Displaying 20 results from an estimated 800 matches similar to: "Computing generalized eigenvalues"
2012 Apr 19
3
Solve an ordinary or generalized eigenvalue problem in R?
Folks:
I'm trying to port some code from python over to R, and I'm running into a
wall finding R code that can solve a generalized eigenvalue problem
following this function model:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html
Any ideas? I don't want to call python from within R for various reasons,
I'd prefer a "native" R solution if one
2012 Apr 23
0
Solve an ordinary or generalized eigenvalue problem in R
This thread reveals that R has some holes in the solution of some of the linear algebra
problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the
generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to
issues with condition of B and making a symmetric/Hermitian problem unsymmetric.
In short, the problem is stated as follows:
2004 Nov 05
1
fast partial spectral decompositions.
hello,
i want to compute the top k eigenvalues+eigenvectors of a (large)
real symmetric matrix. since it doesn't look like any top-level R
function does this, i'll call LAPACK from a C shlib and then
use .Call. the only LAPACK function i see to do this in
R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr
can also return a partial eigendecomposition. why is dsyevr not
2004 Nov 05
1
fast partial spectral decompositions.
hello,
i want to compute the top k eigenvalues+eigenvectors of a (large)
real symmetric matrix. since it doesn't look like any top-level R
function does this, i'll call LAPACK from a C shlib and then
use .Call. the only LAPACK function i see to do this in
R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr
can also return a partial eigendecomposition. why is dsyevr not
2006 Jan 18
1
function 'eigen' (PR#8503)
Full_Name: Pierre Legendre
Version: 2.1.1
OS: Mac OSX 10.4.3
Submission from: (NULL) (132.204.120.81)
I am reporting the mis-behaviour of the function 'eigen' in 'base', for the
following input matrix:
A <- matrix(c(2,3,4,-1,3,1,1,-2,0),3,3)
eigen(A)
I obtain the following results, which are incorrect for eigenvalues and
eigenvectors 2 and 3 (incorrect imaginary portions):
2008 Jun 18
2
highest eigenvalues of a matrix
DeaR list,
I happily use eigen() to compute the eigenvalues and eigenvectors of
a fairly large matrix (200x200, say), but it seems over-killed as its
rank is limited to typically 2 or 3. I sort of remember being taught
that numerical techniques can find iteratively decreasing eigenvalues
and corresponding orthogonal eigenvectors, which would provide a nice
alternative (once I have the
2004 Oct 19
3
matrix of eigenvalues
I thought that the function
eigen(A)
will return a matrix with eigenvectors that are independent of each
other (thus forming a base and the matrix being invertible). This
seems not to be the case in the following example
A=matrix(c(1,2,0,1),nrow=2,byrow=T)
eigen(A) ->ev
solve(ev$vectors)
note that I try to get the upper triangular form with eigenvalues on
the diagonal and (possibly) 1 just
2009 Dec 01
1
eigenvalues of complex matrices
Dear all,
I want to compute the eigenvalues of a complex matrix for some statistics.
Comparing it to its matlab/octave sibling, I don't get the same eigenvalues
in R computing it from the exact same matrix.
In R, I used eigen() and arpack() that give different eigenvalues. In
matlab/octave I used eig() and eigs() that give out the same eigenvalues but
different to the R ones.
For real
2011 May 27
1
eigenvalues and correlation matrices
I'm trying to test if a correlation matrix is positive semidefinite.
My understanding is that a matrix is positive semidefinite if it is
Hermitian and all its eigenvalues are positive. The values in my
correlation matrix are real and the layout means that it is symmetric.
This seems to satisfy the Hermitian criterion so I figure that my real
challenge is to check if the eigenvalues are all
2003 Jun 03
3
lda: how to get the eigenvalues
Dear R-users
How can I get the eigenvalues out of an lda analysis?
thanks a lot
christoph
--
Christoph Lehmann <christoph.lehmann at gmx.ch>
2013 Mar 14
2
Same eigenvalues but different eigenvectors using 'prcomp' and 'principal' commands
Dear all,
I've used the 'prcomp' command to
calculate the eigenvalues and eigenvectors of a matrix(gg).
Using the command 'principal' from the
'psych' packageĀ I've performed the same exercise. I got the same
eigenvalues but different eigenvectors. Is there any reason for that
difference?
