which reports how much of the true network is recovered at each sample size (sensitivity, specificity and the correlation between estimated and true edge weights), and powerly::powerly(), which recommends the sample size needed to reach a declared performance with a given probability. It also writes the reproducible R script and a draft of the Participants section.

Usage

run_netpowerlab(launch.browser = TRUE, port = NULL, ...)

Arguments

Details

The application needs shiny, bslib and powerly, which are suggested rather than required by InterconectaR: install them with install.packages(c("shiny", "bslib", "powerly")) the first time. Both simulations are computationally intensive; the app distributes them across cores (nCores in bootnet, cores in powerly), which turns minutes into seconds.

Value

Invisibly NULL. Called for its side effect: the running app.

References

Constantin, M. A., Schuurman, N. K., & Vermunt, J. K. (2026). A general Monte Carlo method for sample size analysis in the context of network models. Psychological Methods, 31(3), 385-405. doi:10.1037/met0000555

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212. doi:10.3758/s13428-017-0862-1

Examples

# The application only runs in an interactive session
if (interactive()) {
  # opens the app in the browser
  run_netpowerlab()

  # on a fixed port, without opening a browser window
  run_netpowerlab(launch.browser = FALSE, port = 7799)
}

Default control parameters for sample_size_EGA_montecarlo()

Description

Returns a fully populated list of advanced defaults. Pass a partial list through the control argument of sample_size_EGA_montecarlo() to override only the entries you care about.

Usage

sample_size_EGA_control(control = list())

Arguments

Details

Defaults are chosen from the EGA / network-psychometrics literature (Christensen et al., 2024; Golino et al., 2020). Each entry supplied in control replaces the default as a whole; for instance, control = list(targets = list(ari = 0.80)) leaves ARI as the only target.

Value

A named list with all advanced parameters resolved.

Examples

# Defaults
str(sample_size_EGA_control(), max.level = 1)

# Use only community recovery as the decisional target
sample_size_EGA_control(list(targets = list(ari = 0.80)))$targets

A Priori Sample Size Estimation for EGA Using Monte Carlo Simulation

Description

Estimates an a priori sample size for psychometric network designs using Monte Carlo simulation. Data are generated from a population correlation matrix or from a hypothesized partial correlation network, then EGAnet::EGA() is fitted across candidate sample sizes.

When the input is a correlation matrix, the implied partial correlation network is computed and used as the ground truth for edge-recovery metrics (since EGAnet::EGA() returns a partial correlation network).

Optionally runs in parallel using future.apply with a per-replication progress bar via progressr.

Usage

sample_size_EGA_montecarlo(
  community_sizes = NULL,
  within_edge = 0.1,
  bridges = NULL,
  community_names = NULL,
  population_network = NULL,
  communities = NULL,
  sample_sizes = c(150, 250, 400, 600, 900, 1300),
  n_rep = 500,
  network_type = c("partial", "correlation"),
  data_type = c("ordinal", "continuous"),
  power_target = 0.8,
  validation_n_rep = 0,
  sensitivity = FALSE,
  plots = FALSE,
  output_dir = NULL,
  parallel = FALSE,
  n_workers = NULL,
  seed = NULL,
  verbose = TRUE,
  control = list(),
  ...
)

Arguments

Value

A list with class sample_size_EGA_montecarlo containing the recommended sample size, summary table (with median, mean, 95 percent Monte Carlo bands and per-metric power curves), raw replication results, criteria, targets, isotonic interpolation, bootstrap distribution and CI for the recommended n, validation results (when requested), the population correlation matrix, the true partial-correlation network used as ground truth, and the expected communities.

Bootstrap interpretation

The 95 percent CI returned in recommended_n_ci (and the band available via plot_power_curve_ci()) captures Monte Carlo uncertainty of the recommended-n estimator conditional on the assumed population model and the chosen pipeline. It is a within-model uncertainty band, not a population-level inferential statement. In particular, it does NOT account for: (1) misspecification of population_network (the wrong "truth"); (2) departures from the simulated data-generating process such as real ordinal scales with floor / ceiling effects, missingness, or non-normality; or (3) differences between the chosen EGA pipeline (corr, model, algorithm) and what users will actually run on their data.

For robust reporting, complement the CI with sensitivity analyses: vary the population network (denser or sparser), edge_threshold_method, combination, true_edge_threshold (e.g. 0.01 vs 0.05 vs 0.10, usually the most influential arbitrary choice in the pipeline when the targets include edge-recovery metrics), and thresholds (use make_likert_thresholds() to build floor / ceiling patterns). The validation list provides an empirical check that the recommended n actually delivers the target power on a fresh batch of replications.

Partial-to-Sigma convention

When network_type = "partial", the implied population correlation matrix Sigma is reconstructed from the partial-correlation network P by setting the precision matrix to K = I - P (unit residual variances on the diagonal, off-diagonal -P), inverting to obtain a covariance, and rescaling to a correlation. This is one valid parameterisation but not the only one: different residual-variance choices are compatible with the same partial structure and produce different Sigma, different simulated data, and ultimately different recommended_n values. The recommendation is therefore conditional on this generative convention. Report it explicitly in the manuscript and, if the partial network admits a natural alternative scaling, run a sensitivity analysis under that alternative.

