similar to: Trying to create a function to extract values from a matrix

Hi, I am using eigen to get an eigen decomposition of a square, symmetric matrix. For some reason, I am getting a column in my eigen vectors (the 52nd column out of 601) that is a column of all NAs. I am using the option, symmetric=T for eigen. I just discovered that I do not get this behavior when I use the option EISPACK=T. With EISPACK=T, the 52nd eigenvector is (up to rounding error) a

Dear all, my question is not directly related to R, however I believe that experts here would not mind anything to have a look on my problem. Please consider a symmetric matrix and it's eigen values: > set.seed(1) > mat <- matrix(rnorm(36), 6) > mat <- mat %*% t(mat) # symmetric matrix > mat [,1] [,2] [,3] [,4] [,5] [,6] [1,]

I think I am missing something with the chol() function. Here is my calculation: ? > mat ???? [,1] [,2] [,3] [,4] [,5] [1,]??? 1??? 3??? 0??? 0??? 0 [2,]??? 0??? 1??? 0??? 0??? 0 [3,]??? 0??? 0??? 1??? 0??? 0 [4,]??? 0??? 0??? 0??? 1??? 0 [5,]??? 0??? 0??? 0??? 0??? 1 > eigen(mat) $values [1] 1 1 1 1 1 $vectors ???? [,1]????????? [,2] [,3] [,4] [,5] [1,]??? 1 -1.000000e+00??? 0??? 0??? 0

Hi I need to extract the data returned by Eigen to plot the eigenvectors. However, when I try and eigv = eigen(covariance); it returns an object with the matrices containing eigenvalues and vectors.. how can I extract the eigenvector matrix from this?? When I try mat = eig["vectors"] it returns a matrix with the "$vectors" string on top , how can I remove this? code: > eig

I'm wanting to perform analysis (e.g. using eigen()) of binary matrices - i.e. matrices comprising 0s and 1s. For example: n<-1000 test.mat<-matrix(round(runif(n^2)),n,n) eigen(test.mat,only.values=T) Is there a more efficient way of setting up test.mat, as each cell only requires a binary digit? I imagine R is setting up a structure which could contain n^2 floats. Thanks in advance

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

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

Full_Name: Jerome Asselin Version: 1.3.0 OS: Windows 98 Submission from: (NULL) (24.77.112.193) I am having accuracy problems involving the computation of inverse of nonnegative definite matrices with solve(). I also have to compute the Choleski decomposition of matrices. My numerical problems involving solve() made me find a bug in the chol() function. Here is an example. #Please, load the

Hello, I did a pca with over 200000 snps for 340 observations (ids). If I plot the eigenvectors (called rotation in prcomp) 2,3 and 4 (e.g. plot (rotation[,2]) I see a strange "column" in my data (see attachment). I suggest it is an artefact (but of what?). Suggestion: I used prcomp this way: prcomp (mat), where mat is a matrix with the column means already substracted followed by a

General goal: Write R code to find the inverse matrix of an nxn positive definite symmetric matrix. Use solve() to verify your code works. Started with a 3x3 matrix example to build the code, but something dosen't seem to be working. I just don't know where I am going wrong. ##Example matrix I found online A<-c(4,1,-1,1,2,1,-1,1,2) m<-matrix(A,nrow=3,ncol=3) ##Caculate the eigen

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

It works! But Once I have the square root of this matrix, how do I convert it to a real (not imaginary) matrix which has the same property? Is that possible? Best, Simon >----Messaggio originale---- >Da: p.dalgaard at biostat.ku.dk >Data: 21-nov-2009 18.56 >A: "Charles C. Berry"<cberry at tajo.ucsd.edu> >Cc: "simona.racioppi at

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

$ 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

> Date: Sat, 8 Jun 2024 19:16:22 -0400 > From: Ben Bolker <bbolker at gmail.com> > > The ASAN errors occur *even if the zero-length object is not actually > accessed*/is used in a perfectly correct manner, i.e. it's perfectly > legal in base R to define `m <- numeric(0)` or `m <- matrix(nrow = 0, > ncol = 0)`, whereas doing the

Is it possible to avoid the loop in the following function (or make the function otherwise more efficient) and can someone point me to a possible solution? (It would be great if hours could be reduced to seconds :-). # --------------------------------------------- RanEigen=function(items=x,cases=y,sample=z) { X=matrix(rnorm(cases*items),nrow=cases,byrow=F) S=crossprod(X-rep(1,cases) %*%

----- Forwarded Message ----- From: vramaiah at neo.tamu.edu To: "bernhard pfaff" <bernhard.pfaff at pfaffikus.de> Sent: Friday, November 11, 2011 9:03:11 AM GMT -06:00 US/Canada Central Subject: Use of R for VECM Hello Fellow R'ers I am a new user of R and I am applying it for solving Bi-Variate (Consumption and Output) VECM with Co-Integration (I(1)) with three lags on

Why do we get Partial correlation values greater than 1? I have used the default function pcor.mat :-- I have manipulated the default pcor.mat function a bit so ignore tha variables corr_type,element1_in_no,element2_in_no,P.Please ignore the ?pairwise? section and have a look at athe ?listwise ? part i.e else part. *pcor.mat <-

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d