Revert the betas and variance components back to the original scale.

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