similar to: replace zeros in a block diagonal matrix with small random values?

Hi r-help community, This question isn't so much a syntax/coding one, but here goes: Let's say I have matrix of arbitrary dimensions and I'd like to reorder the rows in such a way that I could maximize the sum of the entries along the diagonal. For example, for this 3x3 matrix: [,1] [,2] [,3] [1,] 3 4 13 [2,] 9 1 2 [3,] 2 11 1 rearranging the rows

Hi Everybody, I have a matrix of about 45 columns. Some of the rows contain zeros. Using >data1<-data[complete.cases(data),], I can remove the "NA" rows. But I am unable to tackle that of zeros. Can anybody give me an idea of how to remove rows containing zeros in a matrix. Thanks so much Best Ogbos [[alternative HTML version deleted]]

Dear friends I have an array consist of r-rows and c-columns e.g. x=c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,0,0,0,0,0,0,0,0); x1=array(x, dim=c(4,6)) output is > x1 [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 2 3 4 0 0 [2,] 1 2 3 4 0 0 [3,] 1 2 3 4 0 0 [4,] 1 2 3 4 0 0 How can i ignore columns having zero sums? Help in this regard

Hi, I am looking for two ways to speed up my computations: 1. Is there a function that efficiently computes the 'sandwich product' of three matrices, say, ZPZ' 2. Is there a function that efficiently computes the determinant of a positive definite symmetric matrix? Thanks, S.A. [[alternative HTML version deleted]]

Hi, I have a matrix that looks like this a <- c(1,1,1,1,2,2,3,3,3,3) b <- c(2,2,2,3,4,4,4,5,5,6) c <- c(1,2,3,4,5,6,7,8,9,10) M <- matrix(nr=10,nc=3) M[,1] <- a M[,2] <- b M[,3] <- c > M [,1] [,2] [,3] [1,] 1 2 1 [2,] 1 2 2 [3,] 1 2 3 [4,] 1 3 4 [5,] 2 4 5 [6,] 2 4 6 [7,] 3 4 7

Dear all, I have  a data series (might be vector or matrix) which is composed only from zeros and ones like the following example 0 0 0 1 1 0 1 0 0 1 1 1 1 0 0 0 I want to be able to return back the length of concecutive zeros and the length of concecutive ones. For that I want to have something like that returned: zeros= [3 1 2 3]; ones=[2 1 4]; How I can do that simply in R? I would like

Hi, I have a correlation matrix of about 3000 items, i.e., a 3000*3000 matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID,

Hi. I have an n x m matrix M some of who's entries are zeros. I want to know how many zeros there are in each row -perhaps stored in a 1 x n vector which lists the number of zeros for each row of M. Before I had a vector V and I was able to get the number of zeros in V by doing length(V[ V==0]) but when I try something similar for M, like M[ M==0] it creates a vector not a matrix and so

Dear Group, I have a following dataset: > a A B C D 1 22 3 31 40 2 26 31 36 32 3 3 7 49 16 4 24 40 27 26 5 20 45 47 0 6 34 43 11 18 7 48 48 24 2 8 3 16 39 48 9 20 49 7 21 10 17 36 47 10 > dput(a) structure(list(A = c(22L, 26L, 3L, 24L, 20L, 34L, 48L, 3L, 20L, 17L), B = c(3L, 31L, 7L, 40L, 45L, 43L, 48L, 16L, 49L, 36L), C = c(31L, 36L, 49L, 27L, 47L, 11L, 24L,

I am using the following command to replace all the missing values and assorted typos in a dataframe with NA: mydata[mydata>80]=NA The problem is that the first column contains values which should be more than 80, so really I want to do it just for mydata[,2:length(mydata)] I can't seem to re-write the code to fit: mydata[,2:length(mydata)>80]=NA # no error message, but doesn't

Dear R helpers Suppose x  <- c(1:3) y  <- matrix(1:12, ncol = 3, nrow = 4) > y      [,1] [,2] [,3] [1,]    1    5    9 [2,]    2    6   10 [3,]    3    7   11 [4,]    4    8   12 I wish to multiply 1st column of y by first element of x i.e. 1, 2nd column of y by 2nd element of x i.e. 2 an so on. Thus the resultant matrix should be like > z      [,1]   [,2]    [,3] [1,]    1   

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

[My previous message rejected, therefore I am sending same one with some modification] I have 3 vectors with object name : dat1, dat2, dat3 Now I want to create a loop, like : for (i in 1:3) { cat(sd(dati)) } How I can do this in R? Regards,

Dear all, I am trying to create a vector name, for example tmax.195012 from tmax., 1950 and 12. Obviously I don't wish to simply type it because the 3 name components are changing in each iteration within a loop. Is there any way of concatenating those 3 components (which are a mixture of numbers and letters)? Thanks for reading, Panos

Dear R list, I have one very elementary question regrading correlation between two variables. x = c(44,46,46,47,45,43,45,44) y = c(44,43,41,41,46,48,44,43) > cov(x, y) [1] -2.428571 However, if I try to calculate the covariance using the formula as covariance = sum((x-mean(x))*(y-mean(y)))/8       # no of of paired obs. = 8 or     covariance = sum(x*y)/8-(mean(x)*mean(y)) gives

Hi everyone: I have a kinda easy question but i do not know how to solve that in a simple way. I want to compare the rows of two matrices. col1 <- c(1,2,3,4,5,6) col2 <- c(6,5,4,3,2,1) m <- cbind(col1, col2) col3 <- c(1,3,2,6) col4 <- c(6,3,5,1) n <- cbind(col3, col4) In matrix n, for example the first row is (1,6), it is also some row

I am very sorry for such a simple question, but I am struggling with "split". I have the following data frame: x<-data.frame(A=c(NA,NA,NA,NA,"split",NA,NA,NA,NA,"split",NA,NA,NA,NA,"split",NA,NA,NA,NA),

I have following matrix : a = matrix(rnorm(36), 6) Now I want to replace the lower-triangular elements with it's upper-triangular elements. That is I want to make a symmetric matrix from a. I have tried with lower.tri() and upper.tri() function, but got desired result. Can anyone please tell me how to do that?

Hi all, I am trying to figure out a way to improve my code's efficiency by avoiding the use of loop. I want to calculate a conditional mean(?) given time. For example, from the data below, I want to calculate sum((value|choice==1)/sum(value)) across time. Is there a way to do it without using a loop? time cum_time choice value 1 4 1 3 1 4

Hello all, Is there a way in R to compute the multivariate normal density of every pair of entries in a vector efficiently instead of using for loop? For example Suppose I have a vector a=c(v_1,...,v_p)=c(0.5343909, -0.7784353, -0.0568370, 1.8772838, -1.3183407, 0.8227418,...) I want to compute density(v_i, v_j) for every pair of entries (i,j) (i!=j) in a. The joint bivariate distribution