docs: def destandardize(self, estimates, se, **kwargs):
          """Revert the betas and variance components back to the original scale.
  
          """
          pvalues = kwargs["pvalues"]
          v = kwargs["v"]
          nonmissing=kwargs["nonmissing"]
          pheno = self.datasource.phenotype_data[self.idx][self.datasource.phenotype_data[self.idx] != PhenoCovar.missing_encoding]
          covariates = []
          mmx = []
          ssx = []
          a = [1,0]
  
          for c in self.datasource.covariate_data:
              covariates.append(c[c!=PhenoCovar.missing_encoding])
              mmx.append(numpy.mean(covariates[-1]))
              ssx.append(numpy.std(covariates[-1]))
              a.append(-mmx[-1]/ssx[-1])
          if len(mmx) < 1:
              mmx = 1
              ssx = 1
          else:
              mmx = numpy.array(mmx)
              ssx = numpy.array(ssx)
          covariates = numpy.array(covariates)
          ssy = numpy.std(pheno)
          mmy = numpy.mean(pheno)
          # Quick scratch pad for Chun's new destandardization
          ccount = len(covariates) + 2
          a=numpy.array(a)
  
          meanpar = list([mmy + ssy * (estimates[0] - numpy.sum(estimates[2:ccount]*mmx/ssx)),
                      estimates[1] * ssy])
  
  
          meanse = [ssy*numpy.sqrt(a.dot(v[0:ccount, 0:ccount]).dot(a.transpose())),
                    ssy * se[1]]
          varpar = [2*numpy.log(ssy) + estimates[ccount] - numpy.sum(estimates[ccount+2:]*mmx/ssx),
                    estimates[ccount+1]]
          varse = [numpy.sqrt(a.dot(v[ccount:, ccount:]).dot(a.transpose())),
                   se[ccount+1]]
          if ccount > 2:
              meanpar += list(estimates[2:ccount] * ssy / ssx)
              meanse += list(ssy * se[2:ccount]/ssx)
              varpar += list(estimates[ccount+2:]/ssx)
              varse += list(se[ccount+2:]/ssx)
          pvals = 2*scipy.stats.norm.cdf(-numpy.absolute(numpy.array(meanpar+varpar)/numpy.array(meanse+varse)))
  
  
  
          return meanpar+varpar, meanse + varse, pvals