如何在R中进行费舍尔测试?
费舍尔检验帮助我们了解分类变量之间是否存在显着的非随机关系。它用于列联表,因为这些表用于表示分类变量的频率,我们可以将其应用于矩阵,并且矩阵具有类似的形式。在R中,我们可以使用fisher.test函数执行fisher测试。
示例
M1<-matrix(1:9,ncol=3)M1
输出结果
[,1] [,2] [,3][1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
fisher.test(M1)
Fisher's Exact Test for CountData
data: M1
p-value = 0.9888
alternative hypothesis: two.sided
示例
M2<-matrix(1:16,ncol=4)M2
输出结果
[,1] [,2] [,3] [,4][1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
fisher.test(M2)
Fisher's Exact Test for Count Datadata: M2
p-value = 0.9993
alternative hypothesis: two.sided
示例
M3<-matrix(sample(0:4,9,replace=TRUE),nrow=3)M3
输出结果
[,1] [,2] [,3][1,] 0 0 4
[2,] 4 0 4
[3,] 1 2 3
fisher.test(M3)
Fisher's Exact Test for CountData
data: M3
p-value = 0.5567
alternative hypothesis: two.sided
示例
M4<-matrix(c(14,27,15,24,27,17,39,19,24),nrow=3)M4
输出结果
[,1] [,2] [,3][1,] 14 24 39
[2,] 27 27 19
[3,] 15 17 24
fisher.test(M4)
Fisher's Exact Test for Count Datadata: M4
p-value = 0.02126
alternative hypothesis: two.sided
fisher.test(M4,alternative =“ greater”)
Fisher's Exact Test for Count Datadata: M4
p-value = 0.02126
alternative hypothesis: greater
fisher.test(M4,alternative =“ less”)
Fisher's Exact Test for Count Datadata: M4
p-value = 0.02126
alternative hypothesis: less
示例
M5<-matrix(sample(c(545,501,576),4,replace=TRUE),nrow=2)M5
输出结果
[,1] [,2][1,] 545 545
[2,] 545 545
fisher.test(M5)
Fisher's Exact Test for Count Datadata: M5
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval: 0.8391933 1.1916205
sample estimates:
odds ratio
1
fisher.test(M5,alternative =“ greater”)
Fisher's Exact Test for Count Datadata: M5
p-value = 0.5175
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval: 0.8626582 Inf
sample estimates:
odds ratio
1
fisher.test(M5,alternative =“ less”)
Fisher's Exact Test for Count Datadata: M5 p-value = 0.5175
alternative hypothesis: true
odds ratio is less than 1
95 percent confidence interval: 0.000000 1.159208
sample estimates:
odds ratio
1
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