R: Least Squares Problems in Surveying

lsq {SparseM} | R Documentation

## Least Squares Problems in Surveying

### Description

One of the four matrices from the least-squares solution of problems in surveying that were used by Michael Saunders and Chris Paige in the testing of LSQR

### Usage

    data(lsq)

### Format

A list of class `matrix.csc.hb` or `matrix.ssc.hb` depending on how the coefficient matrix is stored with the following components:

- ra ra component of the csc or ssc format of the coefficient matrix, X.

- ja ja component of the csc or ssc format of the coefficient matrix, X.

- ia ia component of the csc or ssc format of the coefficient matrix, X.

- rhs.ra ra component of the right-hand-side, y, if stored in csc or ssc format; right-hand-side stored in dense vector or matrix otherwise.

- rhs.ja ja component of the right-hand-side, y, if stored in csc or ssc format; a null vector otherwise.

- rhs.ia ia component of the right-hand-side, y, if stored in csc or ssc format; a null vector otherwise.

- xexactvector of the exact solutions, b, if they exist; a null vector o therwise.

- guessvector of the initial guess of the solutions if they exist; a null vector otherwise.

- dimdimenson of the coefficient matrix, X.

- rhs.dimdimenson of the right-hand-side, y.

- rhs.modestorage mode of the right-hand-side; can be full storage or same format as the coefficient matrix.

### References

Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research

Matrix Market, http://math.nist.gov/MatrixMarket/data/Harwell-Boeing/lsq/lsq.html

### See Also

`read.matrix.hb`

### Examples

    data(lsq)
    class(lsq) # -> [1] "matrix.csc.hb"
    model.matrix(lsq)->X
    class(X) # -> "matrix.csr"
    dim(X) # -> [1] 1850  712
    y <- model.response(lsq) # extract the rhs
    length(y) # [1] 1850

[Package SparseM version 1.77 Index]