building random matrices from vectors of random parameters

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

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

[[3]]
 ????????? [,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))