# statsmodels.multivariate.factor.FactorResults¶

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

Factor results class

Parameters: factor (Factor) – Fitted Factor class
uniqueness

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

Type: ndarray
communality

1 - uniqueness

Type: ndarray
loadings

Type: ndarray
loadings_no_rot

Type: ndarray
eigenvalues

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

Type: ndarray
n_comp

Number of components (factors)

Type: int
nbs

Number of observations

Type: int
fa_method

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

Type: string
df

Degrees of freedom of the factor model.

Type: int

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
 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.