Package: EFAfactors 1.2.4

Haijiang Qin

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:Haijiang Qin [aut, cre, cph], Lei Guo [aut, cph]

EFAfactors_1.2.4.tar.gz
EFAfactors_1.2.4.tar.gz(r-4.7-arm64)EFAfactors_1.2.4.tar.gz(r-4.7-x86_64)EFAfactors_1.2.4.tar.gz(r-4.6-arm64)EFAfactors_1.2.4.tar.gz(r-4.6-x86_64)
EFAfactors_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EFAfactors/json (API)
NEWS

# Install 'EFAfactors' in R:
install.packages('EFAfactors', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • data.bfi - 25 Personality Items Representing 5 Factors
  • data.DAPCS - 20-item Dependency-Oriented and Achievement-Oriented Psychological Control Scale
  • data.datasets.DNN - Subset Dataset for Training the Deep Neural Network
  • data.datasets.LSTM - Subset Dataset for Training the Long Short Term Memory (LSTM) Network
  • data.scaler.DNN - The Scaler for the pre-trained Deep Neural Network
  • data.scaler.LSTM - The Scaler for the pre-trained Long Short Term Memory (LSTM) Network
  • model.xgb - The Tuned XGBoost Model for Determining the Number of Facotrs

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

2.36 score 1 packages 38 scripts 289 downloads 26 exports 116 dependencies

Last updated from:d0b8d80fd3. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK190
linux-devel-x86_64OK210
source / vignettesOK238
linux-release-arm64OK200
linux-release-x86_64OK209
wasm-releaseOK181

Exports:af.softmaxCDCDFcheck_python_librariesEFAhclustEFAindexEFAkmeansEFAscreetEFAsim.dataEFAvoteEKCextractor.feature.FFextractor.feature.NNfactor.analysisFFGenDataHullKGCload.NNload.scalerload.xgbMAPNNnormalizorPASTOC

Dependencies:askpassbackportsbase64encBBmiscbitbit64bslibcachemcheckmateclicliprcommonmarkcpp11crayoncrosstalkcurldata.tableddpcrdigestdplyrDTevaluateevdfarverfastmapfastmatchfontawesomefsgenericsggplot2glueGPArotationgtableherehighrhmshtmltoolshtmlwidgetshttpuvhttrineqisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemimemixtoolsmlrmnormtnlmeopensslotelparallelMapParamHelperspillarpkgconfigplotlyplyrpngprettyunitsprogresspromisesproxypsychpurrrR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLreadrreticulaterlangrmarkdownrprojrootS7sassscalessegmentedshinyshinydisconnectshinyjsSimCorMultRessourcetoolsstringistringrsurvivalsystibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevroomwithrxfunxgboostXMLxtableyaml

Readme and manuals

Help Manual

Help pageTopics
An Activation Function: Softmaxaf.softmax
the Comparison Data (CD) ApproachCD
the Comparison Data Forest (CDF) ApproachCDF
Check and Install Python Libraries (numpy and onnxruntime)check_python_libraries
25 Personality Items Representing 5 Factorsdata.bfi
20-item Dependency-Oriented and Achievement-Oriented Psychological Control Scale (DAPCS)data.DAPCS
Subset Dataset for Training the Deep Neural Network (DNN)data.datasets.DNN
Subset Dataset for Training the Long Short Term Memory (LSTM) Networkdata.datasets.LSTM
the Scaler for the pre-trained Deep Neural Network (DNN)data.scaler.DNN
the Scaler for the pre-trained Long Short Term Memory (LSTM) Networkdata.scaler.LSTM
Hierarchical Clustering for EFAEFAhclust
Various Indeces in EFAEFAindex
K-means for EFAEFAkmeans
Scree PlotEFAscreet
Simulate Data that Conforms to the theory of Exploratory Factor Analysis.EFAsim.data
Voting Method for Number of Factors in EFAEFAvote
Empirical Kaiser CriterionEKC
Extracting features According to Goretzko & Buhner (2020)extractor.feature.FF
Extracting features for the pre-trained Neural Networks for Determining the Number of Factorsextractor.feature.NN
Factor Analysis by Principal Axis Factoringfactor.analysis
Factor Forest (FF) Powered by An Tuned XGBoost Model for Determining the Number of FactorsFF
Simulating Data Following John Ruscio's RGenDataGenData
the Hull ApproachHull
Kaiser-Guttman CriterionKGC
Load the the pre-trained Neural Networks for Determining the Number of Factorsload.NN
Load the Scaler for the pre-trained Neural Networks for Determining the Number of Factorsload.scaler
Load the Tuned XGBoost Modelload.xgb
Minimum Average Partial (MAP) TestMAP
the Tuned XGBoost Model for Determining the Number of Facotrsmodel.xgb
the pre-trained Neural Networks for Determining the Number of FactorsNN
Feature Normalization for the pre-trained Neural Networks for Determining the Number of Factorsnormalizor
Parallel AnalysisPA
Plot Methodsplot plot.CD plot.CDF plot.EFAhclust plot.EFAkmeans plot.EFAscreet plot.EFAvote plot.EKC plot.FF plot.Hull plot.KGC plot.MAP plot.NN plot.PA plot.STOC
Prediction Function for the Tuned XGBoost Model with Early StoppingpredictLearner.classif.xgboost.earlystop
Print Methodsprint print.CD print.CDF print.EFAdata print.EFAhclust print.EFAscreet print.EFAvote print.EKC print.FF print.Hull print.KGC print.MAP print.NN print.PA
Scree Test Optimal Coordinate (STOC)STOC