# lsq

0th

Percentile

##### Least Squares Problems in Surveying

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

Keywords
datasets
##### 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

read.matrix.hb

• lsq
##### Examples
# NOT RUN {
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
# }

Documentation reproduced from package SparseM, version 1.78, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.