similar to: Calculation of Eigen values.

Professor Ripley commented on LAPACK error codes: https://stat.ethz.ch/pipermail/r-help/2007-March/127702.html and says "Internal LAPACK errors are usually problems with arithmetic accuracy, and as such are compiler- and CPU-specific." Is there a listing for the error codes from Lapack routine 'dsyevr'? Especially I am interested about the meaning and handling of error codes 1

Thank you for your reply. Do you have any idea of how to get rid of the errors? I tried Null function to calculate eigenvectors and nearPD to get approximate positive definite matrix first but they also had errors. -- View this message in context: http://r.789695.n4.nabble.com/error-code-1-from-Lapack-routine-dsyevr-tp4702571p4702639.html Sent from the R devel mailing list archive at

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

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

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

Hi, I got an error message in my program saying "Error in eigen(gene_intersection.kernel) : error code 1 from Lapack routine 'dsyevr' Execution halted". As you see, I was trying to compute the eigenvalues of a matrix but got this error. Is there anyone who knows what this error means and how I can fix it? Theoretically the eigenvalues should be nonnegative, if it helps.

On 2 February 2015 at 10:07, William Dunlap <wdunlap at tibco.com> wrote: <snip> > > If all goes well then > eigen(lastEigenX) > will cause the same error and you or someone on this list can see what > is odd about that matrix (e.g., by looking at its singular values). Preferably *not* this list as this doesn't really seem to be about developing R or with/for

Hi, I am Inderjit, and have some issues with configuration of samba with ADS 2008. I am able to connect to ADS 2008, but command "getent group" doesn't show always the output with ADS groups. We have more that 25000 users and domain controller is not located at same location. Could you please give me a hints or suggestions, what can be changed to solve this issue. Regards

I need to find solutions to a tridiagonal system. By this I mean a set of linear equations Ax = d where A is a square matrix containing elements A[i,i-1], A[i,i] and A[i,i+1] for i in 1:nrow, and zero elsewhere. R is probably not the ideal way to do this, but this is part of a larger problem that requires R. In my application it is much easier (and much faster) to generate the diagonal and

Dear R Users, I am trying to solve a tridiagonal matrix in R. I am wondering if there is an inbuilt R function or package to solve that. I tried looking on google but couldn't find something that would help directly. Any help is highly appreciated. Thanks. Janesh [[alternative HTML version deleted]]

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

Hello everyone. I am doing a logistic gam (package mgcv) on a pretty large dataframe (130.000 cases with 100 variables). Because of that, the gam is fitted on a random subset of 10000. Now when I want to predict the values for the rest of the data, I get the following error: > gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1, +

Hi, I am generating large Kac matrices (also known as Clement matrix). This a tridiagonal matrix. I was wondering whether there is a vectorized solution that avoids the `for' loops to the following code: n <- 1000 Kacmat <- matrix(0, n+1, n+1) for (i in 1:n) Kacmat[i, i+1] <- n - i + 1 for (i in 2:(n+1)) Kacmat[i, i-1] <- i-1 The above code is fast, but I am curious about

Hi All. We have a samba server at our location. We are facing out with some issue. User who have the account on the server are able to access "/" root access. I have tried to add an extra line In Home sharing, which is "path = %H", this lined solved my issue, but gave other issue. After implementing this line under Home share, I am not able to open any other user's

While using the rmvnorm function, I get the error: Error in eigen(sigma, sym = TRUE) : error code 5 from Lapack routine 'dsyevr' The same thing happens when I try the eigen() function on my covariance matrix. The matrix is a symmetric 111x111 matrix. Well, it is almost symmetric; there are slight deviations from symmetry (the largest is 3e-18). I have this in an MCMC loop, and it

Dear all, It is not a R related problem rather than statistical/mathematical. However I am posting this query hoping that anyone can help me on this matter. My problem is to get the Geometrical Interpretation of Eigen value and Eigen vector of any square matrix. Can anyone give me a light on it? Thanks and regards, Arun [[alternative HTML version deleted]]

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,

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):

I'm using R version 2.0.1 on a Windows 2000 operating system. Here is some actual code I executed: > test [,1] [,2] [1,] 1000 500 [2,] 500 250 > eigen(test, symmetric=T)$values [1] 1.250000e+03 -3.153033e-15 > eigen(test, symmetric=T)$values[2] >= 0 [1] FALSE > eigen(test, symmetric=T, only.values=T)$values [1] 1250 0 > eigen(test, symmetric=T,

Dear all I am so glad the R can provide the efficient calculate about eigenvector and eigenvalue. However, i have some puzzle about the procedure of eigen. Fristly, what kind of procedue does the R utilize such that the eigen are obtained? For example, A=matrix(c(1,2,4,3),2,2) we can define the eigenvalue lamda, such as det | 1-lamda 4 | =0 | 2 3-lamda | then