transf_ function(model, C=0, plotit=F,
                 labs=c(rep("d",model$rows[1]),
                        rep("c",sum(model$ptab.len) - 
                                        model$rows[1]),
                        rep("p",dim(model$X)[1] - 
                                        sum(model$ptab.len)))
                 )
{
    X_ model$X
    rX1_ model$rows[1]
    rX1.1_ sum(model$ptab.len)
    rX_  dim(X)[1]
    e_ as.matrix(model$mean.theta)
    yhat_ X %*% e + C
    E_ model$Y - yhat - C
    sE_ as.vector(E * model$Ghe)
    svX_ svd(model$X * model$Ghe)
    V_ svX$u %*% t(svX$u)
    yhat_ yhat[1:rX1]
    if (any(yhat <= 0)) stop("\nnegative fitted values, choose C>0?")
    G_ as.matrix(c(yhat*log(yhat) - yhat + 1, 
                            rep(0, rX - rX1))) * model$Ghe
    avtx_ as.vector((diag(rX) - V) %*% G)
    fit.avtx_ lsfit(avtx,sE)
    if(plotit) {
        plot(avtx, sE, 
            xlab = "Andrews added variable for transformation", 
            ylab = "Scaled residuals", type="n")
        text(avtx,sE,labs, cex = 1)
        abline(fit.avtx)
    }
    z_ list(avtx = avtx, scal.res = sE, C=C, 
                lambda = 1 - fit.avtx$coef[2], 
                std.err = ls.diag(fit.avtx)$std.err[2])
    z
}