ssolve | R Documentation |
Covariance matrices of variables with very different orders of magnitude can have very large ratios between their greatest and their least eigenvalues, causing them to appear to the algorithms to be near-singular when they are actually very much SPD. These functions first scale the matrix's rows and/or columns by its diagonal elements and then undo the scaling on the result.
ssolve(a, b, ..., snnd = TRUE)
sginv(X, ..., snnd = TRUE)
srcond(x, ..., snnd = TRUE)
snearPD(x, ...)
xTAx_ssolve(x, A, ...)
xTAx_qrssolve(x, A, tol = 1e-07, ...)
sandwich_ssolve(A, B, ...)
snnd |
assume that the matrix is symmetric non-negative definite (SNND). If it's "obvious" that it's not (e.g., negative diagonal elements), an error is raised. |
x, a, b, X, A, B, tol, ... |
corresponding arguments of the wrapped functions. |
ssolve()
, sginv()
, and snearPD()
wrap solve()
,
MASS::ginv()
, and Matrix::nearPD()
, respectively. srcond()
returns the reciprocal condition number of rcond()
net of the
above scaling. xTAx_ssolve
, xTAx_qrssolve
, and
sandwich_ssolve
wrap the corresponding statnet.common
functions.
x <- rnorm(2, sd=c(1,1e12))
x <- c(x, sum(x))
A <- matrix(c(1, 0, 1,
0, 1e24, 1e24,
1, 1e24, 1e24), 3, 3)
stopifnot(all.equal(
xTAx_qrssolve(x,A),
structure(drop(x%*%sginv(A)%*%x), rank = 2L, nullity = 1L)
))
x <- rnorm(2, sd=c(1,1e12))
x <- c(x, rnorm(1, sd=1e12))
A <- matrix(c(1, 0, 1,
0, 1e24, 1e24,
1, 1e24, 1e24), 3, 3)
stopifnot(try(xTAx_qrssolve(x,A), silent=TRUE) ==
"Error in xTAx_qrssolve(x, A) : x is not in the span of A\n")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.