class_docstr: Class to contain results from likelihood models 
class_docstr_tok: ["Class","to","contain","results","from","likelihood","models"]
docstr: Compute a Tcontrast for a row vector `matrix`
  
  To get the t-statistic for a single column, use the 't' method.
  
  Parameters
  ----------
  matrix : 1D array-like
      contrast matrix
  store : sequence, optional
      components of t to store in results output object.  Defaults to all
      components ('t', 'effect', 'sd').
  dispersion : None or float, optional
  
  Returns
  -------
  res : ``TContrastResults`` object
docstr_tok: ["Compute","a","Tcontrast","for","a","row","vector","`","matrix","`","To","get","the","t-statistic","for","a","single","column",",","use","the","'t","'","method",".","Parameters","--","--","--","--","--","matrix",":","1D","array-like","contrast","matrix","store",":","sequence",",","optional","components","of","t","to","store","in","results","output","object",".","Defaults","to","all","components","(","'t","'",",","'effect","'",",","'sd","'",")",".","dispersion",":","None","or","float",",","optional","Returns","--","--","--","-","res",":","``","TContrastResults","``","object"]
code: def Tcontrast(
      self, matrix, store=("t", "effect", "sd"), dispersion=None
  ):
      """Compute a Tcontrast for a row vector `matrix`
  
      To get the t-statistic for a single column, use the 't' method.
  
      Parameters
      ----------
      matrix : 1D array-like
          contrast matrix
      store : sequence, optional
          components of t to store in results output object.  Defaults to all
          components ('t', 'effect', 'sd').
      dispersion : None or float, optional
  
      Returns
      -------
      res : ``TContrastResults`` object
      """
      matrix = np.asarray(matrix)
      # 1D vectors assumed to be row vector
      if matrix.ndim == 1:
          matrix = matrix[None]
      if matrix.shape[0] != 1:
          raise ValueError("t contrasts should have only one row")
      if matrix.shape[1] != self.theta.shape[0]:
          raise ValueError(
              "t contrasts should be length P=%d, "
              "but this is length %d"
              % (self.theta.shape[0], matrix.shape[1])
          )
      store = set(store)
      if not store.issubset(("t", "effect", "sd")):
          raise ValueError(f"Unexpected store request in {store}")
      st_t = st_effect = st_sd = effect = sd = None
      if "t" in store or "effect" in store:
          effect = np.dot(matrix, self.theta)
          if "effect" in store:
              st_effect = np.squeeze(effect)
      if "t" in store or "sd" in store:
          sd = np.sqrt(self.vcov(matrix=matrix, dispersion=dispersion))
          if "sd" in store:
              st_sd = np.squeeze(sd)
      if "t" in store:
          st_t = np.squeeze(effect * pos_recipr(sd))
      return TContrastResults(
          effect=st_effect, t=st_t, sd=st_sd, df_den=self.df_resid
      )
  
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 7 for calling the LikelihoodModelResults obj's underlying member method code with example usage: obj.Tcontrast(matrix, store, dispersion) and returns: TContrastResults