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我想从多项分布中抽样。我会通过使用样本并指定一些概率来做到这一点。例如：我有 3 个类别，我想采样 10 次。

> my_prob = c(0.2, 0.3, 0.5)
> x = sample(c(0:2), 100, replace = T, prob = my_prob)
> head(x)
[1] 2 0 2 1 1 2

我的设置现在只在以下方面有所不同：我想采样很多（例如 1e09）数字。实际上我只对每个类别的频率感兴趣。所以在上面提到的例子中，这意味着：

> table(x)
x
 0  1  2 
27 29 44

有人知道如何尽可能高效地计算它吗？

谢谢，斯蒂菲

4

2 回答 2

6

你需要rmultinom.

my_prob <- c(0.2,0.3,0.5)
number_of_experiments <- 10
number_of_samples <- 100
experiments <- rmultinom(n=number_of_experiments, size=number_of_samples, prob=my_prob)
experiments

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
     [1,]   14   18   15   19   14   17   23   18   24    15
     [2,]   33   34   36   30   40   30   27   38   24    30
     [3,]   53   48   49   51   46   53   50   44   52    55
于 2011-10-27T12:28:06.917 回答
1

If the problem is that you can't fit a vector of length 1e9 into RAM, then you can repeatedly calculate the table for a smaller number of samples and add up the totals.

n_total <- 1e9
n_chunk <- 1e6
n_iter <- n_total / n_chunk
my_prob = c(0.2, 0.3, 0.5)
totals <- numeric(3)
for(i in seq_len(n_iter))
{
  totals <- totals + table(sample(0:2, n_chunk, replace = TRUE, prob = my_prob))
}
totals
stopifnot(sum(totals) == n_total)

Like Max said, you might prefer rmultinom over sample. Take the rowSums of his experiments variable.

于 2011-10-27T12:39:10.610 回答