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]

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