statsmodels.multivariate.factor.FactorResults¶

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

Factor results class

Parameters:
factor`Factor`

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

loadings`ndarray`

loadings_no_rot`ndarray`

eigenvals`ndarray`

The eigenvalues for a factor analysis obtained using principal components; not available under ML estimation.

n_comp`int`

Number of components (factors)

nbs`int`

Number of observations

fa_method`str`

The method used to obtain the decomposition, either ‘pa’ for ‘principal axes’ or ‘ml’ for maximum likelihood.

df`int`

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.

Last update: Dec 14, 2023