Package {PsyMetricTools}

Version:	1.2.2
Title:	Psychometric and Statistical Analysis Tools
Description:	Provides tools for psychometric and statistical analysis in social sciences, including functions for data preprocessing, factor analysis, reliability testing, descriptive statistics, visualization of Likert-scale items, measurement invariance analysis, and handling of multi-class imbalance. Facilitates advanced data analysis tasks commonly encountered in psychological and educational research. Includes an updated easy_invariance() function for ordinal data following Wu and Estabrook (2016) <doi:10.1037/met0000051>.
License:	GPL (≥ 3)
URL:	https://github.com/jventural/PsyMetricTools
BugReports:	https://github.com/jventural/PsyMetricTools/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Imports:	lavaan, semTools, psych, dplyr, tidyr, ggplot2, purrr, tibble, stringr, gridExtra, gtable, grid, pbapply, tidyselect, rlang, grDevices, scales, stats, utils
Suggests:	testthat (≥ 3.0.0), EGAnet, bootnet, future, future.apply, progressr, progress, careless, qgraph, semPlot, ggridges, ggpubr, forcats, RColorBrewer, openxlsx, lavaan.mi, blavaan, HDInterval, ggplotify, patchwork, wesanderson, circlize
Config/testthat/edition:	3
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2026-07-28 17:49:55 UTC; PC
Author:	Jose Ventura-Leon [aut, cre]
Maintainer:	Jose Ventura-Leon <jventuraleon@gmail.com>
Repository:	CRAN
Date/Publication:	2026-08-06 13:10:21 UTC

PsyMetricTools: Psychometric and Statistical Analysis Tools

Description

Provides tools for psychometric and statistical analysis in social sciences, including functions for data preprocessing, factor analysis, reliability testing, descriptive statistics, visualization of Likert-scale items, measurement invariance analysis, and handling of multi-class imbalance. Facilitates advanced data analysis tasks commonly encountered in psychological and educational research. Includes an updated easy_invariance() function for ordinal data following Wu and Estabrook (2016) doi:10.1037/met0000051.

Author(s)

Maintainer: Jose Ventura-Leon jventuraleon@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/jventural/PsyMetricTools

• Report bugs at https://github.com/jventural/PsyMetricTools/issues

Modern Exploratory Factor Analysis

Description

Performs exploratory factor analysis using lavaan with rotation.

Usage

EFA_modern(
  n_factors,
  n_items,
  name_items,
  data,
  apply_threshold,
  estimator = "WLSMV",
  rotation = "oblimin",
  exclude_items = NULL
)

Arguments

Value

A list containing fit indices, specifications, interfactor correlations, and pattern matrix.

Examples

# Create sample data with 15 Likert-type items
set.seed(123)
n <- 300
data_efa <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE),
  Item7 = sample(1:5, n, replace = TRUE),
  Item8 = sample(1:5, n, replace = TRUE),
  Item9 = sample(1:5, n, replace = TRUE),
  Item10 = sample(1:5, n, replace = TRUE),
  Item11 = sample(1:5, n, replace = TRUE),
  Item12 = sample(1:5, n, replace = TRUE),
  Item13 = sample(1:5, n, replace = TRUE),
  Item14 = sample(1:5, n, replace = TRUE),
  Item15 = sample(1:5, n, replace = TRUE)
)

# Perform EFA with 3 factors using WLSMV estimator
result <- EFA_modern(
  n_factors = 3,
  n_items = 15,
  name_items = "Item",
  data = data_efa,
  apply_threshold = TRUE,
  estimator = "WLSMV",
  rotation = "oblimin"
)

# Access fit indices
result$Bondades_Original

# Access pattern matrix (factor loadings)
result$result_df

# Access interfactor correlations
result$InterFactor

# Example excluding specific items
result2 <- EFA_modern(
  n_factors = 3,
  n_items = 15,
  name_items = "Item",
  data = data_efa,
  apply_threshold = TRUE,
  estimator = "WLSMV",
  rotation = "oblimin",
  exclude_items = c("Item5", "Item10")
)

Plot EFA Factor Loadings

Description

Creates a visualization of factor loadings from EFA.

Usage

EFA_plot(specifications, item_prefix)

Arguments

Value

A ggplot object showing factor loadings.

Examples

# Example 1: Using with EFA_modern() result
set.seed(123)
n <- 300
data_efa <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE),
  Item7 = sample(1:5, n, replace = TRUE),
  Item8 = sample(1:5, n, replace = TRUE),
  Item9 = sample(1:5, n, replace = TRUE)
)

# First run EFA
efa_result <- EFA_modern(
  n_factors = 3,
  n_items = 9,
  name_items = "Item",
  data = data_efa,
  apply_threshold = TRUE
)

# Plot factor loadings using the lavaan specification
plot <- EFA_plot(
  specifications = efa_result$Specifications[[3]],
  item_prefix = "Item"
)
print(plot)

# Example 2: Using with a lavaan object directly
library(lavaan)
model <- "
  efa('efa')*f1 +
  efa('efa')*f2 +
  efa('efa')*f3 =~ Item1 + Item2 + Item3 + Item4 + Item5 +
                   Item6 + Item7 + Item8 + Item9
"
fit <- sem(model, data = data_efa, rotation = "oblimin")
EFA_plot(fit, item_prefix = "Item")

Create Lambda Matrix for CFA

Description

Creates a binary lambda matrix specifying item-factor relationships for CFA models.

Usage

Matrix_lambda(n_items, factores, n_tamanos)

Arguments

Value

A matrix with 1s indicating item-factor loadings.

Plot Likert Scale Items

Description

Creates a visualization for Likert scale items.

Usage

Plot_Likert(Data, name_items, ranges, exclude = NULL, text_size = 3)

Arguments

Value

A ggplot object.

Examples

# Create sample survey data with Likert items (1-5 scale)
set.seed(123)
n <- 200
survey_data <- data.frame(
  Q1 = sample(1:5, n, replace = TRUE),
  Q2 = sample(1:5, n, replace = TRUE),
  Q3 = sample(1:5, n, replace = TRUE),
  Q4 = sample(1:5, n, replace = TRUE),
  Q5 = sample(1:5, n, replace = TRUE),
  Q6 = sample(1:5, n, replace = TRUE)
)

# Plot all items Q1 to Q6
plot <- Plot_Likert(
  Data = survey_data,
  name_items = "Q",
  ranges = 1:6
)
print(plot)

# Plot items Q1 to Q4, excluding Q2
plot2 <- Plot_Likert(
  Data = survey_data,
  name_items = "Q",
  ranges = 1:4,
  exclude = 2,
  text_size = 4
)
print(plot2)

Plot Likert Scale Items (Diverging Stacked Bar)

Description

Creates a diverging stacked bar chart visualization for Likert scale items.

Usage

Plot_Likert2(
  data,
  prefix = "IS",
  labels = c("Not at all true", "Completely true"),
  palette = "Blues",
  text_size = 3,
  threshold = 5,
  show_text = TRUE,
  item_range = 1:21
)

Arguments

Value

A ggplot object.

Examples

# Create sample survey data with binary Likert items
set.seed(123)
n <- 200
survey_data <- data.frame(
  IS1 = sample(1:2, n, replace = TRUE),
  IS2 = sample(1:2, n, replace = TRUE),
  IS3 = sample(1:2, n, replace = TRUE),
  IS4 = sample(1:2, n, replace = TRUE),
  IS5 = sample(1:2, n, replace = TRUE)
)

# Create diverging stacked bar chart
plot <- Plot_Likert2(
  data = survey_data,
  prefix = "IS",
  labels = c("Disagree", "Agree"),
  palette = "Blues",
  text_size = 3,
  threshold = 5,
  show_text = TRUE,
  item_range = 1:5
)
print(plot)

# Example with 5-point scale
survey_data2 <- data.frame(
  Q1 = sample(1:5, n, replace = TRUE),
  Q2 = sample(1:5, n, replace = TRUE),
  Q3 = sample(1:5, n, replace = TRUE),
  Q4 = sample(1:5, n, replace = TRUE)
)

plot2 <- Plot_Likert2(
  data = survey_data2,
  prefix = "Q",
  labels = c("Strongly disagree", "Disagree", "Neutral",
             "Agree", "Strongly agree"),
  palette = "RdYlBu",
  item_range = 1:4
)
print(plot2)

Preliminary Response Frequency Table

Description

Creates a table of response frequencies for items.

Usage

Preliminar_table(data)

Arguments

Value

A tibble with response frequencies as percentages.

Examples

# Create sample Likert data (1-5 scale)
set.seed(123)
data <- data.frame(
  Item1 = sample(1:5, 100, replace = TRUE),
  Item2 = sample(1:5, 100, replace = TRUE),
  Item3 = sample(1:5, 100, replace = TRUE)
)

# Get response frequency table
freq_table <- Preliminar_table(data)
print(freq_table)
# Output shows percentage of responses for each response option (1-5)
# Items   1     2     3     4     5
# 1      20.0  18.0  22.0  21.0  19.0
# 2      19.0  21.0  20.0  22.0  18.0
# 3      21.0  19.0  18.0  20.0  22.0

Standardized Factor Solutions for EFA

Description

Extracts and formats standardized factor loadings from lavaan EFA.

Usage

Standardized_solutions(specification, name_items, apply_threshold = TRUE)

Arguments

Value

A data frame with items and factor loadings.

Examples

# First run EFA_modern to get the specification object
set.seed(123)
n <- 300
data_efa <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE),
  Item7 = sample(1:5, n, replace = TRUE),
  Item8 = sample(1:5, n, replace = TRUE),
  Item9 = sample(1:5, n, replace = TRUE)
)

# Run EFA
efa_result <- EFA_modern(
  n_factors = 3,
  n_items = 9,
  name_items = "Item",
  data = data_efa,
  apply_threshold = TRUE
)

# Extract standardized solutions from the 3-factor model
loadings <- Standardized_solutions(
  specification = efa_result$Specifications[[3]],
  name_items = "Item",
  apply_threshold = TRUE
)
print(loadings)

# Without threshold (show all loadings)
loadings_full <- Standardized_solutions(
  specification = efa_result$Specifications[[3]],
  name_items = "Item",
  apply_threshold = FALSE
)
print(loadings_full)

Standardized Factor Solutions for CFA

Description

Extracts and formats standardized factor loadings from lavaan CFA.

Usage

Standardized_solutions_cfa(specification, name_items, apply_threshold = TRUE)

Arguments

Value

A data frame with items and factor loadings.

Examples

library(lavaan)

# Create sample data
set.seed(123)
n <- 300
data_cfa <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

# Define CFA model
model <- "
  f1 =~ Item1 + Item2 + Item3
  f2 =~ Item4 + Item5 + Item6
"

# Fit the model
fit <- cfa(model, data = data_cfa, ordered = TRUE, estimator = "WLSMV")

# Extract standardized solutions with threshold
loadings <- Standardized_solutions_cfa(
  specification = fit,
  name_items = "Item",
  apply_threshold = TRUE
)
print(loadings)

# Without threshold (show all loadings)
loadings_full <- Standardized_solutions_cfa(
  specification = fit,
  name_items = "Item",
  apply_threshold = FALSE
)
print(loadings_full)

Simple Admissibility Check for CFA

Description

Checks whether a fitted CFA model has admissible solutions by detecting negative residual variances (Heywood cases) and standardized factor loadings with absolute value greater than 1.

