Package: EFAtools 0.4.4

EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.

Authors:Markus Steiner [aut, cre], Silvia Grieder [aut], William Revelle [ctb], Max Auerswald [ctb], Morten Moshagen [ctb], John Ruscio [ctb], Brendan Roche [ctb], Urbano Lorenzo-Seva [ctb], David Navarro-Gonzalez [ctb]

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EFAtools.pdf |EFAtools.html✨
EFAtools/json (API)
NEWS

# Install 'EFAtools' in R:
install.packages('EFAtools', repos = c('https://mdsteiner.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mdsteiner/efatools/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

17 exports 9 stars 1.84 score 59 dependencies 1 dependents 1 mentions 74 scripts 784 downloads

Last updated 2 years agofrom:c05cd79cf9. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-win-x86_64NOTEAug 20 2024
R-4.5-linux-x86_64NOTEAug 20 2024
R-4.4-win-x86_64NOTEAug 20 2024
R-4.4-mac-x86_64NOTEAug 20 2024
R-4.4-mac-aarch64NOTEAug 20 2024
R-4.3-win-x86_64NOTEAug 20 2024
R-4.3-mac-x86_64NOTEAug 20 2024
R-4.3-mac-aarch64NOTEAug 20 2024

Exports:%>%BARTLETTCDCOMPAREEFAEFA_AVERAGEEKCFACTOR_SCORESHULLKGCKMON_FACTORSOMEGAPARALLELSCREESLSMT

Dependencies:backportscheckmateclicodetoolscolorspacecpp11crayondigestdplyrfansifarverfuturefuture.applygenericsggplot2globalsglueGPArotationgtablehmsisobandlabelinglatticelavaanlifecyclelistenvmagrittrMASSMatrixmgcvmnormtmunsellnlmenumDerivparallellypbivnormpillarpkgconfigprettyunitsprogressprogressrpsychpurrrquadprogR6RColorBrewerRcppRcppArmadillorlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

EFAtools

Rendered fromEFAtools.Rmdusingknitr::rmarkdownon Aug 20 2024.

Last update: 2022-03-21
Started: 2020-06-22

Replicate SPSS and R psych results with EFAtools

Rendered fromReplicate_SPSS_psych.Rmdusingknitr::rmarkdownon Aug 20 2024.

Last update: 2020-12-28
Started: 2020-07-28

Readme and manuals

Help Manual

Help pageTopics
Compute explained variances from loadings.compute_vars
Compute number of non-matching indicator-to-factor correspondences.factor_corres
Format numbers for print method.numformat
Perform the iterative PAF procedure.paf_iter
Parallel analysis on simulated data..parallel_sim
Bartlett's test of sphericityBARTLETT
Comparison DataCD
Compare two vectors or matrices (communalities or loadings)COMPARE
DOSPERTDOSPERT
DOSPERT_rawDOSPERT_raw
Exploratory factor analysis (EFA)EFA
Model averaging across different EFA methods and typesEFA_AVERAGE
Empirical Kaiser CriterionEKC
Estimate factor scores for an EFA modelFACTOR_SCORES
GRiPS_rawGRiPS_raw
Hull method for determining the number of factors to retainHULL
Intelligence subtests from the Intelligence and Development Scales-2IDS2_R
Kaiser-Guttman CriterionKGC
Kaiser-Meyer-Olkin criterionKMO
Various Factor Retention CriteriaN_FACTORS
McDonald's omegaOMEGA
Parallel analysisPARALLEL
Plot CD objectplot.CD
Plot EFA_AVERAGE objectplot.EFA_AVERAGE
Plot EKC objectplot.EKC
Plot HULL objectplot.HULL
Plot KGC objectplot.KGC
Plot PARALLEL objectplot.PARALLEL
Plot SCREE objectplot.SCREE
population_modelspopulation_models
Print BARTLETT objectprint.BARTLETT
Print function for CD objectsprint.CD
Print COMPARE objectprint.COMPARE
Print EFA objectprint.EFA
Print EFA_AVERAGE objectprint.EFA_AVERAGE
Print function for EKC objectsprint.EKC
Print function for HULL objectsprint.HULL
Print function for KGC objectsprint.KGC
Print KMO objectprint.KMO
Print LOADINGS objectprint.LOADINGS
Print function for N_FACTORS objectsprint.N_FACTORS
Print OMEGA objectprint.OMEGA
Print function for PARALLEL objectsprint.PARALLEL
Print function for SCREE objectsprint.SCREE
Print SL objectprint.SL
Print SLLOADINGS objectprint.SLLOADINGS
Print SMT objectprint.SMT
RiskDimensionsRiskDimensions
Scree PlotSCREE
Schmid-Leiman TransformationSL
Sequential Chi Square Model Tests, RMSEA lower bound, and AICSMT
Various outputs from SPSS (version 23) FACTORSPSS_23
Various outputs from SPSS (version 27) FACTORSPSS_27
Four test models used in Grieder and Steiner (2020)test_models
UPPS_rawUPPS_raw
Woodcock Johnson IV: ages 14 to 19WJIV_ages_14_19
Woodcock Johnson IV: ages 20 to 39WJIV_ages_20_39
Woodcock Johnson IV: ages 3 to 5WJIV_ages_3_5
Woodcock Johnson IV: ages 40 to 90 plusWJIV_ages_40_90
Woodcock Johnson IV: ages 6 to 8WJIV_ages_6_8
Woodcock Johnson IV: ages 9 to 13WJIV_ages_9_13