docstr: Estimate voxel-level group parameters using mixed effects
  variational Bayes algorithm.
docstr_tok: ["Estimate","voxel-level","group","parameters","using","mixed","effects","variational","Bayes","algorithm","."]
code: def _voxel_level_inference(self, mfx=True):
      """
      Estimate voxel-level group parameters using mixed effects
      variational Bayes algorithm.
      """
      beta, s2, dof = two_level_glm(
          self.cons, self.vcons, self.design_matrix
      )
  
      self.beta = np.dot(self.cvect, beta)
      if self.design_matrix.ndim == 1:
          self.vbeta = s2 * (
              self.cvect[0] ** 2 / np.sum(self.design_matrix**2)
          )
      else:
          tmp = np.linalg.inv(
              np.dot(self.design_matrix.T, self.design_matrix)
          )
          self.vbeta = s2 * np.dot(
              self.cvect.T, np.dot(tmp, self.cvect)
          )
      self.dof = dof
  
imports: import warnings
  from os.path import join
  import numpy
  import scipy.ndimage
  import scipy.stats
  from nibabel import io_orientation
  from ...None import save_image
  from ...core.image.image_spaces import make_xyz_image, xyz_affine
  from ..kernel_smooth import fwhm2sigma
  from ..registration import resample
  from ..statistics.bayesian_mixed_effects import two_level_glm
  from ..statistics.histogram import histogram
label_desc: Use image node_id 3 for calling the ParcelAnalysis obj's underlying member method code with example usage: obj._voxel_level_inference(mfx) without return types