Displaying 20 results from an estimated 800 matches similar to: "How to compute eigenvectors and eigenvalues?"
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,]
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
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
[[alternative HTML version deleted]]
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):
2005 Mar 14
1
r: eviews and r // eigen analysis
hi all
i have a question that about the eigen analysis found in R and in
eviews.
i used the same data set in the two packages and found different
answers. which is incorrect?
the data is:
aa ( a correlation matrix)
1 0.9801 0.9801 0.9801 0.9801
0.9801 1 0.9801 0.9801 0.9801
0.9801 0.9801 1 0.9801 0.9801
0.9801 0.9801 0.9801 1 0.9801
0.9801 0.9801 0.9801 0.9801 1
now
> svd(aa)
$d
[1] 4.9204
2010 Jan 11
3
Eigenvectors and values in R and SAS
Hi,
I was wondering if function eigen() does something different from the
function call eigen() in SAS.
I'm in the process of translating a SAS code into a R code and the values of
the eigenvectors and eigenvalues of a square matrix came out to be different
from the values in SAS.
I would also appreciate it if someone can explain the difference in simple
terms. I'm pretty new to both
2002 Nov 05
2
eigenvectors order
Hi,
How the eigenvectors output by the eigen() function are ordered. The
first column corresponds to the largest eigenvalue? or is the last
column as in Octave?
I'm performing a spatial-temporal analysis of some climatic variables
so my matrices are MxN (locations*time)and I'm looking for the leading
EOF's. As I have understand the eigenvectors columns represent those
EOF's
2003 Jun 09
1
understanding eigen(): getting non-normalized eigenvectors
Hi, dear R pros
I try to understand eigen(). I have seen, that eigen() gives the
eigenvectors normalized to unit length.
What shall I do to get the eigenvectors not normalized to unit length?
E.g. take the example:
A
[,1] [,2]
V1 0.7714286 -0.2571429
V2 -0.4224490 0.1408163
Calculating eigen(A) "by hand" gives the eigenvectors (example from
Backhaus,
2003 Nov 04
2
real eigenvectors
Hello list,
Sorry, these questions are not directly linked to R.
If I consider an indefinte real matrix, I would like to know if the
symmetry of the matrix is sufficient to say that their eigenvectors are real ?
And what is the conditions to ensure that eigenvectors are real in the case
of an asymmetric matrix (if some conditions exist)?
Thanks in Advance,
St?phane DRAY
2010 Mar 19
1
Howto get unnormalized eigenvectors?
Hi,
I try to calculate the angle between two first eigenvectors of different covariance matrices of biological phenotypic traits for different populations. My issue here is, that all possibilities to do so seem to normalize the eigenvectors to length 1. Although the helpfile of eigen() states, that using eigen(, symmetric = FALSE, EISPACK =TRUE) skips normalization this is (I guess) not applicable
2003 Apr 03
2
Matrix eigenvectors in R and MatLab
Dear R-listers
Is there anyone who knows why I get different eigenvectors when I run
MatLab and R? I run both programs in Windows Me. Can I make R to produce
the same vectors as MatLab?
#R Matrix
PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43
,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24
,58/53 ,26/244 ,0/1 ,5/43)
#R-syntax
2008 Jul 08
1
Help with eigenvectors
Hi everybody,
I have some problems with the function eigen. I have a square matrix and I
want to calculate the eigenvalues and eigenvectors. I apply the function
eigen and I get it, however when I solve the same problem in Statistica
software, I realise that some eigenvectors are the opposite. How can I get
the same values?
Thanks in advance
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2010 Jun 15
1
Getting the eigenvectors for the dependent variables from principal components analysis
Dear listserv,
I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that.
For instance, if I try the example from ?prcomp
> prcomp(USArrests,
2003 Jun 08
2
LDA: normalization of eigenvectors (see SPSS)
Hi dear R-users
I try to reproduce the steps included in a LDA. Concerning the eigenvectors there is
a difference to SPSS. In my textbook (Bortz)
it says, that the matrix with the eigenvectors
V
usually are not normalized to the length of 1, but in the way that the
following holds (SPSS does the same thing):
t(Vstar)%*%Derror%*%Vstar = I
where Vstar are the normalized eigenvectors. Derror
2005 May 02
14
eigenvalues of a circulant matrix
Hi,
It is my understanding that the eigenvectors of a circulant matrix are given as
follows:
1,omega,omega^2,....,omega^{p-1}
where the matrix has dimension given by p x p and omega is one of p complex
roots of unity. (See Bellman for an excellent discussion on this).
The matrix created by the attached row and obtained using the following
commands
indicates no imaginary parts for the
2011 Apr 09
2
Orthoblique rotation on eigenvectors (SAS VARCLUS)
Hi All,
I'd like to build a package for the community that replicates the output
produced by SAS "proc varclus". According to the SAS documentation, the
first few steps are:
1. Find the first two principal components.
2. Perform an orthoblique rotation (quartimax rotation) on eigenvectors.
3. Assign each variable to the rotated component with which it has the
higher
squared
2011 May 28
1
prcomp & eigenvectors ... ??
Hi ...
Please could you help with probably a very simple problem I have. I'm completely new to R and am trying to follow a tutorial using R for Force Distribution Analysis that I got from ... http://projects.eml.org/mbm/website/fda_gromacs.htm. Basically, the MDS I preform outputs a force matrix (.fm) from the force simulation I perform.
Then, this matrix is read into R and prcomp is
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
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
2006 Aug 16
1
help about agnes
Hello.
I have the following distance matrix between 8 points:
[1,] 0.000000 3.162278 7.280110 8.544004 7.071068 9.899495 6.403124 8.062258
[2,] 3.162278 0.000000 5.000000 6.403124 4.472136 8.944272 6.082763 8.062258
[3,] 7.280110 5.000000 0.000000 1.414214 1.000000 5.000000 4.242641 5.830952
[4,] 8.544004 6.403124 1.414214 0.000000 2.236068 4.123106 4.472136 5.656854
[5,] 7.071068 4.472136