similar to: Help with eigenvectors

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]

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,

Hello everyone, I need reshape an array. For example, if we have next array: > a <- c(1,2,3,4,5,6,7,8,9,10,11,12) > dim(a) <- c(2,2,3) > a , , 1 [,1] [,2] [1,] 1 3 [2,] 2 4 , , 2 [,1] [,2] [1,] 5 7 [2,] 6 8 , , 3 [,1] [,2] [1,] 9 11 [2,] 10 12 I need to get next matrices: 1 2 3 4 5 6 7 8 9 10 11 12 1 3 2 4 5

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

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

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

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

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

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' package  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

Dear R community, I'm working with PCA and CCA methods, and I have a theoretical question. Why is it necesary to have more temporal values than variables when the CCA O PCA are going to be used? Could you advise to me some any paper about it? Thanks in advance, [[alternative HTML version deleted]]

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,]

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

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]]

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

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

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,

Dear All, how do I compute the left eigenvector of a matrix? I gather that "eigen" computes the right eigenvectors... Regards, Federico Calboli -- ================================= Federico C. F. Calboli PLEASE NOTE NEW ADDRESS Dipartimento di Biologia Via Selmi 3 40126 Bologna Italy tel (+39) 051 209 4187 fax (+39) 051 251 208 f.calboli at ucl.ac.uk

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]]

$ 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

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