similar to: Sparse matrix performance question

I have data in i,j,r format, where r is the value in location A[i,j] for some imaginary matrix A. I need to build this matrix A, but given the sizes of i and j, I believe that using a sparse format would be most adequate. Hopefully this will allow me to perform some basic matrix manipulation such as multiplication, addition, rowsums, transpositions, subsetting etc etc. Is there any way

> sessionInfo() R version 2.13.1 (2011-07-08) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] Matrix_0.999375-50 lattice_0.19-30 loaded via a namespace (and not attached): [1] grid_2.13.1

Hi: I was trying to use apply on a sparse matrix from package Matrix, and I get the error: Error in asMethod(object) : Cholmod error 'problem too large' at file ../Core/cholmod_dense.c, line 106 Is there a way to apply a function to all the rows without bumping into this problem? Here is a simplified example: > dim(sm) [1] 72913 43052 > class(sm) [1] "dgCMatrix"

Hi, I am trying to read in a large and highly sparse ARFF file into R which was produced by WEKA. However the package 'RWeka' just chokes on this file. The data set has about 40k observations and about 20k dimensions. Even after 1hr read.arff method of RWeka is still trying to read in the file, whereas WEKA is able to read it in in less than 20seconds. What are my options at this

Dear all, I am trying to use "do.call", but I don't think I totally understand this function. Here is an simple example. -------------------------------------------- > B <- matrix(c(.5,.1,.2,.3),2,2) > B [,1] [,2] [1,] 0.5 0.2 [2,] 0.1 0.3 > x <- c(.1,.2) > X <- cbind(1,x) > X x [1,] 1 0.1 [2,] 1 0.2 > > lt <-

I cleaned up my old benchmarking code and added checks for missing data to compare various ways of finding OLS regression coefficients. I thought I would share this for others. the long and short of it is that I would recommend ols.crossprod = function (y, x) { x <- as.matrix(x) ok <- (!is.na(y))&(!is.na(rowSums(x))) y <- y[ok]; x

Hi, I'm using colSums and rowSums to sum the first dimensions of arrays. It works ok but the resulting object is different. See > a3d <- array(rnorm(120, mean=2), dim=c(20,6,1)) > dim(colSums(a3d)) [1] 6 1 > dim(rowSums(a3d)) NULL > class(colSums(a3d)) [1] "matrix" > class(rowSums(a3d)) [1] "numeric" I was expecting rowSums to preserve the array

Working with an NxMxO sized matrix, currently I can do this in my code: if (max(colSums(array)) >= number) But to get an equivalent result using rowSums, I have to do: for (i in 1:10) { if (max(rowSums(array[,,i])) >= number) } I'm running both in a much larger loop that loops millions of times, so speed and such is quite a big factor for me. Currently, the colSums line uses about

Dear all, Even though one of R users answered my question, I cannot understand, so I re-ask this question. I am trying to use "do.call", but I don't think I totally understand this function. Here is an simple example. -------------------------------------------- > B <- matrix(c(.5,.1,.2,.3),2,2) > B [,1] [,2] [1,] 0.5 0.2 [2,] 0.1 0.3 > x <- c(.1,.2) >

Hello, sorry for posting this independently of the original thread, but it is not that easy to answer to mails, when receiving the r-help as digest... ... The question was: > I compare each row of a matrix with each row of another matrix. > > testmat1 <- matrix(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16), nrow=4) > testmat2 <- matrix(c(1,2,3,5,5,6,7,8,9,10,11,12,13,14,15,16),

Hi all I have a data frame with duplicate columns and i want to remove duplicates by adding rows in each group of duplicates, but have lots of NA's. Data: dfrm <- data.frame(a = 1:4, b= 1:4, cc= 1:4, dd=1:10, ee=1:4) names(dfrm) <- c("a", "a", "b", "b", "b") dfrm[3,2:3]<-NA dfrm a a b b b 1 1 1 1 1 1 2 2 2 2 2 2 3

Hi [R-2.6.0, macOSX 10.4.10]. The helppage says that rowSums() and colSums() are equivalent to 'apply' with 'FUN = sum'. But I came across this: > a <- matrix(1:30,5,6) > is.integer(apply(a,1,sum)) [1] TRUE > is.integer(rowSums(a)) [1] FALSE > so rowSums() returns a float. Why is this? -- Robin Hankin Uncertainty Analyst National Oceanography Centre,

I have a question on RowSums. Lets say i have a timeSeries table A B C 1/1/90 NA 1 1 1/2/90 NA 1 1 1/3/90 NA 1 1 1/4/90 NA 1 1 1/5/90 1 1 1 1/6/90 1 1 1 if i use RowSums, i will get 1/5/90 3 1/6/90 3 but i want 1/1/90 2 1/2/90 2 1/3/90 2 1/4/90 2 1/5/90 3 1/6/90 3 I cant

Dear all, I am trying to extract rows from a data.frame based on the rowSums != 0. I want to preserve rownames in the first column in the subset. Does anyone know how to extract all species that don't have rowSums equal to zero? Here it is: # dataset x <- data.frame( species=c("sp.1","sp.2","sp.3","sp.4"), site1=c(2,3,0,0), site2=c(0,0,0,0),

Dear list, I have some trouble generating a frequency table over a number of vectors. Creating these tables over simple numbers is no problem with table() > table(c(1,1,1,3,4,5)) 1 3 4 5 3 1 1 1 , but how can i for example turn: 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 into 0 0 1 1 1 0 0 2 0 1 0 3 My second problem is, sorting rows and columns of a matrix by the rowSums/colSums. I did it

> with the entirely different rowSums, but it has been around > for a long time.) A lot longer than rowSums ... > Bill Dunlap > Spotfire, TIBCO Software --- This made me smile. The rowsums function was originally an internal part of the survival package, used for fast computation of certain sums when there is a cluster() statement. It was Statistical

Should not the result be NULL if you have removed the NA with na.rm=TRUE ? B. > On Mar 21, 2018, at 11:44 AM, Stefano Sofia <stefano.sofia at regione.marche.it> wrote: > > Dear list users, > let me ask you this trivial question. I worked on that for a long time, by now. > Suppose to have a data frame with NAs and to sum some columns with rowSums: > > df <-

Say I have the following data: testDat <- data.frame(A = c(1,NA,3), B = c(NA, NA, 3)) > testDat A B 1 1 NA 2 NA NA 3 3 3 rowsums() with na.rm=TRUE generates the following, which is not desired: > rowSums(testDat[, c('A', 'B')], na.rm=T) [1] 1 0 6 rowsums() with na.rm=F generates the following, which is also not desired: > rowSums(testDat[, c('A',

From ?NA Numerical computations using ?NA? will normally result in ?NA?: a possible exception is where ?NaN? is also involved, in which case either might result. and ?NaN Computations involving ?NaN? will return ?NaN? or perhaps ?NA?: which of those two is not guaranteed and may depend on the R platform (since compilers may re-order computations).

Dear R-helpers, I need to generate all the binary sequences of length n (here n = 8) that start with 1 and have no fewer than two of each digit, and are not cyclic permutations of each other. Here is what I have done: len <- 8 df <- as.data.frame(numeric(2^(len - 1)) %o% numeric(len)) require(partitions) for (i in 1:2^(len - 1)) df[i, ] <- binary(i, dim = len)[[1]] df <-