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)
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
| a vector or matrix of right-hand sides of equations. |
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
\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.qy, qr.qty,
qr.resid, and qr.fitted use a computed
QR decomposition to compute various quantities of interest.
qr.solve solves systems of equations via the QR decomposition.
is.qr returns TRUE if x is a list with a
component named qr and FALSE otherwise.
It is not possible to coerce objects to mode qr. Objects either are qr decompositions or they are not. Coercion is not possible.
qr
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
ncol(x) which contains
additional information on Q.
rank
x as computed by the decomposition.
pivot
solve.qr, lsfit.