qr: The QR Decomposition of a Matrix

Description

qr computes the QR decomposition of a matrix.

Usage

qr(x, ...)
"qr"(x, tol = 1e-07 , LAPACK = FALSE, ...)
qr.coef(qr, y)
qr.qy(qr, y)
qr.qty(qr, y)
qr.resid(qr, y)
qr.fitted(qr, y, k = qr$rank)
qr.solve(a, b, tol = 1e-7)
"solve"(a, b, ...)
is.qr(x)
as.qr(x)

Arguments

a numeric or complex matrix whose QR decomposition is to be computed. Logical matrices are coerced to numeric.

tol

the tolerance for detecting linear dependencies in the columns of x. Only used if LAPACK is false and x is real.

a QR decomposition of the type computed by qr.

y, b

a vector or matrix of right-hand sides of equations.

a QR decomposition or (qr.solve only) a rectangular matrix.

effective rank.

LAPACK

logical. For real x, if true use LAPACK otherwise use LINPACK (the default).

...

further arguments passed to or from other methods

Value

qr: a matrix with the same dimensions as x. The upper triangle contains the $\bold{R}$ of the decomposition and the lower triangle contains information on the $\bold{Q}$ of the decomposition (stored in compact form). Note that the storage used by DQRDC and DGEQP3 differs.
qraux: a vector of length ncol(x) which contains additional information on $\bold{Q}$.
rank: the rank of x as computed by the decomposition: always full rank in the LAPACK case.
pivot: information on the pivoting strategy used during the decomposition.

Source

For qr, the LINPACK routine DQRDC and the LAPACK routines DGEQP3 and ZGEQP3. Further LINPACK and LAPACK routines are used for qr.coef, qr.qy and qr.aty. LAPACK and LINPACK are from http://www.netlib.org/lapack and http://www.netlib.org/linpack and their guides are listed in the references.

Details

The QR decomposition plays an important role in many statistical techniques. In particular it can be used to solve the equation $\bold{Ax} = \bold{b}$ for given matrix $\bold{A}$, and vector $\bold{b}$. It is useful for computing regression coefficients and in applying the Newton-Raphson algorithm.

The functions qr.coef, qr.resid, and qr.fitted return the coefficients, residuals and fitted values obtained when fitting y to the matrix with QR decomposition qr. (If pivoting is used, some of the coefficients will be NA.) qr.qy and qr.qty return Q %*% y and t(Q) %*% y, where Q is the (complete) $\bold{Q}$ matrix.

All the above functions keep dimnames (and names) of x and y if there are any.

solve.qr is the method for solve for qr objects. qr.solve solves systems of equations via the QR decomposition: if a is a QR decomposition it is the same as solve.qr, but if a is a rectangular matrix the QR decomposition is computed first. Either will handle over- and under-determined systems, providing a least-squares fit if appropriate.

is.qr returns TRUE if x is a list with components named qr, rank and qraux and FALSE otherwise.

It is not possible to coerce objects to mode "qr". Objects either are QR decompositions or they are not.

The LINPACK interface is restricted to matrices x with less than $2^31$ elements.

qr.fitted and qr.resid only support the LINPACK interface.

Unsuccessful results from the underlying LAPACK code will result in an error giving a positive error code: these can only be interpreted by detailed study of the FORTRAN code.

References

Anderson. E. and ten others (1999) LAPACK Users' Guide. Third Edition. SIAM. Available on-line at http://www.netlib.org/lapack/lug/lapack_lug.html.

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Dongarra, J. J., Bunch, J. R., Moler, C. B. and Stewart, G. W. (1978) LINPACK Users Guide. Philadelphia: SIAM Publications.

Examples

Run this code

hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") }
h9 <- hilbert(9); h9
qr(h9)$rank           #--> only 7
qrh9 <- qr(h9, tol = 1e-10)
qrh9$rank             #--> 9
##-- Solve linear equation system  H %*% x = y :
y <- 1:9/10
x <- qr.solve(h9, y, tol = 1e-10) # or equivalently :
x <- qr.coef(qrh9, y) #-- is == but much better than
                      #-- solve(h9) %*% y
h9 %*% x              # = y


## overdetermined system
A <- matrix(runif(12), 4)
b <- 1:4
qr.solve(A, b) # or solve(qr(A), b)
solve(qr(A, LAPACK = TRUE), b)
# this is a least-squares solution, cf. lm(b ~ 0 + A)

## underdetermined system
A <- matrix(runif(12), 3)
b <- 1:3
qr.solve(A, b)
solve(qr(A, LAPACK = TRUE), b)
# solutions will have one zero, not necessarily the same one

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