Package: EFAfactors 1.1.1
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.
Authors:
EFAfactors_1.1.1.tar.gz
EFAfactors_1.1.1.tar.gz(r-4.5-noble)EFAfactors_1.1.1.tar.gz(r-4.4-noble)
EFAfactors.pdf |EFAfactors.html✨
EFAfactors/json (API)
NEWS
# Install 'EFAfactors' in R: |
install.packages('EFAfactors', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- data.bfi - 25 Personality Items Representing 5 Factors
- data.datasets - Subset Dataset for Training the Pre-Trained Deep Neural Network
- data.scaler - The Scaler for the Pre-Trained Deep Neural Network
- model.xgb - The Tuned XGBoost Model for Determining the Number of Facotrs
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 16 days agofrom:0c6498c80c. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 20 2024 |
R-4.5-linux-x86_64 | OK | Nov 20 2024 |
Exports:af.softmaxCDCDFDNN_predictorEFAhclustEFAindexEFAkmeansEFAscreetEFAsim.dataEFAvoteEKCextractor.feature.DNNextractor.feature.FFfactor.analysisFFGenDataHullKGCload_DNNload_scalerload_xgbnormalizorPA
Dependencies:askpassbackportsbase64encBBmiscbitbit64bslibcachemcheckmateclicliprcolorspacecommonmarkcpp11crayoncrosstalkcurldata.tableddpcrdigestdplyrDTevaluateevdfansifarverfastmapfastmatchfontawesomefsgenericsggplot2glueGPArotationgtableherehighrhmshtmltoolshtmlwidgetshttpuvhttrineqisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemixtoolsmlrmnormtmunsellnlmeopensslparallelMapParamHelperspillarpkgconfigplotlyplyrpngprettyunitsprogresspromisesproxypsychpurrrR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLreadrreticulaterlangrmarkdownrprojrootsassscalessegmentedshinyshinydisconnectshinyjsSimCorMultRessourcetoolsstringistringrsurvivalsystibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevroomwithrxfunxgboostXMLxtableyaml