sparseQR-class {Matrix} R Documentation

## Sparse QR decomposition of a sparse matrix

### Description

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

### Details

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 from the Class

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`.

### Slots

`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.

### Methods

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")`: 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