Displaying 5 results from an estimated 5 matches for "mpinv".
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mping
2012 Dec 05
1
Understanding svd usage and its necessity in generalized inverse calculation
...9.90 1.82 9.90 1.82
"match" 9.80 1.80 22.94 4.22
"conditionMessage" 8.92 1.64 14.50 2.67
"svd" 8.70 1.60 185.88 34.19
"mpinv" 8.40 1.55 221.56 40.76
"c" 8.16 1.50 8.16 1.50
".deparseOpts" 7.92 1.46 12.42 2.28
"$" 6.94 1.28 6.94...
2012 Dec 12
3
R-2.15.2 changes in computation speed. Numerical precision?
...e's the output from my computer
http://pj.freefaculty.org/scraps/profile/prof-puzzle-1.Rout
That includes the profile of the calculations that depend on the
ordinary generalized inverse algorithm based on svd and the new one.
See? The KP algorithm is faster. And just as accurate as
Amelia:::mpinv or MASS::ginv (for details on that, please review my
notes in http://pj.freefaculty.org/scraps/profile/qrginv.R).
So I asked WIndows users for more detailed feedback, including
sessionInfo(), and I noticed that my proposed algorithm is not faster
on Windows--WITH OLD R!
Here's the script outp...
2001 Oct 18
1
AW: General Matrix Inverse
Thorsten is right. There is a direct formula for computing the Moore-Penrose
inverse
using the singular value composition of a matrix. This is incorporated in
the following:
mpinv <- function(A, eps = 1e-13) {
s <- svd(A)
e <- s$d
e[e > eps] <- 1/e[e > eps]
return(s$v %*% diag(e) %*% t(s$u))
}
Hope it helps.
Dietrich
****************************************************************************
*****
Dr. Dietrich Trenkler (dtren...
2004 Jun 16
1
off topic: C/C++ codes for pseudo inverse
Hi,
I am looking for C/C++ codes for computing generalized
inverse of a matrix. Can anyone help me in this
regard?
Thanks,
Mahbub.
1999 Jun 30
1
qr and Moore-Penrose
...many solutions
for the regression of y on X, and the Moore-Penrose one is just one choice
(that assumes that the coefficients are somehow comparable).
> X <- cbind(1, diag(3)); # singular matrix
> y <- 1:3
> Xp <- qr(X);
> b1 <- qr.coef(Xp, y); # contains NA
> b2 <- mpinv(X)%*%y # least square fit using Moore-Penrose
> X%*%b2 # == y
Those `;' are unnecessary: either CR or ; separates expressions in
S-like languages.
I find
> drop(b2)
[1] 1.5 -0.5 0.5 1.5
> b1
[1] 3 -2 -1 NA
> lm(y ~ X + 0)
Call:
lm(formula = y ~ X + 0)
Coefficients:
X1...