# statsmodels.multivariate.factor.FactorResults¶

class statsmodels.multivariate.factor.FactorResults(factor)[source]

Factor results class

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

Attributes
uniqueness: ndarray

The uniqueness (variance of uncorrelated errors unique to each variable)

communality: ndarray

1 - uniqueness

loadingsndarray

loadings_no_rotndarray

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_methodstr

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 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

Properties

 fitted_cov Returns the fitted covariance matrix. load_stderr The standard errors of the loadings. uniq_stderr The standard errors of the uniquenesses.