如何在 R 中创建具有给定概率的二元随机变量?
r programmingserver side programmingprogramming更新于 2025/4/8 14:22:17
要在 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，其样本大小参数为 n，成功大小参数为 size，概率参数为 prob。要了解如何操作，请查看以下示例。
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
执行时，上述脚本会生成以下输出(由于随机化，此输出将因您的系统而异) −
[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
