| SparseM.ontology {SparseM} | R Documentation |
This group of functions evaluates and coerces changes in class structure.
## S3 method for class 'matrix.csr' as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...) ## S3 method for class 'matrix.csc' as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...) ## S3 method for class 'matrix.ssr' as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...) ## S3 method for class 'matrix.ssc' as(x, nrow = 1, ncol = 1, eps = .Machine$double.eps, ...) ## S3 method for class 'matrix.csr' is(x, ...) ## S3 method for class 'matrix.csc' is(x, ...) ## S3 method for class 'matrix.ssr' is(x, ...) ## S3 method for class 'matrix.ssc' is(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
|
... |
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.
as.matrix.csr(x), when x is an object of class matrix.csr.chol
(that is, an object returned by a call to chol(a) when a
is an object of class matrix.csr or matric.csc),
by default returns an upper triangular matrix, which
is not consistent with the result of chol in the base
package. To get an lower triangular matric.csr matrix, use either
as.matrix.csr(x, upper.tri = FALSE) or
t(as.matrix.csr(x)).
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