similar to: Spectral analysis with mtm-svd Multi-Taper Method Combined with Singular Value Decomposition

hi all, how can I create sequences that start from a known vector, say x1 and end with another say x2- also suppose all the sequences will be the same length. I don't want to use a for loop x1<-c(1,2,3,4); x2<-(3,4,5,6); what I want is 1 2 3 4 2 3 4 5 3 4 5 6 Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context:

Hi, How can I generate a random signal that's correlated with a given signal at a given correlation (say 0.7)? I've been looking at rmvnorm etc but don't seem to figure it out. Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/generating-a-random-signal-with-a-known-correlation-tp18932541p18932541.html Sent from the R help

I am trying to load a .so file and get the following error message: > dyn.load("taper.so",local=F) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "/home/hpc1367/runs/taper/taper.so": ld.so.1: /usr/local/lib/R/bin/R.bin: fatal: relocation error: file /home/hpc1367/runs/taper/taper.so: symbol f90_init: referenced symbol not

Full_Name: Michael Stein Version: Version 2.1.1 OS: linux Submission from: (NULL) (128.135.149.112) The documentation for spec.taper says p: The total proportion to be tapered, either a scalar or a vector of the length of the number of series. Details: The cosine-bell taper is applied to the first and last 'p[i]/2' observations of time series 'x[,

[I know this is the 'port' list, but I can't find a better place to post this, and with the garbage going on @slashdot I figured I'd get this out. This belongs on sci.crypt or a general OpenSSH mailing list] First, a quick rehash of stuff everyone here already knows, OpenSSH can use two major forms of authentication: 1. Password 2. RSA keys The RSA method is good because it

Hi everybody, I'm a beginner R user and I'm trying to use the package "sapa" to estimate the spectral density function of several time series using the SDF function. For each time series, I want to calculate the density function at two temporal resolutions (daily and monthly). The monthly values calculated as a mean of the daily values. I first create a ts object for both series

Dear expeRts, I have run some simulations under R 2.15.1 on a Mac, and I have rerun a sample of them under R 3.0.1 on Windows (and also for comparison under R2.14.1 on Windows). For most cases, I get exactly the same results in all three runs. However, for those cases that depend on principal components computed with prcomp, where the particular choice of the orthogonalization is arbitrary

Dear expeRts, I have run some simulations under R 2.15.1 on a Mac, and I have rerun a sample of them under R 3.0.1 on Windows (and also for comparison under R2.14.1 on Windows). For most cases, I get exactly the same results in all three runs. However, for those cases that depend on principal components computed with prcomp, where the particular choice of the orthogonalization is arbitrary

Hi again, I've got few very good options from the list and since they were not posted to the list, I will provide a summary. Thank you very much to all who answered and I hope this summary will benefit others interested in solving similar problems like that. Yasir Kaheil re-wrote my original code in a more streamlined way: pr<-array(0,c(dim(x)[2],dim(x)[2])); for (i in 1:dim(x)[2]) for

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 <-

Hi, Samba team! I am trying to install samba4 on FreeBSD 9.2 as a domain DC to join an existing samba4 domain controller on FreeBSD 9.2. I followed the instruction of: Samba4/HOWTO/Join a domain as a DC Everything is OK until I run the following command: root at mtm:/var/named/etc/namedb # samba-tool drs showrepl Default-First-Site-Name\MTM DSA Options: 0x00000001 DSA object GUID:

Hi All, I performed an svd on a matrix X and saved the first three column of the left singular matrix U. ( I assume that they correspond to the projection of the matrix on the first three eigen vectors that corresponds to the first three largest eigenvalues). I would like to know how much variance is explained by the first eigenvectors? how can I find that. Thanks for your help -- View this

> By the way, since the tcrossprod function in the Matrix package is so fast, the Euclidean distance can be computed very fast: Indeed. > euc_dist <- function(m) {mtm <- Matrix::tcrossprod(m); sq <- rowSums(m*m); sqrt(outer(sq,sq,"+") - 2*mtm)} There are two reasons why I didn't use this optimization in "wordspace": 1) It can be inaccurate for small

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

Hello all, I have the square symetric matrix A: 2 1 1 1 2 1 1 1 2 My first question is what is the easiest way to enter this matriz in R? Second, matrix A has an eigenvalue with multiplicity 2, in this case, how could I find the two related ortogonal eigenvectors given below by R, without the help of R, I mean, I want to know how R calculate this eigenvectors related to the same eigenvalue.

I have attached a text file representing the centralized amplitude of a signal, sampled at 30Hz, whose length N = 6922 My goal is to remove the trend. I am using package "simsalabim". I ran command decompSSA with L = length(Amps)/5 The reason is that I have SSA/MTM toolkit running in Mac/OS. SSA/MTM documentation, relative to SSA, recommends that N/10 <= L <= N/5.

All of my resources for numerical analysis show that the spectral decomposition is A = CBC' Where C are the eigenvectors and B is a diagonal matrix of eigen values. Now, using the eigen function in R # Original matrix aa <- matrix(c(1,-1,-1,1), ncol=2) ss <- eigen(aa) # This results yields back the original matrix according to the formula above ss$vectors %*% diag(ss$values) %*%

I am reading the Mining of Massive Datasets Book by Rajaraman and Ullman. It has a good explanation of Recommendation System at Chapter 9. But what are the relationship between 1) SVD (Singular Decomposition) 2) UV-Decomposition 3) NMF (Non-negative Matrix Factorization) In particular, it seems 2) and 3) can be very similar. Is it right? Thanks. -- View this message in context:

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

Hi Stefan, Thank you very much for pointing me to the wordspace package. It does the job a bit faster than my C code but is 100 times more convenient. By the way, since the tcrossprod function in the Matrix package is so fast, the Euclidean distance can be computed very fast: euc_dist <- function(m) {mtm <- Matrix::tcrossprod(m); sq <- rowSums(m*m);? sqrt(outer(sq,sq,"+") -