statsmodels.multivariate.factor.FactorResults¶
-
class
statsmodels.multivariate.factor.
FactorResults
(factor)[source]¶ Factor results class
For result summary, scree/loading plots and factor rotations
Parameters: factor (Factor) – Fitted Factor class -
uniqueness
¶ The uniqueness (variance of uncorrelated errors unique to each variable)
Type: ndarray
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communality
¶ 1 - uniqueness
Type: ndarray
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loadings
¶ Each column is the loading vector for one factor
Type: ndarray
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loadings_no_rot
¶ Unrotated loadings, not available under maximum likelihood analyis.
Type: ndarray
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eigenvalues
¶ The eigenvalues for a factor analysis obtained using principal components; not available under ML estimation.
Type: ndarray
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fa_method
¶ The method used to obtain the decomposition, either ‘pa’ for ‘principal axes’ or ‘ml’ for maximum likelihood.
Type: string
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 fitted_cov
()Returns the fitted covariance matrix. get_loadings_frame
([style, sort_, …])get loadings matrix as DataFrame or pandas Styler load_stderr
()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
()uniq_stderr
([kurt])The standard errors of the uniquenesses. -