It seems that if I have a regression model such as $y_i sim beta_0 + beta_1 x_i+beta_2 x_i^2 +beta_3 x_i^3$ I can either fit a raw polynomial and get unreliable results or fit an orthogonal polynomial and get coefficients that don't have a direct physical interpretation (e.g. I cannot use them to find the locations of the extrema on the original scale). Seems like I should be able to have the best of both worlds and be able to transform the fitted orthogonal coefficients and their variances back to the raw scale. I've taken a graduate course in applied linear regression (using Kutner, 5ed) and I looked through the polynomial regression chapter in Draper (3ed, referred to by Kutner) but found no discussion of how to do this. The help text for the `poly()`

function in R does not. Nor have I found anything in my web searching, including here. Is reconstructing raw coefficients (and obtaining their variances) from coefficients fitted to an orthogonal polynomial…

- impossible to do and I'm wasting my time.
- maybe possible but not known how in the general case.
- possible but not discussed because "who would want to?"
- possible but not discussed because "it's obvious".

If the answer is 3 or 4, I would be very grateful if someone would have the patience to explain how to do this or point to a source that does so. If it's 1 or 2, I'd still be curious to know what the obstacle is. Thank you very much for reading this, and I apologize in advance if I'm overlooking something obvious.

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#### Best Answer

Yes, it's possible.

Let $z_1, z_2, z_3$ be the non-constant parts of the orthogonal polynomials computed from the $x_i$. (Each is a column vector.) Regressing these against the $x_i$ must give a perfect fit. You can perform this with the software *even when it does not document its procedures to compute orthogonal polynomials.* The regression of $z_j$ yields coefficients $gamma_{ij}$ for which

$$z_{ij} = gamma_{j0} + x_igamma_{j1} + x_i^2gamma_{j2} + x_i^3gamma_{j3}.$$

The result is a $4times 4$ matrix $Gamma$ that, upon right multiplication, converts the design matrix $X=pmatrix{1;&x;&x^2;&x^3}$ into $$Z=pmatrix{1;&z_1;&z_2;&z_3} = XGamma.tag{1}$$

After fitting the model

$$mathbb{E}(Y) = Zbeta$$

and obtaining estimated coefficients $hatbeta$ (a four-element column vector), you may substitute $(1)$ to obtain

$$hat Y = Zhatbeta = (XGamma)hatbeta = X(Gammahatbeta).$$

Therefore $Gammahatbeta$ is the estimated coefficient vector for the model in terms of the original (raw, un-orthogonalized) powers of $x$.

The following `R`

code illustrates these procedures and tests them with synthetic data.

`n <- 10 # Number of observations d <- 3 # Degree # # Synthesize a regressor, its powers, and orthogonal polynomials thereof. # x <- rnorm(n) x.p <- outer(x, 0:d, `^`); colnames(x.p) <- c("Intercept", paste0("x.", 1:d)) z <- poly(x, d) # # Compute the orthogonal polynomials in terms of the powers via OLS. # xform <- lm(cbind(1, z) ~ x.p-1) gamma <- coef(xform) # # Verify the transformation: all components should be tiny, certainly # infinitesimal compared to 1. # if (!all.equal(as.vector(1 + crossprod(x.p %*% gamma - cbind(1,z)) - 1), rep(0, (d+1)^2))) warning("Transformation is inaccurate.") # # Fit the model with orthogonal polynomials. # y <- x + rnorm(n) fit <- lm(y ~ z) #summary(fit) # # As a check, fit the model with raw powers. # fit.p <- lm(y ~ .-1, data.frame(x.p)) #summary(fit.p) # # Compare the results. # (rbind(Computed=as.vector(gamma %*% coef(fit)), Fit=coef(fit.p))) if (!all.equal(as.vector(gamma %*% coef(fit)), as.vector(coef(fit.p)))) warning("Results were not the same.") `

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