statsmodels.regression.mixed_linear_model.MixedLM.fit¶

MixedLM.fit(start_params=None, reml=True, niter_sa=0, do_cg=True, fe_pen=None, cov_pen=None, free=None, full_output=False, method=None, **fit_kwargs)[source]¶

Fit a linear mixed model to the data.

Parameters:¶

start_params : array_like or MixedLMParams¶

Starting values for the profile log-likelihood. If not a MixedLMParams instance, this should be an array containing the packed parameters for the profile log-likelihood, including the fixed effects parameters.

reml : bool¶

If true, fit according to the REML likelihood, else fit the standard likelihood using ML.

niter_sa : int¶

Currently this argument is ignored and has no effect on the results.

cov_pen : CovariancePenalty object¶

A penalty for the random effects covariance matrix

do_cg : bool, defaults to True¶

If False, the optimization is skipped and a results object at the given (or default) starting values is returned.

fe_pen : Penalty object¶

A penalty on the fixed effects

free : MixedLMParams object¶

If not None, this is a mask that allows parameters to be held fixed at specified values. A 1 indicates that the corresponding parameter is estimated, a 0 indicates that it is fixed at its starting value. Setting the cov_re component to the identity matrix fits a model with independent random effects. Note that some optimization methods do not respect this constraint (bfgs and lbfgs both work).

full_output : bool¶

If true, attach iteration history to results

method : str¶

Optimization method. Can be a scipy.optimize method name, or a list of such names to be tried in sequence.

**fit_kwargs¶

Additional keyword arguments passed to fit.

Return type:¶

A MixedLMResults instance.