similar to: add/subtract matrices, ignoring NA or missing values

m1 <- matrix(rnorm(40), ncol=4) m2 <- matrix(rnorm(40), ncol=4) I would like to subtract first column of m1 from all columns of m2, subtract 2nd of m1 from all columns of m2, and so on. Obviously, I am not using the appropriate function outer(m1, m1, "-"), since the first column isn't all 0s. _________________________________________________________________

I would like to be able to randomise presence-absence (i.e. binary) matrices whilst keeping both the row and column totals constant. Is there a function in R that would allow me to do this? I'm working with vegetation presence-absence matrices based on field observations. The matrices are formatted to have sites as rows and species as columns. The presence of a species on a site is

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value

Dear Members, I have two estimated transition matrices and I want to compare them. In fact I want to check the hypothesis if they come from the same process. I tried to look for some test but all I found was independence test of contingency tables. The following code shows that the usual chi-squared test statistic does not follow chisq distribution. MCRepl <- 5000 khi12 <- rep(0,MCRepl)

I've been scratching my head over this one for too long. I'm hoping someone out there can solve this riddle. I have two vectors of characters, v1 and v2, both of length L, and two matrices of logicals, m1 and m2, both of dimension N*L. The first matrix of logicals corresponds to the first vector of characters, and the second to the second. The matrices are telling me which of the

Dear R-helpers, I have a huge data-set so need to avoid for loops as much as possible. Can someone think how I can compute the result in the following example (that uses a for-loop) using some version of apply instead (or any other similarly super-efficient function)? example: #Suppose a matrix: m1=cbind(1:5,1:5,1:5) #The aim is to create a new matrix with every column containing the

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Hi, May be this helps: m1<- as.matrix(read.table(text=" y1 g24 y1 0 1 g24 1 0 ",sep="",header=TRUE)) m2<-as.matrix(read.table(text="y1 c1 c2 l17 ?y1 0 1 1 1 ?c1 1 0 1 1 ?c2 1 1 0 1 ?l17 1 1 1 0",sep="",header=TRUE)) m3<- as.matrix(read.table(text="y1 h4??? s2???? s30 ?y1 0 1 1 1 ?h4 1 0 1 1 ?s2 1 1 0 1 ?s30 1 1 1

Dear R users, I want to check matrices when i change the order of the rows or/and the order of the columns or/and the combination of them i will give an example what i want  1  -1  1  1      1  1   1  1 -1  -1 -1 -1    -1 -1  -1  -1   1  1    1  1     1 -1    1   1 these 2 matrices are identical because i change the first row and make it third   1  -1  1  1      -1  1   1  1 -1  -1 -1

Hi, I am trying to find an efficient way of applying a linear regression model to different factor combinations in a data frame. I want to obtain the output with minimal or no use of loops if possible. Please let me know if this query is unclear. Thanks, Murtaza

Hi, Given test <- matrix(c(0,2,0,1,3,5), 3,2) > test[test>0] [1] 2 1 3 5 These are values >0 > which(test>0) [1] 2 4 5 6 These are array indices of those values >0 > which(apply(test>0, 1, all)) [1] 2 This gives the row whose elements are all >0 I can't seem to get indices of rows containing one or more elements >0 [[alternative HTML version deleted]]

I'm looking for a good form in which to store matrix results of a simulation. I am doing a simulation study. Each simulation generates some data and then analyzes it. I want to record the results of many simulations and analyze them. Say r has the results of one simulation, and I care about r$coefficients, a vector of coefficients, and r$var, the estimated covariance matrix. I'll do

A list contains several matrices. Over all matrices (list elements) I'd like to access one matrix cell: m <- matrix(1:9, nrow=3, dimnames=list(LETTERS[1:3], letters[1:3])) l <- list(m1=m, m2=m*2, m3=m*3) l[[3]] # works l[[3]][1:2, ] # works l[[1:3]][1, 1] # does not work How can I slice all C-c combinations in the list? S?ren -- S?ren Vogel, Dipl.-Psych. (Univ.), PhD-Student, Eawag,

Hi, Given a test matrix, test <- matrix(c(1,2,3,NA,2,3,NA,NA,2), 3,3) A) How to compute the counts of each column (excluding the NA) i.e., 3, 2, 1 B) How to compute the counts of each column (excluding the NA) that are greater than the column means ? i.e., 1, 1, 0 I could write a for loop, but hope to use better alternative. [[alternative HTML version deleted]]

Hi everyone, I'm having trouble applying the Cor() function to two matrices, both of which contain NAs. I am doing the following: a<-cor(m1, m2, use="complete.obs") ... and I get the following error message: Error in cor(m1, m2, use = "complete.obs") : no complete element pairs Does anyone know how I can apply a correlation, ignoring any NAs? Thanks, rcoder --

Hello, I have another stupid question. I hope you can give me a hint how to solve this: I have a list and one element is again a list containing matrices, all of the same dimensions. Now, I'd like to set the dimnames for all matrices: example code: m1 <- matrix(1:25, nrow=5) m2 <- matrix(26:50, nrow=5) # ... there can be much more than two matrices l <- list() l[[1]] <-

Hi R users: How can I built a direct sum function of matrices in R? I mean A(mxn), B(pxq), C(rxs),... X<-ds(A,B,C,...) X = [ A, 0, 0 0, B, 0 0, 0, C] ((m+p+r+...) x (n+q+s+...)) Thank you for your help. Kenneth -- Using Opera's revolutionary e-mail client: http://www.opera.com/m2/

Dear R-ers, my task is to simple: to assign cases to desired groupings based on the combined values on 2 variables. I can think of 3 methods of doing it. Method 1 seems to me pretty r-like, but it requires a lot of lines of code - onerous. Method 2 is a loop, so not very good - as it loops through all rows of mydata. Method 3 is a loop but loops through fewer lines, so it seems to me more

Hi, I''m trying to get a handle on hfsc. Here is my configuration: root@jmnrouter:/jmn# tc class show dev vlan1 class hfsc 1: root class hfsc 1:1 parent 1: ls m1 0bit d 0us m2 225000bit ul m1 0bit d 0us m2 225000bit class hfsc 1:10 parent 1:1 rt m1 191000bit d 25.0ms m2 135000bit ls m1 0bit d 0us m2 135000bit ul m1 0bit d 0us m2 225000bit class hfsc 1:20 parent 1:1 rt m1 22008bit d