similar to: transpose of complex matrices in R

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, Need some help as I was trying to follow the guide below. https://wiki.samba.org/index.php/Samba4/HOWTO/Join_a_domain_as_a_DC Until the steps of ldbsearch -H /usr/local/samba/private/sam.ldb '(invocationid=*)' --cross-ncs objectguid and my ldbsearch reply with such a result. ldb: unable to dlopen /usr/lib64/samba/ldb/acl.so : /usr/lib64/ldb/libreplace.so: version

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'm trying to test if a correlation matrix is positive semidefinite. My understanding is that a matrix is positive semidefinite if it is Hermitian and all its eigenvalues are positive. The values in my correlation matrix are real and the layout means that it is symmetric. This seems to satisfy the Hermitian criterion so I figure that my real challenge is to check if the eigenvalues are all

I have a dataframe ("mydf") that contains "differences of means". I wish to test whether these differences are significantly different from zero. Below, I calculate the t-statistic for each column. What is a "good" method to calculate/look-up the p-value for each column? mydf=data.frame(a=c(1,-22,3,-4),b=c(5,-6,-7,9)) mymean=mean(mydf) mysd=sd(mydf)

Dear List, I am having problems converting a file with fractional seconds to class POSIXct. I have set my options to include digits.secs and my format to just time, but my output is the current date with my time lacking the fractions of a second. For example: options(digits.secs=3) t<-c("06:00:00.100","06:00:01.231") myt<-as.POSIXct(t,format="%H:%M:%S")

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Hi all, I am currently using the code snippet below to minimize a function which I wasn't able to minimize properly with optim (cliffy surface, constraints, poles). Obviously this iteration is probably the poorest way of implementing such a function and it is expectedly slow. I've already written some vectorized functions, but I'm afraid that I'm missing the "big

Hi, All: Is there a simple way to detect complex conjugates in the roots returned by 'polyroot'? The obvious comparison of each root with the complex conjugate of the next sometimes produces roundoff error, and I don't know how to bound its magnitude: (tst <- polyroot(c(1, -.6, .4))) tst[-1]-Conj(tst[-2]) [1] 3.108624e-15+2.22045e-16i

Dear R-users, I am trying to translate a matlab code for calculating the Local Whittle estimator in time series with long memory originally written by Shimotsu and available free in his webpage ( http://www.econ.queensu.ca/pub/faculty/shimotsu/ ) The Matlab code is ======================================================================================= function[r] = whittle(d,x,m) % WHITTLE.M

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, Man page for 'convolve' says: conj: logical; if 'TRUE', take the complex _conjugate_ before back-transforming (default, and used for usual convolution). The complex conjugate of 'x', of 'y', of both? In fact it seems that it takes the complex conjugate of 'y' only which is OK but might be worth mentioning because (1) conj=TRUE is the

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently

Dear R-ophiles, I've found something very odd when I apply convolve to ever larger vectors. Here is an example below with vectors ranging from 2^11 to 2^17. There is a funny bump up at 2^12. Then it gets very slow at 2^16. > for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o"))) user system elapsed 0.002 0.000 0.002 user system elapsed 0.373

Hi I have written a whole bunch of methods for objects of class "octonion". [ an octonion is a single column of an eight-row matrix. Octonions have their own multiplication rules and are a generalization of quaternions, which are columns of a four-row matrix. ] So far I've done about a dozen generic functions such as seq.octonion(), rep.octonion(), [<-.octonion(), and so on

Hello, I am work with a linear regression model: y=ax+b with the function of lm. y= observed migration distance of butterflies x= predicted migration distance of butterflies Usually the result will show if the linear term a is significantly different from zero based on the p-value. Now I would like to test if the linear term is significantly different from one. (because I want to know

Hello, everybody! I'm back to ask the obvious on behalf of the silent majority :) Today a student asked me "what standard error was used in this t.test output". I looked into it and was a little surprised that a t.test output object does not have a "slot" for the standard error. Of course, we can reconstruct the se=mu-hat/t, but I was surprised. Do you think it would

Fangrui Song via cfe-dev <cfe-dev at lists.llvm.org> writes: > > One property of "Squash and merge" is that it will add intermediate > commits as bullet points (`* `). In many cases the merger does not spend > more time cleaning up the description so a commit may look like: > > ``` > RFC: treat small negative λ as 0 for sqrt(::Hermitian) (#35057) > >

This thread reveals that R has some holes in the solution of some of the linear algebra problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to issues with condition of B and making a symmetric/Hermitian problem unsymmetric. In short, the problem is stated as follows:

> On Jul 11, 2017, at 6:25 AM, Cristina Silva <csilva at ipma.pt> wrote: > > Dear Jeff, > > I am sorry, I didn't notice that it was not plain text. I hope that it is now in the correct format. I explain the problem again, now with more detais. > I am collecting the track positions of our research vessel from www.marinetraffic.com. In the page, the data appear in a