similar to: problem with generalized singular value decomposition using LAPACK

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}}

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]

Hi, I need to computed the GSVD of two matrices. For doing so I used in my C code the lapack routine dggsvd. But when I source my file gsvd.R I have the following error: Error in eval.with.vis(expr, envir, enclos) : BLAS/LAPACK routine 'DGGSVD' gave error code -1 Is there a problem with the parameters passed through the gsvd.R script? Is there a problem within the C script?

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/

Dear R-list users, this question is not strictly related to R, but hopefully somebody will be able to answer. In a schematic way, which is the algorithm to sample from a multivariate normal distribution using the singular value decomposition? thank you for your help Stefano AVVISO IMPORTANTE: Questo messaggio di posta elettronica pu? contenere informazioni confidenziali, pertanto ? destinato

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

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

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

Hi I have a problem with this error, I have searched the archives and found previous discussion about this, can I cannot understand how the explanations apply to what I am trying to do. I am trying to do Log_rank Survival analysis, I have included tables and str command, is it a factor/integer problem? If so how do I correct this, as all my attempt to recode the data have failed. >

Hi there! I´m a noob when it comes to R and I´m using it to run statisc analysis. With the code for ARIMA below I´m getting this error: Error in solve.default(res$hessian * n.used) :Lapack routine dgesv: system is exactly singular The code is: > s.ts <- ts(x[,7], start = 2004, fre=12) > get.best.arima <- function (x.ts, maxord=c(1,1,1,1,1,1)) + { + best.aic <- 1e8 + n <-

Hi R-users: When I run the R script (as the following), I got the error message: Error in solve.default(C[ind, ind]) : Lapack routine dgesv: system is exactly singular. Any help is appreciated. Ying library(sem) R.pw <- matrix(c( 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2137356, 0.2137356, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

but maybe of use to some: http://gifi.stat.ucla.edu/psychoR/derivatives Computes generalized eigenvalue solutions Ax=\lambda Bx and generalized singular value solutions Rz=\gamma Px and R'x=\gamma Qy for matrices that are differentiable functions of a vector of parameters. Along with the decomposition the code returns arrays with all first-order partial derivatives of the values/vector wrt

Dear all, I made a simple test of the Cholesky decomposition in the package 'Matrix', by considering 2 variables 100% correlated. http://blogs.sas.com/content/iml/2012/02/08/use-the-cholesky-transformation-to-correlate-and-uncorrelate-variables/ The full code is below and can be simply copy&paste in the R prompt. After uncorrelation I still have a correlation of +-100%...

Dear R-listers, does anybody know of any package developed to implement the Oaxaca-Blinder decomposition in R? I've been googling around and my reserch has been unfruitful. The latest news I've found were 1 year old. Does anybody know of any recent development? Has R ever been employed to run a Oaxaca-Blinder decomposition? Thanks for your kind support, f. [[alternative HTML version

Dear All, I have developed a programme the anable the decomposition of the Gini index, it complets tha valuable work of Achim Zeileis, the author of the ineq package. I would like to make it to be part of all R package. How should I proceed. Must I sent it to the the Core developement team ? The proogramme is written in R. Many thanks for your advice, Best regards, Souleymane

The gchol function in library(kinship) does an LDL decomposition. An updated version has just recently been posted on Rforge, in the bdsmatrix library which is part of survival. > temp <- matrix(c(1,1,1,1,5,8,1,8,14), 3) > gt <- gchol(temp) > as.matrix(gt) # L [,1] [,2] [,3] [1,] 1 0.00 0 [2,] 1 1.00 0 [3,] 1 1.75 1 > diag(gt) # D [1]

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

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:

Dear all, I have a time series with a frequency of 10 days (so 36 yearly). one year is completely NA. Now I want to do a stl-decomposition, but using e.g. na.action= na.approx makes no sense for a whole year, of course. Is there a way of simulating this single year or to just make stl not using this year for the decomposition? -- View this message in context: