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How to perform fisher test in R?
The fisher test helps us to understand whether there exists a significant non-random relationship among categorical variables or not. It is applied on contingency tables because these tables are used to represent the frequency for categorical variables and we can apply it on a matrix as well as matrices have the similar form. In R, we can use fisher.test function to perform the fisher test.
Example
M1<-matrix(1:9,ncol=3) M1
Output
[,1] [,2] [,3] [1,] 1 4 7 [2,] 2 5 8 [3,] 3 6 9
fisher.test(M1)
Fisher's Exact Test for Count Data data: M1 p-value = 0.9888 alternative hypothesis: two.sided
Example
M2<-matrix(1:16,ncol=4) M2
Output
[,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 Data data: M2 p-value = 0.9993 alternative hypothesis: two.sided
Example
M3<-matrix(sample(0:4,9,replace=TRUE),nrow=3) M3
Output
[,1] [,2] [,3] [1,] 0 0 4 [2,] 4 0 4 [3,] 1 2 3
fisher.test(M3)
Fisher's Exact Test for Count Data data: M3 p-value = 0.5567 alternative hypothesis: two.sided
Example
M4<-matrix(c(14,27,15,24,27,17,39,19,24),nrow=3) M4
Output
[,1] [,2] [,3] [1,] 14 24 39 [2,] 27 27 19 [3,] 15 17 24
fisher.test(M4)
Fisher's Exact Test for Count Data data: M4 p-value = 0.02126 alternative hypothesis: two.sided
fisher.test(M4,alternative="greater")
Fisher's Exact Test for Count Data data: M4 p-value = 0.02126 alternative hypothesis: greater
fisher.test(M4,alternative="less")
Fisher's Exact Test for Count Data data: M4 p-value = 0.02126 alternative hypothesis: less
Example
M5<-matrix(sample(c(545,501,576),4,replace=TRUE),nrow=2) M5
Output
[,1] [,2] [1,] 545 545 [2,] 545 545
fisher.test(M5)
Fisher's Exact Test for Count Data data: 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 Data data: 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 Data data: 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
Updated on: 19-Oct-2020
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