Usage

admis_simple(fit, latents)

Arguments

Value

A character string: "OK" if admissible, "NONCONV/FAIL" if the model is NULL, or a description of the problems found.

Examples

set.seed(123)
mydata <- data.frame(
  x1 = sample(1:5, 200, replace = TRUE),
  x2 = sample(1:5, 200, replace = TRUE),
  x3 = sample(1:5, 200, replace = TRUE)
)
model <- 'F1 =~ x1 + x2 + x3'
fit <- safe_cfa(model, data = mydata, items = c("x1", "x2", "x3"))
latents <- lavaan::lavNames(fit, type = "lv")
admis_simple(fit, latents)

Group Items by Factor Loadings

Description

Groups items by their factor loadings above a threshold.

Usage

agrupar_por_factor(df, item_col = "Items", threshold = 0)

Arguments

Value

A named list with items grouped by factor.

Examples

# Create a data frame with factor loadings
loadings_df <- data.frame(
  Items = c("Item1", "Item2", "Item3", "Item4", "Item5", "Item6"),
  f1 = c(0.75, 0.68, 0.72, 0.10, 0.05, 0.08),
  f2 = c(0.08, 0.12, 0.05, 0.70, 0.65, 0.78)
)

# Group items by factor (using threshold of 0.30)
groups <- agrupar_por_factor(
  df = loadings_df,
  item_col = "Items",
  threshold = 0.30
)
print(groups)
# $f1
# [1] "Item1" "Item2" "Item3"
# $f2
# [1] "Item4" "Item5" "Item6"

Bootstrap Confirmatory Factor Analysis

Description

Performs bootstrap CFA with reliability estimation.

Usage

boot_cfa(
  new_df,
  model_string,
  item_prefix,
  seed = NULL,
  n_replications = 1000,
  ordered = TRUE,
  estimator = "WLSMV"
)

Arguments

Value

Data frame with bootstrap results including fit measures and reliability.

Examples

# Create sample data with 9 Likert-type items (3 factors, 3 items each)
set.seed(123)
n <- 300
data_cfa <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE),
  Item7 = sample(1:5, n, replace = TRUE),
  Item8 = sample(1:5, n, replace = TRUE),
  Item9 = sample(1:5, n, replace = TRUE)
)

# Define the CFA model
model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
  F3 =~ Item7 + Item8 + Item9
"

# Perform bootstrap CFA with 100 replications (use more in practice)
result <- boot_cfa(
  new_df = data_cfa,
  model_string = model,
  item_prefix = "Item",
  seed = 2023,
  n_replications = 25,
  ordered = TRUE,
  estimator = "WLSMV"
)

# Access fit measures
result$fit_measures1

# Check convergence rates
mean(result$converged1)

# Access reliability estimates (omega)
result[, c("Rel1", "Rel2", "Rel3")]

Density Plot para Bootstrap CFA

Description

Visualiza la dispersion de resultados bootstrap mediante graficos de densidad con intervalos de confianza y valores de referencia.

Usage

boot_cfa_density(
  df,
  save = FALSE,
  path = NULL,
  dpi = 600,
  exclude_indices = NULL,
  show_ci = TRUE,
  ci_level = 0.95,
  show_reference = TRUE,
  fill_color = "#3498db",
  ci_color = "#e74c3c",
  ...
)

Arguments

Value

Un objeto ggplot. La tabla de estadisticos bootstrap se adjunta como atributo "stats" y puede extraerse con attr(x, "stats").

Examples

# First run boot_cfa to get bootstrap results
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"

boot_results <- boot_cfa(
  new_df = data,
  model_string = model,
  item_prefix = "Item",
  n_replications = 25
)

# Create density plot with confidence intervals
boot_cfa_density(boot_results,
                 save = TRUE,
                 path = file.path(tempdir(), "bootstrap_density.jpg"),
                 show_ci = TRUE,
                 ci_level = 0.95,
                 show_reference = TRUE)

# Custom colors
boot_cfa_density(boot_results,
                 save = FALSE,
                 fill_color = "#2ecc71",
                 ci_color = "#e74c3c")

Bootstrap CFA Plot

Description

Creates boxplot visualizations for bootstrap CFA results.

Usage

boot_cfa_plot(
  df,
  save = FALSE,
  path = NULL,
  dpi = 600,
  omega_ymin_annot = NULL,
  omega_ymax_annot = NULL,
  comp_ymin_annot = NULL,
  comp_ymax_annot = NULL,
  abs_ymin_annot = NULL,
  abs_ymax_annot = NULL,
  palette = "grey",
  exclude_indices = NULL,
  show_tables = TRUE,
  ...
)

Arguments

Value

A combined ggplot object (invisibly).

Examples

# First run boot_cfa to get bootstrap results
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"

boot_results <- boot_cfa(
  new_df = data,
  model_string = model,
  item_prefix = "Item",
  n_replications = 25
)

# Create boxplot visualization
boot_cfa_plot(boot_results,
              save = TRUE,
              path = file.path(tempdir(), "bootstrap_cfa_results.jpg"),
              palette = "grey",
              show_tables = TRUE)

# Exclude certain indices
boot_cfa_plot(boot_results,
              save = FALSE,
              exclude_indices = c("CRMR"),
              palette = "grey")

Boot CFA Plot Enhanced (panel A enriquecido)

Description

Versión mejorada de boot_cfa_plot() que mantiene el lenguaje visual base (boxplot azul + tabla descriptiva) pero agrega capas que responden más preguntas de un vistazo:

1. Banda verde (pasa) y roja (falla) detrás del boxplot.

2. Línea de cutoff (rojo punteado) con etiqueta lateral por columna.

3. Jitter de las réplicas (azul muy claro) detrás del boxplot.

4. Mediana con valor numérico anotado al lado del boxplot.

5. Porcentaje de réplicas que cumplen el criterio (compliance %) en verde bold sobre el panel.

6. Tabla descriptiva con cabecera coloreada y bordes APA.

La función original boot_cfa_plot() permanece intacta.

Usage

boot_cfa_plot_enhanced(
  df,
  save = FALSE,
  path = NULL,
  dpi = 600,
  palette = NULL,
  accent_color = NULL,
  semantic_colors = TRUE,
  pass_palette = c("#bae4b3", "#74c476", "#238b45"),
  fail_palette = c("#fcae91", "#fb6a4a", "#cb181d"),
  pass_accent = "#00441b",
  fail_accent = "#67000d",
  compliance_threshold = 90,
  lang = c("es", "en"),
  table_style = c("iqr_min_max", "mean_sd_ci", "cv_min_max"),
  cutoffs = list(omega = 0.7, CFI = 0.95, TLI = 0.95, RMSEA = 0.08, SRMR = 0.08, CRMR =
    0.08),
  higher_better = list(omega = TRUE, CFI = TRUE, TLI = TRUE, RMSEA = FALSE, SRMR = FALSE,
    CRMR = FALSE),
  show_jitter = TRUE,
  show_compliance = TRUE,
  show_median_label = TRUE,
  show_zone_bands = TRUE,
  show_cutoff_label = TRUE,
  show_tables = TRUE,
  exclude_indices = NULL,
  ...
)

Arguments

Value

Un objeto patchwork listo para imprimirse o componer con patchwork::wrap_elements().

Examples

set.seed(123)
n <- 300
df <- as.data.frame(lapply(setNames(1:6, paste0("PHQ", 1:6)),
                           function(i) sample(1:5, n, replace = TRUE)))
m <- "F1 =~ PHQ1 + PHQ2 + PHQ3
      F2 =~ PHQ4 + PHQ5 + PHQ6"
results_boot <- boot_cfa(new_df = df, model_string = m,
                         item_prefix = "PHQ", n_replications = 30)
boot_cfa_plot_enhanced(results_boot)

Raincloud Plot para Bootstrap CFA

Description

Crea visualizaciones raincloud elegantes para resultados de bootstrap CFA.

Usage

boot_cfa_raincloud(
  df,
  save = FALSE,
  path = NULL,
  dpi = 600,
  exclude_indices = NULL,
  color_scheme = "ocean",
  show_stats = TRUE,
  theme_style = "modern",
  ...
)

Arguments

Value

Un objeto ggplot.

Examples

# First run boot_cfa to get bootstrap results
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"

boot_results <- boot_cfa(
  new_df = data,
  model_string = model,
  item_prefix = "Item",
  n_replications = 25
)

# Create raincloud plot with ocean color scheme
boot_cfa_raincloud(boot_results,
                   save = TRUE,
                   path = file.path(tempdir(), "bootstrap_raincloud.jpg"),
                   color_scheme = "ocean",
                   show_stats = TRUE,
                   theme_style = "modern")

# Dark theme with sunset colors
boot_cfa_raincloud(boot_results,
                   save = FALSE,
                   color_scheme = "sunset",
                   theme_style = "dark")

# Available color schemes: "ocean", "sunset", "forest", "lavender", "monochrome", "elegant"

Bootstrap CFA Stability Analysis

Description

Performs bootstrap CFA stability analysis across different sample sizes.

Usage

boot_cfa_stability(
  modelo,
  data,
  num_replicas,
  estimator,
  seed = NULL,
  n_cores = 2
)

Arguments

Value

Data frame with fit measures and reliability across sample sizes.

Examples

# Create sample data
set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)

# Define CFA model
model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"

# Run stability analysis (testing 90% to 30% of sample sizes)
stability_results <- boot_cfa_stability(
  modelo = model,
  data = data,
  num_replicas = 10,
  estimator = "WLSMV",
  seed = 2023,
  n_cores = 2
)

# View results - fit indices and reliability at different sample sizes
head(stability_results)

# Plot stability results
plot_cfa_stability(stability_results)

Bootstrap EFA (Exploratory Factor Analysis)

Description

Performs bootstrap resampling for Exploratory Factor Analysis using lavaan, calculating fit measures, factor loadings, and McDonald's omega reliability for each replication.

Usage

boot_efa(
  data,
  n_factors,
  n_items,
  name_items,
  exclude_items = NULL,
  rotation = "oblimin",
  estimator = "WLSMV",
  ordered = TRUE,
  apply_threshold = FALSE,
  seed = NULL,
  n_replications = 1000
)

Arguments

Value

A list containing:

• Replicaciones: Data frame with all bootstrap results

• fit_summary: Summary statistics of fit measures

• loadings_summary: Summary statistics of factor loadings

• n_converged: Number of converged models

• n_valid: Number of valid models

• parameters: List of parameters used

Examples

set.seed(123)
n <- 200
data <- as.data.frame(lapply(setNames(1:6, paste0("PBA", 1:6)),
                             function(i) sample(1:5, n, replace = TRUE)))
results <- boot_efa(
  data = data,
  n_factors = 2,
  n_items = 6,
  name_items = "PBA",
  rotation = "oblimin",
  n_replications = 5
)

Forest Plot for Bootstrap EFA Results

Description

Creates a forest plot showing factor loadings with 95 from bootstrap EFA results. Style inspired by Solomon Kurz.

