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
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docstr: Compute a t-test for a each linear hypothesis of the form Rb = q
  
  Parameters
  ----------
  r_matrix : array_like
      If an array is given, a p x k 2d array or length k 1d
      array specifying the linear restrictions. It is assumed
      that the linear combination is equal to zero.
  scale : float, optional
      An optional `scale` to use.  Default is the scale specified
      by the model fit.
  use_t : bool, optional
      If use_t is None, then the default of the model is used.
      If use_t is True, then the p-values are based on the t
      distribution.
      If use_t is False, then the p-values are based on the normal
      distribution.
  
  Returns
  -------
  res : ContrastResults instance
      The results for the test are attributes of this results instance.
      The available results have the same elements as the parameter table
      in `summary()`.
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code: def t_test(self, r_matrix, use_t=None):
      """
      Compute a t-test for a each linear hypothesis of the form Rb = q
  
      Parameters
      ----------
      r_matrix : array_like
          If an array is given, a p x k 2d array or length k 1d
          array specifying the linear restrictions. It is assumed
          that the linear combination is equal to zero.
      scale : float, optional
          An optional `scale` to use.  Default is the scale specified
          by the model fit.
      use_t : bool, optional
          If use_t is None, then the default of the model is used.
          If use_t is True, then the p-values are based on the t
          distribution.
          If use_t is False, then the p-values are based on the normal
          distribution.
  
      Returns
      -------
      res : ContrastResults instance
          The results for the test are attributes of this results instance.
          The available results have the same elements as the parameter table
          in `summary()`.
      """
      if r_matrix.shape[1] != self.k_fe:
          raise ValueError(
              "r_matrix for t-test should have %d columns" % self.k_fe
          )
  
      d = self.k_re2 + self.k_vc
      z0 = np.zeros((r_matrix.shape[0], d))
      r_matrix = np.concatenate((r_matrix, z0), axis=1)
      tst_rslt = super().t_test(r_matrix, use_t=use_t)
      return tst_rslt
  
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 9 for calling the MixedLMResults obj's underlying member method code with example usage: obj.t_test(r_matrix, use_t) and returns: tst_rslt