class_docstr: This class represents a parameter state for a mixed linear model.
  
  Parameters
  ----------
  k_fe : int
      The number of covariates with fixed effects.
  k_re : int
      The number of covariates with random coefficients (excluding
      variance components).
  k_vc : int
      The number of variance components parameters.
  
  Notes
  -----
  This object represents the parameter state for the model in which
  the scale parameter has been profiled out.
class_docstr_tok: ["This","class","represents","a","parameter","state","for","a","mixed","linear","model",".","Parameters","--","--","--","--","--","k_fe",":","int","The","number","of","covariates","with","fixed","effects",".","k_re",":","int","The","number","of","covariates","with","random","coefficients","(","excluding","variance","components",")",".","k_vc",":","int","The","number","of","variance","components","parameters",".","Notes","--","--","-","This","object","represents","the","parameter","state","for","the","model","in","which","the","scale","parameter","has","been","profiled","out","."]
docstr: Returns a copy of the object.
docstr_tok: ["Returns","a","copy","of","the","object","."]
code: def copy(self):
      """
      Returns a copy of the object.
      """
      obj = MixedLMParams(self.k_fe, self.k_re, self.k_vc)
      obj.fe_params = self.fe_params.copy()
      obj.cov_re = self.cov_re.copy()
      obj.vcomp = self.vcomp.copy()
      return obj
  
imports: import warnings
  import numpy
  import pandas
  import patsy
  from scipy import sparse
  from scipy.stats.distributions import norm
  from statsmodels.base._penalties import Penalty
  import statsmodels.base.model
  from statsmodels.tools import data
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
label_desc: Use image node_id 4 for calling the MixedLMParams obj's underlying member method code with example usage: obj.copy() and returns: obj