random sampling - matrix

问题

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.

回答1:

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.

回答2:

Sample seems the best bet for you. To get 1000 random positions you can do something like:

rows = sample(1:nrow(dataset), 1000, replace = TRUE)
columns = sample(1:ncol(dataset), 1000, replace = TRUE)

I think this gives what you want, but ofcourse I could be mistaken.

Extracting the items from the matrix can be done like:

random_sample = mapply(function(row, col) 
                           return(dataset[row,col]), 
                    row = rows, col = columns)

Sampling strategies

In the comments you speak that your sample needs to have spread. A random sample has no garantuees that there will be no clusters, because of its random nature. There are several more sampling schemes that might be interesting to explore:

Regular sampling, skip the randomness and just sample regularly. Samples the entire matrix space evenly, but there is no randomness.
Stratified random sampling, you divide your matrix space into regular subset, and then sample randomly in those subsets. Presents a mix between random and regular.

To check if your random sampling produces good results, I'd repeat the random sampling a few times and compare the results (as I assume that the sampling will be input for another analysis?).

来源：https://stackoverflow.com/questions/9109778/random-sampling-matrix

标签

random-sample