[英]R-How to generate random sample of a discrete random variables?
In R, I want to generate a random sample of a discrete random variable: X
, where: P(X=a)=P(X=-a)=1/2
.在 R 中,我想生成一个离散随机变量的随机样本:
X
,其中: P(X=a)=P(X=-a)=1/2
。 I have been searching for a function online, but there seems no direct function doing this.我一直在网上搜索一个函数,但似乎没有直接的函数这样做。
I think you are looking to generate samples of a Bernoulli random variable.我认为您正在寻找生成伯努利随机变量的样本。 A Bernoulli random variable is a special case of a binomial random variable.
伯努利随机变量是二项式随机变量的特例。 Therefore, you can try
rbinom(N,1,p)
.因此,您可以尝试
rbinom(N,1,p)
。 This will generate N
samples, with value 1
with probability p
, value 0
with probability (1-p)
.这将生成
N
样本,值为1
的概率为p
,值为0
的概率为(1-p)
。 To get values of a
and -a
you can use a*(2*rbinom(N,1,p)-1)
.要获取
a
和-a
值,您可以使用a*(2*rbinom(N,1,p)-1)
。
1) If you use sample
, this is sufficient: 1)如果您使用
sample
,这就足够了:
sample(c(-a,a),1)
eg:例如:
a <- 10
sample(c(-a,a),1)
[1] -10
Try another couple:尝试另一对:
> sample(c(-a,a),1)
[1] -10
> sample(c(-a,a),1)
[1] 10
Works.作品。
If you need to sample more than one element, then set replace=TRUE
... here we sample 12 times:如果您需要对多个元素进行采样,则设置
replace=TRUE
... 这里我们采样 12 次:
sample(c(-a,a),12,replace=TRUE)
[1] 10 10 -10 10 10 10 -10 -10 10 -10 10 -10
2) you can use runif
; 2) 你可以使用
runif
; here's a sample of size 9:这是一个大小为 9 的样本:
a <- 1
ifelse(runif(9)<.5,-a,a)
[1] -1 1 -1 1 -1 1 -1 1 1
3) you can use rbinom
; 3)你可以使用
rbinom
; here's a sample of size 4:这是一个大小为 4 的样本:
a <- 6
ifelse(rbinom(4,1,.5),-a,a)
[1] -6 6 -6 6
Or this:或这个:
> n=10
> X=rep(0,n)
> Y=rbinom(n,1,1/2)
> #Since they the probability is 1/2 for both cases, I assigned "a" when Y=1 and "-a" otherwise.
> X[Y==1]="a"
> X[Y==0]="-a"
> X
[1] "a" "-a" "a" "a" "a" "-a" "a" "-a" "-a" "-a"
> Y
[1] 1 0 1 1 1 0 1 0 0 0
>
index <- sample(1,c(1,2),replace=T)
if (index == 1) {xx = a} else {xx = -a}
Each distribution generating procedure begins with using $\\text{uniform}(0,1)$.每个分布生成过程都从使用 $\\text{uniform}(0,1)$ 开始。 Since discrete distributions are much easier to generate with $\\text{uniform}(0,1)$, people don't wrap up a function for them.
由于使用 $\\text{uniform}(0,1)$ 更容易生成离散分布,因此人们不会为它们封装函数。 However, you can write your own function and just pick them up next time you're going to use them.
但是,您可以编写自己的函数,并在下次使用它们时拿起它们。
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