Package index • EFAtools

Fitting

Fit exploratory factor models, average across solutions, pool across imputed data sets, compare groups, and configure the estimation and rotation engines.

efa_fit(): Exploratory factor analysis (EFA)
efa_average(): Model averaging across different EFA estimators and types
efa_group(): Multigroup exploratory factor analysis
efa_mi(): Exploratory factor analysis on multiple data imputations
plot(<efa_group>): Plot a multigroup factor analysis
print(<efa_group>) format(<efa_group>): Print and format a multigroup factor analysis
estimate_control() rotate_control(): Control objects for estimation and rotation settings
print(<efa_estimate_control>) format(<efa_estimate_control>) print(<efa_rotate_control>) format(<efa_rotate_control>): Print and format a control object
print(<efa>) print(<efa_mi>) format(<efa>) format(<efa_mi>) summary(<efa>) summary(<efa_mi>) print(<summary.efa>) format(<summary.efa>): Print and summarise an efa object
print(<efa_average>) format(<efa_average>): Print and format an efa_average object
plot(<efa_average>): Plot efa_average object
print(<efa_loadings>) format(<efa_loadings>): Print a loading matrix
residuals(<efa>): Extract residuals from an efa object

Factor retention

Criteria for determining the number of factors to retain, and a wrapper to run several of them at once.

efa_cd(): Comparison data
efa_ekc(): Empirical Kaiser criterion
efa_hull(): Hull method for determining the number of factors to retain
efa_kgc(): Kaiser-Guttman criterion
efa_map(): Velicer's minimum average partial (MAP) criterion
efa_nest(): Next eigenvalue sufficiency test (NEST)
efa_parallel(): Parallel analysis
efa_retain(): Various factor retention criteria
efa_scree(): Scree plot
efa_smt(): Sequential chi square model tests, RMSEA lower bound, and AIC
print(<efa_retain>): Print method for efa_retain objects
format(<efa_retain>): Format method for efa_retain objects
plot(<efa_retain>): Plot method for efa_retain objects
print(<efa_retention>): Print method for efa_retention objects
format(<efa_retention>): Format method for efa_retention objects
plot(<efa_retention>): Plot method for efa_retention objects

Rotation and transformation

Align a solution with a target and transform an oblique solution into a hierarchical one.

efa_procrustes(): Rotate a loading matrix to a target using Procrustes alignment
efa_schmid_leiman(): Schmid-Leiman transformation
print(<efa_schmid_leiman>) format(<efa_schmid_leiman>): Print and format an efa_schmid_leiman object
print(<efa_sl_loadings>) format(<efa_sl_loadings>): Print an efa_sl_loadings object

Reliability

Reliability coefficients for a fitted or user-specified factor solution.

efa_reliability(): Reliability and common-variance coefficients for a factor solution
print(<efa_reliability>) format(<efa_reliability>): Print and format a reliability object

Scores and comparison

Factor scores with score-quality diagnostics, and comparison of two solutions.

efa_scores(): Estimate factor scores and score-quality diagnostics for an EFA model
print(<efa_scores>) format(<efa_scores>) summary(<efa_scores>) print(<summary.efa_scores>) format(<summary.efa_scores>): Print and format an efa_scores object
efa_compare(): Compare two vectors or matrices (communalities or loadings)
print(<efa_compare>) format(<efa_compare>): Print and format an efa_compare object
plot(<efa_compare>): Plot efa_compare object

Screening and simulation

Check whether data are suitable for factor analysis and simulate data from a common factor model.

efa_bartlett(): Bartlett's test of sphericity
efa_kmo(): Kaiser-Meyer-Olkin criterion
efa_screen(): Screen data for exploratory factor analysis
print(<efa_screen>) format(<efa_screen>): Print and format an efa_screen object
efa_simulate(): Simulate data from a common-factor population model
print(<efa_simulated>) format(<efa_simulated>): Print and format an efa_simulated object
print(<efa_bartlett>) format(<efa_bartlett>): Print and format an efa_bartlett object
print(<efa_kmo>) format(<efa_kmo>): Print and format an efa_kmo object

Power

Analytic and simulation-based power analysis.

efa_power(): Power analysis for exploratory factor analysis
plot(<efa_power>): Plot the RMSEA power curve
print(<efa_power>) format(<efa_power>): Print and format an efa_power object

Datasets

Example data sets and models.

DOSPERT: DOSPERT
DOSPERT_raw: DOSPERT_raw
GRiPS_raw: GRiPS_raw
IDS2_R: Intelligence subtests from the Intelligence and Development Scales–2
RiskDimensions: RiskDimensions
SPSS_23: Various outputs from SPSS (version 23) FACTOR
SPSS_27: Various outputs from SPSS (version 27) FACTOR
UPPS_raw: UPPS_raw
WJIV_ages_14_19: Woodcock Johnson IV: ages 14 to 19
WJIV_ages_20_39: Woodcock Johnson IV: ages 20 to 39
WJIV_ages_3_5: Woodcock Johnson IV: ages 3 to 5
WJIV_ages_40_90: Woodcock Johnson IV: ages 40 to 90 plus
WJIV_ages_6_8: Woodcock Johnson IV: ages 6 to 8
WJIV_ages_9_13: Woodcock Johnson IV: ages 9 to 13
population_models: population_models
test_models: Four test models used in Grieder and Steiner (2022)

Superseded

The original uppercase names. They remain exported and keep their original argument lists, but the lowercase efa_* equivalents are the recommended interface.

EFA() superseded: Exploratory factor analysis (EFA)
EFA_POOLED() superseded: Exploratory factor analysis on multiple data imputations
EFA_AVERAGE() superseded: Model averaging across different EFA methods and types
N_FACTORS() superseded: Various factor retention criteria
BARTLETT() superseded: Bartlett's test of sphericity
KMO() superseded: Kaiser-Meyer-Olkin criterion
CD() superseded: Comparison data
EKC() superseded: Empirical Kaiser criterion
HULL() superseded: Hull method
KGC() superseded: Kaiser-Guttman criterion
MAP() superseded: Minimum average partial
NEST() superseded: Next eigenvalue sufficiency test
PARALLEL() superseded: Parallel analysis
SCREE() superseded: Scree plot
SMT() superseded: Sequential model tests
SL() superseded: Schmid-Leiman transformation
OMEGA() superseded: McDonald's omega
print(<OMEGA>) format(<OMEGA>): Print and format an OMEGA object
FACTOR_SCORES() superseded: Estimate factor scores for an EFA model
COMPARE() superseded: Compare two vectors or matrices (communalities or loadings)
PROCRUSTES() superseded: Rotate a loading matrix to a target using Procrustes alignment