Usage

boot_efa_forest_plot(
  boot_efa_results,
  save = FALSE,
  path = NULL,
  dpi = 600,
  title = "Factor Loadings with 95% CI (Bootstrap)",
  threshold_low = 0.4,
  threshold_mid = 0.7,
  ...
)

Arguments

Value

Invisibly returns a list with the plot and summary data.

Examples

set.seed(123)
n <- 200
data <- as.data.frame(lapply(setNames(1:6, paste0("PBA", 1:6)),
                             function(i) sample(1:5, n, replace = TRUE)))
results_boot_efa <- boot_efa(
  data = data, n_factors = 2, n_items = 6, name_items = "PBA",
  rotation = "oblimin", n_replications = 5
)
boot_efa_forest_plot(results_boot_efa,
                     save = TRUE,
                     path = file.path(tempdir(), "Forest_PBA.jpg"),
                     title = "Factor Loadings PBA - Bootstrap EFA")

Plot Bootstrap EFA Results

Description

Creates a three-panel plot showing omega reliability, CFI/TLI, and RMSEA/SRMR/CRMR from bootstrap EFA results.

Usage

boot_efa_plot(
  boot_efa_results,
  save = FALSE,
  path = NULL,
  dpi = 600,
  omega_ymin_annot = NULL,
  omega_ymax_annot = NULL,
  comp_ymin_annot = NULL,
  comp_ymax_annot = NULL,
  abs_ymin_annot = NULL,
  abs_ymax_annot = NULL,
  palette = "grey",
  ...
)

Arguments

Value

Invisibly returns the plot.

Examples

set.seed(123)
n <- 200
data <- as.data.frame(lapply(setNames(1:6, paste0("PBA", 1:6)),
                             function(i) sample(1:5, n, replace = TRUE)))
results_boot_efa <- boot_efa(
  data = data, n_factors = 2, n_items = 6, name_items = "PBA",
  rotation = "oblimin", n_replications = 5
)
boot_efa_plot(results_boot_efa,
              save = TRUE,
              path = file.path(tempdir(), "mi_grafico_efa.jpg"),
              omega_ymin_annot = 0.70,
              omega_ymax_annot = 0.80)

Calculate McDonald's Omega Coefficient

Description

Calculates McDonald's omega reliability coefficient from factor loadings.

Usage

calcula_omega_mcdonald(
  loadings_df,
  groups = NULL,
  item_col = NULL,
  method = c("comunalidad", "sum_loadings")
)

Arguments

Value

A list with omega values and item statistics.

Examples

# Create a sample loadings data frame (from EFA or CFA)
loadings_df <- data.frame(
  Items = c("Item1", "Item2", "Item3", "Item4", "Item5", "Item6"),
  f1 = c(0.75, 0.68, 0.72, 0.10, 0.05, 0.08),
  f2 = c(0.08, 0.12, 0.05, 0.70, 0.65, 0.78)
)

# Calculate omega for all items
result <- calcula_omega_mcdonald(
  loadings_df = loadings_df,
  groups = NULL,
  method = "comunalidad"
)
result$omegas

# Calculate omega for specific groups of items
result2 <- calcula_omega_mcdonald(
  loadings_df = loadings_df,
  groups = list(
    Factor1 = c("Item1", "Item2", "Item3"),
    Factor2 = c("Item4", "Item5", "Item6")
  ),
  method = "comunalidad"
)
result2$omegas

# View item-level statistics
result2$item_stats$Factor1

Omega Coefficient from a Fitted lavaan Model

Description

Computes McDonald's omega using the R-squared values (communalities) extracted directly from a fitted lavaan object. Accepts either a single character vector of item names or a named list of vectors to compute omega for multiple factors at once.

Usage

calculate_omega_J(items, fit, digits = 3)

Arguments

Value

When items is a single vector, a numeric scalar (omega). When items is a named list, a named numeric vector with one omega per factor.

Examples

library(lavaan)
model <- 'F1 =~ x1 + x2 + x3
           F2 =~ x4 + x5 + x6'
fit <- cfa(model, data = HolzingerSwineford1939)

# Single factor
calculate_omega_J(c("x1", "x2", "x3"), fit)

# Multiple factors at once
calculate_omega_J(
  list(F1 = c("x1", "x2", "x3"),
       F2 = c("x4", "x5", "x6")),
  fit
)

Calculate Percentage of Fit Indices Meeting Thresholds

Description

Calculates the percentage of bootstrap replications meeting specified fit thresholds.

Usage

calculate_per_fit(df_repli, thresholds_str)

Arguments

Value

A data frame with percentages for each fit measure.

Examples

# First run boot_cfa to get bootstrap results
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"

boot_results <- boot_cfa(
  new_df = data,
  model_string = model,
  item_prefix = "Item",
  n_replications = 25
)

# Define threshold string
thresholds <- "CFI > 0.95, TLI > 0.95, RMSEA < 0.06, SRMR < 0.08"

# Calculate percentage of bootstrap replications meeting thresholds
fit_percentages <- calculate_per_fit(boot_results, thresholds)
print(fit_percentages)

# Different thresholds (more lenient)
thresholds2 <- "CFI > 0.90, TLI > 0.90, RMSEA < 0.08"
calculate_per_fit(boot_results, thresholds2)

Calculate Percentage of Bootstrap EFA Models Meeting Fit Thresholds

Description

Calculates the percentage of bootstrap EFA replications that meet specified fit measure thresholds.

Usage

calculate_per_fit_efa(boot_efa_results, thresholds_str)

Arguments

Value

A data frame with percentages for each threshold.

Examples

set.seed(123)
n <- 200
data <- as.data.frame(lapply(setNames(1:6, paste0("PBA", 1:6)),
                             function(i) sample(1:5, n, replace = TRUE)))
results_boot_efa <- boot_efa(
  data = data, n_factors = 2, n_items = 6, name_items = "PBA",
  rotation = "oblimin", n_replications = 5
)
thresholds <- "CFI > 0.95, TLI > 0.95, RMSEA < 0.06, SRMR < 0.08"
calculate_per_fit_efa(results_boot_efa, thresholds)

Combine Likert Plot with SEM Path Diagram

Description

Creates a combined visualization with a Likert plot and SEM path diagram.

Usage

combine_likert_sem(
  plot_likert,
  fit_sem,
  sem_args = list(),
  nodeLabels = NULL,
  ncol = 2,
  widths = rep(1, ncol),
  tag_levels = "A",
  tag_suffix = "",
  tag_pos = c(0.05, 0.95),
  tag_offset_y = 0.02,
  tag_size = 16,
  tag_face = "bold"
)

Arguments

Value

A combined patchwork plot.

Examples

library(lavaan)
library(ggplot2)

# Create sample data
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

# Create Likert plot
likert_plot <- Plot_Likert(
  Data = data,
  name_items = "Item",
  ranges = 1:6
)

# Fit CFA model
model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"
fit <- cfa(model, data = data, ordered = TRUE, estimator = "WLSMV")

# Combine Likert plot with SEM path diagram
combined_plot <- combine_likert_sem(
  plot_likert = likert_plot,
  fit_sem = fit,
  ncol = 2,
  widths = c(1, 1),
  tag_levels = "A",
  tag_size = 14
)

# Display combined plot
print(combined_plot)

# Save combined plot
ggsave(file.path(tempdir(), "combined_likert_sem.jpg"), combined_plot,
       width = 14, height = 8, dpi = 300)

Compare Fit Metrics Across Models

Description

Compares fit metrics (AIC, BIC, CFI, TLI, RMSEA, etc.) across multiple models.

Usage

comparation_metric(...)

Arguments

Value

A data frame with fit metrics for each model.

Extract Factor Correlations from EFA

Description

Extracts the inter-factor correlation matrix (Phi) from EFA results.

Usage

cor_afe(x)

Arguments

Value

A matrix of factor correlations rounded to 2 decimals.

Examples

library(psych)

# Create sample data
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

# Run EFA with psych package
efa_result <- fa(data, nfactors = 2, rotate = "oblimin", fm = "pa")

# Extract factor correlations
factor_cors <- cor_afe(efa_result)
print(factor_cors)
# Output: 2x2 matrix of factor correlations

Count Excluded Items from Bootstrap Analysis

Description

Counts how many times each item was excluded across bootstrap iterations.

Usage

count_excluded_items(res)

Arguments

Value

A tibble with item names and exclusion counts, sorted by frequency.

Create CFA Syntax from Data Frame

Description

Generates lavaan CFA model syntax from a data frame with items and factors.

Usage

crearSintaxisCFA(df)

Arguments

Value

A character string with lavaan model syntax.

Examples

# Example 1: Create CFA syntax from EFA results with factor loadings
# Assume you have EFA results with items and their factor loadings
efa_loadings <- data.frame(
  Items = c("Item1", "Item2", "Item3", "Item4", "Item5", "Item6"),
  f1 = c(0.75, 0.68, 0.72, 0, 0, 0),
  f2 = c(0, 0, 0, 0.70, 0.65, 0.78)
)

# Generate CFA syntax
model <- crearSintaxisCFA(efa_loadings)
cat(model)
# Output:
# f1 =~ Item1 + Item2 + Item3
# f2 =~ Item4 + Item5 + Item6

# Example 2: Create CFA syntax from a data frame with explicit factor assignments
factor_assignments <- data.frame(
  Items = c("Q1", "Q2", "Q3", "Q4", "Q5", "Q6"),
  Factores = c("Anxiety", "Anxiety", "Anxiety", "Depression", "Depression", "Depression")
)

model2 <- crearSintaxisCFA(factor_assignments)
cat(model2)
# Output:
# F1 =~ Q1 + Q2 + Q3
# F2 =~ Q4 + Q5 + Q6

Create Lavaan Model Syntax

Description

Creates lavaan model syntax from item prefix and factor specifications.

Usage

crear_modelo_lavaan(nombre, ...)

Arguments

Value

A character string with lavaan model syntax.

Examples

# Create a 3-factor CFA model with item prefix "Item"
model <- crear_modelo_lavaan(
  nombre = "Item",
  F1 = 1:4,      # Items 1-4 load on Factor 1
  F2 = 5:8,      # Items 5-8 load on Factor 2
  F3 = 9:12      # Items 9-12 load on Factor 3
)
cat(model)
# Output:
# F1 =~ Item1 + Item2 + Item3 + Item4
# F2 =~ Item5 + Item6 + Item7 + Item8
# F3 =~ Item9 + Item10 + Item11 + Item12

# Create a 2-factor model with non-consecutive items
model2 <- crear_modelo_lavaan(
  nombre = "Q",
  Anxiety = c(1, 3, 5, 7),
  Depression = c(2, 4, 6, 8)
)
cat(model2)

Create Initial Model Configuration

Description

Creates an initial model configuration list for factor analysis.

Usage

createInitialModel(n_factors, n_items, name_items, exclude_items = NULL)

Arguments

Value

A list with model configuration parameters.

Create Item Groups for Factor Analysis

Description

Creates a list of item index groups for factor definitions.

Usage

create_groups(names, values)

Arguments

Value

A named list with item indices for each group.

