SparseM.ontology {SparseM} | R Documentation |
This group of functions evaluates and coerces changes in class structure.
as.matrix.csr(x, nrow = 1, ncol = 1, eps = .Machine$double.eps) as.matrix.csc(x, nrow = 1, ncol = 1, eps = .Machine$double.eps) as.matrix.ssr(x, nrow = 1, ncol = 1, eps = .Machine$double.eps) as.matrix.ssc(x, nrow = 1, ncol = 1, eps = .Machine$double.eps) is.matrix.csr(x, ...) is.matrix.csc(x, ...) is.matrix.ssr(x, ...) is.matrix.ssc(x, ...)
x |
is a matrix, or vector object, of either dense or sparse form |
nrow |
number of rows of matrix |
ncol |
number of columns of matrix |
eps |
A tolerance parameter: elements of x such that abs(x) < eps set to zero.
This argument is only relevant when coercing matrices from dense to sparse form. Defaults to
eps = .Machine$double.eps |
... |
other arguments |
The function matrix.csc
acts like matrix
to coerce a vector object to
a sparse matrix object of class matrix.csr
.
This aspect of the code is in the process of conversion from S3 to S4 classes.
For the most part the S3 syntax prevails. An exception is the code to
coerce vectors to diagonal matrix form which uses as(v,"matrix.diag.csr"
.
The generic functions as.matrix.xxx
coerce a matrix x
into
a matrix of storage class matrix.xxx
. The argument matrix x
may be of conventional dense form, or of any of the four supported
classes: matrix.csr, matrix.csc, matrix.ssr, matrix.ssc
.
The generic functions is.matrix.xxx
evaluate whether the
argument is of class matrix.xxx
. The function
as.matrix
transforms a matrix of any sparse class into conventional
dense form. The primary storage class for sparse matrices is the
compressed sparse row matrix.csr
class.
An n by m matrix A with real elements a_{ij},
stored in matrix.csr
format consists of three arrays:
ra
: a real array of nnz elements containing the non-zero
elements of A, stored in row order. Thus, if i<j, all elements of row i
precede elements from row j. The order of elements within the rows is immaterial.
ja
: an integer array of nnz elements containing the column
indices of the elements stored in ra
.
ia
: an integer array of n+1 elements containing pointers to
the beginning of each row in the arrays ra
and ja
. Thus
ia[i]
indicates the position in the arrays ra
and
ja
where the ith row begins. The last, (n+1)st, element of
ia
indicates where the n+1 row would start, if it existed.
The compressed sparse column class matrix.csc
is defined in
an analogous way, as are the matrix.ssr
, symmetric sparse row, and
matrix.ssc
, symmetric sparse column classes.
as.matrix.ssr
and as.matrix.ssc
should ONLY be used with
symmetric matrices.
Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research
SparseM.hb
for handling Harwell-Boeing sparse matrices.
n1 <- 10 p <- 5 a <- rnorm(n1*p) a[abs(a)<0.5] <- 0 A <- matrix(a,n1,p) B <- t(A)%*%A A.csr <- as.matrix.csr(A) A.csc <- as.matrix.csc(A) B.ssr <- as.matrix.ssr(B) B.ssc <- as.matrix.ssc(B) is.matrix.csr(A.csr) # -> TRUE is.matrix.csc(A.csc) # -> TRUE is.matrix.ssr(B.ssr) # -> TRUE is.matrix.ssc(B.ssc) # -> TRUE as.matrix(A.csr) as.matrix(A.csc) as.matrix(B.ssr) as.matrix(B.ssc) as.matrix.csr(rep(0,9),3,3) #sparse matrix of all zeros as(4,"matrix.diag.csr") #identity matrix of dimension 4