如何以给定的概率在R中创建一个二元随机变量？
要以给定的概率在R中创建二元随机变量，我们可以使用带有样本大小参数n、成功大小参数大小和概率参数prob的rbinom函数。要了解如何完成，请查看以下示例。
示例1
使用rbinom函数创建向量，n=500，大小=1，概率=0.05，如下所示-
x1<-rbinom(n=500,size=1,prob=0.05) x1输出结果
执行时，上述脚本生成以下内容output(thisoutputwillvaryonyoursystemduetorandomization)-
[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，n=500，大小=1，概率=0.10，如下所示-
x2<-rbinom(n=500,size=1,prob=0.10) x2输出结果
[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，n=500，大小=1，概率=0.50，如下所示-
x3<-rbinom(n=500,size=1,prob=0.50) x3输出结果
[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，n=500，大小=1，概率=0.90，如下所示-
x4<-rbinom(n=500,size=1,prob=0.90) x4输出结果
[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