Examples

# Create groups for a 3-factor model with different items per factor
groups <- create_groups(
  names = c("Anxiety", "Depression", "Stress"),
  values = c(5, 4, 6)  # 5 items for Anxiety, 4 for Depression, 6 for Stress
)

# View the groups
groups
# $Anxiety
# [1] 1 2 3 4 5
# $Depression
# [1] 6 7 8 9
# $Stress
# [1] 10 11 12 13 14 15

# Use with crear_modelo_lavaan
model <- crear_modelo_lavaan(
  nombre = "Item",
  Anxiety = groups$Anxiety,
  Depression = groups$Depression,
  Stress = groups$Stress
)
cat(model)

# Example for a 2-factor scale
groups2 <- create_groups(
  names = c("Factor1", "Factor2"),
  values = c(8, 7)
)

Analisis de Invarianza Factorial Simplificado

Description

Realiza analisis de invarianza factorial para datos ordinales utilizando el enfoque de Wu y Estabrook (2016) con estimador WLSMV.

Usage

easy_invariance(
  model,
  data,
  estimator,
  ordered,
  ID.cat,
  group,
  levels_of_invariance,
  group.partial = NULL,
  fit_indices = c("CFI", "TLI", "RMSEA", "SRMR")
)

Arguments

Value

Lista con combined_data (tabla resumen) y los modelos ajustados.

Examples

set.seed(123)
n <- 400
theta <- rnorm(n)
sim_item <- function(theta) {
  as.numeric(cut(theta + rnorm(length(theta), 0, 0.8),
                 breaks = c(-Inf, -1, 0, 1, Inf)))
}
mydata <- data.frame(
  item1 = sim_item(theta), item2 = sim_item(theta),
  item3 = sim_item(theta), item4 = sim_item(theta),
  sex = rep(c("M", "F"), each = n / 2)
)

# Ejemplo con todos los indices por defecto
res <- easy_invariance(
  model = "F =~ item1 + item2 + item3 + item4",
  data = mydata,
  estimator = "WLSMV",
  ordered = TRUE,
  ID.cat = "Wu.Estabrook.2016",
  group = "sex",
  levels_of_invariance = c("configural", "threshold", "metric", "strict")
)

# Ejemplo excluyendo RMSEA
res <- easy_invariance(
  model = "F =~ item1 + item2 + item3 + item4",
  data = mydata,
  estimator = "WLSMV",
  ordered = TRUE,
  ID.cat = "Wu.Estabrook.2016",
  group = "sex",
  levels_of_invariance = c("configural", "threshold", "metric", "strict"),
  fit_indices = c("CFI", "TLI", "SRMR", "CRMR")
)

Exploratory Factor Analysis with Bootstrap

Description

Performs EFA with optional bootstrap resampling for stability analysis.

Usage

efa_with_bootstrap(
  n_factors,
  n_items,
  name_items,
  data,
  apply_threshold,
  estimator = "WLSMV",
  rotation = "oblimin",
  exclude_items = NULL,
  bootstrap = FALSE,
  n_bootstrap = 1000,
  bootstrap_seed = NULL
)

Arguments

Value

A list with EFA results and optional bootstrap statistics.

Examples

# Create sample data
set.seed(123)
n <- 200
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)

# Basic EFA without bootstrap
result_basic <- efa_with_bootstrap(
  n_factors = 2,
  n_items = 6,
  name_items = "Item",
  data = data,
  apply_threshold = TRUE,
  bootstrap = FALSE
)

# View fit indices
result_basic$Bondades_Original

# View factor loadings
result_basic$result_df

# EFA with bootstrap for stability analysis
result_boot <- efa_with_bootstrap(
  n_factors = 2,
  n_items = 6,
  name_items = "Item",
  data = data,
  apply_threshold = TRUE,
  bootstrap = TRUE,
  n_bootstrap = 5,  # Use 1000 in practice
  bootstrap_seed = 123
)

# View bootstrap summary of fit indices
result_boot$Bootstrap$fit_indices_summary$summary

# View bootstrap summary of factor loadings
result_boot$Bootstrap$loadings_summary

# View global loadings summary
result_boot$Bootstrap$global_loadings_summary

Extreme-Response Proportion by Item

Description

Computes the Extreme-Response Proportion (ERP) for each item in a Likert-type scale. ERP is the proportion of respondents who selected either the lowest or highest response option, which can signal floor/ceiling effects or poor item discrimination.

Usage

erp_by_item(data, items, extremes = c(1, 5), threshold = 0.3)

Arguments

Value

A data frame with one row per item containing: Item, n, p_low, p_high, ERP, Median, IQR, Flag_ERP, and formatted percentage columns. Rows are sorted by descending ERP.

Examples

set.seed(123)
df <- data.frame(
  IT1 = sample(1:5, 200, replace = TRUE),
  IT2 = sample(1:5, 200, replace = TRUE),
  IT3 = sample(1:5, 200, replace = TRUE)
)
erp <- erp_by_item(df, c("IT1", "IT2", "IT3"))
print(erp)

Export Summary Tables to Excel

Description

Exports summary tables to an Excel file with formatted headers.

Usage

export_summary_tables(
  summary_Tabla_Total,
  summary_Tabla_EFA,
  summary_Tabla_CFA,
  file_name
)

Arguments

Value

Invisibly returns the merged data frame.

Extract All Fit Measures from Bootstrap Results

Description

Extracts and combines fit measures from all bootstrap replications.

Usage

extractAllFitMeasures(res)

Arguments

Value

A data frame with fit measures from all replications and iterations.

Extract All Inter-Factor Correlations from Bootstrap Results

Description

Extracts and combines inter-factor correlations from all bootstrap replications.

Usage

extractAllInterFactors(res)

Arguments

Value

A data frame with factor correlations from all replications and iterations.

Extract All Results from Bootstrap Analysis

Description

Extracts and combines all results from bootstrap replications.

Usage

extractAllResults(res)

Arguments

Value

A data frame with results from all replications and iterations.

Extract Excluded Items from Bootstrap Results

Description

Extracts all excluded items across bootstrap samples.

Usage

extract_ExcludedItems_boot(resultados_bootstrap)

Arguments

Value

A data frame with excluded items and sample IDs.

Extract Modification Indices from CFA Results

Description

Extracts and combines modification indices from multiple CFA analyses.

Usage

extract_ModIndex_AFC(results)

Arguments

Value

A combined data frame of modification indices with sample identifiers.

Examples

library(lavaan)

# Create sample data
set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)

# Fit the same CFA model on three bootstrap samples
model <- "F1 =~ Item1 + Item2 + Item3
          F2 =~ Item4 + Item5 + Item6"
fit1 <- cfa(model, data = data[sample(n, n, replace = TRUE), ])
fit2 <- cfa(model, data = data[sample(n, n, replace = TRUE), ])
fit3 <- cfa(model, data = data[sample(n, n, replace = TRUE), ])

# Each result contains a ModificationsDf element
results_list <- list(
  list(ModificationsDf = modificationIndices(fit1, sort = TRUE)),
  list(ModificationsDf = modificationIndices(fit2, sort = TRUE)),
  list(ModificationsDf = modificationIndices(fit3, sort = TRUE))
)

# Extract and combine modification indices
all_mod_indices <- extract_ModIndex_AFC(results_list)
head(all_mod_indices)

Extract Fit Measures from Bootstrap CFA Results

Description

Extracts and combines fit measures from bootstrap CFA analyses.

Usage

extract_bondad_boot_AFC(results)

Arguments

Value

A combined data frame of fit measures with sample and iteration identifiers.

Examples

# Bootstrap CFA results contain one FitMeasuresDf element per sample
results_list <- list(
  list(FitMeasuresDf = data.frame(CFI = 0.96, TLI = 0.95, RMSEA = 0.05)),
  list(FitMeasuresDf = data.frame(CFI = 0.97, TLI = 0.96, RMSEA = 0.04))
)

# Extract and combine fit measures across all bootstrap samples
all_fit_measures <- extract_bondad_boot_AFC(results_list)
head(all_fit_measures)

Extract Fit Measures from Bootstrap EFA Results

Description

Extracts and combines fit measures from bootstrap EFA analyses.

Usage

extract_bondad_boot_EFA(resultados_bootstrap)

Arguments

Value

A data frame of fit measures with sample identifiers.

Examples

# Bootstrap results contain one Bondades list per sample
resultados_bootstrap <- list(
  Results = list(
    list(CombinedResults = list(Bondades = list(
      data.frame(CFI = 0.96, TLI = 0.95, RMSEA = 0.05)
    ))),
    list(CombinedResults = list(Bondades = list(
      data.frame(CFI = 0.97, TLI = 0.96, RMSEA = 0.04)
    )))
  )
)

# Extract and combine fit measures across all bootstrap samples
fit_measures <- extract_bondad_boot_EFA(resultados_bootstrap)
head(fit_measures)

Extract Factor Items for Lavaan Syntax

Description

Creates lavaan model syntax by extracting items with non-zero loadings per factor.

Usage

extract_f_items(data, prefixes)

Arguments

Value

A character string with lavaan model syntax.

Examples

# Create a data frame with factor loadings (after EFA)
loadings_df <- data.frame(
  Items = c("Item1", "Item2", "Item3", "Item4", "Item5", "Item6"),
  f1 = c(0.75, 0.68, 0.72, 0, 0, 0),
  f2 = c(0, 0, 0, 0.70, 0.65, 0.78)
)

# Extract lavaan syntax for factors f1 and f2
model_syntax <- extract_f_items(
  data = loadings_df,
  prefixes = c("f1", "f2")
)
cat(model_syntax)
# Output:
# f1 =~ Item1 + Item2 + Item3
# f2 =~ Item4 + Item5 + Item6

Extract Fit Indices from Factor Analysis

Description

Extracts fit indices (RMSEA, TLI, CFI, BIC) from psych factor analysis results.

Usage

extract_fit(factors_data)

Arguments

Value

A data frame with fit index names and values.

Examples

library(psych)

# Create sample data
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

# Run factor analysis with psych package
fa_result <- fa(data, nfactors = 2, rotate = "oblimin", fm = "pa")

# Extract fit indices
fit_indices <- extract_fit(fa_result)
print(fit_indices)

# The result contains: RMSEA, lower_RMSEA, upper_RMSEA,
# confidence, TLI, CFI, null.chisq, objective, BIC

Extract Fit Measures from Lavaan Specifications

Description

Extracts fit measures (chi-square, SRMR, WRMR, CFI, TLI, RMSEA) from lavaan models.

Usage

extract_fit_measures(Specifications)

Arguments

Value

A data frame with fit measures for each factor solution.

Examples

# First, run EFA_modern to get specifications
set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data_efa <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)

# Run EFA
efa_result <- EFA_modern(
  n_factors = 2,
  n_items = 6,
  name_items = "Item",
  data = data_efa,
  apply_threshold = TRUE
)

# Extract fit measures from all factor solutions
fit_table <- extract_fit_measures(efa_result$Specifications)
print(fit_table)

# The result contains: Factores, chisq.scaled, df.scaled, srmr, wrmr,
# cfi.scaled, tli.scaled, rmsea.scaled

Summarize Modification Indices Across Replications

Description

Creates summary statistics for modification indices across bootstrap samples.

Usage

extract_summary_modifications(combined_results, min_count = 10)

Arguments

Value

A summary data frame with mean, SD, min, and max MI values.

Extract Items by Maximum Loading

Description

Assigns items to factors based on their highest loading value.

