similar to: symmetric matrix multiplication

Just to avoid possible confusion, let me correct a typo (at step [2] in the example below). Apologies! -----FW: <XFMail.111023084327.ted.harding at wlandres.net>----- Date: Sun, 23 Oct 2011 08:43:27 +0100 (BST) Sender: r-help-bounces at r-project.org From: (Ted Harding) <ted.harding at wlandres.net> To: r-help at r-project.org Subject: Re: [R] symmetric matrix multiplication On

Full_Name: cajo ter Braak Version: 2.1.1 OS: Windows Submission from: (NULL) (137.224.10.105) # I would like to attach the matrix C in the Rdata file; it is 50x50 and comes from a geostatistical problem (spherical covariogram) > rm(list=ls(all=TRUE)) > load(file= "test.eigen.Rdata") > ls() [1] "C" "eW" > > sym.check = max(abs(C - t(C))) # should

I would presume this is another manifestation of what I reported (reproduced below) on 2003-12-01. cajo.terbraak at wur.nl wrote: >Full_Name: cajo ter Braak >Version: 2.1.1 >OS: Windows >Submission from: (NULL) (137.224.10.105) > > ># I would like to attach the matrix C in the Rdata file; it is 50x50 and comes >from a geostatistical problem (spherical covariogram) >

Is there any way to know how the "dmvt" function computes the hypergeometric function needed in the calculation for the density of multivariate t distribution? -- View this message in context: http://r.789695.n4.nabble.com/hypergeometric-function-in-mvtnorm-tp4483730p4483730.html Sent from the R help mailing list archive at Nabble.com.

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

I am working with the pmt function in the {mnormt} package, and i am getting negative values returned. the following is an example of one of my outputs: pmt(x = c(3.024960, -1.010898), mean = c(21.18844, 21.18844), S = matrix(c(.319,.139,.139,0.319), 2, 2),df = 42) # -6.585641e-18 Any help on why i'm getting negative numbers would be very much appreciated. THanks! -- View this message in

I am trying to use the dmt function in the package {mnormt}. Throughout my algorithm, the covariance matrix is sometime calculated to be singular. When attempting to calculate the dmt function with a covariance that is not positive definite, I would like it to return Inf or NaN instead of an error message. I have been using the try function, however it is not yeilding the desired result. (I did

from ?eigen symmetric: if 'TRUE', the matrix is assumed to be symmetric (or Hermitian if complex) and only its lower triangle is used. If 'symmetric' is not specified, the matrix is inspected for symmetry. I think that could mislead a naive reader as it suggests that, with symmetric=TRUE, the result of eigen() (vectors and values) depends only on

Hi, I have two products which are substitudes. I try to fix a system as below to mydata. Demand1 = A1 -B1*Price1 + C1*Price2 Demand2 = A2 +B2*Price1 - C2*Price2 I would expect C1 & B2 to be symmetric, If they are truly substitude. How can I enforce this symmetry when creating a system of equations via SystemFit ? -- View this message in context:

Dear R users, I am not good in R-language programming. So, i need your help. I want to convert my lower-triangle value of symmetry matrix into a vector with their row and column name. I found a function called "sm2vec" in "corpcor" package but it give only a vector of values but not row and column names. But i also want ROW and COLUMN name together with their corresponding

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 R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Please help, I tried a program on S-plus, and it worked. Also I tried the same program on R but not worked. Here is the programme. I put it in a function form. The model and assumption are at the bottom. where counts<-c(22,2,2,0,5,7,14,0,0,2,36,0,0,1,17,10) which is name.data, i is row size and j is the column size. symmetry function(i, j, name.data) { row <- (c(1:i)) col <-

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

Full_Name: Mike Danilov Version: 2.9.0 OS: Fedora Core 9 Submission from: (NULL) (142.103.121.198) When checking for the symmetry of a matrix, function isSymmetric.matrix() gets confused by the discrepancy of colnames/rownames if its argument. See the code snippet below. Perhaps it's a problem of the matrix product which copies colnames of the first argument but not the rownames of the

Hi I need to use rep() to get a vector out, but I have spotted something very strange. See the reproducible example below. N <- 79 seg <- 5 segN <- N / seg # = 15.8 d1 <- seg - ( segN - floor(segN) ) * seg d1 # = 1 rep(2, d1) # = numeric(0), strange - why doesn't it print one "2"? rep(2, 1) # 2, ok rep(2, d1 / 1,1) # 2, this

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

Dear all r-users, I am getting a big problem with matrix multiplication suppose I have, > weight Weight 1 1067640 2 8871500 3 42948778 4 127583735 5 22000000 6 44000000 7 56850000 8 23805662 and, > s a b c d e f g h a 402493.18 -133931.62 461483.3 -94042.86 674493.8

1. Martin Maechler's comments should be taken as replacements for anything I wrote where appropriate. Any apparent conflict is a result of his superior knowledge. 2. 'eigen' returns the eigenvalue decomposition assuming the matrix is symmetric, ignoring anything in m[upper.tri(m)]. 3. The basic idea behind both posdefify and nearPD is to compute the

Hi, I was looking at some bugs to play with, and I started with http://llvm.org/bugs/show_bug.cgi?id=18606 As I commented there, a loop is unrolled and exhibit this pattern: %mul.1 = mul i32 %mul, %mul %mul.2 = mul i32 %mul.1, %mul.1 .... With an unroll factor of 32, the last multiply has 2^32 terms in its SCEV expression. (I mean I expect it would have those terms if I was patient