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

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

communality

ndarray – 1 - uniqueness

loadings

ndarray – Each column is the loading vector for one factor

loadings_no_rot

ndarray – Unrotated loadings, not available under maximum likelihood analyis.

eigenvalues

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

string – 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.

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.