如何在R中建立具有給定機率的二元隨機變數？

為了在R中建立具有給定機率的二元隨機變數，我們可以使用rbinom函式，其中包含樣本大小引數n、成功次數引數size和機率引數prob。要了解如何做到這一點，請檢視下面的示例。

示例1

使用rbinom函式建立向量，其中n = 500，size = 1，prob = 0.05，如下所示：

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

輸出

執行上述指令碼後，將生成以下輸出（由於隨機化，此輸出在您的系統上會有所不同）：

 [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函式建立向量，其中n = 500，size = 1，prob = 0.10，如下所示：

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

輸出

 [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函式建立向量，其中n = 500，size = 1，prob = 0.50，如下所示：

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

輸出

 [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函式建立向量，其中n = 500，size = 1，prob = 0.90，如下所示：

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

輸出

 [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

更新於： 2021年11月8日

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