Usage

extracted_items2(data)

Arguments

Value

A list with items assigned to each factor.

Extract Fit Metrics from Model

Description

Extracts and formats common fit metrics (AIC, BIC, CFI, TLI, RMSEA) from a model.

Usage

extraer_metricas(modelo_fit)

Arguments

Value

A data frame with formatted fit metrics.

Create Factor Analysis Summary

Description

Creates a summary table with loadings, communalities, and uniquenesses.

Usage

factor_summary(factors_data, num_items, num_factors)

Arguments

Value

A data frame with items, factor loadings, h2, and u2.

Examples

library(psych)

# Create sample data
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

# Run EFA with psych package
efa_result <- fa(data, nfactors = 2, rotate = "oblimin", fm = "pa")

# Create summary table
summary_table <- factor_summary(
  factors_data = efa_result,
  num_items = 6,
  num_factors = 2
)
print(summary_table)
# Output:
#   Items   F1    F2   h2   u2
# 1 Item1 0.75  0.10 0.58 0.42
# 2 Item2 0.68  0.05 0.47 0.53
# ...

Combine Factor Data with Item Information

Description

Combines factor loadings with item descriptors and applies loading thresholds.

Usage

factors_data_items(
  summary_data,
  data_items,
  num_factors,
  apply_threshold = TRUE
)

Arguments

Value

A tibble with items, loadings, and descriptors.

Filter Aberrant Response Patterns

Description

Identifies and filters cases with aberrant response patterns using Mahalanobis distance.

Usage

filtrar_aberrantes(data, items, plot = FALSE)

Arguments

Value

A list with filtered data and a table of aberrant cases.

Examples

# Create sample data with some aberrant response patterns
set.seed(123)
n <- 200
data <- data.frame(
  ID = 1:n,
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE)
)

# Add some aberrant patterns (all same responses)
data[1, 2:6] <- rep(1, 5)
data[2, 2:6] <- rep(5, 5)

# Filter aberrant cases
result <- filtrar_aberrantes(
  data = data,
  items = c("Item1", "Item2", "Item3", "Item4", "Item5"),
  plot = FALSE
)

# Access filtered data (without aberrant cases)
clean_data <- result$data_filtrada
nrow(clean_data)

# View table of aberrant cases with Mahalanobis distances
result$tabla_aberrantes

# With visualization of Mahalanobis distances
result_plot <- filtrar_aberrantes(
  data = data,
  items = c("Item1", "Item2", "Item3", "Item4", "Item5"),
  plot = TRUE
)

Create Fit Index Table

Description

Creates a comprehensive table combining fit indices and reliability measures.

Usage

fit_index_Table(resultados)

Arguments

Value

A data frame with model fit indices and omega reliability values.

Examples

# First run multi_cfa to get results
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

models <- list(
  "F1 =~ Item1 + Item2 + Item3 + Item4 + Item5 + Item6",
  "F1 =~ Item1 + Item2 + Item3\nF2 =~ Item4 + Item5 + Item6"
)

results <- multi_cfa(modelos = models, data = data,
                     estimator = "WLSMV", ordered = TRUE)

# Create fit index table combining fit measures and reliability
fit_table <- fit_index_Table(results)
print(fit_table)
# Output:
# Model   x2   df  SRMR   CFI   TLI  RMSEA  CRMR omega_F1 omega_F2
# 1      ...  ...  ...   ...   ...   ...    ...   0.78     NA
# 2      ...  ...  ...   ...   ...   ...    ...   0.75     0.80

Generate Lavaan Model with Thresholds

Description

Generates complete lavaan model syntax including factor loadings and item thresholds.

Usage

generate_model_lavaan(num_items, item_value, t_values, excluded_items = "none")

Arguments

Value

A character string with complete lavaan model syntax.

Examples

# Generate a simple 1-factor model with 5 items and 4 thresholds
model <- generate_model_lavaan(
  num_items = 5,
  item_value = c(0.7, 0.8, 0.75, 0.6, 0.65),  # Factor loadings
  t_values = c(-1.5, -0.5, 0.5, 1.5)           # Threshold values
)
cat(model)
# Output:
# F1 =~ 0.7*Item1 + 0.8*Item2 + 0.75*Item3 + 0.6*Item4 + 0.65*Item5
# F1 ~~ 1*F1
# Item1 | -1.5*t1
# Item1 | -0.5*t2
# ...

# Generate model with some items excluded (commented out)
model2 <- generate_model_lavaan(
  num_items = 5,
  item_value = c(0.7, 0.8, 0.75, 0.6, 0.65),
  t_values = c(-1.5, -0.5, 0.5, 1.5),
  excluded_items = c("Item3t1", "Item3t2")  # Exclude Item3 thresholds 1 and 2
)
cat(model2)

Generate EFA Models for Multiple Factor Solutions

Description

Generates lavaan EFA model syntax for 1 to n_factors solutions.

Usage

generate_modelos(
  n_factors,
  specific_items = NULL,
  name_items = NULL,
  n_items = NULL,
  exclude_items = NULL
)

Arguments

Value

A list of model syntax strings for each factor solution.

Examples

# Generate models for 1 to 4 factor solutions using item prefix
models <- generate_modelos(
  n_factors = 4,
  name_items = "Item",
  n_items = 12
)

# View the 3-factor model syntax
cat(models[[3]])

# Generate models excluding specific items
models2 <- generate_modelos(
  n_factors = 3,
  name_items = "Q",
  n_items = 10,
  exclude_items = c("Q3", "Q7")
)

# Generate models using specific item names
models3 <- generate_modelos(
  n_factors = 2,
  specific_items = c("Anxiety1", "Anxiety2", "Anxiety3",
                     "Depression1", "Depression2", "Depression3")
)
cat(models3[[2]])

Generate Summary Statistics

Description

Generates summary statistics for sociodemographic variables.

Usage

generate_summary(data, variables)

Arguments

Value

A list of summary tables for each variable.

Generate Summary Table

Description

Converts summary results into a formatted table.

Usage

generate_table(summary_results)

Arguments

Value

A data frame formatted as a summary table.

Average Variance Extracted and Composite Reliability

Description

Computes the Average Variance Extracted (AVE) and Composite Reliability (CR) for each latent factor in a fitted lavaan CFA model, using standardized factor loadings and error variances.

Usage

get_ave_cr(fit)

Arguments

Value

A data frame with one row per latent factor containing: latent, CR, and AVE, both rounded to three decimal places.

Examples

library(lavaan)
model <- 'F1 =~ x1 + x2 + x3
           F2 =~ x4 + x5 + x6'
fit <- cfa(model, data = HolzingerSwineford1939)
get_ave_cr(fit)

Interpret Modification Index Decisions

Description

Interprets modification indices and power decisions from SEM analysis.

Usage

interpret_decision_SSV(MI_Saris)

Arguments

Value

A data frame with interpretations of each decision.

Reverse Score Items

Description

Reverses the scoring of specified items in a data frame.

Usage

invertir_items(df, items, num_respuestas, comienza_con_cero = TRUE)

Arguments

Value

Data frame with reversed items.

Examples

# Create sample data with 5-point Likert scale (1-5)
set.seed(123)
data <- data.frame(
  Item1 = sample(1:5, 100, replace = TRUE),
  Item2 = sample(1:5, 100, replace = TRUE),
  Item3 = sample(1:5, 100, replace = TRUE),  # Item to reverse
  Item4 = sample(1:5, 100, replace = TRUE),
  Item5 = sample(1:5, 100, replace = TRUE)   # Item to reverse
)

# Reverse score Items 3 and 5 (scale starts at 1)
data_reversed <- invertir_items(
  df = data,
  items = c("Item3", "Item5"),
  num_respuestas = 5,
  comienza_con_cero = FALSE
)

# Check the reversal: 1->5, 2->4, 3->3, 4->2, 5->1
head(data[, c("Item3", "Item5")])
head(data_reversed[, c("Item3", "Item5")])

# Example with 0-4 scale (starts at 0)
data2 <- data.frame(
  Q1 = sample(0:4, 100, replace = TRUE),
  Q2 = sample(0:4, 100, replace = TRUE)
)

data2_reversed <- invertir_items(
  df = data2,
  items = "Q1",
  num_respuestas = 5,
  comienza_con_cero = TRUE
)
# Check: 0->5, 1->4, 2->3, 3->2, 4->1

Run Multiple CFA Models

Description

Fits multiple CFA models and extracts fit indices, modification indices, and reliability.

Usage

multi_cfa(modelos, data, estimator, ordered = TRUE, orthogonal_indices = NULL)

Arguments

Value

A list with fits, fit indices, modification indices, correlations, and reliability.

Examples

# Create sample data
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE),
  Item7 = sample(1:5, n, replace = TRUE),
  Item8 = sample(1:5, n, replace = TRUE),
  Item9 = sample(1:5, n, replace = TRUE)
)

# Define multiple CFA models to compare
model1 <- "F1 =~ Item1 + Item2 + Item3 + Item4 + Item5 + Item6 + Item7 + Item8 + Item9"

model2 <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
  F3 =~ Item7 + Item8 + Item9
"

model3 <- "
  F1 =~ Item1 + Item2 + Item3 + Item4 + Item5
  F2 =~ Item6 + Item7 + Item8 + Item9
"

modelos <- list(model1, model2, model3)

# Run multiple CFAs
results <- multi_cfa(
  modelos = modelos,
  data = data,
  estimator = "WLSMV",
  ordered = TRUE
)

# Compare fit indices across models
results$bondades_ajuste

# Check modification indices for Model 2
results$indices_modificacion[[2]]

# View factor correlations for Model 2
results$correlaciones_factores$Modelo2

# Check reliability (omega) for each model
results$fiabilidad

# Fit model 3 with orthogonal factors
results_orth <- multi_cfa(
  modelos = modelos,
  data = data,
  estimator = "WLSMV",
  ordered = TRUE,
  orthogonal_indices = 3
)

Plot CFA Stability Results

Description

Creates line plots showing CFA fit indices across sample sizes.

Usage

plot_cfa_stability(
  resultados,
  num_factors = 3,
  y_min_omega = 0.7,
  y_max_omega = 1,
  y_breaks_omega = 0.05,
  y_min_cfi = 0.9,
  y_max_cfi = 1,
  y_min_tli = 0.9,
  y_max_tli = 1,
  y_min_rmsea = 0,
  y_max_rmsea = 0.15,
  y_min_srmr = 0,
  y_max_srmr = 0.15,
  y_min_crmr = 0,
  y_max_crmr = 0.15,
  y_min_reliability = 0.5,
  y_max_reliability = 1,
  y_breaks_comparative = 0.02,
  y_breaks_absolutes = 0.05,
  hline_color = "red",
  xlab_size = 12,
  ylab_size = 12
)

Arguments

Value

A combined ggplot figure.

Examples

# First run boot_cfa_stability to get stability results
set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)

model <- "
  F1 =~ Item1 + Item2 + Item3
  F2 =~ Item4 + Item5 + Item6
"

stability_results <- boot_cfa_stability(
  modelo = model,
  data = data,
  num_replicas = 10,
  estimator = "WLSMV",
  n_cores = 2
)

# Create stability plots
figure <- plot_cfa_stability(
  resultados = stability_results,
  num_factors = 2,
  y_min_omega = 0.6,
  y_max_omega = 1,
  y_min_cfi = 0.85,
  y_max_cfi = 1,
  hline_color = "red"
)

# Save the figure
ggplot2::ggsave(file.path(tempdir(), "stability_analysis.jpg"), figure,
                width = 12, height = 8, dpi = 300)

Plot CFA Stability Results (Boxplot)

Description

Creates boxplots showing CFA fit indices distributions across sample sizes.

