search for: decompositions

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

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 everyone: I try to use r to do the Cholesky Decomposition,which is A=LDL',so far I only found how to decomposite A in to LL' by using chol(A),the function Cholesky(A) doesnt work,any one know other command to decomposte A in to LDL' My r code is: library(Matrix) A=matrix(c(1,1,1,1,5,5,1,5,14),nrow=3) > chol(A) [,1] [,2] [,3] [1,] 1 1 1 [2,] 0 2 2

Hello, I would like to perform a generalized singular value decomposition with R. The only possibility I found is "GSVD" that is based on LAPACK/BLAS. Are there other possibilities too? If not, has anybody used LAPACK/BLAS under Windows XP? How can I install them? Following [1] did not help. I hope this is the right place for my question. Thank you very much! Oana Tomescu [1]

Can we do Cholesky Decompositon in R for any matrix --------------------------------- [[alternative HTML version deleted]]

Dear R-users, I couldn't find a function or some help in R-project web about the Generalized Singular Value Decomposition. In MatLab there is a simple function for this algebric issue (gsvd). Is there anything like that in R? And, if not, could you help me to apply this method in R? Thanks in advance, Giancarlo +++++ This mail has been sent through the MPI for Demographic Rese...{{dropped}}

Hi all, This question is not really R related, rather on Statistics subject itself. Even I did not do those using R. however still I want to post it here, because my hope is I could get help from great statisticians who are the very active member of this group. My problem is to interpret Variance decomposition of VAR model in layman's language. Using EViews I got following : Variance

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

I've written a piece of code (see below) to do a wavelet image decomposition, during the evaluation of this code I would like to write the results of some calculations back to the R root directory. I used assign() to do so because the names should vary when going thrue a while() loop. For some unknown reason I get an error that says: Error in assign(varname[i], imwrImage) :

Hi all, I wonder if this kind of questions are ok in this list... Quick question: What does it mean than the rank of the QR decomposition of a NxN matrix is N-1 ? m: NxN matrix qr(m)$rank equal to (N-1) Long version: I'm doing a manova on a matrix of 10 variables and 16 observations. > dim(tmp) [1] 16 10 > fit <- manova( tmp ~ treatment*mouse ) >results <-

Hi, Would anyone know if it is possible to run a Beveridge Nelson decomposition of a univariate time series object in R? I searched in the help files but didn't come across any potential methods. Thanks very much, Shruthi -- View this message in context: http://www.nabble.com/Beveridge-Nelson-Decomposition-tp21789452p21789452.html Sent from the R help mailing list archive at Nabble.com.

Dear all, Using R function "dwt", it seems that I cannot specify the wavelet transformation like Haar. What's the R code for wavelet decomposition which allows me to specify Haar wavelet transformation? Of course, if it can include "db2", that is even better. In general, I want an R function like matlab code "dwt". Thanks in advance! Zhen Li

Hi wizards, I have seen the function svd of R for singular value decomposition, but I need to computes the ``economy size'' or ``thin'' singular value decomposition of a matrix in R. Somebody knows how to do that?. Thanks in advance. -- Web Page http://geocities.com/lord_tyranus_96/

Say a matrix of size of thousands? I am looking for an eigen-value decomposition algo in R to give good eigenvalues... Is that a hopeful thing? Thank you! [[alternative HTML version deleted]]

Hi all, I am trying to analyse a time series data and want to make trend-season decomposition using STL approach in R. However I found the decomposition result seems to be sensitive to data points even with the robust option. More specifically, suppose I have a few years of monthly data. Using stl, I got a decomposition T1 + S1 + R1. Then I deleted the most recent two or three data points, the

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 <-

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) %*%

Dear fellow R Users: I am doing a Cholesky decomposition on a correlation matrix and get error message the matrix is not semi-definite. Does anyone know: 1- a work around to this issue? 2- Is there any approach to try and figure out what vector might be co-linear with another in thr Matrix? 3- any way to perturb the data to work around this? Thanks for any suggestions.

# Dear R list, # # A have a doubt about SSQ decomposition and contrasts with ANOVAs. # So, I would like a tip from more advanced R users. # Below my data, the basic script and my doubts: # Data r = paste('r', gl(3, 8), sep='') e = paste('e', rep(gl(2, 4), 3), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4,

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 25.2, 25.7, 26.3, 25.1, 26.4, 19.6,