

   TThhee QQRR DDeeccoommppoossiittiioonn ooff aa MMaattrriixx

        qr(x, tol=1e-07)
        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)

        is.qr(x)
        as.qr(x)

   AArrgguummeennttss::

          x: a matrix whose QR decomposition is to be computed.

        tol: the tolerance for detecting linear dependencies in
             the columns of `x'.

         qr: a QR decomposition of the type computed by `qr'.

       y, b: a vector or matrix of right-hand sides of equa-
             tions.

          a: A matrix or QR decomposition.

   DDeessccrriippttiioonn::

        `qr' provides an interface to the techniques used in
        the LINPACK routine DQRDC.  The QR decomposition plays
        an important role in many statistical techniques.  In
        particular it can be used to solve the equation Ax = b
        for given matrix A, and vector 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 decom-
        position `qr'.  `qr.qy' and `qr.qty' return `Q %*% y'
        and `t(Q) %*% y', where `Q' is the Q matrix.

        `qr.solve' solves systems of equations via the QR
        decomposition.

        `is.qr' returns `TRUE' if `x' is a list with a compo-
        nent named `qr' and `FALSE' otherwise.

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

   VVaalluuee::

        The QR decomposition of the matrix as computed by LIN-
        PACK.  The components in the returned value correspond
        directly to the values returned by DQRDC.

         qr: a matrix with the same dimensions as `x'.  The
             upper triangle contains the R of the decomposition
             and the lower triangle contains information on the
             Q of the decomposition (stored in compact form).

      qraux: a vector of length `ncol(x)' which contains addi-
             tional information on Q.

       rank: the rank of `x' as computed by the decomposition.

      pivot: information on the pivoting strategy used during
             the decomposition.

   RReeffeerreenncceess::

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

   SSeeee AAllssoo::

        `qr.Q',  `qr.R',  `qr.X' for reconstruction of the
        matrices.  `solve.qr',  `lsfit', `eigen', `svd'.

   EExxaammpplleess::

        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

