class_docstr: Class to contain results from likelihood models 
class_docstr_tok: ["Class","to","contain","results","from","likelihood","models"]
docstr: Variance/covariance matrix of linear contrast
  
  Parameters
  ----------
  matrix: (dim, self.theta.shape[0]) array, optional
      numerical contrast specification, where ``dim`` refers to the
      'dimension' of the contrast i.e. 1 for t contrasts, 1 or more
      for F contrasts.
  column: int, optional
      alternative way of specifying contrasts (column index)
  dispersion: float or (n_voxels,) array, optional
      value(s) for the dispersion parameters
  other: (dim, self.theta.shape[0]) array, optional
      alternative contrast specification (?)
  
  Returns
  -------
  cov: (dim, dim) or (n_voxels, dim, dim) array
      the estimated covariance matrix/matrices
  
  Returns the variance/covariance matrix of a linear contrast of the
  estimates of theta, multiplied by `dispersion` which will often be an
  estimate of `dispersion`, like, sigma^2.
  
  The covariance of interest is either specified as a (set of) column(s)
  or a matrix.
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code: def vcov(self, matrix=None, column=None, dispersion=None, other=None):
      """Variance/covariance matrix of linear contrast
  
      Parameters
      ----------
      matrix: (dim, self.theta.shape[0]) array, optional
          numerical contrast specification, where ``dim`` refers to the
          'dimension' of the contrast i.e. 1 for t contrasts, 1 or more
          for F contrasts.
      column: int, optional
          alternative way of specifying contrasts (column index)
      dispersion: float or (n_voxels,) array, optional
          value(s) for the dispersion parameters
      other: (dim, self.theta.shape[0]) array, optional
          alternative contrast specification (?)
  
      Returns
      -------
      cov: (dim, dim) or (n_voxels, dim, dim) array
          the estimated covariance matrix/matrices
  
      Returns the variance/covariance matrix of a linear contrast of the
      estimates of theta, multiplied by `dispersion` which will often be an
      estimate of `dispersion`, like, sigma^2.
  
      The covariance of interest is either specified as a (set of) column(s)
      or a matrix.
      """
      if self.cov is None:
          raise ValueError(
              "need covariance of parameters for computing"
              + "(unnormalized) covariances"
          )
  
      if dispersion is None:
          dispersion = self.dispersion
  
      if column is not None:
          column = np.asarray(column)
          if column.shape == ():
              return self.cov[column, column] * dispersion
          else:
              return self.cov[column][:, column] * dispersion
  
      elif matrix is not None:
          if other is None:
              other = matrix
          tmp = np.dot(matrix, np.dot(self.cov, np.transpose(other)))
          if np.isscalar(dispersion):
              return tmp * dispersion
          else:
              return tmp[:, :, np.newaxis] * dispersion
      if matrix is None and column is None:
          return self.cov * dispersion
  
imports: import numpy
  from nibabel.onetime import auto_attr
  from numpy.linalg import inv
  from scipy.stats import t
  from ...utils.matrices import pos_recipr
label_desc: Use image node_id 6 for calling the LikelihoodModelResults obj's underlying member method code with example usage: obj.vcov(matrix, column, dispersion, other) and returns: unknown, unknown, unknown, unknown, unknown