Usage

plot_cfa_stability_box(
  resultados,
  y_min_cfi = 0.9,
  y_max_cfi = 1,
  y_min_tli = 0.9,
  y_max_tli = 1,
  y_min_rmsea = 0,
  y_max_rmsea = 0.15,
  y_min_srmr = 0,
  y_max_srmr = 0.15,
  y_min_crmr = 0,
  y_max_crmr = 0.15,
  y_min_reliability = 0.5,
  y_max_reliability = 1,
  y_breaks = 0.01,
  y_breaks_reliability = 0.05,
  hline_color = "red",
  xlab_size = 12,
  ylab_size = 12,
  label_size = 10
)

Arguments

Value

A combined ggplot figure with boxplots.

Plot CFA Stability with Confidence Intervals

Description

Creates plots with confidence intervals for CFA fit indices.

Usage

plot_cfa_stability_intervals(
  resultados,
  num_factors = 1,
  indices_to_plot = "All",
  indices_to_exclude = NULL,
  fill_color = "skyblue",
  alpha_ribbon = 0.2,
  rename_factors = NULL,
  ...
)

Arguments

Value

A ggplot object with faceted confidence intervals.

Plot CFA Stability with Grouped Intervals

Description

Creates grouped interval plots for CFA fit indices.

Usage

plot_cfa_stability_intervals2(
  resultados,
  num_factors = 1,
  alpha_ribbon = 0.2,
  rename_factors = NULL,
  ...
)

Arguments

Value

A list with three ggplot panels for different index groups.

Grafico Pointrange de Estabilidad CFA

Description

Crea graficos pointrange (punto + intervalo de confianza) para visualizar la estabilidad de indices de ajuste y fiabilidad segun tamano muestral.

Usage

plot_cfa_stability_pointrange(
  resultados,
  indices = c("cfi.scaled", "tli.scaled", "rmsea.scaled", "srmr"),
  ci_level = 0.95,
  point_size = 2.5,
  line_width = 0.8,
  hline_values = list(cfi.scaled = 0.95, tli.scaled = 0.95, rmsea.scaled = 0.08, srmr =
    0.08, crmr = 0.05),
  hline_color = "red",
  hline_linetype = "dashed",
  facet_scales = "free_y",
  color_palette = NULL,
  theme_style = "minimal"
)

Arguments

Value

Un objeto ggplot.

Examples

set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)
model <- "F1 =~ Item1 + Item2 + Item3
          F2 =~ Item4 + Item5 + Item6"
resultado_stability <- boot_cfa_stability(
  modelo = model, data = data, num_replicas = 10,
  estimator = "WLSMV", n_cores = 2
)
plot_cfa_stability_pointrange(resultado_stability)
plot_cfa_stability_pointrange(resultado_stability, indices = c("cfi.scaled", "rmsea.scaled"))

Grafico Ridgeline de Estabilidad CFA

Description

Crea graficos ridgeline (joy plots) para visualizar la distribucion de los indices de ajuste y fiabilidad segun tamano muestral.

Usage

plot_cfa_stability_ridgeline(
  resultados,
  indices = c("cfi.scaled", "tli.scaled", "rmsea.scaled", "srmr"),
  scale_ridges = 1.2,
  alpha_fill = 0.7,
  fill_color = NULL,
  gradient_colors = c("steelblue", "darkblue"),
  show_quantiles = TRUE,
  quantile_lines = c(0.025, 0.5, 0.975),
  vline_values = list(cfi.scaled = 0.95, tli.scaled = 0.95, rmsea.scaled = 0.08, srmr =
    0.08, crmr = 0.05),
  vline_color = "red",
  vline_linetype = "dashed",
  theme_style = "minimal",
  rel_min_height = 0.01,
  lang = c("es", "en")
)

Arguments

Value

Un objeto ggplot.

Examples

set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)
model <- "F1 =~ Item1 + Item2 + Item3
          F2 =~ Item4 + Item5 + Item6"
resultado_stability <- boot_cfa_stability(
  modelo = model, data = data, num_replicas = 10,
  estimator = "WLSMV", n_cores = 2
)
plot_cfa_stability_ridgeline(resultado_stability)
plot_cfa_stability_ridgeline(resultado_stability, indices = c("cfi.scaled", "rmsea.scaled"))

Plot Extreme-Response Proportion

Description

Creates a horizontal bar chart of ERP values per item, with a dashed reference line at the chosen threshold. Items exceeding the threshold are visually distinguished by fill colour.

Usage

plot_erp(erp_table, threshold = 0.3)

Arguments

Value

A ggplot object.

Examples

set.seed(123)
df <- data.frame(
  IT1 = sample(1:5, 200, replace = TRUE),
  IT2 = sample(1:5, 200, replace = TRUE),
  IT3 = sample(1:5, 200, replace = TRUE)
)
erp <- erp_by_item(df, c("IT1", "IT2", "IT3"))
plot_erp(erp)

Chord diagram of distal-predictor by mediator associations

Description

Visualización circular de cuerdas (chord diagram) que muestra las asociaciones bivariadas entre los predictores distales y los indicadores de los mediadores en un modelo de mediación múltiple paralela. Replica el estilo de la Figura 1 de Wang et al. (2025, BMC Medicine): cada arco perimetral representa una variable agrupada por bloque conceptual (color), y cada cuerda interna representa una asociación con grosor proporcional a |\beta| y color por signo.

Usage

plot_mediation_chord(
  fit,
  predictors,
  mediators,
  outcome = NULL,
  node_groups = NULL,
  node_labels = NULL,
  palette = NULL,
  sig_threshold = 0.05,
  show_n_s = TRUE,
  sig_alpha = 0.15,
  positive_color = NULL,
  negative_color = NULL,
  chord_width_range = c(0.5, 6),
  gap_degree = 2,
  big_gap = 8,
  label_cex = 0.85,
  title = NULL
)

Arguments

Details

La función calcula los \beta estandarizados de lavaan::standardizedSolution() para cada par (predictor, mediador) y construye una matriz adyacencia que se pasa a circlize::chordDiagram(). Requiere el paquete circlize.

Value

Invoca circlize::chordDiagram() y devuelve invisiblemente la matriz de asociaciones usada para el dibujo.

References

Wang, X., Cao, Z., Yin, S., Duan, T., Sun, T., & Xu, C. (2025). Childhood maltreatment and depression: Mediating role of lifestyle factors, personality traits, adult traumas, and social connections among middle-aged and elderly participants. BMC Medicine, 23, 319. doi:10.1186/s12916-025-04147-2

Gu, Z., Gu, L., Eils, R., Schlesner, M., & Brors, B. (2014). circlize implements and enhances circular visualization in R. Bioinformatics, 30(19), 2811–2812. doi:10.1093/bioinformatics/btu393

Examples

library(lavaan); library(circlize)

set.seed(123)
n <- 300
X1 <- rnorm(n); X2 <- rnorm(n); X3 <- rnorm(n)
M1 <- 0.5 * X1 + 0.3 * X2 + rnorm(n, 0, 0.8)
M2 <- 0.4 * X2 + 0.3 * X3 + rnorm(n, 0, 0.8)
Y  <- 0.4 * M1 + 0.3 * M2 + 0.2 * X1 + rnorm(n, 0, 0.7)
your_data <- data.frame(X1, X2, X3, M1, M2, Y)

mod <- '
  M1 ~ X1 + X2 + X3
  M2 ~ X1 + X2 + X3
  Y  ~ M1 + M2
'
fit <- sem(mod, data = your_data)

plot_mediation_chord(
  fit,
  predictors = c("X1","X2","X3"),
  mediators  = c("M1","M2"),
  node_groups = c(X1 = "Block A", X2 = "Block A", X3 = "Block B",
                  M1 = "Mediator", M2 = "Mediator"),
  palette = c("Block A" = "#FFE082", "Block B" = "#B0BEC5",
              "Mediator" = "#F4A8A8")
)

Donut panel of mediation proportions per parallel mediator

Description

Genera un panel de donuts (gráficos circulares con centro hueco) que cuantifica visualmente la proporción del efecto total mediado por cada uno de los mediadores paralelos para un predictor distal determinado. Cada donut muestra el porcentaje de la asociación X -> Y atribuible al mediador en cuestión, replicando el estilo de la Figura 2 superior de Wang et al. (2025, BMC Medicine).

Usage

plot_mediation_donuts(
  fit,
  predictor,
  mediators,
  outcome = "suic",
  predictor_regression = NULL,
  indirect_prefix = "ind_",
  mediator_colors = NULL,
  remaining_color = "grey85",
  show_total = TRUE,
  ncol = NULL,
  title = NULL,
  subtitle = NULL
)

Arguments

Details

La función calcula la proporción mediada por cada mediador como

P_j = \frac{a_j \times b_j}{|a_1 b_1| + |a_2 b_2| + ... + |a_m b_m|}

donde a_j es el coeficiente M_j ~ X y b_j es el coeficiente Y ~ M_j, ambos estandarizados. Cada donut representa el valor absoluto de P_j con la fracción restante en gris, acompañado por el porcentaje en el centro y el signo del efecto en la parte inferior.

Requiere los paquetes ggplot2 y patchwork.

Value

Un objeto patchwork (composición de ggplots) listo para imprimir o guardar con ggsave().

References

Wang, X., Cao, Z., Yin, S., Duan, T., Sun, T., & Xu, C. (2025). Childhood maltreatment and depression: Mediating role of lifestyle factors, personality traits, adult traumas, and social connections among middle-aged and elderly participants. BMC Medicine, 23, 319. doi:10.1186/s12916-025-04147-2

Examples

library(lavaan); library(patchwork)

set.seed(123)
n <- 300
sueno_num <- rnorm(n)
DASS_dep <- 0.5 * sueno_num + rnorm(n, 0, 0.8)
DASS_anx <- 0.4 * sueno_num + rnorm(n, 0, 0.8)
suic <- 0.4 * DASS_dep + 0.3 * DASS_anx + 0.2 * sueno_num + rnorm(n, 0, 0.7)
dat <- data.frame(sueno_num, DASS_dep, DASS_anx, suic)

mod <- '
  DASS_dep ~ sueno_num
  DASS_anx ~ sueno_num
  suic ~ DASS_dep + DASS_anx + sueno_num
'
fit <- sem(mod, data = dat)

plot_mediation_donuts(
  fit,
  predictor = "sueno",
  mediators = c("Depresión" = "DASS_dep", "Ansiedad" = "DASS_anx"),
  outcome = "suic",
  predictor_regression = "sueno_num",
  title = "Mediación de la calidad del sueño sobre la conducta suicida"
)

Forest plot of direct, indirect, and total effects in a SEM model

Description

Visualización alternativa al diagrama de senderos clásico cuando el modelo tiene muchos predictores distales. Genera un forest plot horizontal que ordena cada predictor por la magnitud absoluta de su efecto \beta estandarizado y muestra simultáneamente los efectos directos, indirectos (a \times b) y totales con intervalos de confianza al 95 %. Sigue la recomendación de Fife y Mendoza (2018) sobre presentar la descomposición de efectos en un panel separado en lugar de saturar el path diagram con los tres tipos de coeficientes.

