Às 12:39 de 22/04/2025, Brian Smith escreveu:
Hi Rui,
Many thanks for your time and insight.
However, I am not sure if I could understand the code. Below is what I
tried based on your code
library(Surrogate)
a <- c(0.015, 0.005)
b <- c(0.070, 0.045)
set.seed(2025)
res <- mapply(\(a, b, s, n, m) RandVec(a, b, s, n, m),
MoreArgs = list(s = 0.05528650577311, n = 2, m = 10000), a, b)
res1 = res[[1]]
res2 = res[[2]]
apply(res1, 1, min) > a ## [1] TRUE TRUE
apply(res2, 1, min) > a ## [1] FALSE TRUE
I could not understand what basically 2 blocks of res signify? Which
one I should take as final simulation of the vector? If I take the
first block then the lower bound condition is fulfilled, but not with
the second block. However with the both blocks, the total equals s is
satisfying.
I appreciate your further insight.
Thanks and regards,
On Mon, 21 Apr 2025 at 20:43, Rui Barradas <ruipbarra...@sapo.pt> wrote:
Hello,
Inline.
Às 16:08 de 21/04/2025, Rui Barradas escreveu:
Às 15:27 de 21/04/2025, Brian Smith escreveu:
Hi,
There is a function called RandVec in the package Surrogate which can
generate andom vectors (continuous number) with a fixed sum
The help page of this function states that:
a
The function RandVec generates an n by m matrix x. Each of the m
columns contain n random values lying in the interval [a,b]. The
argument a specifies the lower limit of the interval. Default 0.
b
The argument b specifies the upper limit of the interval. Default 1.
However in my case, the lower and upper limits are not same. For
example, if I need to draw a pair of number x, y, such that x + y = 1,
then the lower and upper limits are different.
I tried with below code
library(Surrogate)
RandVec(a=c(0.1, 0.2), b=c(0.2, 0.8), s=1, n=2, m=5)$RandVecOutput
This generates error with message
Error in if (b - a == 0) { : the condition has length > 1
Is there any way to generate the numbers with different lower and
upper limits?
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Hello,
Use ?mapply to cycle through all values of a and b.
Note that the output matrices are transposed, the random vectors are the
rows.
Sorry, this is not true. The columns are the random vectors, as
documented. An example setting the RNG seed, for reproducibility.
library(Surrogate)
a <- c(0.1, 0.2)
b <- c(0.2, 0.8)
set.seed(2025)
res <- mapply(\(a, b, s, n, m) RandVec(a, b, s, n, m),
MoreArgs = list(s = 1, n = 2, m = 5), a, b)
res
#> $RandVecOutput
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.146079 0.1649319 0.1413759 0.257086 0.1715478
#> [2,] 0.253921 0.2350681 0.2586241 0.142914 0.2284522
#>
#> $RandVecOutput
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.5930918 0.2154583 0.6915523 0.7167089 0.3617918
#> [2,] 0.4069082 0.7845417 0.3084477 0.2832911 0.6382082
lapply(res, colSums)
#> $RandVecOutput
#> [1] 0.4 0.4 0.4 0.4 0.4
#>
#> $RandVecOutput
#> [1] 1 1 1 1 1
Hope this helps,
Rui Barradas
library(Surrogate)
a <- c(0.1, 0.2)
b <- c(0.2, 0.8)
mapply(\(a, b, s, n, m) RandVec(a, b, s, n, m),
MoreArgs = list(s = 1, n = 2, m = 5), a, b)
#> $RandVecOutput
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.2004363 0.1552328 0.2391742 0.1744857 0.1949236
#> [2,] 0.1995637 0.2447672 0.1608258 0.2255143 0.2050764
#>
#> $RandVecOutput
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.2157416 0.4691191 0.5067447 0.7749258 0.7728955
#> [2,] 0.7842584 0.5308809 0.4932553 0.2250742 0.2271045
Hope this helps,
Rui Barradas
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Hello,
The two blocks of res are the two random matrices, one for each
combination of (a,b). mapply passes each of the values in its arguments
list (the ellipses in the help page) and computes the anonymous function
with the pairs (a[1], b[1]), (a[2], b[2]).
Since a and b are two elements vectors the output res is a two members
named list. Your error is to compare the result of apply(res2, 1, min)
to a, when you should compare to a[2]. See the code below.
library(Surrogate)
a <- c(0.015, 0.005)
b <- c(0.070, 0.045)
set.seed(2025)
res <- mapply(\(a, b, s, n, m) RandVec(a, b, s, n, m),
MoreArgs = list(s = 0.05528650577311, n = 2, m = 10000),
a, b)
res1 = res[[1]]
res2 = res[[2]]
# first check that the sums are correct
# these sums should be s = 0.05528650577311, up to floating-point accuracy
lapply(res, \(x) colSums(x[, 1:5]) |> print(digits = 14L))
#> [1] 0.05528650577311 0.05528650577311 0.05528650577311 0.05528650577311
#> [5] 0.05528650577311
#> [1] 0.05528650577311 0.05528650577311 0.05528650577311 0.05528650577311
#> [5] 0.05528650577311
#> $RandVecOutput
#> [1] 0.05528651 0.05528651 0.05528651 0.05528651 0.05528651
#>
#> $RandVecOutput
#> [1] 0.05528651 0.05528651 0.05528651 0.05528651 0.05528651
# now check the min and max
apply(res1, 1, min) > a[1L] ## [1] TRUE TRUE
#> [1] TRUE TRUE
apply(res2, 1, min) > a[2L] ## [1] TRUE TRUE
#> [1] TRUE TRUE
apply(res1, 1, max) < b[1L] ## [1] TRUE TRUE
#> [1] TRUE TRUE
apply(res2, 1, max) < b[2L] ## [1] TRUE TRUE
#> [1] TRUE TRUE
Which one should you take as final simulation of the vector? Both.
The first matrix values are bounded by c(a[1], b[1]) with column sums
equal to s.
The second matrix values are bounded by c(a[2], b[2]) with column sums
also equal to s.
Hoep this helps,
Rui Barradas
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