Prop.test 和 R 中的费舍尔精确检验

Prop.test and fisher's exact test in R

我对使用 Fisher 精确检验比较两个比例感到困惑。例如,我想测试两个比例 9/13 和 3/18 之间是否存在差异。我可以简单地输入

A <-  c( 9, 3)
B <-  c( 13, 18)
prop.test(A , B)

但是如何使用 Fisher 精确检验来执行此操作?我不确定这是否正确:

A = matrix(c(9, 3,
             13,18), nrow = 2)

fisher.test(A)

感谢任何想法

是的,与 prop.test() 相比,您的 Fisher 检验设置不正确。

来自 prop.test 帮助文件:

prop.test(x, n, p = NULL, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, correct = TRUE)

x a vector of counts of successes,...

n a vector of counts of trials ...

费舍尔检验

fisher.test(x, y = NULL, workspace = 200000, hybrid = FALSE,
            hybridPars = c(expect = 5, percent = 80, Emin = 1),
            control = list(), or = 1, alternative = "two.sided",
            conf.int = TRUE, conf.level = 0.95,
            simulate.p.value = FALSE, B = 2000)

x a two-dimensional contingency table in matrix form.

因此,如果您的 13 次和 18 次试验的 2 次测试分别成功了 9 次和 3 次,这意味着失败次数为 4 次和 15 次,因此 Fishers 测试应该是:

A = matrix(c(9, 3, 4, 15), nrow = 2)
#Row sums are the total number of trials
#Column sums are the total number of True/False
 
fisher.test(A)

    Fisher's Exact Test for Count Data

data:  A
p-value = 0.007518
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.61038 89.70868
sample estimates:
odds ratio 
  10.18122 

这提供了与 prop.test 结果相当的结果。