Diagonal | R Documentation |

Create a diagonal matrix object, i.e., an object inheriting from
`diagonalMatrix`

(or a “standard”
`CsparseMatrix`

diagonal matrix in cases that is prefered).

Diagonal(n, x = NULL) .symDiagonal(n, x = rep.int(1,n), uplo = "U", kind) .trDiagonal(n, x = 1, uplo = "U", unitri=TRUE, kind) .sparseDiagonal(n, x = 1, uplo = "U", shape = if(missing(cols)) "t" else "g", unitri, kind, cols = if(n) 0:(n - 1L) else integer(0))

`n` |
integer specifying the dimension of the (square) matrix. If
missing, |

`x` |
numeric or logical; if missing, a |

`uplo` |
for |

`shape` |
string of 1 character, one of |

`unitri` |
optional logical indicating if a triangular result
should be “unit-triangular”, i.e., with |

`kind` |
string of 1 character, one of |

`cols` |
integer vector with values from |

`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. Analogously,
`.triDiagonal`

gives a sparse `triangularMatrix`

.
This can be more efficient than `Diagonal(n)`

when the result is combined
with further symmetric (sparse) matrices, e.g., in `kronecker`

,
however *not* for
matrix multiplications where `Diagonal()`

is clearly preferred.

`.sparseDiagonal()`

, the workhorse of `.symDiagonal`

and
`.trDiagonal`

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. `band(A)`

returns a
band matrix containing some sub-/super - diagonals of `A`

.

`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")) (I4 <- .sparseDiagonal(4, shape="t"))# now (2012-10) unitriangular stopifnot(I4@diag == "U", all(I4 == diag(4)))

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