More specifically, I want all elements other than the diagonal ones (X_11, X_22, X_33,...,X_jj) to be zero.
E.g. I want:
[1 4 5
2 3 5
3 9 8]
to be:
[1 0 0
0 3 0
0 0 8]
Is this possible? Sorry I'm a complete noob at this..
It's a simple one liner. First, get the data in:
> (a <- matrix(scan(),nr=3,byrow=TRUE))
1: 1 4 5 2 3 5 3 9 8
10:
Read 9 items
[,1] [,2] [,3]
[1,] 1 4 5
[2,] 2 3 5
[3,] 3 9 8
Method 1:
> diag(diag(a))
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 3 0
[3,] 0 0 8
The thing is, if its argument is a matrix, diag extracts the diagonal... but if the argument is a vector, it's a function that creates a diagonal matrix. So just use it twice. (In fact diag has four different uses, depending on what you give it, though two of the cases are quite similar.) See ?diag
If your matrices are huge this isn't likely to be the most efficient way, but for moderate size cases that's the way I do it.
---
Method 2:
A completely different one-liner that also works -
ifelse(row(a)==col(a),a,0)
The two work the same on square matrices. But they have a different result on non-square matrices - the first one returns a square matrix (of dimension the smaller of the two original dimensions), while the second one returns an object of the same shape as its argument; this can be useful depending on the situation.
The simplest way to do this, is to create a new matrix filled with 0s, then replace its diagonal with the diagonal of the old matrix.
So if you have:
m <- cbind(c(1,2,3), c(4,3,9), c(5, 5, 8)) # The original matrix
diagonal <- diag(m)
m <- matrix(0, nrow(m), ncol(m)) # Overwrite the old matrix
diag(m) <- diagonal
For a matrix of n x m
for i to n
for j to m
if i != j
matrix[i][j] = 0;
If m is your matrix try:
m = matrix(c(1,4,5,2,3,5,3,9,8),3,3)
m[upper.tri(m) | lower.tri(m)] = 0
m
## [,1] [,2] [,3]
## [1,] 1 0 0
## [2,] 0 3 0
## [3,] 0 0 8
It simply dependes on the size of the matrix you're dealing with, let's say you have a nxn matrix then the diagonals are gonna be at these places 0, n+1 , 2(n+1), 3(n+1),... if your matrix as you mentioned is not multi dimensional and is linear! so by simply writing a for loop it is possible.
I'm transferring my answer from your second post on this topic.
You can use the following to compute a logical matrix which describes the non-diagonal entries of a n×n matrix:
outer(1:n, 1:n, function(i,j) i!=j)
Applied to your example:
> m <- matrix(c(1,2,3,4,3,9,5,5,8),ncol=3)
> m
[,1] [,2] [,3]
[1,] 1 4 5
[2,] 2 3 5
[3,] 3 9 8
> m[outer(1:3, 1:3, function(i,j) i!=j)] <- 0
> m
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 3 0
[3,] 0 0 8
I also like the triangle approach by #e4e5f4. That might be a bit faster than this code here, but this code here might be easier to adapt to different situations. So it's good to know this one, even if that one might be preferrable for your current application.
Related
Suppose I have a list of matrices. Suppose further I have found the smallest values by the column.
Here is my last question
I really need to know from which matrix each smallest value is selected. My original function is very complicated. Therefore, I provided a simple example. I have one idea and really do not know to implement it correctly in R.
My idea is:
Suppose that [i,j] is the elements of the matrix. Then,
if(d[[1]][i,j] < d[[2]][i,j]){
d[[1]][i,j] <– "x"
}else { d[[2]][i,j] <– "z"}
So, I would like to sign the name of the matrix that corresponds to each smallest value. Then, store the names in a separate matrix. So, then I can see the values in one matrix and their corresponding names (from where they come from) in another matrix
For example,
y <- c(3,2,4,5,6, 4,5,5,6,7)
x[lower.tri(x,diag=F)] <- y
> x
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 0 0
[2,] 3 0 0 0 0
[3,] 2 6 0 0 0
[4,] 4 4 5 0 0
[5,] 5 5 6 7 0
k <- c(1,4,5,2,5,-4,4,4,4,5)
z[lower.tri(z,diag=F)] <- k
> z
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 0 0
[2,] 1 0 0 0 0
[3,] 4 5 0 0 0
[4,] 5 -4 4 0 0
[5,] 2 4 4 5 0
d <- list(z, x)
Then:
do.call(pmin, d) (answered by #akrun)
Then, I will only get the matrix with smallest values. I would like to know where each value is come from?