Usage

plot_mediation_forest(
  fit,
  predictors,
  outcome = "suic",
  predictor_regression = NULL,
  indirect_prefix = "ind_",
  total_prefix = "tot_",
  predictor_labels = NULL,
  effect_levels = c("Directo", "Indirecto", "Total"),
  effect_colors = c(Directo = "#5B8DBE", Indirecto = "#E07A5F", Total = "#3D405B"),
  layout = c("facet", "stacked"),
  effect_dodge = 0.55,
  sig_threshold = 0.05,
  sort_by = c("total", "indirect", "direct"),
  show_zero = TRUE,
  show_p_text = TRUE,
  show_beta_text = FALSE,
  p_text_position = c("right", "panel"),
  point_size = 2.4,
  error_width = 0.7,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  x_lim = NULL,
  facet_scales = "fixed"
)

Arguments

Details

La función toma los efectos definidos con := en la sintaxis lavaan y los reorganiza en un long format apto para ggplot2. Cada predictor genera un bloque vertical con los tres tipos de efecto (cuando están disponibles); las barras representan IC 95 % obtenidos directamente de lavaan::standardizedSolution().

Comparado con el path diagram de plot_path_mediation, este formato es preferible cuando \ge 8 predictores distales saturan visualmente el diagrama de tres columnas, o cuando se necesita comunicar la descomposición directo + indirecto = total con precisión cuantitativa.

Value

Objeto ggplot listo para imprimir o guardar con ggsave().

References

Fife, D. A., & Mendoza, J. L. (2018). Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization. Addictive Behaviors, 94, 232–239. doi:10.1016/j.addbeh.2018.08.030

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. doi:10.3758/BRM.40.3.879

Examples

library(lavaan)

set.seed(123)
n <- 300
X1 <- rnorm(n); X2 <- rnorm(n); X3 <- rnorm(n)
M1 <- 0.5 * X1 + 0.3 * X2 + rnorm(n, 0, 0.8)
M2 <- 0.4 * X2 + 0.3 * X3 + rnorm(n, 0, 0.8)
Y  <- 0.4 * M1 + 0.3 * M2 + 0.2 * X1 + rnorm(n, 0, 0.7)
your_data <- data.frame(X1, X2, X3, M1, M2, Y)

mod <- '
  M1 ~ a1*X1 + a2*X2
  Y  ~ b1*M1 + c1*X1 + c2*X2
  ind_X1 := a1*b1
  ind_X2 := a2*b1
  tot_X1 := c1 + ind_X1
  tot_X2 := c2 + ind_X2
'
fit <- sem(mod, data = your_data)

plot_mediation_forest(
  fit,
  predictors = c("X1","X2"),
  outcome = "Y",
  predictor_labels = c(X1 = "Predictor 1", X2 = "Predictor 2")
)

Plot Multiple SEM Models

Description

Creates a panel of SEM path diagrams for multiple models. The panel can be arranged in a single row (default) or in an arbitrary grid via the nrow / ncol arguments; empty cells are left blank when the number of models does not fill the grid.

Usage

plot_multi_sem(
  models,
  titles = NULL,
  layout = "tree2",
  rotation = 2,
  whatLabels = "std",
  residuals = FALSE,
  intercepts = FALSE,
  thresholds = FALSE,
  sizeMan = 10,
  sizeMan2 = 5,
  sizeLat = 12,
  label.cex = 1,
  edge.label.cex = 2,
  edge.color = "grey40",
  color = list(lat = "grey80", man = "grey90"),
  mar = c(4, 4, 4, 4),
  outerMar = c(1, 1, 3, 1),
  nrow = NULL,
  ncol = NULL,
  show_fit_indices = TRUE,
  fit_indices = c("cfi", "tli", "rmsea", "srmr"),
  use_scaled = FALSE,
  model_descriptions = NULL,
  custom_titles = NULL,
  title.cex = 0.8,
  title.font = 2,
  save_plot = FALSE,
  filename = NULL,
  file_format = "png",
  width_per = 4,
  height = 4,
  dpi = 300,
  units = "in"
)

Arguments

Value

NULL (plots are drawn to device).

Examples

library(lavaan)

# Create sample data
set.seed(123)
n <- 300
data <- data.frame(
  Item1 = sample(1:5, n, replace = TRUE),
  Item2 = sample(1:5, n, replace = TRUE),
  Item3 = sample(1:5, n, replace = TRUE),
  Item4 = sample(1:5, n, replace = TRUE),
  Item5 = sample(1:5, n, replace = TRUE),
  Item6 = sample(1:5, n, replace = TRUE)
)

# Fit multiple models
model1 <- "F1 =~ Item1 + Item2 + Item3 + Item4 + Item5 + Item6"
model2 <- "F1 =~ Item1 + Item2 + Item3\nF2 =~ Item4 + Item5 + Item6"

fit1 <- cfa(model1, data = data, ordered = TRUE, estimator = "WLSMV")
fit2 <- cfa(model2, data = data, ordered = TRUE, estimator = "WLSMV")

# Plot multiple models side by side (single row, default)
plot_multi_sem(
  models = list(fit1, fit2),
  model_descriptions = c("1-Factor Model", "2-Factor Model"),
  show_fit_indices = TRUE,
  fit_indices = c("cfi", "tli", "rmsea", "srmr"),
  use_scaled = TRUE
)

# Arrange three models in two rows (2 x 2 grid; last cell left blank)
plot_multi_sem(
  models = list(fit1, fit2, fit1),
  model_descriptions = c("Model 1", "Model 2", "Model 3"),
  nrow = 2, ncol = 2,
  save_plot = TRUE, filename = file.path(tempdir(), "sem_grid"),
  width_per = 6, height = 6
)

Path diagram for a CFA model (psychometric convention, ggplot2)

Description

Genera un diagrama de senderos para un modelo CFA ajustado con lavaan::cfa() siguiendo la convencion psicometrica estandar: factores latentes como elipses (circulos), items observados como rectangulos, y cargas dibujadas en abanico desde el borde del circulo del factor hacia cada item. Las correlaciones latentes se representan como curvas dobles entre factores. Por defecto el dibujo es lineal (blanco y negro), tipo diagrama de Brown (2015) o Kline; el usuario puede pedir paleta de color con bw = FALSE o pasando palette.

Usage

plot_path_cfa(
  fit,
  loading_threshold = 0.3,
  show_loading_labels = TRUE,
  loading_label_decimals = 2,
  loading_label_position = 0.55,
  correlate_factors = TRUE,
  show_correlation_labels = TRUE,
  correlation_curvature = 0.45,
  bw = TRUE,
  palette = NULL,
  factor_radius = 0.85,
  item_half_width = 0.65,
  item_half_height = 0.28,
  edge_width_range = c(0.4, 1.6),
  node_label_size = 3,
  edge_label_size = 2.7,
  item_x = 0,
  factor_x = 8,
  item_spacing = 1,
  group_gap = 1.2,
  fit_indices = c("cfi.scaled", "tli.scaled", "rmsea.scaled", "srmr"),
  show_n = TRUE,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  factor_label_map = NULL
)

Arguments

Details

Toma lavaan::standardizedSolution() y filtra:

• op == "=~" para construir las cargas factor \to item.

• op == "~~" con lhs != rhs entre factores latentes para las correlaciones \phi.

Cada flecha sale del borde del circulo del factor (no del centro) gracias a un calculo trigonometrico que las distribuye en abanico, evitando la convergencia visual al mismo punto.

Las varianzas residuales se omiten para mantener la lectura limpia. Si las necesitas explicitas, usa semPlot::semPaths(..., what = "std").

Value

Un objeto ggplot listo para imprimir, guardar con ggsave() o componer con patchwork.

References

Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.

Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.

Examples

library(lavaan)

set.seed(123)
n <- 400
g <- rnorm(n)
sim_f <- function(fscore, k) {
  sapply(seq_len(k), function(i) {
    as.numeric(cut(0.8 * fscore + rnorm(length(fscore), 0, 0.6),
                   c(-Inf, -1, 0, 1, Inf)))
  })
}
F1s <- 0.6 * g + rnorm(n, 0, 0.8)
F2s <- 0.6 * g + rnorm(n, 0, 0.8)
F3s <- 0.6 * g + rnorm(n, 0, 0.8)
my_data <- data.frame(sim_f(F1s, 5), sim_f(F2s, 5), sim_f(F3s, 5))
names(my_data) <- paste0("ITEM", 1:15)

mod <- '
  F1 =~ ITEM1 + ITEM2 + ITEM3 + ITEM4 + ITEM5
  F2 =~ ITEM6 + ITEM7 + ITEM8 + ITEM9 + ITEM10
  F3 =~ ITEM11 + ITEM12 + ITEM13 + ITEM14 + ITEM15
'
fit <- cfa(mod, data = my_data, ordered = paste0("ITEM", 1:15),
           estimator = "WLSMV", std.lv = TRUE)

# Default: black & white (textbook style)
plot_path_cfa(fit, title = "DERS — final structure")

# With colour palette
plot_path_cfa(fit, bw = FALSE,
  palette = c(F1 = "#90CAF9", F2 = "#F4A8A8", F3 = "#A5D6A7"))

Path-mediation diagram for SEM models with multiple distal predictors

Description

Genera un diagrama de senderos en tres columnas (predictores distales \to mediadores \to outcome) a partir de un objeto lavaan ajustado. Las aristas tienen grosor proporcional a |\beta| estandarizado, color por bloque de origen, y se atenúan cuando p \ge .05. Los nodos se rellenan con una paleta pastel agrupada por bloque conceptual. Diseñado para modelos con muchos predictores distales (10+) en los que semPlot/lavaanPlot producen layouts ilegibles.

Usage

plot_path_mediation(
  fit,
  nodes,
  edges_extra = NULL,
  x_positions = c(0, 6, 12),
  palette = NULL,
  sig_alpha = 0.18,
  edge_width_range = c(0.3, 2.4),
  sig_threshold = 0.05,
  show_n_s = TRUE,
  show_edge_labels = TRUE,
  edge_label_decimals = 2,
  shrink_dx = 1.2,
  shrink_dy = 0.7,
  node_size = 3.4,
  node_label_size = 3.4,
  edge_label_size = 2.7,
  fit_indices = c("cfi.robust", "tli.robust", "rmsea.robust", "srmr"),
  show_r2 = TRUE,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  layer_labels = c("Predictores distales", "Mediadores", "Variable dependiente"),
  show_layer_labels = TRUE
)

Arguments

Details

La función toma los coeficientes estandarizados de lavaan::standardizedSolution() y construye el data frame de aristas filtrando las regresiones (op == "~") que conectan nodos del data frame nodes. Los efectos indirectos definidos con := en la sintaxis lavaan no se grafican aquí; para visualizarlos en otra capa, use plot_mediation_forest.