Edge-recovery metrics under network_type = "correlation"

When network_type = "correlation" the implied true partial network is computed by inverting Sigma and is typically dense. Because model = "glasso" produces sparse estimates, binary edge-recovery metrics (sensitivity, specificity, precision, fdr) become structurally biased regardless of sample size: the design itself imposes a sparsity mismatch. In this regime, prefer targets based on ari (community recovery) or edge_correlation (continuous agreement) and treat binary edge targets as untrustworthy. The function emits a warning if you combine network_type = "correlation" with binary edge metrics in targets.

Examples

# A toy run: two communities of four items, three candidate sample sizes
# and 10 replications each. Real studies need n_rep = 500 or more.
res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4),
  within_edge = 0.20,
  sample_sizes = c(100, 200, 400),
  n_rep = 10,
  data_type = "continuous",
  seed = 1,
  verbose = FALSE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
res
res$summary[, c("Sample_Size", "power_combined")]

Visualize all plots stored in a sample_size_EGA_montecarlo object

Description

Prints each plot in x$plots sequentially. Use this after calling sample_size_EGA_montecarlo(..., plots = TRUE) to inspect the simulation graphically without having to write each print().

Usage

show_plots(x, which = NULL)

Arguments

Value

Invisibly returns the list of plots.

Examples

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4), within_edge = 0.20,
  sample_sizes = c(100, 200, 400), n_rep = 10,
  data_type = "continuous", seed = 1, verbose = FALSE, plots = TRUE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
show_plots(res, which = "power_curve_ci")

One-line dynamic recommendation message

Description

Reports, as a message, a single sentence with the recommended sample size and the structural assumptions that produced it (items, communities, sizes, data type, power target, bootstrap CI when available). Use this when you want the answer fast and don't need the full print() report.

Usage

show_recommendation(x)

Arguments

Value

Invisibly returns the recommended n.

Examples

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4), within_edge = 0.20,
  sample_sizes = c(100, 200, 400), n_rep = 10,
  data_type = "continuous", seed = 1, verbose = FALSE,
  control = list(boots = 100, corr = "pearson", algorithm = "walktrap")
)
show_recommendation(res)

Print plots without the warnings of their internal layers

Description

Several plotting functions of the package tag the returned object with the class silent_gg or silent_plot. These print methods draw the plot exactly as ggplot2 or patchwork would, but silence the warnings that some layers emit while drawing (for example, rows removed outside a fixed scale).

Usage

## S3 method for class 'silent_gg'
print(x, ...)

## S3 method for class 'silent_plot'
print(x, ...)

Arguments

Value

Invisibly returns x.

Structure Groups

Description

Creates a named list of sequential integer groups.

Usage

structure_groups(names, values)

Arguments

Value

A named list of integer sequences.

Examples

# Two scales: the first four items and the next five
structure_groups(c("Anxiety", "Depression"), c(4, 5))

Summarise Case Drop Stability

Description

Summarises case-dropping bootstrap stability results from bootnet.

Usage

summarise_case_drop_stability(
  caseDroppingBoot,
  probs = c(0.025, 0.975),
  correlation_use = "pairwise.complete.obs",
  quiet = TRUE
)

Arguments

Value

A data frame with stability summary statistics.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
case_boot <- bootnet::bootnet(net, nBoots = 20, type = "case",
                              statistics = c("strength", "expectedInfluence"),
                              nCores = 1, verbose = FALSE)
summarise_case_drop_stability(case_boot)

Summarise Nonparametric Edges

Description

Summarises nonparametric bootstrap results for network edges from bootnet.

Usage

summarise_nonparametric_edges(
  nonParametricBoot,
  stat_type = "edge",
  probs = c(0.025, 0.975),
  quiet = TRUE
)

Arguments

Value

A data frame with edge bootstrap summary statistics.

Examples

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))

net <- bootnet::estimateNetwork(items, default = "EBICglasso")
edge_boot <- bootnet::bootnet(net, nBoots = 20, nCores = 1, verbose = FALSE)
summarise_nonparametric_edges(edge_boot)

Summarize Network Performance Metrics

Description

Calculates median values of network performance metrics grouped by algorithm, correlation method, and sample size.

Usage

summary_metrics(results)

Arguments

Value

A data frame with averaged metrics.

Examples

# A small table with the structure returned by boot_and_evaluate()
set.seed(1)
res <- expand.grid(Algorithm = c("louvain", "walktrap"),
                   Correlation_Method = c("pearson", "spearman"),
                   Sample_Size = c(100, 250, 500), Simulation = 1:5,
                   stringsAsFactors = FALSE)
k <- nrow(res)
res$sensitivity <- runif(k, 0.6, 0.9)
res$specificity <- runif(k, 0.7, 0.95)
res$precision <- runif(k, 0.6, 0.9)
res$correlation <- runif(k, 0.7, 0.95)
res$abs_cor <- runif(k, 0.7, 0.95)
res$bias <- runif(k, 0.01, 0.05)
res$TEFI <- rnorm(k, -5, 0.3)
res$FDR <- runif(k, 0.05, 0.2)

summary_metrics(res)