Any idea or help, please?
You can use Map and do.call to create your own functions that will be applied element-wise to a list of inputs,
in your case a list of matrices.
pwhich.min <- function(...) {
which.min(c(...)) # which.min takes a single vector as input
}
di <- unlist(do.call(Map, c(list(f = pwhich.min), d)))
dim(di) <- dim(x) # take dimension from one of the inputs
di
[,1] [,2] [,3] [,4] [,5]
[1,] 1 1 1 1 1
[2,] 2 1 1 1 1
[3,] 1 2 1 1 1
[4,] 1 2 2 1 1
[5,] 2 2 2 2 1
EDIT:
To elaborate,
you could do something like Map(f = min, z, x) to apply min to each pair of values in z and x,
although in that case min already supports arbitrary amount of inputs through an ellipsis (...).
By contrast,
which.min only takes a single vector as input,
so you need a wrapper with an ellipsis that combines all values into a vector
(pwhich.min above).
Since you may want to have more than two matrices,
you can put them all in a list,
and use do.call to put each element in the list as a parameter to the function you specify in f.
Or another option would be to convert it to a 3D array and use apply with which.min
apply(array(unlist(d), c(5, 5, 2)), c(1, 2), which.min)
Or with pmap from purrr
library(purrr)
pmap_int(d, ~ which.min(c(...))) %>%
array(., dim(x))
I have a vector where the order of the elements are important, say
x <- c(1,2,3,4)
I would like to arrange my vector into a lower triangular matrix with a specific order where each row contains the preceding element of the vector. My goal is to obtain the following matrix
lower_diag_matrix
[,1] [,2] [,3] [,4]
[1,] 4 0 0 0
[2,] 3 4 0 0
[3,] 2 3 4 0
[4,] 1 2 3 4
I know I can fill the lower triangular area using lower_diag_matrix[lower.tri(lower_diag_matrix,diag = T)]<-some_vector but I can't seem to figure out the arrangement of the vector used to fill the lower triangular area. In practice the numbers will be random, so I would need a generic way to fill the area.
Here's one way:
x <- c(2, 4, 7)
M <- matrix(0, length(x), length(x))
M[lower.tri(M, diag = TRUE)] <- rev(x)[sequence(length(x):1)]
M
# [,1] [,2] [,3]
# [1,] 7 0 0
# [2,] 4 7 0
# [3,] 2 4 7
I have a matrix that should be symmetric according to theory, but might not be observed as symmetric in my data. I would like to force this to be symmetric by using the maximum of the two compared cells.
test_matrix <- matrix(c(0,1,2,1,0,1,1.5,1,0), nrow = 3)
test_matrix
#> [,1] [,2] [,3]
#>[1,] 0 1 1.5
#>[2,] 1 0 1.0
#>[3,] 2 1 0.0
It's easy enough to do this with a double loop.
for(i in 1:3){
for(j in 1:3){
test_matrix[i, j] <- max(test_matrix[i, j], test_matrix[j, i])
}
}
test_matrix
#> [,1] [,2] [,3]
#> [1,] 0 1 2
#> [2,] 1 0 1
#> [3,] 2 1 0
But my matrix is larger than $3x3$, and R's problems with loops are well-documented. I'm also interested in making my code as clean as possible. In fact, I considered putting this on code golf, but this is a real problem that I think others might be interested in.
I've seen this one as well as this one, but mine is different in that those op's seemed to actually have a symmetric matrix that just needed reordering, and I have a matrix that I need to change to be symmetric.
You could use pmax(), which returns the element-wise maxima of a pair of vectors.
pmax(test_matrix, t(test_matrix))
# [,1] [,2] [,3]
# [1,] 0 1 2
# [2,] 1 0 1
# [3,] 2 1 0
It'll work with a pair of matrices, as here, because: (1) in R, matrices are 'just' vectors with attached (dimension) attributes; and (2) the code used to implement pmax() is nice enough to reattach the attributes of it's first argument to the value that it returns.
This question already has answers here:
How to replace non-diagonal elements in a matrix?
(6 answers)
Closed 9 years ago.
Okay, I asked this question earlier but I got bashed (deservedly) for not specifying anything and showing no sign of previous attempt. So let me try again..
I'm using R, and I have a 463✕463 matrix. What I would like to do is to replace all elements other than the diagonal ones (X11, X22, X33,...,Xjj) with zero.
