Installation

install.packages("EFAtools")

install.packages("devtools")devtools::install_github("mdsteiner/EFAtools")

install.packages("devtools")devtools::install_github("mdsteiner/EFAtools",build_vignettes=TRUE)

Example

# load the packagelibrary(EFAtools)# Run multiple factor retention methodsN_FACTORS(test_models$baseline$cormat,N=500)#> Warning in N_FACTORS(test_models$baseline$cormat, N = 500): ! 'x' was a correlation matrix but CD needs raw data. Skipping CD.#> ℹ The default implementation of EKC has changed compared to EFAtools version <= 0.5.0 to reflect the original version by Braeken and van Assen (2017). The previous version (which often yields different results from the original) is available with type = 'AM2019'. See details in the help page.#>#> ── Tests for the suitability of the data for factor analysis ───────────────────#>#> Bartlett's test of sphericity#>#> ✔ The Bartlett's test of sphericity was significant at an alpha level of .05.#>   These data are probably suitable for factor analysis.#>#>   𝜒²(153) = 2173.28, p < .001#>#> Kaiser-Meyer-Olkin criterion (KMO)#>#> ✔ The overall KMO value for your data is marvellous with 0.916.#>   These data are probably suitable for factor analysis.#>#> ── Number of factors suggested by the different factor retention criteria ──────#>#> • Comparison data: NA#> • EKC (original implementation, type 'BvA2017'): 3#> • Hull method with CAF: 3#> • Hull method with CFI: 1#> • Hull method with RMSEA: 1#> • Parallel analysis with SMC: 3#> • NEST: 3# A type SPSS EFA to mimick the SPSS implementation with# promax rotationEFA_SPSS<- EFA(test_models$baseline$cormat,n_factors=3,type="SPSS",rotation="promax")# look at solutionEFA_SPSS#>#> EFA performed with type = 'SPSS', method = 'PAF', and rotation = 'promax'.#>#> ── Rotated Loadings ────────────────────────────────────────────────────────────#>#>       F1      F2      F3      h2      u2#> V1   -.048    .035    .613    .367    .633#> V2   -.001    .067    .482    .277    .723#> V3    .060    .056    .453    .283    .717#> V4    .101   -.009    .551    .378    .622#> V5    .157   -.018    .438    .293    .707#> V6   -.072   -.049    .704    .399    .601#> V7    .001    .533    .093    .357    .643#> V8   -.016    .581    .030    .349    .651#> V9    .038    .550   -.001    .330    .670#> V10  -.022    .674   -.071    .383    .617#> V11   .015    .356    .232    .297    .703#> V12   .020    .651   -.010    .432    .568#> V13   .614    .086   -.067    .394    .606#> V14   .548   -.068    .088    .322    .678#> V15   .561    .128   -.070    .363    .637#> V16   .555   -.050    .091    .344    .656#> V17   .664   -.037   -.027    .390    .610#> V18   .555    .004    .050    .350    .650#>#> ── Factor Intercorrelations ────────────────────────────────────────────────────#>#>       F1      F2      F3#> F1    1.000   0.617   0.648#> F2    0.617   1.000   0.632#> F3    0.648   0.632   1.000#>#> ── Variances Accounted for ─────────────────────────────────────────────────────#>#>                       F1      F2      F3#> SS loadings           2.198   2.074   2.034#> Prop Tot Var          0.122   0.115   0.113#> Cum Prop Tot Var      0.122   0.237   0.350#> Prop Comm Var         0.349   0.329   0.323#> Cum Prop Comm Var     0.349   0.677   1.000#>#> ── Model Fit ───────────────────────────────────────────────────────────────────#>#> CAF: .50#> df: 102# A type psych EFA to mimick the psych::fa() implementation with# promax rotationEFA_psych<- EFA(test_models$baseline$cormat,n_factors=3,type="psych",rotation="promax")# compare the type psych and type SPSS implementationsCOMPARE(EFA_SPSS$rot_loadings,EFA_psych$rot_loadings,x_labels= c("SPSS","psych"))#> Mean [min, max] absolute difference:  0.0090 [ 0.0001,  0.0245]#> Median absolute difference:  0.0095#> Max decimals where all numbers are equal: 0#> Minimum number of decimals provided: 17#>#>        F1      F2      F3#> V1    0.0150  0.0142 -0.0195#> V2    0.0109  0.0109 -0.0138#> V3    0.0095  0.0103 -0.0119#> V4    0.0118  0.0131 -0.0154#> V5    0.0084  0.0105 -0.0109#> V6    0.0183  0.0169 -0.0245#> V7   -0.0026 -0.0017  0.0076#> V8   -0.0043 -0.0035  0.0102#> V9   -0.0055 -0.0040  0.0117#> V10  -0.0075 -0.0066  0.0151#> V11   0.0021  0.0029  0.0001#> V12  -0.0064 -0.0050  0.0136#> V13  -0.0109 -0.0019  0.0163#> V14  -0.0049  0.0028  0.0070#> V15  -0.0107 -0.0023  0.0161#> V16  -0.0051  0.0028  0.0074#> V17  -0.0096 -0.0001  0.0136#> V18  -0.0066  0.0014  0.0098

