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.
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)
.
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. |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.multivariate.factor.FactorResults.html