sparseQR-class {Matrix} | R Documentation |

Objects class `"sparseQR"`

represent a QR decomposition of a
sparse *n x p* rectangular matrix *X*,
typically resulting from `qr()`

The decomposition is of the form `A[p+1,] == Q %*% R`

, if
the `q`

slot is of length 0 or `A[p+1,q+1] == Q %*% R`

where A is a sparse *m by n* matrix (*m >= n*),
*R* is an *m by n* matrix that is zero below the
main diagonal. The `p`

slot is a 0-based permutation of
`1:m`

applied to the rows of the original matrix. If the `q`

slot has length `n`

it is a 0-based permutation of `1:n`

applied to the columns of the original matrix to reduce the amount
of “fill-in” in the matrix *R*.

The matrix *Q* is a "virtual matrix". It is the product of
*n* Householder transformations. The information to generate
these Householder transformations is stored in the `V`

and
`beta`

slots.

The `"sparseQR"`

methods for the `qr.*`

functions return
objects of class `"dgeMatrix"`

(see
`dgeMatrix`

). Results from `qr.coef`

,
`qr.resid`

and `qr.fitted`

(when `k == ncol(R)`

) are
well-defined and should match those from the corresponding dense matrix
calculations. However, because the matrix `Q`

is not uniquely
defined, the results of `qr.qy`

and `qr.qty`

do not
necessarily match those from the corresponding dense matrix
calculations.

Also, the results of `qr.qy`

and `qr.qty`

apply to the
permuted column order when the `q`

slot has length `n`

.

Objects can be created by calls of the form `new("sparseQR", ...)`

but are more commonly created by function `qr`

applied
to a sparse matrix such as a matrix of class
`dgCMatrix`

.

`V`

:Object of class

`"dgCMatrix"`

. The columns of`V`

are the vectors that generate the Householder transformations of which the matrix Q is composed.`beta`

:Object of class

`"numeric"`

, the normalizing factors for the Householder transformations.`p`

:Object of class

`"integer"`

: Permutation (of`0:(n-1)`

) applied to the rows of the original matrix.`R`

:Object of class

`"dgCMatrix"`

: An upper triangular matrix of dimension \

`q`

:Object of class

`"integer"`

: Permutation applied from the right. Can be of length 0 which implies no permutation.

- qr.R
`signature(qr = "sparseQR")`

: compute the upper triangular*R*matrix of the QR decomposition. Note that this currently warns because of possible permutation mismatch with the classical`qr.R()`

result,*and*you can suppress these warnings by setting`options()`

either`"Matrix.quiet.qr.R"`

or (the more general) either`"Matrix.quiet"`

to`TRUE`

.- qr.Q
`signature(qr = "sparseQR")`

: compute the orthogonal*Q*matrix of the QR decomposition.- qr.coef
`signature(qr = "sparseQR", y = "ddenseMatrix")`

: ...- qr.coef
`signature(qr = "sparseQR", y = "matrix")`

: ...- qr.coef
`signature(qr = "sparseQR", y = "numeric")`

: ...- qr.fitted
`signature(qr = "sparseQR", y = "ddenseMatrix")`

: ...- qr.fitted
`signature(qr = "sparseQR", y = "matrix")`

: ...- qr.fitted
`signature(qr = "sparseQR", y = "numeric")`

: ...- qr.qty
`signature(qr = "sparseQR", y = "ddenseMatrix")`

: ...- qr.qty
`signature(qr = "sparseQR", y = "matrix")`

: ...- qr.qty
`signature(qr = "sparseQR", y = "numeric")`

: ...- qr.qy
`signature(qr = "sparseQR", y = "ddenseMatrix")`

: ...- qr.qy
`signature(qr = "sparseQR", y = "matrix")`

: ...- qr.qy
`signature(qr = "sparseQR", y = "numeric")`

: ...- qr.resid
`signature(qr = "sparseQR", y = "ddenseMatrix")`

: ...- qr.resid
`signature(qr = "sparseQR", y = "matrix")`

: ...- qr.resid
`signature(qr = "sparseQR", y = "numeric")`

: ...- solve
`signature(a = "sparseQR", b = "ANY")`

: For`solve(a,b)`

, simply uses`qr.coef(a,b)`

.

`qr`

, `qr.Q`

,
`qr.R`

, `qr.fitted`

,
`qr.resid`

, `qr.coef`

,
`qr.qty`

, `qr.qy`

,
`dgCMatrix`

, `dgeMatrix`

.

data(KNex) mm <- KNex $ mm y <- KNex $ y y. <- as(as.matrix(y), "dgCMatrix") str(qrm <- qr(mm)) qc <- qr.coef (qrm, y); qc. <- qr.coef (qrm, y.) # 2nd failed in Matrix <= 1.1-0 qf <- qr.fitted(qrm, y); qf. <- qr.fitted(qrm, y.) qs <- qr.resid (qrm, y); qs. <- qr.resid (qrm, y.) stopifnot(all.equal(qc, as.numeric(qc.), tolerance=1e-12), all.equal(qf, as.numeric(qf.), tolerance=1e-12), all.equal(qs, as.numeric(qs.), tolerance=1e-12), all.equal(qf+qs, y, tolerance=1e-12))

[Package *Matrix* version 1.2-2 Index]