# Average solution across many different EFAs with oblique rotationsEFA_AV<- EFA_AVERAGE(test_models$baseline$cormat,n_factors=3,N=500,method= c("PAF","ML","ULS"),rotation="oblique",show_progress=FALSE)# look at solutionEFA_AV#>#> Averaging performed with averaging method mean (trim = 0) across 162 EFAs, varying the following settings: method, init_comm, criterion_type, start_method, rotation, k_promax, P_type, and varimax_type.#>#> The error rate is at 0%. Of the solutions that did not result in an error, 100% converged, 0% contained Heywood cases, and 100% were admissible.#>#>#> ══ Indicator-to-Factor Correspondences ═════════════════════════════════════════#>#> For each cell, the proportion of solutions including the respective indicator-to-factor correspondence. A salience threshold of 0.3 was used to determine indicator-to-factor correspondences.#>#>       F1      F2      F3#> V1    .11     .00    1.00#> V2    .11     .00    1.00#> V3    .11     .00     .94#> V4    .11     .00    1.00#> V5    .11     .00     .94#> V6    .11     .00    1.00#> V7    .11     .94     .00#> V8    .11    1.00     .00#> V9    .11     .94     .00#> V10   .11    1.00     .00#> V11   .11     .89     .00#> V12   .11    1.00     .00#> V13  1.00     .00     .00#> V14  1.00     .00     .00#> V15  1.00     .00     .00#> V16  1.00     .00     .00#> V17  1.00     .00     .00#> V18  1.00     .00     .00#>#>#> ══ Loadings ════════════════════════════════════════════════════════════════════#>#> ── Mean ────────────────────────────────────────────────────────────────────────#>#>       F1      F2      F3#> V1    .025    .048    .576#> V2    .060    .077    .451#> V3    .115    .066    .425#> V4    .157    .007    .518#> V5    .198   -.002    .412#> V6    .002   -.028    .658#> V7    .074    .497    .102#> V8    .056    .538    .046#> V9    .100    .510    .018#> V10   .048    .625   -.046#> V11   .082    .336    .228#> V12   .094    .606    .007#> V13   .597    .083   -.047#> V14   .531   -.056    .093#> V15   .548    .122   -.049#> V16   .540   -.041    .097#> V17   .633   -.033   -.009#> V18   .542    .009    .060#>#> ── Range ───────────────────────────────────────────────────────────────────────#>#>       F1      F2      F3#> V1    0.513   0.086   0.239#> V2    0.431   0.093   0.186#> V3    0.394   0.108   0.179#> V4    0.415   0.110   0.214#> V5    0.315   0.122   0.177#> V6    0.514   0.104   0.267#> V7    0.527   0.255   0.089#> V8    0.520   0.275   0.078#> V9    0.470   0.276   0.080#> V10   0.533   0.313   0.097#> V11   0.482   0.176   0.102#> V12   0.548   0.324   0.103#> V13   0.081   0.289   0.114#> V14   0.063   0.220   0.117#> V15   0.091   0.280   0.107#> V16   0.072   0.230   0.122#> V17   0.108   0.270   0.124#> V18   0.081   0.246   0.118#>#>#> ══ Factor Intercorrelations from Oblique Solutions ═════════════════════════════#>#> ── Mean ────────────────────────────────────────────────────────────────────────#>#>       F1      F2      F3#> F1    1.000   0.431   0.518#> F2    0.431   1.000   0.454#> F3    0.518   0.454   1.000#>#> ── Range ───────────────────────────────────────────────────────────────────────#>#>       F1      F2      F3#> F1    0.000   1.276   0.679#> F2    1.276   0.000   1.316#> F3    0.679   1.316   0.000#>#>#> ══ Variances Accounted for ═════════════════════════════════════════════════════#>#> ── Mean ────────────────────────────────────────────────────────────────────────#>#>                   F1      F2      F3#> SS loadings       2.443   1.929   1.904#> Prop Tot Var      0.136   0.107   0.106#> Prop Comm Var     0.389   0.307   0.303#>#> ── Range ───────────────────────────────────────────────────────────────────────#>#>                   F1      F2      F3#> SS loadings       2.831   1.356   1.291#> Prop Tot Var      0.157   0.075   0.072#> Prop Comm Var     0.419   0.215   0.215#>#>#> ══ Model Fit ═══════════════════════════════════════════════════════════════════#>#>        M (SD) [Min; Max]#> 𝜒²: 101.73 (34.62) [53.23; 125.98]#> df: 102#> p: .369 (.450) [.054; 1.000]#> CFI: 1.00 (.00) [1.00; 1.00]#> RMSEA: .01 (.01) [.00; .02]#> AIC: -102.27 (34.62) [-150.77; -78.02]#> BIC: -532.16 (34.62) [-580.66; -507.91]#> CAF: .50 (.00) [.50; .50]

# Perform a Schmid-Leiman transformationSL<- SL(EFA_psych)# Based on a specific salience threshold for the loadings (here: .20):factor_corres<-SL$sl[, c("F1","F2","F3")]>=.2# Compute omegas from the Schmid-Leiman solutionOMEGA(SL,factor_corres=factor_corres)#> Omega total, omega hierarchical, omega subscale, H index, explained common variance (ECV), and percent of uncontaminated correlations (PUC) for the general factor (top row) and omegas and H index for the group factors:#>#>      tot  hier   sub     H   ECV   PUC#> g  0.883 0.750 0.122 0.845 0.668 0.706#> F1 0.769 0.498 0.272 0.465#> F2 0.764 0.494 0.270 0.473#> F3 0.745 0.543 0.202 0.380

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