# 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
endogarray_like

Variables in columns, observations in rows. May be None if corr is not None.

n_factorint

The number of factors to extract

corrarray_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.

methodstr

The method to extract factors, currently must be either ‘pa’ for principal axis factor analysis or ‘ml’ for maximum likelihood estimation.

smc

Whether or not to apply squared multiple correlations (method=’pa’)

endog_namesstr

Names of endogenous variables. If specified, it will be used instead of the column names in endog

nobsint

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).

Properties

 endog_names Names of endogenous variables exog_names Names of exogenous variables.