fastMisc  R Documentation 
“SemiAPI” functions used internally by Matrix,
often to bypass S4 dispatch and avoid the associated overhead.
These are exported to provide this capability to expert users.
Typical users should continue to rely on S4 generic functions
to dispatch suitable methods, by calling,
e.g., as(., <class>)
for coercions.
.M2kind(from, kind = ".", sparse = NA)
.M2gen(from, kind = ".")
.M2sym(from, ...)
.M2tri(from, ...)
.M2diag(from)
.M2v(from)
.M2m(from)
.M2unpacked(from)
.M2packed(from)
.M2C(from)
.M2R(from)
.M2T(from)
.M2V(from)
.m2V(from, kind = ".")
.sparse2dense(from, packed = FALSE)
.diag2dense(from, kind = ".", shape = "t", packed = FALSE, uplo = "U")
.ind2dense(from, kind = "n")
.m2dense(from, class = ".ge", uplo = "U", diag = "N", trans = FALSE)
.dense2sparse(from, repr = "C")
.diag2sparse(from, kind = ".", shape = "t", repr = "C", uplo = "U")
.ind2sparse(from, kind = "n", repr = ".")
.m2sparse(from, class = ".gC", uplo = "U", diag = "N", trans = FALSE)
.tCRT(x, lazy = TRUE)
.diag.dsC(x, Chx = Cholesky(x, LDL = TRUE), res.kind = "diag")
.solve.dgC.lu (a, b, tol = .Machine$double.eps, check = TRUE)
.solve.dgC.qr (a, b, order = 3L, check = TRUE)
.solve.dgC.chol(a, b, check = TRUE)
.updateCHMfactor(object, parent, mult = 0)
from, x, a, b 
a 
kind 
a string ( 
shape 
a string ( 
repr 
a string ( 
packed 
a logical indicating if the result should
inherit from 
sparse 
a logical indicating if the result should inherit
from 
uplo 
a string ( 
diag 
a string ( 
trans 
a logical indicating if the result should be a 1row
matrix rather than a 1column matrix where 
class 
a string whose first three characters specify the class
of the result. It should match the pattern

... 
optional arguments passed to 
lazy 
a logical indicating if the transpose should be constructed with minimal allocation, but possibly without preserving representation. 
Chx 
optionally, the 
res.kind 
a string in 
tol 
see 
order 
see 
check 
a logical indicating if the first argument should be
tested for inheritance from 
object 
a Cholesky factorization inheriting from virtual class

parent 
an object of class 
mult 
a numeric vector of postive length. Only the first element is used, and that must be finite. 
Functions with names of the form .<A>2<B>
implement coercions
from virtual class A to the “nearest” nonvirtual subclass of
virtual class B, where the virtual classes are abbreviated as follows:
M
Matrix
V
sparseVector
m
matrix
v
vector
dense
denseMatrix
unpacked
unpackedMatrix
packed
packedMatrix
sparse
CsparseMatrix
,
RsparseMatrix
, or
TsparseMatrix
C
CsparseMatrix
R
RsparseMatrix
T
TsparseMatrix
gen
generalMatrix
sym
symmetricMatrix
tri
triangularMatrix
diag
diagonalMatrix
ind
indMatrix
Abbreviations should be seen as a guide, rather than as an
exact description of behaviour. Notably, .m2dense
,
.m2sparse
, and .m2V
accept vectors that are
not matrices.
.tCRT(x)
If lazy = TRUE
, then .tCRT
constructs the transpose
of x
using the most efficient representation,
which for ‘CRT’ is ‘RCT’. If lazy = FALSE
,
then .tCRT
preserves the representation of x
,
behaving as the corresponding methods for generic function t
.
.diag.dsC(x)
.diag.dsC
computes (or uses if Chx
is supplied)
the Cholesky factorization of x
as L D L'
in order
to calculate one of several possible statistics from the diagonal
entries of D
. See res.kind
under ‘Arguments’.
.solve.dgC.*(a, b)
.solve.dgC.lu(a, b)
needs a square matrix a
.
.solve.dgC.qr(a, b)
needs a “long” matrix a
,
with nrow(a) >= ncol(a)
.
.solve.dgC.chol(a, b)
needs a “wide” matrix a
,
with nrow(a) <= ncol(a)
.
All three may be used to solve sparse linear systems directly.
Only .solve.dgC.qr
and .solve.dgC.chol
be used
to solve sparse least squares problems.
.updateCHMfactor(object, parent, mult)
.updateCHMfactor
updates object
with the result
of Cholesky factorizing
F(parent) + mult[1] * diag(nrow(parent))
,
i.e., F(parent)
plus mult[1]
times the identity matrix,
where F = identity
if parent
is a dsCMatrix
and F = tcrossprod
if parent
is a dgCMatrix
.
The nonzero pattern of F(parent)
must match
that of S
if object = Cholesky(S, ...)
.
D. < diag(x = c(1, 1, 2, 3, 5, 8))
D.0 < Diagonal(x = c(0, 0, 0, 3, 5, 8))
S. < toeplitz(as.double(1:6))
C. < new("dgCMatrix", Dim = c(3L, 4L),
p = c(0L, 1L, 1L, 1L, 3L), i = c(1L, 0L, 2L), x = c(8, 2, 3))
stopifnot(exprs = {
identical(.M2tri (D.), as(D., "triangularMatrix"))
identical(.M2sym (D.), as(D., "symmetricMatrix"))
identical(.M2diag(D.), as(D., "diagonalMatrix"))
identical(.M2kind(C., "l"),
as(C., "lMatrix"))
identical(.M2kind(.sparse2dense(C.), "l"),
as(as(C., "denseMatrix"), "lMatrix"))
identical(.diag2sparse(D.0, ".", "t", "C"),
.dense2sparse(.diag2dense(D.0, ".", "t", TRUE), "C"))
identical(.M2gen(.diag2dense(D.0, ".", "s", FALSE)),
.sparse2dense(.M2gen(.diag2sparse(D.0, ".", "s", "T"))))
identical(S.,
.M2m(.m2sparse(S., ".sR")))
identical(S. * lower.tri(S.) + diag(1, 6L),
.M2m(.m2dense (S., ".tr", "L", "U")))
identical(.M2R(C.), .M2R(.M2T(C.)))
identical(.tCRT(C.), .M2R(t(C.)))
})
A < tcrossprod(C.)/6 + Diagonal(3, 1/3); A[1,2] < 3; A
stopifnot(exprs = {
is.numeric( x. < c(2.2, 0, 1.2) )
all.equal(x., .solve.dgC.lu(A, c(1,0,0), check=FALSE))
all.equal(x., .solve.dgC.qr(A, c(1,0,0), check=FALSE))
})
## Solving sparse least squares:
X < rbind(A, Diagonal(3)) # design matrix X (for L.S.)
Xt < t(X) # *transposed* X (for L.S.)
(y < drop(crossprod(Xt, 1:3)) + c(1,1)/1000) # small rand.err.
str(solveCh < .solve.dgC.chol(Xt, y, check=FALSE)) # Xt *is* dgC..
stopifnot(exprs = {
all.equal(solveCh$coef, 1:3, tol = 1e3)# rel.err ~ 1e4
all.equal(solveCh$coef, drop(solve(tcrossprod(Xt), Xt %*% y)))
all.equal(solveCh$coef, .solve.dgC.qr(X, y, check=FALSE))
})
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