start_params: array-like or MixedLMParams :
If a MixedLMParams the state provides the starting
value. If array-like, this is the packed parameter
vector, assumed to be in the same state as this model.
reml : bool
If true, fit according to the REML likelihood, else
fit the standard likelihood using ML.
niter_sa : integer
The number of steepest ascent iterations
niter_em : non-negative integer
The number of EM steps. The EM steps always
preceed steepest descent and conjugate gradient
optimization. The EM algorithm implemented here
is for ML estimation.
do_cg : bool
If True, a conjugate gradient algorithm is
used for optimization (following any steepest
descent or EM steps).
cov_pen : CovariancePenalty object
A penalty for the random effects covariance matrix
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. The state of use_sqrt for
free must agree with that of the parent model.
full_output : bool
If true, attach iteration history to results