Re: [R] building random matrices from vectors of random parameters

[Evan Cooch]

Makes sense, although (re-)learning what aperm does wasn't a wasted 
exercise.

Thanks!

On 9/28/2017 1:22 PM, Jeff Newmiller wrote:
> The use of aperm is unnecessary if you call array() properly.
>
> ms <- array(c(rep(0, 5),so,sa*m,sa), c(5, 2, 2))


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Re: [R] building random matrices from vectors of random parameters

[Jeff Newmiller]

The use of aperm is unnecessary if you call array() properly. 

ms <- array(c(rep(0, 5),so,sa*m,sa), c(5, 2, 2))
-- 
Sent from my phone. Please excuse my brevity.

On September 28, 2017 9:10:26 AM PDT, Evan Cooch  wrote:
>Sure -- thanks -- only took me 3-4 attempts to get aperm to work (as 
>opposed to really thinking hard about how it works ;-)
>
>On 9/28/2017 11:55 AM, Duncan Murdoch wrote:
>> On 28/09/2017 9:10 AM, Evan Cooch wrote:
>>> 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,] 0.000 0.8643679
>>> [2,] 0.4731249 0.7750431
>>>
>>> [[2]]
>>>    [,1]  [,2]
>>> [1,] 0.000 0.8838286
>>> [2,] 0.5895258 0.7880983
>>>
>>> [[3]]
>>>    [,1]  [,2]
>>> [1,] 0.000 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,] 0.000 0.4731249
>>> [2,] 0.8643679 0.7750431
>>>
>>>    [,1]  [,2]
>>> [1,] 0.000 0.5895258
>>> [2,] 0.8838286 0.7880983
>>>
>>>   [,1]  [,2]
>>> [1,] 0.00 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)) 
>>>
>>
>>
>> Sorry about that -- I didn't notice the "byrow = T" in your original.
>>
>> Duncan Murdoch
>>
>
>
>   [[alternative HTML version deleted]]
>
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>and provide commented, minimal, self-contained, reproducible code.

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Re: [R] building random matrices from vectors of random parameters

[Evan Cooch]

Sure -- thanks -- only took me 3-4 attempts to get aperm to work (as 
opposed to really thinking hard about how it works ;-)

On 9/28/2017 11:55 AM, Duncan Murdoch wrote:
> On 28/09/2017 9:10 AM, Evan Cooch wrote:
>> 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,] 0.000 0.8643679
>> [2,] 0.4731249 0.7750431
>>
>> [[2]]
>>    [,1]  [,2]
>> [1,] 0.000 0.8838286
>> [2,] 0.5895258 0.7880983
>>
>> [[3]]
>>    [,1]  [,2]
>> [1,] 0.000 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,] 0.000 0.4731249
>> [2,] 0.8643679 0.7750431
>>
>>    [,1]  [,2]
>> [1,] 0.000 0.5895258
>> [2,] 0.8838286 0.7880983
>>
>>   [,1]  [,2]
>> [1,] 0.00 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)) 
>>
>
>
> Sorry about that -- I didn't notice the "byrow = T" in your original.
>
> Duncan Murdoch
>


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Re: [R] building random matrices from vectors of random parameters

[Duncan Murdoch]

On 28/09/2017 9:10 AM, Evan Cooch wrote:
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,] 0.000 0.8643679
[2,] 0.4731249 0.7750431

[[2]]
   [,1]  [,2]
[1,] 0.000 0.8838286
[2,] 0.5895258 0.7880983

[[3]]
   [,1]  [,2]
[1,] 0.000 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,] 0.000 0.4731249
[2,] 0.8643679 0.7750431

   [,1]  [,2]
[1,] 0.000 0.5895258
[2,] 0.8838286 0.7880983

  [,1]  [,2]
[1,] 0.00 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)) 





Sorry about that -- I didn't notice the "byrow = T" in your original.

Duncan Murdoch

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Re: [R] building random matrices from vectors of random parameters

[Evan Cooch]

>
> 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.

