similar to: Segfaults of eigen

Dear All i have a little puzzle about eigenvector in the R. As we know that the eigenvector can be displayed on several form. For example A=matrix(c(1,2,4,3),2,2) if we want to get the eigenvalue and eigenvector, the code followed eigen(A) $values [1] 5 -1 $vectors [,1] [,2] [1,] -0.7071068 -0.8944272 [2,] -0.7071068 0.4472136 however, we also can calculate the vector matrix

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

Hi, All: What tools exist for diagnosing singular gradient problems with 'nls'? Consider the following toy example: DF1 <- data.frame(y=1:9, one=rep(1,9)) nlsToyProblem <- nls(y~(a+2*b)*one, DF1, start=list(a=1, b=1), control=nls.control(warnOnly=TRUE)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

Hi, All: Attached please find a symmetric, indefinite matrix for which 'eigen(...)$vectors' included NAs: > load("eigenBug.Rdata") > sum(is.na(eigen(eigenBug)$vectors)) [1] 5670 > sessioninfo() Error: could not find function "sessioninfo" > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32 locale: LC_COLLATE=English_United

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

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):

Thanks for telling me that you could not get my message, I hope this work better... so my question was: I built a population matrix to which I applied the fonction eigen in order to find the main parameters about my population. I know that the first eigen value correspond to lambda or exponential growth rate of my population. My problem is that I want to have the 95% confidence interval of the

In the manual page for prcomp(), it says that sdev is "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)." ?However, this is not what I'm finding. ?The values appear to be the standard deviations of a reprojection of

Ladies and Gentlemen, using > A <- structure(c(1, 0, 0, 3, 2, 1, 4, 5, -3, -2, 1, 0), .Dim = as.integer(c(3,4))) I get > dim(A) [1] 3 4 > qr.R(qr(A),complete=TRUE) [,1] [,2] [,3] [,4] [1,] -1 -3.000000 -4.000000 2.0000000 [2,] 0 -2.236068 -3.130495 -0.8944272 [3,] 0 0.000000 -4.919350 -0.4472136 > qr.R(qr(A),complete=FALSE) [,1]

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

Hi there, Given a positive definite symmetric matrix, I can use chol(x) to obtain U where U is upper triangular and x=U'U. For example, x=matrix(c(5,1,2,1,3,1,2,1,4),3,3) U=chol(x) U # [,1] [,2] [,3] #[1,] 2.236068 0.4472136 0.8944272 #[2,] 0.000000 1.6733201 0.3585686 #[3,] 0.000000 0.0000000 1.7525492 t(U)%*%U # this is exactly x Does anyone know how to obtain L such

The following snippet suggests that there is either a bug in qr(,LAPACK=T), or some bug in my understanding. Note that the detected rank is correct (= 2) using the default LINPACK qr, but incorrect (=3) using LAPACK. This is running on Linux Redhat 9.0, using the lapack library that comes with the Redhat distribution. I'm running R 1.7.1 compiled from the source. If the bug is in my

Using the 64-bit Solaris compilers make check dumps core in La_rs at 73 F77_CALL(dsyev)(jobv, uplo, &n, rx, &n, rvalues, work, &lwork, &info); I can't reproduce it easily, but example(eigen) occasionally stops with Error: abs(sm - V %*% diag(lam) %*% t(V)) < 60 * Meps is not TRUE which might be connected. The tiny rounding errors in example(eigen) aren't

Sorry if this bug is fixed in the meantime, but what is this?? > m [,1] [,2] [,3] [1,] 1 2 1 [2,] 0 2 0 [3,] 1 2 1 > eigen(m) $values [1] 2 2 0 $vectors [,1] [,2] [,3] [1,] -4.194304e+06 0.7071068 -0.7071068 [2,] -4.656613e-10 0.0000000 0.0000000 [3,] -4.194304e+06 0.7071068 0.7071068 > eigen(m, symmetric=T) $values [1]

Dear R Development Team, I have encountered the following difficulty in using the function polyroot under either NT4.0 (R version 1.2.1) or linux (R version 0.90.1). In the provided example, the non-zero root of c(0,0,0,1) depends on the results of the previous call of polyroot. R : Copyright 2001, The R Development Core Team Version 1.2.1 (2001-01-15) R is free software and comes with

Consider this matrix: > sg X1 X2 X3 X4 X5 1 3.240 2.592 2.592 2.592 2.592 2 2.592 3.240 2.592 2.592 2.592 3 2.592 2.592 3.240 2.592 2.592 4 2.592 2.592 2.592 3.240 2.592 5 2.592 2.592 2.592 2.592 3.240 If I compute the eigenvalues of the 'sg' matrix using R Version 1.5.0 (2002-04-29) under Linux (or using Version 1.4.0 (2001-12-19) under Solaris), I obtain: >

I'd just like to report a possible R bug--or rather, confirm an existing one (bug #751). I have had some difficulty using the polyroot() function. For example, in Win 98, R 1.1.1, > polyroot(c(2,1,1)) correctly (per the help index) gives the roots of 1 + (1*x) + (2*x^2) as [1] -0.5+1.322876i -0.5-1.322876i However, > polyroot(c(-100,0,1)) gives the roots of [1] 10+0i -10+0i

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species svm> model <- svm(x, y) svm>

[I thought I'd submitted this bug report some time ago, but it's never showed up on the bug tracking system, so I'm submitting again.] qr.solve() gives incorrect results when dealing with complex matrices or with qr objects that have been computed with LAPACK=TRUE, whenever the b argument has more than one column. This bug flows from qr.coef(), which has a similar problem. I believe

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