Title: Automated Item Removal Strategies for Exploratory Factor Analysis
Version: 0.2.3
Description: Automates the identification and comparative evaluation of item-removal strategies in exploratory factor analysis, producing transparent summaries (explained variance, loading ranges, reliability) to support comfortable, reproducible decisions. The criteria are based on best practices and established heuristics (e.g., Costello & Osborne (2005) <doi:10.7275/jyj1-4868>, Howard (2016) <doi:10.1080/10447318.2015.1087664>).
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (≥ 4.1)
Imports: gtools, psych, qgraph, stats, utils
URL: https://github.com/ahmetcaliskan1987/ItemRest
BugReports: https://github.com/ahmetcaliskan1987/ItemRest/issues
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2025-10-06 14:02:37 UTC; ahmetcaliskan
Author: Ahmet Çalışkan [aut, cre], Abdullah Faruk Kılıç [aut]
Maintainer: Ahmet Çalışkan <ahmetcaliskan1987@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-09 12:10:17 UTC

Calculate a correlation matrix.

Description

Calculate a correlation matrix.

Usage

cor_matrix_custom(data, method = "polychoric")

Calculate basic descriptive statistics for a dataset.

Description

Calculate basic descriptive statistics for a dataset.

Usage

descriptive_stats(data)

Determine the number of factors using Parallel Analysis.

Description

Determine the number of factors using Parallel Analysis.

Usage

determine_n_factors(data, cor_method = "pearson")

Run a custom EFA.

Description

Run a custom EFA.

Usage

efa_custom(
  data,
  n_factors = 1,
  cor_method = "polychoric",
  extract = "uls",
  rotate = "oblimin"
)

Generate combinations of items to remove.

Description

Generate combinations of items to remove.

Usage

get_combinations(items)

Evaluate Item Removal Strategies for Exploratory Factor Analysis (EFA)

Description

This function automates the process of identifying low-quality items (those with low factor loadings or significant cross-loadings) in an Exploratory Factor Analysis (EFA). It systematically tests various combinations of removing these problematic items and evaluates the impact on model fit, returning a comprehensive summary of all tested strategies.

Usage

itemrest(
  data,
  cor_method = "pearson",
  n_factors = NULL,
  extract = "uls",
  rotate = "oblimin"
)

Arguments

data

A numeric data.frame or matrix for the analysis.

cor_method

The correlation method to use, e.g., "pearson" or "polychoric".

n_factors

The number of factors. If NULL, it's determined automatically by parallel analysis.

extract

The factor extraction (estimation) method. See psych::fa. Default is "uls".

rotate

The rotation method. See psych::fa. Default is "oblimin".

Value

An object of class itemrest_result. This is a list containing the following components:

descriptive_stats

Basic descriptive statistics of the input data.

initial_efa

The results of the initial EFA before any items are removed.

problem_items

A list of items identified as low-loading or cross-loading.

removal_summary

A data.frame summarizing the results of all tested removal strategies.

optimal_strategy

The best-performing strategy that resulted in a clean factor structure (no cross-loadings).

settings

A list of the settings used for the analysis.

Examples


# We will use the 'bfi' dataset from the 'psych' package.
# This requires the 'psych' package to be installed.
if (requireNamespace("psych", quietly = TRUE)) {
  data(bfi, package = "psych")

  # 1. Prepare the data: Select the personality items (first 25 columns)
  #    and remove rows with missing values for this example.
  example_data <- bfi[, 1:25]
  example_data <- na.omit(example_data)

  # 2. Run the item removal analysis.
  #    Based on theory, the Big Five model has 5 factors.
  results <- itemrest(
    data = example_data,
    n_factors = 5,
    cor_method = "pearson" # Data is not ordinal, so pearson is appropriate
  )

  # 3. Print the report for optimal strategies (default).
  print(results)

  # 4. Print the report for all tested strategies.
  print(results, report = "all")
}

Print method for itemrest_result class

Description

Print method for itemrest_result class

Usage

## S3 method for class 'itemrest_result'
print(x, report = "optimal", ...)

Arguments

x

An object of class itemrest_result.

report

The type of report to generate: "optimal" (default) or "all".

...

Other arguments (not used).

Value

No return value, called for side effects (prints the report to the console).

Examples


if (requireNamespace("psych", quietly = TRUE)) {
  data(bfi, package = "psych")
  example_data <- na.omit(bfi[, 1:25])
  results <- itemrest(example_data, n_factors = 5)

  # Print the default optimal report
  print(results)

  # Print the report of all strategies
  print(results, report = "all")
}

Sort item IDs numerically.

Description

Sort item IDs numerically.

Usage

sort_item_ids(x)

Test removal strategies and build a summary table.

Description

Test removal strategies and build a summary table.

Usage

test_removals(data, base_items, combs, n_factors, cor_method, extract, rotate)