tests/testthat/_snaps/snapshots.md

In EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

print.efa_retention output is stable for SMT

Code
  print(smt)
Output
  -- Sequential model tests ------------------------------------------------------

  * Sequential chi-square model tests: 3
  * Lower bound of RMSEA 90% CI: 2
  * Akaike Information Criterion: 3

Code
  print(smt_id)
Output
  -- Sequential model tests ------------------------------------------------------

  * Sequential chi-square model tests: 0
  * Lower bound of RMSEA 90% CI: 0
  * Akaike Information Criterion: 0

print.efa_retention output is stable for SCREE

Code
  print(scree)
Output
  -- Scree plot ------------------------------------------------------------------
  Eigenvalues found using PCA, SMC, and EFA.

  i Scree plot is a visual criterion; call plot(x) to identify the elbow.

Code
  print(scree_smc)
Output
  -- Scree plot ------------------------------------------------------------------
  Eigenvalues found using SMC.

  i Scree plot is a visual criterion; call plot(x) to identify the elbow.

print.efa_retention output is stable for CD

Code
  print(cd)
Output
  -- Comparison data -------------------------------------------------------------

  * Suggested number of factors: 1

print.efa_retention output is stable for PARALLEL

Code
  print(pa)
Output
  -- Parallel analysis -----------------------------------------------------------
  Eigenvalues found using PCA; 1000 simulated datasets.

  * PCA: 3

  i Number of factors retained using the "means" decision rule.

Code
  print(pa_nodat)
Output
  -- Parallel analysis -----------------------------------------------------------
  Eigenvalues found using PCA, SMC, and EFA; 1000 simulated datasets.

  i No data were entered; showing the simulated eigenvalues only. No number of
  factors is suggested.

print.efa_retention output is stable for KGC

Code
  print(kgc)
Output
  -- Kaiser-Guttman criterion ----------------------------------------------------
  Eigenvalues found using PCA, SMC, and EFA.

  * PCA: 3
  * SMC: 1
  * EFA: 1

Code
  print(kgc_smc)
Output
  -- Kaiser-Guttman criterion ----------------------------------------------------
  Eigenvalues found using SMC.

  * SMC: 1

print.efa_retention output is stable for NEST

Code
  print(nest)
Output
  -- Next Eigenvalue Sufficiency Test --------------------------------------------

  * Suggested number of factors: 3

print.efa_retention output is stable for EKC

Code
  print(ekc)
Output
  -- Empirical Kaiser Criterion --------------------------------------------------

  * Braeken & van Assen (2017): 3

print.efa_retention output is stable for HULL

Code
  print(hull)
Output
  -- Hull method -----------------------------------------------------------------
  Estimator: ML

  * CAF: 3
  * CFI: 1
  * RMSEA: 1

Code
  print(hull_paf)
Output
  -- Hull method -----------------------------------------------------------------
  Estimator: PAF

  * CAF: 3

print.efa_retention output is stable for MAP

Code
  print(map)
Output
  -- Minimum average partial -----------------------------------------------------

  * Original implementation (TR2): 1
  * Revised implementation (TR4): 3

print.efa_retain output is stable

Code
  print(nf)
Output
  -- Suggested number of factors -------------------------------------------------

  8 suggestions from 4 criteria, ranging from 1 to 3 factors (most common: 3).

  Empirical Kaiser Criterion
  * Braeken & van Assen (2017): 3

  Kaiser-Guttman criterion
  Eigenvalues found using PCA and SMC.
  * PCA: 3
  * SMC: 1

  Minimum average partial
  * Original implementation (TR2): 1
  * Revised implementation (TR4): 3

  Sequential model tests
  * Sequential chi-square model tests: 3
  * Lower bound of RMSEA 90% CI: 2
  * Akaike Information Criterion: 3

print.efa_retain summarises the criteria's suggestions

Code
  print(nf)
Output
  -- Suggested number of factors -------------------------------------------------

  4 suggestions from 3 criteria, ranging from 1 to 3 factors (most common: 3).

  Empirical Kaiser Criterion
  * Braeken & van Assen (2017): 3

  Kaiser-Guttman criterion
  Eigenvalues found using PCA.
  * PCA: 3

  Minimum average partial
  * Original implementation (TR2): 1
  * Revised implementation (TR4): 3

EFAtools documentation built on Aug. 21, 2026, 5:16 p.m.