MixedLM.fit_regularized(start_params=None, method='l1', alpha=0, ceps=0.0001, ptol=1e-06, maxit=200, **fit_kwargs)
[source]
Fit a model in which the fixed effects parameters are penalized. The dependence parameters are held fixed at their estimated values in the unpenalized model.
Parameters: |
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Returns: | |
Return type: |
A MixedLMResults instance containing the results. |
The covariance structure is not updated as the fixed effects parameters are varied.
The algorithm used here for L1 regularization is a”shooting” or cyclic coordinate descent algorithm.
If method is ‘l1’, then fe_pen
and cov_pen
are used to obtain the covariance structure, but are ignored during the L1-penalized fitting.
Friedman, J. H., Hastie, T. and Tibshirani, R. Regularized Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1) (2008) http://www.jstatsoft.org/v33/i01/paper
http://statweb.stanford.edu/~tibs/stat315a/Supplements/fuse.pdf
© 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.regression.mixed_linear_model.MixedLM.fit_regularized.html