statsmodels.multivariate.factor.FactorResults¶
-
class
statsmodels.multivariate.factor.FactorResults(factor)[source]¶ Factor results class
For result summary, scree/loading plots and factor rotations
- Parameters
- factorFactor
Fitted Factor class
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.
- Attributes
- uniqueness: ndarray
The uniqueness (variance of uncorrelated errors unique to each variable)
- communality: ndarray
1 - uniqueness
- loadingsndarray
Each column is the loading vector for one factor
- loadings_no_rotndarray
Unrotated loadings, not available under maximum likelihood analyis.
- eigenvaluesndarray
The eigenvalues for a factor analysis obtained using principal components; not available under ML estimation.
- n_compint
Number of components (factors)
- nbsint
Number of observations
- fa_methodstring
The method used to obtain the decomposition, either ‘pa’ for ‘principal axes’ or ‘ml’ for maximum likelihood.
- dfint
Degrees of freedom of the factor model.
Methods
factor_score_params([method])Compute factor scoring coefficient matrix
factor_scoring([endog, method, transform])factor scoring: compute factors for endog
Returns the fitted covariance matrix.
get_loadings_frame([style, sort_, …])get loadings matrix as DataFrame or pandas Styler
The standard errors of the loadings.
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
uniq_stderr([kurt])The standard errors of the uniquenesses.