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

Attributes:¶

uniquenessndarray

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

communalityndarray

1 - uniqueness

loadingsndarray

Each column is the loading vector for one factor

loadings_no_rotndarray

Unrotated loadings, not available under maximum likelihood analysis.

eigenvalsndarray

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.

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
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()	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.