docstr: Return a parametric approximation of the p-value associated
  with the null hypothesis: (H0) 'contrast equals baseline'
docstr_tok: ["Return","a","parametric","approximation","of","the","p-value","associated","with","the","null","hypothesis",":","(","H0",")","'contrast","equals","baseline","'"]
code: def pvalue(self, baseline=0.0):
      """
      Return a parametric approximation of the p-value associated
      with the null hypothesis: (H0) 'contrast equals baseline'
      """
      if self._stat is None or self._baseline != baseline:
          self._stat = self.stat(baseline)
      # Valid conjunction as in Nichols et al, Neuroimage 25, 2005.
      if self.type in ["t", "tmin"]:
          p = sps.t.sf(self._stat, np.minimum(self.dof, self._dofmax))
      elif self.type == "F":
          p = sps.f.sf(
              self._stat, self.dim, np.minimum(self.dof, self._dofmax)
          )
      else:
          raise ValueError("Unknown statistic type")
      self._pvalue = p
      return p
  
imports: import numpy
  import scipy.stats
  from nipy.algorithms.statistics.utils import z_score
  from ..utils import mahalanobis
  from .None import kalman
label_desc: Use image node_id 4 for calling the contrast obj's underlying member method code with example usage: obj.pvalue(baseline) and returns: p