statsmodels.multivariate.factor.FactorResults¶
- class statsmodels.multivariate.factor.FactorResults(factor)[source]¶
Factor results class
For result summary, scree/loading plots and factor rotations
- loadings_no_rot¶
Unrotated loadings, not available under maximum likelihood analysis.
- Type:¶
ndarray
- eigenvals¶
The eigenvalues for a factor analysis obtained using principal components; not available under ML estimation.
- Type:¶
ndarray
- fa_method¶
The method used to obtain the decomposition, either ‘pa’ for ‘principal axes’ or ‘ml’ for maximum likelihood.
Notes
Under ML estimation, the default rotation (used for loadings) is condition IC3 of Bai and Li (2012). Under this rotation, the factor scores are iid and standardized. If G is the canonical loadings and U is the vector of uniquenesses, then the covariance matrix implied by the factor analysis is GG’ + diag(U).
- Status: experimental, Some refactoring will be necessary when new
features are added.
Methods
factor_score_params([method])Compute factor scoring coefficient matrix
factor_scoring([endog, method, transform])factor scoring: compute factors for endog
get_loadings_frame([style, sort_, ...])get loadings matrix as DataFrame or pandas Styler
plot_loadings([loading_pairs, plot_prerotated])Plot factor loadings in 2-d plots
plot_scree([ncomp])Plot of the ordered eigenvalues and variance explained for the loadings
rotate(method)Apply rotation, inplace modification of this Results instance
summary()Summary
Properties
Returns the fitted covariance matrix.
The standard errors of the loadings.
The standard errors of the uniquenesses.