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, **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 – 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 (boolean, 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 correspondinig 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 (string) – Optimization method. Can be a scipy.optimize method name, or a list of such names to be tried in sequence.
Returns:

Return type:

A MixedLMResults instance.