Displaying 20 results from an estimated 2000 matches similar to: "eigenvectors order"
2011 Nov 14
2
How to compute eigenvectors and eigenvalues?
Hello.
Consider the following matrix:
mp <- matrix(c(0,1/4,1/4,3/4,0,1/4,1/4,3/4,1/2),3,3,byrow=T)
> mp
[,1] [,2] [,3]
[1,] 0.00 0.25 0.25
[2,] 0.75 0.00 0.25
[3,] 0.25 0.75 0.50
The eigenvectors of the previous matrix are 1, 0.25 and 0.25 and it is not a diagonalizable matrix.
When you try to find the eigenvalues and eigenvectors with R, R responses:
> eigen(mp)
$values
[1]
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 Aug 06
2
help with lagged scatterplot
Hi,
How do I can make a lagged scatterplot of two variables:
Yt (nao) versus Xt-h (mei)
if they have the following structure:
>series
mei nao
Jan 1950 -1.036 0.55
Feb 1950 -1.133 3.31
Mar 1950 -1.259 0.81
Apr 1950 -1.027 1.60
May 1950 -1.399 -1.73
Jun 1950 -1.366 1.26
Jul 1950 -1.300 -0.87
.
.
.
I've tried with lag.plot but I don't understanf how to use it
Thanks in
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
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
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
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
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
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,
2010 Jun 25
2
Forcing scalar multiplication.
I am trying to check the results from an Eigen decomposition and I need to force a scalar multiplication. The fundamental equation is: Ax = lx. Where 'l' is the eigen value and x is the eigen vector corresponding to the eigenvalue. 'R' returns the eigenvalues as a vector (e <- eigen(A); e$values). So in order to 'check' the result I would multiply the eigenvalues
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
[[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):
2012 Apr 27
2
find the eigenvector corresponding to the largest eigenvalue
Hi,
If I use the eigen() function to find the eigenvalues of a matrix, how can I find the eigenvector corresponding to the largest eigen value?
Thanks!
[[alternative HTML version deleted]]
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]]
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,
2007 Jun 29
4
Dominant eigenvector displayed as third (Marco Visser)
Dear R users & Experts,
This is just a curiousity, I was wondering why the dominant eigenvetor and eigenvalue
of the following matrix is given as the third. I guess this could complicate automatic selection
procedures.
0 0 0 0 0 5
1 0 0 0 0 0
0 1 0 0 0 0
0 0 1 0 0 0
0 0 0 1 0 0
0 0 0 0 1 0
Please
2010 Nov 10
2
prcomp function
Hello,
I have a short question about the prcomp function. First I cite the
associated help page (help(prcomp)):
"Value:
...
SDEV the standard deviations of the principal components (i.e., the square
roots of the eigenvalues of the covariance/correlation matrix, though the
calculation is actually done with the singular values of the data matrix).
ROTATION the matrix of variable loadings
2003 Apr 11
2
princomp with not non-negative definite correlation matrix
$ R --version
R 1.6.1 (2002-11-01).
So I would like to perform principal components analysis on a 16X16
correlation matrix, [princomp(cov.mat=x) where x is correlation matrix],
the problem is princomp complains that it is not non-negative definite.
I called eigen() on the correlation matrix and found that one of the
eigenvectors is close to zero & negative (-0.001832311). Is there any
way
2008 Jun 03
3
matlab eigs function in R
Hello
Does anybody know how one can compute d largest eigenvalues/eigenvectors in
R, like in MATLAB eigs function ? eigen function computes all
eigenvectors/eigenvalues, and they are slightly different than those
generated by matlab eigs.
Thanks in advance
--
View this message in context: http://www.nabble.com/matlab-eigs-function-in-R-tp17619641p17619641.html
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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