docs: def getVarianceCompStdErrors(self,univariance=False):
          """
          Return the standard errors on the estimated variance components (for variance component estimates \see getVarianceComps)
  
          Args:
              univariance:   Boolean indicator, if True variance components are normalized to sum up to 1 for each trait
          Returns:
              standard errors on variance components [P, n_randEffs matrix]
          """
          RV=sp.zeros((self.P,self.n_randEffs))
          for term_i in range(self.n_randEffs):
              #RV[:,term_i] = self.getTraitCovarStdErrors(term_i).diagonal()
              RV[:,term_i] = self.getTraitCovarStdErrors(term_i)
          var = self.getVarianceComps()
          if univariance:
              RV /= var.sum(1)[:,sp.newaxis]
          return RV