random sampling - matrix
r, random
Solution
There is a very easy way to sample a matrix that works if you understand that R represents a matrix internally as a vector.
This means you can use `sample` directly on your matrix. For example, let's assume you want to sample 10 points with replacement:
n <- 10
replace=TRUE
Now just use `sample` on your matrix:
set.seed(1)
sample(dataset, n, replace=replace)
[1] 1 0 0 1 0 1 1 0 0 1
To demonstrate how this works, let's decompose it into two steps. Step 1 is to generate an index of sampling positions, and step 2 is to find those positions in your matrix:
set.seed(1)
mysample <- sample(length(dataset), n, replace=replace)
mysample
[1] 8 12 18 28 7 27 29 20 19 2
dataset[mysample]
[1] 1 0 0 1 0 1 1 0 0 1
And, hey presto, the results of the two methods are identical.
Problem
How can I take a sample of n random points from a matrix populated with 1's and 0's ? ``` a=rep(0:1,5) b=rep(0,10) c=rep(1,10) dataset=matrix(cbind(a,b,c),nrow=10,ncol=3) dataset [,1] [,2] [,3] [1,] 0 0 1 [2,] 1 0 1 [3,] 0 0 1 [4,] 1 0 1 [5,] 0 0 1 [6,] 1 0 1 [7,] 0 0 1 [8,] 1 0 1 [9,] 0 0 1 [10,] 1 0 1 ``` I want to be sure that the positions(row,col) from were I take the N samples are random. I know `sample {base}` but it doesn't seem to allow me to do that, other methods I know are spatial methods that will force me to add x,y and change it to a spatial object and again back to a normal matrix. More information By random I mean also spread inside the "matrix space", e.g. if I make a sampling of 4 points I don't want to have as a result 4 neighboring points, I want them spread in the "matrix space". Knowing the position(row,col) in the matrix where I took out the random points would also be important.