Welcome to the FactorAnalyzer documentation!¶
This is a Python module to perform exploratory and factor analysis (EFA), with several optional rotations. It also includes a class to perform confirmatory factor analysis (CFA), with certain pre-defined constraints. In exploratory factor analysis, factor extraction can be performed using a variety of estimation techniques. The factor_analyzer package allows users to perform EFA using either (1) a minimum residual (MINRES) solution, (2) a maximum likelihood (ML) solution, or (3) a principal factor solution. However, CFA can only be performed using an ML solution.
Both the EFA and CFA classes within this package are fully compatible with
scikit-learn
. Portions of this code are ported from the excellent R library
psych
, and the sem
package provided inspiration for the CFA class.
Important
Please make sure to read the important notes section if you encounter any unexpected results.
Documentation¶
- Introduction
- Important notes
- API documentation
factor_analyzer.factor_analyzer
Modulefactor_analyzer.confirmatory_factor_analyzer
Modulefactor_analyzer.rotator
Modulefactor_analyzer.utils
Moduleapply_impute_nan()
commutation_matrix()
corr()
cov()
covariance_to_correlation()
duplication_matrix()
duplication_matrix_pre_post()
fill_lower_diag()
get_first_idxs_from_values()
get_free_parameter_idxs()
get_symmetric_lower_idxs()
get_symmetric_upper_idxs()
impute_values()
inv_chol()
merge_variance_covariance()
partial_correlations()
smc()
unique_elements()