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.
- xexact
vector of the exact solutions, b, if they exist; a null vector o therwise.
- guess
vector of the initial guess of the solutions if they exist; a null vector otherwise.
- dim
dimenson of the coefficient matrix, X.
- rhs.dim
dimenson of the right-hand-side, y.
- rhs.mode
storage 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/home.html
Matrix Market, https://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