El layout en tres columnas se inspira en Li et al. (2026, BMC Public Health, figura del modelo de mediación dual de presión académica) y en Bakioglu, Korkmaz y Ercan (2020, IJMHA, modelo de mediación del miedo a la COVID-19).

Value

Un objeto ggplot listo para imprimir, guardar con ggsave() o componer con patchwork.

References

Bakioglu, F., Korkmaz, O., & Ercan, H. (2020). Fear of COVID-19 and positivity: Mediating role of intolerance of uncertainty, depression, anxiety and stress. International Journal of Mental Health and Addiction. doi:10.1007/s11469-020-00331-y

Li, Y., Wang, J., Luo, W., Zhou, H., Zhang, J., Xu, J., et al. (2026). Academic pressure and suicide risk among adolescents: A dual mediation model of depression and school cohesion. BMC Public Health, 26, 335. doi:10.1186/s12889-025-25805-3

Examples

library(lavaan)

set.seed(123)
n <- 300
X1 <- rnorm(n); X2 <- rnorm(n); X3 <- rnorm(n)
M1 <- 0.5 * X1 + 0.3 * X2 + rnorm(n, 0, 0.8)
M2 <- 0.4 * X2 + 0.3 * X3 + rnorm(n, 0, 0.8)
Y  <- 0.4 * M1 + 0.3 * M2 + 0.2 * X1 + rnorm(n, 0, 0.7)
your_data <- data.frame(X1, X2, X3, M1, M2, Y)

# Modelo de senderos: 3 predictores distales -> 2 mediadores -> 1 outcome
mod <- '
  M1 ~ X1 + X2 + X3
  M2 ~ X1 + X2 + X3
  Y  ~ M1 + M2 + X1 + X2 + X3
'
fit <- sem(mod, data = your_data, estimator = "MLR")

nodes <- data.frame(
  id    = c("X1","X2","X3","M1","M2","Y"),
  label = c("Estilo 1","Estilo 2","Demogr","Mediador 1","Mediador 2","Outcome"),
  block = c("Habito","Habito","Demogr","Afecto","Afecto","Outcome"),
  layer = c(1,1,1,2,2,3),
  y     = c(2,0,-2,1,-1,0)
)

plot_path_mediation(fit, nodes,
                    title = "Modelo de mediación afectiva",
                    palette = c(Habito = "#FFE082", Demogr = "#B0BEC5",
                                Afecto = "#F4A8A8", Outcome = "#90CAF9"))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

%>%

Safe CFA Wrapper

Description

Fits a CFA model using cfa with error handling. Returns NULL instead of raising an error when the model fails to converge or encounters estimation problems.

Usage

safe_cfa(syntax, data, items, estimator = "WLSMV", stdlv = TRUE)

Arguments

Value

A fitted lavaan object, or NULL if the model failed.

Examples

set.seed(123)
mydata <- data.frame(
  x1 = sample(1:5, 200, replace = TRUE),
  x2 = sample(1:5, 200, replace = TRUE),
  x3 = sample(1:5, 200, replace = TRUE)
)
model <- 'F1 =~ x1 + x2 + x3'
fit <- safe_cfa(model, data = mydata, items = c("x1", "x2", "x3"))
if (!is.null(fit)) summary(fit)

Safe Fit Measures Extraction

Description

Extracts selected fit measures from a lavaan object. Returns a one-row data frame with NA values for any measures that could not be retrieved (e.g., when the model is NULL).

Usage

safe_measures(
  fit,
  wanted = c("chisq.scaled", "df.scaled", "cfi.scaled", "tli.scaled", "rmsea.scaled",
    "srmr", "wrmr")
)

Arguments

Value

A one-row data frame with the requested fit measures.

Examples

set.seed(123)
mydata <- data.frame(
  x1 = sample(1:5, 200, replace = TRUE),
  x2 = sample(1:5, 200, replace = TRUE),
  x3 = sample(1:5, 200, replace = TRUE)
)
model <- 'F1 =~ x1 + x2 + x3'
fit <- safe_cfa(model, data = mydata, items = c("x1", "x2", "x3"))
safe_measures(fit)

Save Tables to Excel File

Description

Saves multiple data frames to an Excel file with separate sheets.

Usage

save_to_excel_table(file_name, ..., na.string = "")

Arguments

Value

Invisibly returns NULL. Writes Excel file to disk.

SMOTE Oversampling for Multiclass Data

Description

Applies SMOTE (Synthetic Minority Over-sampling Technique) for multiclass imbalanced data.

Usage

smote_multiclass(data, outcome, perc_maj = 100, k = 5, seed = NULL)

Arguments

Value

A data frame with oversampled minority classes.

Examples

# Create imbalanced multiclass data
set.seed(123)
data <- data.frame(
  Item1 = sample(1:5, 200, replace = TRUE),
  Item2 = sample(1:5, 200, replace = TRUE),
  Item3 = sample(1:5, 200, replace = TRUE),
  Group = c(rep("A", 100), rep("B", 70), rep("C", 30))  # Imbalanced classes
)

# Check original class distribution
table(data$Group)
# A   B   C
# 100 70  30

# Apply SMOTE to balance classes
balanced_data <- smote_multiclass(
  data = data,
  outcome = "Group",
  perc_maj = 100,  # Target 100% of majority class size
  k = 5,
  seed = 123
)

# Check balanced class distribution
table(balanced_data$Group)

# Partial balancing (50% of majority)
partial_balanced <- smote_multiclass(
  data = data,
  outcome = "Group",
  perc_maj = 50,
  seed = 456
)
table(partial_balanced$Group)

Fit Multiple Lavaan Model Specifications

Description

Fits multiple lavaan EFA/CFA models and returns fitted model objects.

Usage

specification_models(
  modelos,
  data,
  estimator,
  rotation = "oblimin",
  ordered = TRUE,
  verbose = FALSE
)

Arguments

Value

A list of fitted lavaan model objects.

Examples

# Create sample data
set.seed(123)
n <- 300
g <- rnorm(n)
t1 <- 0.7 * g + rnorm(n, 0, 0.7)
t2 <- 0.7 * g + rnorm(n, 0, 0.7)
sim_item <- function(t) {
  as.numeric(cut(t + rnorm(length(t), 0, 0.8), c(-Inf, -1, 0, 1, Inf)))
}
data <- data.frame(
  Item1 = sim_item(t1), Item2 = sim_item(t1), Item3 = sim_item(t1),
  Item4 = sim_item(t2), Item5 = sim_item(t2), Item6 = sim_item(t2)
)

# Generate EFA models for 1 to 3 factors
models <- generate_modelos(
  n_factors = 2,
  name_items = "Item",
  n_items = 6
)

# Fit all models
specifications <- specification_models(
  modelos = models,
  data = data,
  estimator = "WLSMV",
  rotation = "oblimin",
  ordered = TRUE
)

# Access fitted model for 2-factor solution
summary(specifications[[2]])

# Extract fit indices
fit_table <- extract_fit_measures(specifications)
print(fit_table)

Split Data with Stratified Clustering

Description

Splits a data frame using stratified and clustered sampling.

Usage

split_data_stratified_clustered(
  df,
  strat_var = "Region_reside",
  cluster_var = "Facultad",
  perc_exploratorio = 0.5,
  seed = NULL
)

Arguments

Value

A list with exploratorio and confirmatorio data frames.

Examples

# Create sample data with stratification and clustering variables
set.seed(123)
data <- data.frame(
  ID = 1:500,
  Item1 = sample(1:5, 500, replace = TRUE),
  Item2 = sample(1:5, 500, replace = TRUE),
  Item3 = sample(1:5, 500, replace = TRUE),
  Region_reside = sample(c("Norte", "Sur", "Centro"), 500, replace = TRUE),
  Facultad = sample(c("Ingenieria", "Medicina", "Derecho"), 500, replace = TRUE)
)

# Split with stratification by Region and clustering by Facultad
splits <- split_data_stratified_clustered(
  df = data,
  strat_var = "Region_reside",
  cluster_var = "Facultad",
  perc_exploratorio = 0.5,
  seed = 123
)

# Access subsets
efa_data <- splits$exploratorio
cfa_data <- splits$confirmatorio

# Verify stratification is maintained
table(efa_data$Region_reside, efa_data$Facultad)
table(cfa_data$Region_reside, cfa_data$Facultad)

# Different proportions
splits2 <- split_data_stratified_clustered(
  df = data,
  strat_var = "Region_reside",
  cluster_var = "Facultad",
  perc_exploratorio = 0.6,
  seed = 456
)

Split Data into Three Subsets

Description

Splits a data frame into pilot, exploratory and confirmatory subsets.

Usage

split_data_three(
  df,
  perc_piloto = 0.1,
  perc_exploratorio = 0.45,
  perc_confirmatorio = 0.45,
  seed = NULL
)

Arguments

Value

A list with piloto, exploratorio and confirmatorio data frames.

Examples

# Create sample data
set.seed(123)
data <- data.frame(
  Item1 = sample(1:5, 1000, replace = TRUE),
  Item2 = sample(1:5, 1000, replace = TRUE),
  Item3 = sample(1:5, 1000, replace = TRUE),
  Item4 = sample(1:5, 1000, replace = TRUE)
)

# Split data into 10% pilot, 45% EFA, 45% CFA
splits <- split_data_three(
  df = data,
  perc_piloto = 0.10,
  perc_exploratorio = 0.45,
  perc_confirmatorio = 0.45,
  seed = 123
)

# Access the subsets
pilot_data <- splits$piloto
efa_data <- splits$exploratorio
cfa_data <- splits$confirmatorio

nrow(pilot_data)  # Should be 100
nrow(efa_data)    # Should be 450
nrow(cfa_data)    # Should be 450

# Custom proportions: 20% pilot, 40% EFA, 40% CFA
splits2 <- split_data_three(
  df = data,
  perc_piloto = 0.20,
  perc_exploratorio = 0.40,
  perc_confirmatorio = 0.40,
  seed = 456
)

Split Data into Two Subsets

Description

Splits a data frame into exploratory and confirmatory subsets.

Usage

split_data_two(
  df,
  perc_exploratorio = 0.5,
  perc_confirmatorio = 0.5,
  seed = NULL
)

Arguments

Value

A list with exploratorio and confirmatorio data frames.

Examples

# Create sample data
set.seed(123)
data <- data.frame(
  Item1 = sample(1:5, 500, replace = TRUE),
  Item2 = sample(1:5, 500, replace = TRUE),
  Item3 = sample(1:5, 500, replace = TRUE),
  Item4 = sample(1:5, 500, replace = TRUE)
)

# Split data 50/50 for EFA and CFA
splits <- split_data_two(
  df = data,
  perc_exploratorio = 0.5,
  perc_confirmatorio = 0.5,
  seed = 123
)

# Access the subsets
efa_data <- splits$exploratorio
cfa_data <- splits$confirmatorio

nrow(efa_data)  # Should be 250
nrow(cfa_data)  # Should be 250

# Different proportions: 60% EFA, 40% CFA
splits2 <- split_data_two(
  df = data,
  perc_exploratorio = 0.6,
  perc_confirmatorio = 0.4,
  seed = 456
)

Summarize Column Statistics

Description

Calculates descriptive statistics for a column, excluding zero values.

Usage

summarise_column(data, col_name)

Arguments

Value

A data frame with mean, SD, min, and max values.