# statsmodels.multivariate.factor.Factor¶

class statsmodels.multivariate.factor.Factor(endog=None, n_factor=1, corr=None, method='pa', smc=True, endog_names=None, nobs=None, missing='drop')[source]

Factor analysis

Parameters: endog (array-like) – Variables in columns, observations in rows. May be None if corr is not None. n_factor (int) – The number of factors to extract corr (array-like) – Directly specify the correlation matrix instead of estimating it from endog. If provided, endog is not used for the factor analysis, it may be used in post-estimation. method (str) – The method to extract factors, currently must be either ‘pa’ for principal axis factor analysis or ‘ml’ for maximum likelihood estimation. smc (True or False) – Whether or not to apply squared multiple correlations (method=’pa’) endog_names (str) – Names of endogeous variables. If specified, it will be used instead of the column names in endog nobs (int) – The number of observations, not used if endog is present. Needs to be provided for inference if endog is None. missing ('none', 'drop', or 'raise') – Missing value handling for endog, default is row-wise deletion ‘drop’ If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised.

Notes

Experimental

Supported rotations: ‘varimax’, ‘quartimax’, ‘biquartimax’, ‘equamax’, ‘oblimin’, ‘parsimax’, ‘parsimony’, ‘biquartimin’, ‘promax’

If method=’ml’, the factors are rotated to satisfy condition IC3 of Bai and Li (2012). This means that the scores have covariance I, so the model for the covariance matrix is L * L’ + diag(U), where L are the loadings and U are the uniquenesses. In addition, L’ * diag(U)^{-1} L must be diagonal.

References

 [*] Hofacker, C. (2004). Exploratory Factor Analysis, Mathematical Marketing. http://www.openaccesstexts.org/pdf/Quant_Chapter_11_efa.pdf
 [†] J Bai, K Li (2012). Statistical analysis of factor models of high dimension. Annals of Statistics. https://arxiv.org/pdf/1205.6617.pdf

Methods

 fit([maxiter, tol, start, opt_method, opt, …]) Estimate factor model parameters. from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe. loglike(par) Evaluate the log-likelihood function. predict(params[, exog]) After a model has been fit predict returns the fitted values. score(par) Evaluate the score function (first derivative of loglike).

Attributes

 endog_names Names of endogenous variables exog_names Names of exogenous variables