Fast and Flexible Implementations of Exploratory Factor Analysis Tools
Bartlett's test of sphericity
Comparison Data
Compare two vectors or matrices (communalities or loadings)
Compute explained variances from loadings
Compute number of non-matching indicator-to-factor correspondences
Get reference values for nest.
Format numbers for print method
Perform the iterative PAF procedure
Parallel analysis on simulated data.
Model averaging across different EFA methods and types
Exploratory factor analysis (EFA)
EFAtools: Fast and Flexible Implementations of Exploratory Factor Anal...
Empirical Kaiser Criterion
Estimate factor scores for an EFA model
Hull method for determining the number of factors to retain
Kaiser-Guttman Criterion
Kaiser-Meyer-Olkin criterion
Various Factor Retention Criteria
Next eigenvalue sufficiency test (NEST)
McDonald's omega
Parallel analysis
Pipe operator
Plot CD object
Plot EFA_AVERAGE object
Plot EKC object
Plot HULL object
Plot KGC object
Plot PARALLEL object
Plot SCREE object
Print BARTLETT object
Print function for CD objects
Print COMPARE object
Print EFA_AVERAGE object
Print EFA object
Print function for EKC objects
Print function for HULL objects
Print function for KGC objects
Print KMO object
Print LOADINGS object
Print function for N_FACTORS objects
Print function for NEST objects
Print OMEGA object
Print function for PARALLEL objects
Print function for SCREE objects
Print SL object
Print SLLOADINGS object
Print SMT object
Scree Plot
Schmid-Leiman Transformation
Sequential Chi Square Model Tests, RMSEA lower bound, and AIC
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++.