similar to: Question about NA's

Hello fellow R users, I have a matrix computation that I imagine should be relatively easy to do, however I cannot figure out a nice way to do it. I have two matrices, for example mat1 <- matrix(c(1:5,rep(NA,5), 6:10), nrow=3, byrow=T) mat2 <- matrix(c(2:6, 6:10, rep(NA,5)), nrow=3, byrow=T) I'd like to compute the element-wise average for non-NA entries. Of course (mat1+mat2)/2

Hi R-users, How do I calculate a number of NA's in a row of every second column in my data frame? As a starting point: dfr <- data.frame(sapply(x, function(x) sample(0:x, 6, replace = TRUE))) dfr[dfr==0] <- NA So, I would like to count the number of NA in row one, two, three etc. of columns X1, X3, X5 etc. Thanks in advance Lauri [[alternative HTML version deleted]]

Hello, #I have two matrices, eg.: y <- matrix( c(20, NA, NA, 45, 50, 19, 32, 101, 10, 22, NA, NA, 80, 49, 61, 190), ncol=4 ) x <- matrix( c(20, NA, NA, NA, 50, 19, 32, 101, 10, 22, NA, NA, 80, 49, 61, 190), ncol=4 ) #Whereas x contains all NA?s from y plus some additional NA?s. #I want to find the index of these additional NA?s. I think, there must be a very

Excellent. That was very helpful. Now I have full control about my NA?s :-) Thank you very much!!! Matthias > > Here is an another way > > count <- is.na(x) + is.na(y) > which( count == 1, arr.ind=TRUE ) > > 'count' gives you the number of missing values at for each > row and column. Then you can find out how many occurances of > both missing,

I am trying to fit a very simple broken stick model using the package "segmented" but I have hit a roadblock. > str(data) 'data.frame': 18 obs. of 2 variables: $ Bin : num 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 ... $ LnFREQ: num 5.06 4.23 3.50 3.47 2.83 ... I fit the lm easily: > fit.lm<-lm(LnFREQ~Bin, data=id07) But I keep getting an error

Hi, I have three matrices which could be, for example: A = 0, NA NA, 3 B = 1, NA 0, NA C = 1, NA 1, 1 (The point is that they all may have NA's in some cells) QUESTION: How do I perform a element-by-element sum of the elements of these three matrices (A + B + C), ignoring NA's, to obtain: D = 2, NA 1, 4

Your message doesn't help us very much. You haven't said what kind of calculation it is you want to do, and that certainly matters. For example, for some kinds of computations the solution you started below would work fine: > M <- matrix(1:16, 4, 4) > is.na(diag(M)) <- TRUE > M [,1] [,2] [,3] [,4] [1,] NA 5 9 13 [2,] 2 NA 10 14 [3,] 3 7 NA

Hello!!! I have a folder (C:/R/) with matrix files, named by number, i.e. 0.mat, 1.mat,...,1250.mat. In this case, they are 5x5 simetric matrices. I would like to compute a property for each matrix and put calculated values into a data frame for posterior ploting and printing. Below there is an example for 7 matrices (0.mat..6.mat) #define data frame L <- data.frame(frame=numeric(7),

The print.dist() method in the mva package currently prints the triangular distance matrices with the ``trick'' of setting the (diagonal and) upper triangular part to NA, and then uses print(mat, na = "") to print that matrix. This is very much undesired if there are true NAs. Reproducible example code: (x <- cbind(c(1,NA,2,3), c(NA,2,NA,1))) (d <- dist(x)) ## does not

Hi Steven, It's not entirely clear what you actually want to achieve in the end. As soon as you "know" x1, and assuming that the different "xi" do not differ in length in the real application, you know the length of the target vector. Instead of the loop, you can use 'Reduce' without having to initialize a starting vector. # generate sample vectors, put them in

Thanks, I tried someting like this, but computation takes times for large matrices btransf <- function(y,X=length(y)^4) { N <- length(y) bm <- matrix(rep(1/N,N^2),N,N) for(j in 1:X){ coord <- sample(1:N,4,replace=T) d <- runif(1,0,min(bm[coord[1],coord[2]],bm[coord[3],coord[4]]))

Hi, when I try to generate LLVM-IR which calls back to native C functions the jit compiler generates invalid code on 64-bit linux. The same code works fine on 32-bit linux, 32-bit OS X and 64-bit OS X. A reproduction case is attached to this mail. It is a simple modification of the "How to use jit" example adding a call to a native function. I am currently using EE->addGlobalMapping

Hi R, I have a list of matrices. I need to get the sum of all the matrices in the list. Example: a=b=c=d=matrix(1:4,2,2) l=list(a,b,c,d) I need: > a+b+c+d [,1] [,2] [1,] 4 12 [2,] 8 16 Something like do.call("+",l) is not working...why is this? I may not be knowing the number of matrices in the list... Thanks, Shubha This e-mail

Apart from the inconsistencies, there are two clear bugs here: 1) miscalculating the number of values needed, in the matrix case. E.g. > AA[idx, 1] <- B[1:4] Error in "[<-"(`*tmp*`, idx, 1, value = B[1:4]) : number of items to replace is not a multiple of replacement length although only 4 values are replaced by AA[idx, 1] <- B. 2) the behaviour of the 3D case.

Hello, I have got two matrices with different length. The matrices have 3 columuns. The first two are coordinates. The third is a measurement. Now I want to get a subtraction between every single value of the third column (between matrix1 and matrix2), but only if the two first coordinates in matrix1 and matrix2 are the same. I tried "FUN=?" in aggregate and ave, but I don't know

Dear all, I observe for each observation several joint distributions of two multinomial random variables (5x5 matrices). Right now, data are arranged so that I have 20 columns for each joint distribution by observation, which is not practical. I would like to work with matrices that would be indexed by observation, just as I work with vectors and matrices for regression like analysis. How

Dear all, I am having trouble creating a "jagged array" in R. I have found no commentary on how to do it in the help files, but maybe I am misunderstanding the purpose of some of the array functions (e.g. tapply). I am using a longitudinal dataset where each individual has a different number of time points (observations). I need to create correlation structures for each

Dear, I have a data frame melted from a list of matrices (melt from reshape package), then I impute some missing values and then want to tabulate the data again to the array (or list, doesn't matter) of matrices form. However when using xtabs function it orders my rows alphabetically and apparently doesn't take "reorder = FALSE" option or anything like it. Is there anything I

I'm wanting to perform analysis (e.g. using eigen()) of binary matrices - i.e. matrices comprising 0s and 1s. For example: n<-1000 test.mat<-matrix(round(runif(n^2)),n,n) eigen(test.mat,only.values=T) Is there a more efficient way of setting up test.mat, as each cell only requires a binary digit? I imagine R is setting up a structure which could contain n^2 floats. Thanks in advance

Hello, I had installed several asterisk, but I every time had a problem with callerID. On each phones I don't reveive the first digit. For example: Caller 0672083516 called an IP Phone 0123456789. The IP Phone see 672083516 as callerID. I think there is a patch for it, but I don't know where to find it out. Thanks. -------------- next part -------------- An HTML attachment was