E.g. I want:
[1 4 5
2 3 5
3 9 8]
to be:
[1 0 0
0 3 0
0 0 8]
When I use the diag() function, it simply gives me a column vector of the diagonal values. I imagine I can use the replace() function somehow combined with a "if not diagonal" logic...but I am lost.
And yes, as some here have guessed, I am probably much younger than many people here and am completely new at this...so please put me in the right direction. Really appreciate all your help!
In R, the diag method has two functions.
It returns the diagonal of a matrix. I.e.
m <- matrix(1:9, ncol=3)
m
# [,1] [,2] [,3]
# [1,] 1 4 7
# [2,] 2 5 8
# [3,] 3 6 9
diag(m)
# [1] 1 5 9
It can construct a diagonal matrix.
diag(1:3)
# [,1] [,2] [,3]
# [1,] 1 0 0
# [2,] 0 2 0
# [3,] 0 0 3
So in your case, extract the diagonal from your existing matrix and supply it to diag:
diag(diag(m))
# [,1] [,2] [,3]
# [1,] 1 0 0
# [2,] 0 5 0
# [3,] 0 0 9
using outer
You can use the following to compute a logical matrix which describes the non-diagonal entries of a n×n matrix:
outer(1:n, 1:n, function(i,j) i!=j)
Applied to your example:
> m <- matrix(c(1,2,3,4,3,9,5,5,8),ncol=3)
> m
[,1] [,2] [,3]
[1,] 1 4 5
[2,] 2 3 5
[3,] 3 9 8
> m[outer(1:3, 1:3, function(i,j) i!=j)] <- 0
> m
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 3 0
[3,] 0 0 8
using triangles
A possible alternative would be combining the two triangles on either side of the diagonal. In this case, you use the matrix m itself as input to determine the size.
upper.tri(m) | lower.tri(m)
Applied to your use case:
> m[upper.tri(m) | lower.tri(m)] <- 0
It seems you already got this answer in response to your original post…
m[ col(m)==row(m) ] <- 0
> m <- matrix(1:9, 3)
> m[ col(m)==row(m) ]
[1] 1 5 9
> m[ col(m)!=row(m) ] <- 0
> m
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 5 0
[3,] 0 0 9
I have a basic matrix mat and I hope to get an R object x = (mat, mat, ...) where mat is repeated for 100 times. If this is possible, then I can pass x to a function which takes a vector of matrix names. I tried rep(mat, 100) but it seems that the matrix class is no longer maintained. Any suggestions? Thanks!
Update: Basically I plan to use
grp.ids <- as.factor(c(rep(1,8), rep(2,4), rep(3,2)))
x <- model.matrix(~grp.ids)
do.call(blockMatrixDiagonal,
replicate(100, x, simplify=FALSE))
where the blockMatrixDiagonal function can be found here. Then R gives an error: number of items to replace is not a multiple of replacement length. What I really hope to get via these coding is a block diagonal matrix. Thanks :)
Your input matrix is not appropriate for building a block diagonal matrix since it's not a square matrix (i.e., the number of rows equals the number of columns).
Let me cite two resources on block diagonal matrices.
1) Wikipedia:
A block diagonal matrix is a block matrix which is a square matrix, and having main diagonal blocks square matrices
2) The description of the function blockMatrixDiagonal:
builds a block matrix whose diagonals are the square matrices provided.
You can combine your non-square matrices with the function adiag from the package magic. With your matrix x:
library(magic)
do.call(adiag, replicate(100, x, simplify = FALSE))
For a base R solution, check out kronecker
?kronecker
# For your block diagonal matrix:
kronecker(diag(1, 100), x)
# or with `%x%` alias
diag(1, 100) %x% x
# example 1
m <- matrix(1:6, nrow = 3)
kronecker(diag(1, 2), m)
# [,1] [,2] [,3] [,4]
# [1,] 1 4 0 0
# [2,] 2 5 0 0
# [3,] 3 6 0 0
# [4,] 0 0 1 4
# [5,] 0 0 2 5
# [6,] 0 0 3 6
# example 2
matrix(1, nrow = 2, ncol = 3) %x% m
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1 4 1 4 1 4
# [2,] 2 5 2 5 2 5
# [3,] 3 6 3 6 3 6
# [4,] 1 4 1 4 1 4
# [5,] 2 5 2 5 2 5
# [6,] 3 6 3 6 3 6