Saving covariance matrix in a for loop - R - r

Question

So there is a hobby project I am currently working on in order to improve my R skills. What I created with my previous code are various subsets of data "returnseries.1, returnseries.2, returnseries.3, ... "(from 1 to 119) which are stored each in a 252x6 matrix.
Now I am building a for loop to calculate the covariance matrix for each subset.
My code goes as the following:
for(k in 1:119){
covmat[k] = matrix(c(cov(returnseries[k])),nrow=6, ncol=6)
}
For some reason I get the error that: "My column index must be at most 7 not 8."
And I don't get why. I tried several other code versions but nothing gives me an answer. Thought that it had to do with the naming but using return series.[k] is providing me an error, that returnseries. is not defined
Would be delighted if somebody could provide a quick

Answer 1

You can use an array. A 3D array in this case.
Generate some data.
> xy <- list(one = matrix(rnorm(9), ncol = 3),
+ two = matrix(rnorm(9), ncol = 3),
+ three = matrix(rnorm(9), ncol = 3))
> xy
$one
[,1] [,2] [,3]
[1,] 0.1341714 -1.27229790 0.22431441
[2,] 1.0853899 0.02335881 -0.05600098
[3,] -1.5645181 0.83745858 -1.47670091
$two
[,1] [,2] [,3]
[1,] 1.4891642 -0.3766222 -0.86981432
[2,] 0.3424295 -1.7882177 1.79601480
[3,] -1.1583058 -0.1604330 0.02690498
$three
[,1] [,2] [,3]
[1,] -0.1511346 -0.3672432 -0.3008405
[2,] -1.9881830 -0.8545396 -0.7108430
[3,] 0.1637134 -0.7958267 1.1923535
Create empty array
> N <- 3
> ar <- array(rep(NA, 3*3*N), dim = c(3, 3, N))
> ar
, , 1
[,1] [,2] [,3]
[1,] NA NA NA
[2,] NA NA NA
[3,] NA NA NA
, , 2
[,1] [,2] [,3]
[1,] NA NA NA
[2,] NA NA NA
[3,] NA NA NA
, , 3
[,1] [,2] [,3]
[1,] NA NA NA
[2,] NA NA NA
[3,] NA NA NA
Fill in values.
> for (i in 1:N) {
+ ar[,, i] <- xy[[i]]
+ }
>
> ar
, , 1
[,1] [,2] [,3]
[1,] 0.1341714 -1.27229790 0.22431441
[2,] 1.0853899 0.02335881 -0.05600098
[3,] -1.5645181 0.83745858 -1.47670091
, , 2
[,1] [,2] [,3]
[1,] 1.4891642 -0.3766222 -0.86981432
[2,] 0.3424295 -1.7882177 1.79601480
[3,] -1.1583058 -0.1604330 0.02690498
, , 3
[,1] [,2] [,3]
[1,] -0.1511346 -0.3672432 -0.3008405
[2,] -1.9881830 -0.8545396 -0.7108430
[3,] 0.1637134 -0.7958267 1.1923535
You can do all sorts of wonderful things with this now. For example, do row sums.
> apply(ar, MARGIN = 3, FUN = rowSums)
[,1] [,2] [,3]
[1,] -0.9138121 0.2427277 -0.8192183
[2,] 1.0527477 0.3502266 -3.5535656
[3,] -2.2037604 -1.2918338 0.5602402
Here's proof for the first matrix. Compare it to the first column:
> rowSums(xy[[1]])
[1] -0.9138121 1.0527477 -2.2037604