EFAfactors: Determining the Number of Factors in Exploratory Factor Analysis
Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.
| Version: |
1.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
BBmisc, ddpcr, ineq, MASS, Matrix, mlr, ParamHelpers, proxy, psych, ranger, reticulate, Rcpp, RcppArmadillo, SimCorMultRes, xgboost |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2024-09-29 |
| DOI: |
10.32614/CRAN.package.EFAfactors |
| Author: |
Haijiang Qin [aut, cre, cph],
Lei Guo [aut, cph] |
| Maintainer: |
Haijiang Qin <haijiang133 at outlook.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
EFAfactors results |
Documentation:
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