class_docstr: Class to contain results of fitting a linear mixed effects model.
  
  MixedLMResults inherits from statsmodels.LikelihoodModelResults
  
  Parameters
  ----------
  See statsmodels.LikelihoodModelResults
  
  Attributes
  ----------
  model : class instance
      Pointer to MixedLM model instance that called fit.
  normalized_cov_params : ndarray
      The sampling covariance matrix of the estimates
  params : ndarray
      A packed parameter vector for the profile parameterization.
      The first `k_fe` elements are the estimated fixed effects
      coefficients.  The remaining elements are the estimated
      variance parameters.  The variance parameters are all divided
      by `scale` and are not the variance parameters shown
      in the summary.
  fe_params : ndarray
      The fitted fixed-effects coefficients
  cov_re : ndarray
      The fitted random-effects covariance matrix
  bse_fe : ndarray
      The standard errors of the fitted fixed effects coefficients
  bse_re : ndarray
      The standard errors of the fitted random effects covariance
      matrix and variance components.  The first `k_re * (k_re + 1)`
      parameters are the standard errors for the lower triangle of
      `cov_re`, the remaining elements are the standard errors for
      the variance components.
  
  See Also
  --------
  statsmodels.LikelihoodModelResults
class_docstr_tok: ["Class","to","contain","results","of","fitting","a","linear","mixed","effects","model",".","MixedLMResults","inherits","from","statsmodels.LikelihoodModelResults","Parameters","--","--","--","--","--","See","statsmodels.LikelihoodModelResults","Attributes","--","--","--","--","--","model",":","class","instance","Pointer","to","MixedLM","model","instance","that","called","fit",".","normalized_cov_params",":","ndarray","The","sampling","covariance","matrix","of","the","estimates","params",":","ndarray","A","packed","parameter","vector","for","the","profile","parameterization",".","The","first","`","k_fe","`","elements","are","the","estimated","fixed","effects","coefficients",".","The","remaining","elements","are","the","estimated","variance","parameters",".","The","variance","parameters","are","all","divided","by","`","scale","`","and","are","not","the","variance","parameters","shown","in","the","summary",".","fe_params",":","ndarray","The","fitted","fixed-effects","coefficients","cov_re",":","ndarray","The","fitted","random-effects","covariance","matrix","bse_fe",":","ndarray","The","standard","errors","of","the","fitted","fixed","effects","coefficients","bse_re",":","ndarray","The","standard","errors","of","the","fitted","random","effects","covariance","matrix","and","variance","components",".","The","first","`","k_re","*","(","k_re","+","1",")","`","parameters","are","the","standard","errors","for","the","lower","triangle","of","`","cov_re","`",",","the","remaining","elements","are","the","standard","errors","for","the","variance","components",".","See","Also","--","--","--","--","statsmodels.LikelihoodModelResults"]
docstr: Returns the standard errors of the variance parameters.
  
  The first `k_re x (k_re + 1)` elements of the returned array
  are the standard errors of the lower triangle of `cov_re`.
  The remaining elements are the standard errors of the variance
  components.
  
  Note that the sampling distribution of variance parameters is
  strongly skewed unless the sample size is large, so these
  standard errors may not give meaningful confidence intervals
  or p-values if used in the usual way.
docstr_tok: ["Returns","the","standard","errors","of","the","variance","parameters",".","The","first","`","k_re","x","(","k_re","+","1",")","`","elements","of","the","returned","array","are","the","standard","errors","of","the","lower","triangle","of","`","cov_re","`",".","The","remaining","elements","are","the","standard","errors","of","the","variance","components",".","Note","that","the","sampling","distribution","of","variance","parameters","is","strongly","skewed","unless","the","sample","size","is","large",",","so","these","standard","errors","may","not","give","meaningful","confidence","intervals","or","p-values","if","used","in","the","usual","way","."]
code: def bse_re(self):
      """
      Returns the standard errors of the variance parameters.
  
      The first `k_re x (k_re + 1)` elements of the returned array
      are the standard errors of the lower triangle of `cov_re`.
      The remaining elements are the standard errors of the variance
      components.
  
      Note that the sampling distribution of variance parameters is
      strongly skewed unless the sample size is large, so these
      standard errors may not give meaningful confidence intervals
      or p-values if used in the usual way.
      """
      p = self.model.exog.shape[1]
      return np.sqrt(self.scale * np.diag(self.cov_params())[p:])
  
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 5 for calling the MixedLMResults obj's underlying member method code with example usage: obj.bse_re() and returns: np