similar to: efficient submatrix extraction

Hi all, I'm working with a large data set (on the order of 300X300) and trying to apply a function which compares the elements of all possible 2x2 submatrices. There are rc(r-1)(c-1) such submatrices, so obviously the naive method of looping through the rows and columns is computationally unfeasible for my data set: for(i in 1:(nrow(data)-1)) { for(j in (i+1):nrow(data)) { for (m

I tried to use gpuCor function in the gputools package of R to calcuate the pairwise correlations of a matrix of 40,000 columns. Becuase there would be memory issues if I use the whole matrix at a time, I splitted the matrix into submatrix of 10,000 columns and then calculate the pairwise correlation of different submatrices. There are altogether 4 submatrices, so I need to calculate the pearson

Yes you are right ... an example is mandatory. So ... I have a matrix of 0 with just a single 1 per row and per column I need to extract all maximal 'diagonal' submatrices Let's say I have the following matrix A B C D E a 0 1 0 0 0 b 1 0 0 0 0 c 0 0 1 0 0 d 0 0 0 1 0 e 0 0 0 0 1 well I would like to get, for this example, the two following submatrices A B C D E

Hello list, I try to optimize some R code and it turns out that the function as.matrix takes a lot of time in my code. There is only one reason why I need as.matrix: My code should work for matrices with ncol=p and p should be allowed to be 1 or larger. Now I have a matrix x with dim(x)=(n,p), and I need to work with the submatrix y <-x[gv,], (gv a vector of n logicals) and to calculate

Hello everybody I'm very new at using R so probably this is a very stupid question. I have a matrix of "p" columns and I have to calculate for each of them the "two sample t-statistic" and p-value and to save the results into two different vectors. I have divided my matrix into two submatrices: submatrix A containing the first "n1" rows (p columns) and submatrix B

Is there a good reason not to allow matrix extents to be zero? I have been writing a function which fits glms under certain linear constraints. I fit the model with a reduced set of variables and then transform back to the original set. This involves operating on submatrices of the covariance matrix and subsets of the coefficients. Sometimes these subsets are empty. A zero-length subset of the

Dear R-help list, Pls I have this problem. Suppose I have a matrix of size nxn say, generated as follows   z<-matrix(rnorm(n*n,0,1),nrow=n)   I want to write a function such that for i in 1:n, I will remove the rows and columns corresponding to i (so, will be left with n-1*n-1 submatrix in each cases). Now I need the sum of the determinant of each of this submatrices. As an example, if n=3, it

Dear Rxperts Below is a small vector of values of zeros and non-zeros... was wondering if there is an efficient way to get the block sizes of submatrices of a big matrix similar to the one shown below? diagonal elements can be zero too. Rows with only a diagonal element may be considered as a unit block size. c(1,0,0,0,0,0,0,0,0,0,0, 1,1,0,0,0,0,0,0,0,0,0, 0,0,1,0,0,0,0,0,0,0,0,

Hi everybody, I'm looking for an optimal way to split a big matrix (e.g. ncol = 8, nrow=8) into small square submatrices (e.g. ncol=2, nrow=2) For example If I have > h [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1 9 17 25 33 41 49 57 [2,] 2 10 18 26 34 42 50 58 [3,] 3 11 19 27 35 43 51 59 [4,] 4 12 20 28 36

Hi all, I have a R script that creates several input files for an analysis program. It loops through the matrix read into R and picks out submatrices and then creates a separate output file for each submatrix. The loop works great, but I am having trouble getting all the separate output files written. The line I have is: write.table(ch1d,

El código que me olvide pegar input m <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5), .Dim = c(10L, 5L)) m ## output output <- c(1:3, 1:5, 1:2) output nfilas <- nrow(m) while(nfilas > 0) { diagonal <- diag(m)

Dear R community, I have trouble implementing a nested variance-covariance matrix in the lme function. The model has two fixed effects called End and logpgc, the response variable is the logarithm to base 2 of Intensity ( log2(Intensity) ) and the random effects are called Probe and ProbeNo. The model has the following nesting structure: A Pixel is nested within the ProbeNo,the ProbeNo is

I need to multiply to very large, nonsparse matrices, and so get the error "allocMatrix: too many elements specified". Is there a way to set the limit for allocMatrix? In my case, the two matrices, A and B, are nxm and mxp where m is small, so I could subdivide each into blocks of submatrices A=rbind(A1,A2,...) and B=cbind(B1,B2,...) then multiply each pair of submatrices, but I was

Dear R users, I have a Hadamard matrix 16x15 and i want to put 16x5 submatrices in an array put i have an error. > A1<-matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, + 1,0,1,0,1,0,1,0,1,0,1,0,1,0,1, + 0,1,1,0,0,1,1,0,0,1,1,0,0,1,1, + 1,1,0,0,1,1,0,0,1,1,0,0,1,1,0, + 0,0,0,1,1,1,1,0,0,0,0,1,1,1,1, + 1,0,1,1,0,1,0,0,1,0,1,1,0,1,0, + 0,1,1,1,1,0,0,0,0,1,1,1,1,0,0, + 1,1,0,1,0,0,1,0,1,1,0,1,0,0,1, +

Estimado Javier, Gracias por tu mensaje. No, lo unico que requiero es la lista de números (i1, 2, 3, 1, 2, 3, 4, 5, 1, 2). Saludos cordiales, Jorge.- 2015-10-28 14:35 GMT-05:00 Javier Rubén Marcuzzi < javier.ruben.marcuzzi en gmail.com>: > Estimado Jorge I Velez > > > > No comprendo un punto, dices que deseas construir sub matrices y extraer > elementos de sub

Does anyone have something like tapply that is extremely fast for matrices when there is a very large number of levels of the grouping variable? I'm referring to, for example, tapply(x, grouping.variable, function.operating.on.submatrix) where x is a matrix and the submatrix is a subset of the rows of x. The grouping variable's length equals the number of rows of x. -- Frank E

Deal all: i want to do the probit with sample selection estimation, the following is my code: probit with sample selection can be done by stata :heckprob The heckprobll is the likelihood function shown in W.H. Greene 5th p714 ¡´ The question is the convergence is very slow compare with Stata using likellihood only. ¡´ Second i did the similar way in matlab using fminsearch , the estimated

Buenos dias a todos, Quisiera extraer algunas entradas de una matrix "m" teniendo en cuenta algunas restricciones. El siguiente ejemplo ilustra la situacion: ## input m <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5), .Dim = c(10L, 5L)) m ## output output <-

Hi all, I'm seeing doubled quotas on Windows 7 when connecting to Samba. By doubled, I mean that if I set a 1GB group quota on Linux, I see a 2GB quota on Windows 7. The quotas seem correct with Samba 3.6.19 running on a Linux 2.6.32 kernel on Mandriva; they are incorrect with Samba 3.6.22 on a Linux 3.12 kernel on Ubuntu. What could be wrong? In more detail, I'm testing on both a

I am trying to run R from an apache C++ module in a shell script to plot some data to display it in apache later. I get the error (reported in apache's logs): Xlib: connection to ":0.0" refused by server Xlib: No protocol specified Error in X11(paste("png::", filename, sep = ""), width, height, pointsize, : unable to start device PNG In addition: Warning