Displaying 20 results from an estimated 8000 matches similar to: "Matrix inversion"
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
2009 Jun 17
3
Matrix inversion-different answers from LAPACK and LINPACK
Hello.
I am trying to invert a matrix, and I am finding that I can get different
answers depending on whether I set LAPACK true or false using "qr". I had
understood that LAPACK is, in general more robust and faster than LINPACK,
so I am confused as to why I am getting what seems to be invalid answers.
The matrix is ostensibly the Hessian for a function I am optimizing. I want
to get
2000 Apr 28
3
Matrix inverse
I haven't found a function that directly calculates the matrix inverse, does it exist? Otherwise what would be the fastest way to do it?
Patrik Waldmann
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2016 Apr 20
6
Solving sparse, singular systems of equations
I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other methods of solving this system that I found online, two of which give me an error and one of which succeeds on the
2016 Apr 20
0
Solving sparse, singular systems of equations
> On 20 Apr 2016, at 13:22, A A via R-help <r-help at r-project.org> wrote:
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> I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other
2016 Apr 20
1
Solving sparse, singular systems of equations
Thanks for the help. Sorry, I am not sure why it looks like that in the mailing list - it looks much more neat on my end (see attached file).
On Wednesday, April 20, 2016 2:01 PM, Berend Hasselman <bhh at xs4all.nl> wrote:
> On 20 Apr 2016, at 13:22, A A via R-help <r-help at r-project.org> wrote:
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> I have a situation in R where I would like to
2007 May 01
1
(PR#9623) qr.coef: permutes dimnames; inserts NA; promises
On Thu, 19 Apr 2007, brech at delphioutpost.com wrote:
> Full_Name: Christian Brechbuehler
> Version: 2.4.1 Patched (2007-03-25 r40917)
> OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS
> Submission from: (NULL) (24.61.47.236)
>
>
> Splus and R have different ideas about what qr.coef(qr()) should return,
> which is fine... but I believe that R has a bug in that it is not
2004 Jul 06
2
Generate a matrix Q satisfying t(Q)%*%Q=Z and XQ=W
Hello,
I have a question that is not directly related to R ... but I try to do it
in R ;-) :
I would like to generate a matrix Q satisfying (for a given Z, X and W) the
two following conditions:
t(Q)%*%Q=Z (1)
XQ=W (2)
where:
Q is m rows and r columns
X is p rows and m columns
D is p rows and r columns
C is r rows and r columns
with m>p,r
e.g:
m=6,
p=2
r=3
2003 Oct 30
3
Change in 'solve' for r-patched
The solve function in r-patched has been changed so that it applies a
tolerance when using Lapack routines to calculate the inverse of a
matrix or to solve a system of linear equations. A tolerance has
always been used with the Linpack routines but not with the Lapack
routines in versions 1.7.x and 1.8.0. (You can use the optional
argument tol = 0 to override this check for computational
2001 Oct 18
0
General Matrix Inverse
Generalised Inverse:
The Moore-Penrose Generalisied Inverse is probably better defined as a
pseudo-Inverse that arises in solving least squares problems.
Another well known pseudo-Inverse is the so-called Drazin pseudo-Inverse.
If memory serves (and it's been 10-12 years!) it can be obtained via a
diagonalisation.
Anyway, I dare say Prof. Ripley (among others) probably has "all the
2003 Aug 14
2
How to get the pseudo left inverse of a singular square matrix?
Dear R-listers,
I have a dxr matrix Z, where d > r.
And the product Z*Z' is a singular square matrix.
The problem is how to get the left inverse U of this
singular matrix Z*Z', such that
U*(Z*Z') = I?
Is there any to figure it out using matrix decomposition method?
Thanks a lot for your help.
Fred
2011 Aug 16
2
generalized inverse using matinv (Design)
i am trying to use matinv from the Design package
to compute the generalized inverse of the normal equations
of a 3x3 design via the sweep operator.
That is, for the linear model
y = ? + x1 + x2 + x1*x2
where x1, x2 are 3-level factors and dummy coding is being used
the matrix to be inverted is
X'X =
9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1
3 3 0 0 1 1 1 1 0 0 1 0 0 1 0 0
3 0 3 0 1 1 1 0 1 0 0 1
2007 Dec 18
2
"gam()" in "gam" package
R-users
E-mail: r-help@r-project.org
I have a quenstion on "gam()" in "gam" package.
The help of gam() says:
'gam' uses the _backfitting
algorithm_ to combine different smoothing or fitting methods.
On the other hand, lm.wfit(), which is a routine of gam.fit() contains:
z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *
2000 Sep 29
2
Matrix inversion
I cannot find what is the function label for matrix inversion in R. I have
found 'ginv' for the moore-penrose in the MASS package, but there is
probably a simple inversion operator in the base package. Where can I find
it?
____________________________________________
Yvonnick Noel, PhD.
University of Lille 3
Department of Psychology
F-59653 Villeneuve d'Ascq Cedex
(+33) 320 41 63 48
2003 Nov 12
2
bug in det using method="qr" (PR#1244) (PR#4450)
I just detected, that det() is not working on complex matrices any more,
due to the fix to the bug reports noted above. I am not happy with this,
as determinants are perfectly usable on complex matrices.
AFAIUI the bugs resulted from less than optimal behaviour of qr() in
certain cases. IMHO this is due to the unhappy decision to use a default for
parameter tol to decide whether the the
2004 Mar 25
1
g-inverse question
I am using the ginv function from MASS and have run across this problem
that I do not understand. If I define the matrix A as below, its
g-inverse does not satisfy the Moore-Penrose condition
A %*% ginv(A) %*% A = A.
The matrix A is X'WX in a quadratic regression using some very large
dollar values.
The much simpler matrix B does satisfy the MP condition. Am I doing
something wrong? Is
2004 Jul 01
1
QR decomposition question
Hi all,
I wonder if this kind of questions are ok in this
list...
Quick question:
What does it mean than the rank of the QR
decomposition of a NxN matrix is N-1 ?
m: NxN matrix
qr(m)$rank equal to (N-1)
Long version:
I'm doing a manova on a matrix of 10 variables
and 16 observations.
> dim(tmp)
[1] 16 10
> fit <- manova( tmp ~ treatment*mouse )
>results <-
2007 May 15
2
QR Decompositon and qr.qty
Dear R people,
I do not have much knowledge about linear algebra but currently I need
to understand what the function qr.qty is actually doing. The
documentation states that it calculates t(Q) %*% y via a previously
performed QR matrix decomposition.
In order to do that, I tried following basic example:
m<-matrix(c(1,0,0,0,1,0,0,0,1,0,0,1),ncol=3) # 4x3 matrix
2008 Jun 05
1
Limit distribution of continuous-time Markov process
I have (below) an attempt at an R script to find the limit distribution
of
a continuous-time Markov process, using the formulae outlined at
http://www.uwm.edu/~ziyu/ctc.pdf, page 5.
First, is there a better exposition of a practical algorithm for doing
this? I have not found an R package that does this specifically, nor
anything on the web.
Second, the script below will give the right
2004 Mar 05
1
row-echelon form (was no subject)
I think one needs an LU decomposition rather than QR.
However, I couldn't find anything off the shelf to do
an LU, other than learning that determinant() now
uses LU instead of QR or SVD, so the code to do it must
be in there for those that want it.
You'll probably need to divide rows of U by the first
entry if you insist on the unique reduced REF.
However, I can't see any reason