class_docstr: Model for
  EM implementation of (repeated measures)
  mixed effects model.
  
  'Maximum Likelihood Computations with Repeated Measures:
  Application of the EM Algorithm'
  
  Nan Laird; Nicholas Lange; Daniel Stram
  
  Journal of the American Statistical Association,
  Vol. 82, No. 397. (Mar., 1987), pp. 97-105.
  
  
  Parameters
  ----------
  units : list of units
     the data for the individual units should be attached to the units
  response, fixed and random : formula expression, called as argument to Formula
  
  
  *available results and alias*
  
  (subject to renaming, and coversion to cached attributes)
  
  params() -> self.a : coefficient for fixed effects or exog
  cov_params() -> self.Sinv : covariance estimate of fixed effects/exog
  bse() : standard deviation of params
  
  cov_random -> self.D : estimate of random effects covariance
  params_random_units -> [self.units[...].b] : random coefficient for each unit
  
  
  *attributes*
  
  (others)
  
  self.m : number of units
  self.p : k_vars_fixed
  self.q : k_vars_random
  self.N : nobs (total)
  
  
  Notes
  -----
  Fit returns a result instance, but not all results that use the inherited
  methods have been checked.
  
  Parameters need to change: drop formula and we require a naming convention for
  the units (currently Y,X,Z). - endog, exog_fe, endog_re ?
  
  logL does not include constant, e.g. sqrt(pi)
  llf is for MLE not for REML
  
  
  convergence criteria for iteration
  Currently convergence in the iterative solver is reached if either the loglikelihood
  *or* the fixed effects parameter do not change above tolerance.
  
  In some examples, the fixed effects parameters converged to 1e-5 within 150 iterations
  while the log likelihood did not converge within 2000 iterations. This might be
  the case if the fixed effects parameters are well estimated, but there are still
  changes in the random effects. If params_rtol and params_atol are set at a higher
  level, then the random effects might not be estimated to a very high precision.
  
  The above was with a misspecified model, without a constant. With a
  correctly specified model convergence is fast, within a few iterations
  (6 in example).
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docstr: Approximate covariance of estimates of fixed effects.
  
  Just after Display (3.10) in Laird, Lange, Stram (see help(Mixed)).
code: def cov_fixed(self):
      """
      Approximate covariance of estimates of fixed effects.
  
      Just after Display (3.10) in Laird, Lange, Stram (see help(Mixed)).
      """
      return self.Sinv
  
imports: import numpy
  import numpy.linalg
  from statsmodels.base.model import LikelihoodModelResults
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 5 for calling the OneWayMixed obj's underlying member method code with example usage: obj.cov_fixed() and returns: self