similar to: Ptak and Candpara

PTAk_1.1-1 ( Principal Tensor Analysis on k modes) has been released on CRAN A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and

PTAk_1.1-1 ( Principal Tensor Analysis on k modes) has been released on CRAN A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and

Hi guys, I need some help to analyzing my data. I start to describe my data: I have 21 matrices, every matrix on the rows has users and on columns has items, in my case films. Element of index (i, j) represent the rating expressed by user i about item j. I have a matrix for each of professions. An example of a this type of matrix is: item 1 item 2 item 3 item4 id

Hello, I must start by saying that I am an R novice and am sorry if this is a no-brainer... I have an csv file that contains a grid of 300x300 data points. Each point represents a 1Km square on a map. Each point is either a floating point number or NA. I load the data in with:   data <- read.csv("matrix.csv", sep=',') Convert the data to a matrix with:   data_matrix <-

Hello, I am trying to make a heatmap in R and am having some trouble. I am very new to the world of R, but have been told that what I am trying to do should be possible. I want to make a heat map that looks like a gene expression heatmap (see http://en.wikipedia.org/wiki/Heat_map). I have 43 samples and 900 genes (yes I know this will be a huge map). I also have copy numbers associated with

I wrote some functions for multiway CANDECOMP, i.e. for least squares fitting of a_{i_1\cdots i_m}\approx\sum_{s=1}^p x^1_{i_1s}x^1_{i_1s}\cdots x^m_{i_ms} with arrays of arbitrary dimension. Reminded me of the good old APL days. I could not find this in the archives, but if it's already there, I would appreciate if someone let me know.

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

Hi, Does anybody know some package or function that computes canonical variates? Luis & Janete -------------------------------------------- sapo.pt/kitadsl -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

Hi All, My paper "R for SAS and SPSS Users" received a bit more of a reaction than I expected. I posted the link (http://oit.utk.edu/scc/RforSAS&SPSSusers.pdf) about 12 days ago on R-help and the equivalent SAS and SPSS lists. Since then people have downloaded it 5,503 times and I've gotten lots of questions along the lines of, "Surely R can't do for free what [fill in

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working

CRAN (and crantastic) updates this week New packages ------------ * EffectStars (1.0) Maintainer: Unknown Author(s): Gunther Schauberger License: GPL-2 http://crantastic.org/packages/EffectStars The package provides functions to visualize regression models with categorical response. The effects of the covariates are plotted with star plots in order to allow for an optical

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]

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}}

I have been playing around with sparse matrices in the Matrix package, in particularly with the Cholesky factorization of matrices of class dsCMatrix. And BTW, what a fantastic package. My problem is that I have to carry out repeated Cholesky factorization of a spares symmetric matrices, say Q_1, Q_2, ...,Q_n, where the Q's have the same non-zero pattern. I know in this case one does

Hi: Does R have a function as gsorth is SAS, that perform a the Gram-Schmidt orthonormal factorization of the m ?n matrix A, where m is greater than or equal to n? That is, the GSORTH subroutine in SAS computes the column-orthonormal m ?n matrix P and the upper triangular n ?n matrix T such that A = P*T. or any other version of Gram-Schmidt orthonormal factorization? I search the help, but I

Hi guys, I have a large number of columns of a dataframe that I want to apply a common factorization to; the columns are all numeric and the factorization collapses some of these values into common groups (labels). How can I do this systematically? Is there an analog to sapply that can pass columns as vectors to FUN? Thanks in advance, Gregory Gentlemen ---------------------------------

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

Yesterday I ported FLAME to OS X (http://www.cs.utexas.edu/users/flame/index.html). FLAME is a system to automatically (currently semi-automatically) generate efficient and correct linear algebra routines in C or MATLAB or MPI from descriptions of the algorithms. It uses code that relies heavily on partitioning of matrices, and avoids indices as much as possible. There are a number of

> check the resulting file for subset blocksizes, sample rates, bits per sample, LPC filter order and rice partition order. That's all parameters that I need to check? FLAC tools have a lots of warnings about non-subset files during encoding, but unfortunately don't have easy way to check/test existing FLAC stream for subset compliance. "flac -a" generates the big text file

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For