# [R] building random matrices from vectors of random parameters

Evan Cooch evan.cooch at gmail.com
Thu Sep 28 15:10:10 CEST 2017

```Thanks for both the mapply and array approaches! However, although
intended to generate the same result, they don't:

# mapply approach

n = 3
sa <- rnorm(n,0.8,0.1)
so <- rnorm(n,0.5,0.1)
m <- rnorm(n,1.2,0.1)
mats = mapply(function(sa1, so1, m1)
matrix(c(0,sa1*m1,so1,sa1),2,2,byrow=T), sa, so, m, SIMPLIFY = FALSE)

print(mats)

[]
[,1]      [,2]
[1,] 0.0000000 0.8643679
[2,] 0.4731249 0.7750431

[]
[,1]      [,2]
[1,] 0.0000000 0.8838286
[2,] 0.5895258 0.7880983

[]
[,1]      [,2]
[1,] 0.0000000 1.1491560
[2,] 0.4947322 0.9744166

Now, the array approach:

# array approach

ms <- array(c(rep(0, 3),sa*m,so,sa), c(3, 2, 2))

for (i in 1:n) { print(ms[i,,])

[,1]      [,2]
[1,] 0.0000000 0.4731249
[2,] 0.8643679 0.7750431

[,1]      [,2]
[1,] 0.0000000 0.5895258
[2,] 0.8838286 0.7880983

[,1]      [,2]
[1,] 0.000000 0.4947322
[2,] 1.149156 0.9744166

These matrices are the transpose of those returned by the mapply
approach. To see if one approach or the other is 'confused', I simply
rerun setting sd=0 for the parameters -- thus, every matrix will be the
same. The correct matrix would be:

[,1] [,2]
[1,]  0.0 0.96
[2,]  0.5 0.80

In fact, this is what is returned by the mapply approach, while the
array approach returns the transpose. I gather the 'missing step' is to
use aperm, but haven't figured out how to get that to work...yet.

On 9/28/2017 5:11 AM, Duncan Murdoch wrote:
> ms <- array(c(rep(0, 5),sa*m,so,sa), c(5, 2, 2))

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