set.seed 在不同版本的 R(和 Ubuntu)上是否一致? [英] Is set.seed consistent over different versions of R (and Ubuntu)?

问题描述

我目前运行的是 R 版本 3.1.0(在 Ubuntu 12.04 LTS 上)，由于我的 R 版本和我的操作系统都很旧，我计划同时更新两者.但是，我有很多依赖于 set.seed() 的模拟，我希望它们在更新 R 和我的操作系统后仍然给我相同的随机数.

I am currently running R version 3.1.0 (on Ubuntu 12.04 LTS) and as both my R version and my operating system is getting rather old, I plan on updating both. However, I have a lot of simulations that rely on set.seed() and I would like them to still give me the same random numbers after updating both R and my operating system.

所以我的问题是三重的.

So my question is three-fold.

1. 我可以在不更改每个种子生成的数字的情况下更新 R 吗?
2. 我可以为我的操作系统做同样的事情吗?
3. 如果 1) 或 2) 都不是，有没有办法更改我代码中的种子，使其与旧种子一致?

推荐答案

跨操作系统一致性:是

如果您在两个不同的操作系统上安装了 R，而没有手动更改默认值或 RProfile，则在使用 set.seed() 时应该得到相同的结果.

Cross-OS consistency: yes

If you installed R on two different operating systems without manually changing defaults or the RProfile, you should get the same results when using set.seed().

过去的情况是 set.seed() 会在 R 版本中给出相同的结果，但由于 R 3.6.0 中的一个小宣布更新，这种情况不再普遍.因此，您可以获得 R 3.6.0 之前的跨版本一致性比较结果，但是如果将 3.6.0 之后的 set.seed() 使用与 3.6.0 之前的 set.seed()，你会得到不同的结果.

It used to be the case that set.seed() would give the same results across R versions, but that's no longer generally true thanks to a little-announced update in R 3.6.0. So you can get cross version consistency comparing results before R 3.6.0, but if you compare a post-3.6.0 use of set.seed() to a pre-3.6.0 use of set.seed(), you will get different results.

您可以在下面的示例中看到这一点:

You can see that in the examples below:

> set.seed(1999)
> sample(LETTERS, 3)
[1] "T" "N" "L"

R 3.5.3

> set.seed(1999)
> sample(LETTERS, 3)
[1] "T" "N" "L"

R 3.6.0

set.seed(1999)
sample(LETTERS, 3)
[1] "D" "Z" "R"

不一致的原因是在 R 3.6.0 中，默认类型的底层随机数生成器已更改.现在，为了让 set.seed() 的结果匹配，你必须首先调用函数 RNGkind(sample.kind = "Rounding").

The reason for the inconsistency is that in R 3.6.0, the default kind of under-the-hood random-number generator was changed. Now, in order to get the results from set.seed() to match, you have to first call the function RNGkind(sample.kind = "Rounding").

> RNGkind(sample.kind = "Rounding")
Warning message:
In RNGkind(sample.kind = "Rounding") : non-uniform 'Rounding' sampler used
> set.seed(1999)
> sample(Letters, 3)
[1] "T" "N" "L"

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