| Diagonal {Matrix} | R Documentation |
Create a diagonal matrix object, i.e., an object inheriting from
diagonalMatrix.
Diagonal(n, x = NULL)
.symDiagonal(n, x = rep.int(1,n), uplo = "U")
.sparseDiagonal(n, x = rep.int(1,m), uplo = "U",
shape = if(missing(cols)) "t" else "g",
kind, cols = if(n) 0:(n - 1L) else integer(0))
n |
integer specifying the dimension of the (square) matrix. If
missing, length(x) is used. |
x |
numeric or logical; if missing, a unit diagonal n x n matrix is created. |
uplo |
for .symDiagonal, the resulting sparse
symmetricMatrix will have slot uplo set
from this argument, either "U" or "L". Only rarely
will it make sense to change this from the default. |
shape |
string of 1 character, one of c("t","s","g"), to
chose a triangular, symmetric or general result matrix. |
kind |
string of 1 character, one of c("d","l","n"), to
chose the storage mode of the result, from classes
dsparseMatrix,
lsparseMatrix, or
nsparseMatrix, respectively. |
cols |
integer vector with values from 0:(n-1), denoting
the columns to subselect conceptually, i.e., get the
equivalent of Diagonal(n,*)[, cols + 1]. |
Diagonal() returns an object of class
ddiMatrix or ldiMatrix
(with “superclass” diagonalMatrix).
.symDiagonal() returns an object of class
dsCMatrix or lsCMatrix,
i.e., a sparse symmetric matrix. This can be
more efficient than Diagonal(n) when the result is combined
with further symmetric (sparse) matrices, however not for
matrix multiplications where Diagonal() is clearly preferred.
.sparseDiagonal(), the workhorse of .symDiagonal returns
a CsparseMatrix (the resulting class depending
on shape and kind) representation of Diagonal(n),
or, when cols are specified, of Diagonal(n)[, cols+1].
Martin Maechler
the generic function diag for extraction
of the diagonal from a matrix works for all “Matrices”.
bandSparse constructs a banded sparse matrix from
its non-zero sub-/super - diagonals.
Matrix for general matrix construction;
further, class diagonalMatrix.
Diagonal(3) Diagonal(x = 10^(3:1)) Diagonal(x = (1:4) >= 2)#-> "ldiMatrix" ## Use Diagonal() + kronecker() for "repeated-block" matrices: M1 <- Matrix(0+0:5, 2,3) (M <- kronecker(Diagonal(3), M1)) (S <- crossprod(Matrix(rbinom(60, size=1, prob=0.1), 10,6))) (SI <- S + 10*.symDiagonal(6)) # sparse symmetric still stopifnot(is(SI, "dsCMatrix"))