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

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

Type:ndarray
communality

1 - uniqueness

Type:ndarray
loadings

Each column is the loading vector for one factor

Type:ndarray
loadings_no_rot

Unrotated loadings, not available under maximum likelihood analyis.

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
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.