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statsmodels.regression.mixed_linear_model.MixedLM.EM

MixedLM.EM(fe_params, cov_re, scale, niter_em=10, hist=None)[source]

Run the EM algorithm from a given starting point. This is for ML (not REML), but it seems to be good enough to use for REML starting values.

Returns:

fe_params : 1d ndarray

The final value of the fixed effects coefficients

cov_re : 2d ndarray

The final value of the random effects covariance matrix

scale : float

The final value of the error variance

Notes

This uses the parameterization of the likelihood scale*I + Z'*V*Z, note that this differs from the profile likelihood used in the gradient calculations.

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