如何在R中创建具有给定概率的二元随机变量？

为了在R中创建具有给定概率的二元随机变量，我们可以使用rbinom函数，其中包含样本大小参数n、成功次数参数size和概率参数prob。要了解如何做到这一点，请查看下面的示例。

示例1

使用rbinom函数创建向量，其中n = 500，size = 1，prob = 0.05，如下所示：

x1<-rbinom(n=500,size=1,prob=0.05)
x1

输出

执行上述脚本后，将生成以下输出（由于随机化，此输出在您的系统上会有所不同）：

 [1]  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38]  0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[75]  0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
[223] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[297] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
[334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[371] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
[445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
[482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

示例2

使用rbinom函数创建向量，其中n = 500，size = 1，prob = 0.10，如下所示：

x2<-rbinom(n=500,size=1,prob=0.10)
x2

输出

 [1]  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38]  0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
[75]  0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0
[186] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
[223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
[260] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0
[297] 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[334] 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0
[371] 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
[408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[445] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
[482] 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1

示例3

使用rbinom函数创建向量，其中n = 500，size = 1，prob = 0.50，如下所示：

x3<-rbinom(n=500,size=1,prob=0.50)
x3

输出

 [1]  1 0 1 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1
[38]  0 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1
[75]  0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0
[112] 1 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 0 1
[149] 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 1 0 1 1 0 0 0 0 1 1
[186] 1 1 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0
[223] 0 1 0 1 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 0
[260] 1 0 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 0 1 1 0 1 1 1 1
[297] 0 0 0 1 1 0 1 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 0 0
[334] 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1
[371] 1 1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1
[408] 1 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 1 1 0 1
[445] 1 0 1 0 1 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 0
[482] 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1 0 1 0 0

示例4

使用rbinom函数创建向量，其中n = 500，size = 1，prob = 0.90，如下所示：

x4<-rbinom(n=500,size=1,prob=0.90)
x4

输出

 [1]  1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1
[38]  1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75]  1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1
[149] 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1
[186] 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1
[223] 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1
[260] 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
[334] 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[371] 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0
[408] 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1
[445] 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
[482] 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1

更新于： 2021年11月8日

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