similar to: Generalized singular value decomposition

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, I'm trying to run generalized singular value decomposition (GSVD) function from LAPACK library. Basically my problem is that I can not run it for large matrices, I get a memory error. I'm using R 2.5.1. I tried this on intel centos5 machines with 2 GB memory and 8 GB memory. I have unlimited max memory,cpu time and virtual memory. LAPACK is already compiled for R

Hi all, I encountered this problem with parameterization in R: I have two factors in a regression. how about if I want to set constraint so that for each factor, the sum of their coefficients equals to zero(instead of choosing a reference category)? for example, I have factor(variable) A(with three categories) and factor(variable) B(with 4 categories), and I want to parameterize so that the sum

Dear all, I have a problem when I was working on Age-Period-Cohort study in R. I tried to put constraint to two coefficients on age (so that to solve the identification problem due to linear dependency). But I don't know how to do this in R(put constraint). If you could give me some suggestion, it will be very helpful! Oana

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

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

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

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

Dear list, I am using the plot.coinertia function from the ade4 package. Can someone tell me if it is possible to change the label size in the upper, right plot? My RV value is so high that the arrows become shorter than the labels. Thank you for your help. Oana Tomescu -- Dipl.-Ing. Oana Tomescu ACIB Core Facility Bioinformatics Institute for Genomics and Bioinformatics, TU-Graz

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.

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

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, 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

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.

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) %*%

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/

Hi, How can I find and download a function in R to do the LU decompostion for finding the upper and lower triangular matrix.  Thank you so much. Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

Can somebody help me with the following issue (SEM in R), please:   When I run the model (includes second order models) in R, it gives me the following:   1)       In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars,  :   Could not compute QR decomposition of Hessian. Optimization probably did not converge.   2)       I have aliased parameters and NaNS   or sometimes when