Suppose I use Least Squares to estimate coefficients in the standard linear model with design matrix $X$'s columns standardized, so the model is $$ E[y] = X^*\beta^* $$ where $X^*$ is $X$ with columns centered and scaled. Assume $X$ has full column rank. To recover the regression coefficients $\beta$ in the model with unstandardized X $$ E[y] = X\beta $$ I should be able to use the equation $$ \qquad \quad X\beta = X^*\beta^* \\ \implies X^T X\beta = X^TX^*\beta^* \\ \qquad \quad \; \; \; \; \implies \qquad \beta = (X^T X)^{-1}X^TX^*\beta^* $$ To test this I ran some simple R code:

# set number of samples
N <- 10
# set number of regressors
p <- 3
y <- runif(N)
X <- matrix(runif(N*p), N)
# Least squares coefficients with unstandardized X
(b1 <- solve(crossprod(X), crossprod(X,y)))

# now standardize X and get new coefficients
Xs <- scale(X)
(b2 <- solve(crossprod(Xs), crossprod(Xs,y)))

# b.orig should be exactly the same as b1, but its not!
(b.orig <- solve(crossprod(X), crossprod(X, Xs %*% b2)))

Here is the output of b1:

[1,]  0.17109189
[2,]  0.52204169
[3,] -0.02115178

and the output of b.orig, which should equal b1, but does not:

[1,] -0.15935376
[2,]  0.16915433
[3,] -0.04696604

What is going wrong here? I have already looked at:http://bit.ly/1HxHS6U, who seems to use a similar idea (equating expected or fitted values of y in both equations).


1 Answer 1


The models are not the same. Therefore the coefficients should differ. When you recenter, you are taking linear combinations of the columns of $X$ with the vector $\mathbf{1}=(1,1,\ldots, 1)^\prime$. This is fine, provided that $\mathbf{1}$ lies in the column space. In your example it does not.

What is worse, when you do include $\mathbf{1}$ as a column, the entire calculation falls apart due to singularities.

In detail, the original model (including a constant term) should be

$$\mathbb{E}(Y) = \beta_0 + \beta_1 X_1 + \cdots + \beta_p X_p = \beta_0 + \sum_{i=1}^p \beta_i X_i.$$

Standardizing to $X_i = \sigma_i Z_i + \mu_i$ yields

$$\mathbb{E}(Y) = \beta_0 + \sum_{i=1}^p \beta_i (\sigma_i Z_i + \mu_i) = \left(\beta_0 + \sum_{i=1}^p \beta_i \mu_i\right) + \sum_{i=1}^p (\beta_i \sigma_i) Z_i = \beta_0^{*} + \sum_{i=1}^p \beta_i^{*} Z_i,$$

with $\beta_i^{*} = \sigma_i \beta_i$ giving the correct relationships between the "standardized" and unstandardized coefficients. (The usual definition of standardized coefficient also standardizes the response variable, so these $\beta_i^{*}$ might better be called "semi-standardized.")

  • $\begingroup$ Thanks! Clearly the models are not the same, since the design matrix has changed. I think you meant to say that the models are not even equivalent (define two models to be equivalent if the coefficients from model 1 can be derived from the coefficients of model 2, using information from X if necessary). I believe what you are saying is that two models are equivalent only if you can start with the expression of model 1, and use algebra to arrive at the expression for model 2. Equating the expectation of $y$ is clearly wrong, which I realized after thinking it through. $\endgroup$
    – ved
    Apr 16, 2015 at 18:44
  • 1
    $\begingroup$ Thanks for the clarification. My meaning of "same" is that the column spaces of the design matrices must be identical. (This is the relevant one for your purposes, because your focus is on achieving the same fit to the response variable, regardless of how it happens to be expressed.) If they are not identical spaces, then the least squares solutions will project $y$ onto two different subspaces. The projections will agree only when they (accidentally) lie on the intersection of those subspaces. $\endgroup$
    – whuber
    Apr 16, 2015 at 21:11
  • $\begingroup$ The column space explanation is perfect. Thank you. $\endgroup$
    – ved
    Apr 17, 2015 at 3:32

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