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

ms_new <- aperm(ms,c(1,3,2));

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

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

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

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


>
>
> 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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Re: [R] building random matrices from vectors of random parameters

[Evan Cooch]

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,] 0.000 0.8643679
[2,] 0.4731249 0.7750431

[[2]]
   [,1]  [,2]
[1,] 0.000 0.8838286
[2,] 0.5895258 0.7880983

[[3]]
   [,1]  [,2]
[1,] 0.000 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,] 0.000 0.4731249
[2,] 0.8643679 0.7750431

   [,1]  [,2]
[1,] 0.000 0.5895258
[2,] 0.8838286 0.7880983

  [,1]  [,2]
[1,] 0.00 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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Re: [R] building random matrices from vectors of random parameters

[Duncan Murdoch]

On 27/09/2017 8:47 PM, Evan Cooch wrote:

Suppose I have interest in a matrix with the following symbolic
structure (specified by 3 parameters: sa, so, m):

matrix(c(0,sa*m,so,sa),2,2,byrow=T)

What I can't figure out is how to construct a series of matrices, where
the elements/parameters are rnorm values. I'd like to construct separate
matrices, with each matrix in the series using the 'next random
parameter value'. While the following works (for generating, say, 5 such
random matrices)

replicate(5,matrix(c(0,rnorm(1,0.8,0.1)*rnorm(1,1.2,0.1),rnorm(1,0.5,0.1),rnorm(1,0.8,0.1)),2,2,byrow=T))

its inelegant, and a real pain if the matrix gets large (say, 20 x 20).

I'm wondering if there is an easier way. I tried

  > sa <- rnorm(5,0.8,0.1)
  > so <- rnorm(5,0.5,0.1)
  > m <- rnorm(5,1.2,0.1)

matrix(c(0,sa*m,so,sa),2,2,byrow=T)

but that only returns a single matrix, not 5 matrices as I'd like. I
also tried several variants of the 'replicate' approach (above), but
didn't stumble across anything that seemed to work.

So, is there a better way than something like:

replicate(5,matrix(c(0,rnorm(1,0.8,0.1)*rnorm(1,1.2,0.1),rnorm(1,0.5,0.1),rnorm(1,0.8,0.1)),2,2,byrow=T))



Peter's mapply solution is probably the best.  Another that might be a 
little faster (but more obscure) is to use a 3-index array.  I think 
this is what you'd want, with sa, so, and m as defined above:


ms <- array(c(rep(0, 5),sa*m,so,sa), c(5, 2, 2))

Then matrix i will be stored as ms[i,,].

Duncan Murdoch

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Re: [R] building random matrices from vectors of random parameters

[Peter Langfelder]

I would try something like

n = 5
a <- 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),
   a, so, m, SIMPLIFY = FALSE)

> mats
[[1]]
  [,1]  [,2]
[1,] 0.000 0.9129962
[2,] 0.4963598 0.7067311

[[2]]
  [,1]  [,2]
[1,] 0.000 1.0150316
[2,] 0.5489887 0.8469046

[[3]]
  [,1]  [,2]
[1,] 0.000 0.9516137
[2,] 0.3724521 0.8306535

[[4]]
  [,1]  [,2]
[1,] 0.000 1.0525355
[2,] 0.8075108 0.8314638

[[5]]
  [,1]  [,2]
[1,] 0.000 0.9400074
[2,] 0.4803386 0.7901753

On Wed, Sep 27, 2017 at 5:47 PM, Evan Cooch  wrote:
> Suppose I have interest in a matrix with the following symbolic structure
> (specified by 3 parameters: sa, so, m):
>
> matrix(c(0,sa*m,so,sa),2,2,byrow=T)
>
> What I can't figure out is how to construct a series of matrices, where the
> elements/parameters are rnorm values. I'd like to construct separate
> matrices, with each matrix in the series using the 'next random parameter
> value'. While the following works (for generating, say, 5 such random
> matrices)
>
> replicate(5,matrix(c(0,rnorm(1,0.8,0.1)*rnorm(1,1.2,0.1),rnorm(1,0.5,0.1),rnorm(1,0.8,0.1)),2,2,byrow=T))
>
> its inelegant, and a real pain if the matrix gets large (say, 20 x 20).
>
> I'm wondering if there is an easier way. I tried
>
>> sa <- rnorm(5,0.8,0.1)
>> so <- rnorm(5,0.5,0.1)
>> m <- rnorm(5,1.2,0.1)
>
> matrix(c(0,sa*m,so,sa),2,2,byrow=T)
>
> but that only returns a single matrix, not 5 matrices as I'd like. I also
> tried several variants of the 'replicate' approach (above), but didn't
> stumble across anything that seemed to work.
>
> So, is there a better way than something like:
>
> replicate(5,matrix(c(0,rnorm(1,0.8,0.1)*rnorm(1,1.2,0.1),rnorm(1,0.5,0.1),rnorm(1,0.8,0.1)),2,2,byrow=T))
>
> Many thanks in advance...
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

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