Question

Assume a table as below:

X = 

        col1    col2    col3
row1    "A"      "0"     "1"
row2    "B"      "2"     "NA"
row3    "C"      "1"     "2"

I select combinations of two rows, using the code below:

pair <- apply(X, 2, combn, m=2)

This returns a matrix of the form:

pair = 

 [,1] [,2] [,3]
[1,] "A"  "0"  "1" 
[2,] "B"  "2"  NA  
[3,] "A"  "0"  "1" 
[4,] "C"  "1"  "2" 
[5,] "B"  "2"  NA  
[6,] "C"  "1"  "2" 

I wish to iterate over pair, taking two rows at a time, i.e. first isolate [1,] and [2,], then [3,] and [4,] and finaly, [5,] and [6,]. These rows will then be passed as arguments to regression models, i.e. lm(Y ~ row[i]*row[j]).

I am dealing with a large dataset. Can anybody advise how to iterate over a matrix two rows at a time, assign those rows to variables and pass as arguments to a function?

Thanks,
S 😉

Answer 1

It is unnecessary to multiply the rows of your matrix like that, and if you have a large data set it is might get problematic. In stead just pick out the relevant rows for each instance. But it is convenient to create the selection beforehand, something like this perhaps:

xselect <- combn(1:nrow(X),2)

To illustrate with your data (assuming you only use columns 2 and 3):

X <- matrix(c("A", "B", "C", 0,2,1,1,NA,2),3,3)
Y <- rnorm(2, 4, 2)

for (i in 1:ncol(xselect))
{
  x1 <- as.numeric(X[xselect[1,i], c(2,3)])
  x2 <- as.numeric(X[xselect[2,i], c(2,3)])
  print(lm(Y ~ x1 * x2))
}