How to calculate variance of least squares estimator using QR decomposition in R?

Question

I\'m trying to learn QR decomposition, but can\'t figure out how to get the variance of beta_hat without resorting to traditional matrix calculations. I\'m practising with

Answer 1

setup (copying in your code)

y <- iris$Sepal.Length
x <- iris$Sepal.Width
X <- cbind(1,x)
n <- nrow(X)
p <- ncol(X)
qr.X <- qr(X)
b <- (t(qr.Q(qr.X)) %*% y)[1:p]  ## can be optimized; see Remark 1 below
R <- qr.R(qr.X)  ## can be optimized; see Remark 2 below
beta <- as.vector(backsolve(R, b))
res <- as.vector(y - X %*% beta)

math

computation

Residual degree of freedom is n - p, so estimated variance is

se2 <- sum(res ^ 2) / (n - p)

Thus, the variance covariance matrix of estimated coefficients is

V <- chol2inv(R) * se2

#           [,1]         [,2]
#[1,]  0.22934170 -0.07352916
#[2,] -0.07352916  0.02405009

validation

Let's check the correctness by comparing with lm:

fit <- lm(Sepal.Length ~ Sepal.Width, iris)

vcov(fit)

#            (Intercept) Sepal.Width
#(Intercept)  0.22934170 -0.07352916
#Sepal.Width -0.07352916  0.02405009

Identical result!

---

Remark 1 (skip forming 'Q' factor)

Instead of b <- (t(qr.Q(qr.X)) %*% y)[1:p], you can use function qr.qty (to avoid forming 'Q' matrix):

b <- qr.qty(qr.X, y)[1:p]

Remark 2 (skip forming 'R' factor)

You don't have to extract R <- qr.R(qr.X) for backsolve; using qr.X$qr is sufficient:

beta <- as.vector(backsolve(qr.X$qr, b))

---

Appendix: A function for estimation

The above is the simplest demonstration. In practice column pivoting and rank-deficiency need be dealt with. The following is an implementation. X is a model matrix and y is the response. Results should be compared with lm(y ~ X + 0).

qr_estimation <- function (X, y) {
  ## QR factorization
  QR <- qr(X)
  r <- QR$rank
  piv <- QR$pivot[1:r]
  ## estimate identifiable coefficients
  b <- qr.qty(QR, y)[1:r]
  beta <- backsolve(QR$qr, b, r)
  ## fitted values
  yhat <- base::c(X[, piv] %*% beta)
  ## residuals
  resi <- y - yhat
  ## error variance
  se2 <- base::c(crossprod(resi)) / (nrow(X) - r)
  ## variance-covariance for coefficients
  V <- chol2inv(QR$qr, r) * se2
  ## post-processing on pivoting and rank-deficiency
  p <- ncol(X)
  beta_full <- rep.int(NA_real_, p)
  beta_full[piv] <- beta
  V_full <- matrix(NA_real_, p, p)
  V_full[piv, piv] <- V
  ## return
  list(coefficients = beta_full, vcov = V_full,
       fitted.values = yhat, residuals = resi, sig = sqrt(se2))
  }