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: |
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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.
[*] | 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 |
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). |
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |
© 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.Factor.html