similar to: Different results for sparse and dense version of model matrix using contrasts and interactions

Hi, I have a sparse Matrix (kronecker product of spline design matrices), and I need to multiply each row by a number to get another matrix. If the matrix is A and the numbers are stored in a vector k, with plain vanilla matrices I would do A*k But when using the Matrix package (class of A is "dgCMatrix"), I get the warning "coercing sparse to dense matrix for arithmetic".

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi, I'm doing some basic operations on large sparse matrices, for example getting a row. it takes close to 30 seconds on a 3Ghz machine, and shots the memory usage up to the sky. I suspect there are dense intermediary steps (which, if true would defeat the purpose of trying to use sparse representaitons). As much as I try understanding the

Hi, I'm trying to write an extremely dense tinc-mini-howto. You can see it at: http://keetweej.vanheusden.com/~folkert/tinc_mini_howto.html Please send me suggestions on how I can make things better and such. Oh, and if someone could tell me how to configure tinc so that it authenticates against the host on the other side, please tell me! (couldn't figure that one out) Folkert. Tinc:

I have not been able to find a way to do dense rank in R Here is an example of what I need rank() gives the following 5 rank 1 7 rank 2 7 rank 2 9 *rank 4* but I want 5 rank 1 7 rank 2 7 rank 2 9 *rank 3* * * thanks SS [[alternative HTML version deleted]]

Dear all, I am including in a plot 6 different lines (?lines) with 6 different line types. The problem is that I have so dense information that the line types are not visible any more.   In the code below myLength<-length(currentSet) plot(seq(from=1,to=myMax,length.out=myLength),currentSet, type="l",xlim=c(1,myMax),axes=F,...) the myLength is 100.000+ elements. I would like to ask

In the latest windows binary (2007-04-03 r41023) pdf() with an argument paper="a4" gives an error, this was still ok in (2007-03-18 r40854) Jan Oosting > pdf("test.pdf",paper="a4") Error in pdf("test.pdf", paper = "a4") : invalid paper type 'a4' (pdf) > sessionInfo() R version 2.5.0 alpha (2007-04-03 r41023) i386-pc-mingw32

Dear R-users, For a data frame (say in this example X) I want to look up the corresponding value in a 'look-up data frame' (in this example Y). The for-loop works but is very time-consuming because 'X' in reality is very big. Therefore I would like to have a solution with apply. However, I do not succeed. Any suggestions? Thanks in advance, Hanneke

I use 2 packages that both implement a S4 plot method, where one package depends on the other (the bioconductor package globaltest which depends on multtest). When the plot method is used from within the package, it seems the default plot method is used, and an error is generated. When the method is invoked from the console, the plot is created correctly. I have reproduced this with 2 small

I have data set that I wish to solve with the Matrix package's sparse matrix functionality. The speed improvements that it has achieved are amazing, with my dense matrix solutions never taking really long enough to time in what I've been able to time so far. However, before I can solve my full linear model, I need to be able to read in all the data, and therein lies the rub.

WinXP-x32, R-21.13.0 Dear list, I have a problem that (I think) relates to the interaction between Windows and R. I am trying to scrape a table with data on the Hawai'ian Islands, This is my code: library(XML) u <- "http://en.wikipedia.org/wiki/Hawaii" tables <- readHTMLTable(u) Islands <- tables[[5]] The output is (first set of columns):

Dear all, Can anyone help me, my R software can not run a nested linear regression by using the lme funcion. The message that appears is  Error: could not find function "lme" I already downloaded and loaded the package, please see below. Thank you in advance for any help! Nadia. > data<-read.csv("/Users/nadiasan1/Desktop/MOE and MOR.csv")> attach(data)>

I've been trying to get some linear classifiers (LiblineaR, kernlab, e1071) to work with a sparse matrix of feature data. In the case of LiblineaR and kernlab, it seems I have to coerce my data into a dense matrix in order to train a model. I've done a number of searches, read through the manuals and vignettes, but I can't seem to see how to use either of these packages with sparse

Dear R helpers: Let's say I have some data X, X <- runif(1000, 1, 10000000000) pdf('X.pdf', width=100,height=5) hist(X, breaks=1000) dev.off() I find that, on x-axis the coordinates are 0e+00, 2e+09, 4e+09, 6e+09, 8e+09, 1e+10. Only five numbers, which is too sparse in a 100x5 pdf file. I want the x-axis coordinates to become more dense, e.g.

Thank you. The line of code you give certainly resolves several of the issues. I didn't realize that font support is such a tough matter to realize. Let me express my gratitude to those who provide this for us in R. On 04-05-11, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote: Oh, please! This is about the contributed package XML, not R and not Windows. Some of

hi all, i have a file of the following format that i want to read into a matrix: 010101001110101 101010010111110 010010100100000 ... it has no headers or row names. I tried to use read.table(), but it doesn't allow me to specify nothing as the column separator (specifying sep='' means whitespace for that function). read.fwf doesn't seem appropriate either. [[alternative HTML

Hi, I have a 60k*600k matrix, which exceed the vector length limit of 2^32-1. But it's rather sparse, only 0.02% has value. So I save is as MarketMatrix (mm) file, it's about 300M in size. I use readMM in Matrix package to read it in. If do so, the data type becomes dgTMatrix in 'Matrix' package instead of the common matrix type. The problem is, if I run k-means only on part of

Hello, I'm working with a very large, very sparse X matrix. Let csr.X <- * as.matrix.csr*(X) as described by the SparseM package. The documentation says that "Indexing .... work just like they do on dense matrices". To me this says that I should be able to perform operations on the rows of csr.X in the same way I would on X itself. E.g. f <- function(x){ for (i in 1:n){

I am trying to estimate a covariance matrix from the Hessian of a posterior mode. However, this Hessian is indefinite (possibly because of numerical/roundoff issues), and thus, the Cholesky decomposition does not exist. So, I want to use a modified Cholesky algorithm to estimate a Cholesky of a pseudovariance that is reasonably close to the original matrix. I know that there are R packages that

hi, I have a dataset (the netflix dataset) which is basically ~18k columns and well variable number of rows but let's assume 25 thousand for now. The dataset is very sparse. I was wondering how to do kmeans/nearest neighbors or kernel density estimation on it. I tired using the spMatrix function in "Matrix" package. I think I'm able to create the matrix but as soon as I pass

This is not a solution but your lsfit attempt #Error in lsfit(A, b) : only 3 cases, but 4 variables lsfit(A,b) gave that error because lsfit adds a column of 1 to its first argument unless you use intercept=FALSE. Then it will give you an answer (but I think it converts your sparse matrix into a dense one before doing any linear algebra). Bill Dunlap TIBCO Software wdunlap tibco.com On