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_factor
int
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.
- 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
orFalse
Whether or not to apply squared multiple correlations (method=’pa’)
- endog_names
str
Names of endogenous 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).
Properties
Names of endogenous variables
Names of exogenous variables.