Below are the steps I've followed:
1. PRCOMP
#defining the matrix
2004 Dec 10
1
How to circumvent negative eigenvalues in the capscale function
Dear All
I am trying to do a partial canonical analysis of principal coordinates
using Bray-Curtis distances. The capscale addin to R appears to be the only
way of doing it, however, when I try and calculate a Bray-Curtis distance
matrix either using Capscale or Vegedist (capscale I understand uses
Vegedist anyway to calculate its distance matrix), R uses up all available
memory on the computer,
2010 Sep 17
1
How to find STRESS criteria in MDS when there are negative eigenvalues....
Hi,
I want to know whether there is any function in R to find STRESS after using cmdscale and estimating the coordinates, I have written these functions to find stress (for p =1,2,3,4,5)
stress<-rep(0,5)
for(p in 1:5)
{
datahat<-cmdscale(d,p)
deltahat<-as.matrix(dist(datahat))
a<-0
b<-0
for(i in 1:n)
{
for(j in 1:n)
{
a<-d[i,j]^2+a
b<-(d[i,j]-deltahat[i,j])^2+b
}
}
2012 Apr 27
0
How to find eigenfunctions and eigenvalues of a fourth order ODE
Dear all,
I am having trouble with the following problem. Suppose we have the fourth
order ODE with boundary conditions:
http://r.789695.n4.nabble.com/file/n4591748/problem.jpg problem.jpg
where q(t) is a known function.
Note here the lambda parameter is changing, so essentially we have a series
of ODEs. lambda is called an eigenvalue, the solution y is called an
eigenfunction associated with
2011 Jan 29
1
Regularization of a matrix that has some tiny negative eigenvalues
Dear all:
In what I am doing I sometimes get a (Hessian) matrix that has a
couple of tiny negative eigenvalues (e.g. -6 * 10^-17). So, I can't
run a Cholesky decomp on it - but I need to.
Is there an established way to regularize my (Hessian) matrix (e.g.,
via some transformation) that would allow me to get a semi-positive
definite matrix to be used in Cholesky decomp?
Or should I try some
2011 Nov 14
0
Fwd: How to compute eigenvectors and eigenvalues?
Inicio del mensaje reenviado:
> De: Arnau Mir <arnau.mir@uib.es>
> Fecha: 14 de noviembre de 2011 13:24:31 GMT+01:00
> Para: Martin Maechler <maechler@stat.math.ethz.ch>
> Asunto: Re: [R] How to compute eigenvectors and eigenvalues?
>
> Sorry, but I can't explain very well.
>
>
> The matrix 4*mp is:
>
> 4*mp
> [,1] [,2] [,3]
> [1,]
2010 May 05
3
Symbolic eigenvalues and eigenvectors
Let's say I had a matrix like this:
library(Ryacas)
x<-Sym("x")
m<-matrix(c(cos (x), sin(x), -sin(x), cos(x)), ncol=2)
How can I use R to obtain the eigenvalues and eigenvectors?
Thanks,
John
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2005 Aug 03
3
prcomp eigenvalues
Hello,
Can you get eigenvalues in addition to eigevectors using prcomp? If so how?
I am unable to use princomp due to small sample sizes.
Thank you in advance for your help!
Rebecca Young
--
Rebecca Young
Graduate Student
Ecology & Evolutionary Biology, Badyaev Lab
University of Arizona
1041 E Lowell
Tucson, AZ 85721-0088
Office: 425BSW
rlyoung at email.arizona.edu
(520) 621-4005
2008 Jan 31
1
Confidence intervals for PCA scores/eigenvalues
Dear all,
I have read various descriptions of employing resampling techniques, such as
the bootstrap, to estimate the uncertainties of the eigenvectors computed by
PCA. When I try
2011 Apr 15
1
How to generate a correlation matrix with restrictions on its eigenvalues
Dear All,
I would like to generate m positive real numbers c_i, I=1,...,m, such that
(1) c_1 + c_2 + ... + c_m=m,
(1) after being ordered into c_1 >= c_2 >= .... >=c_m>0, we have that c_m is of the same order of m^(-1/8), when m is sufficiently large.
Thanks,
-Chee
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