R：prop.test根据矩阵或向量是否传递给它返回不同的值

Question

为什么r的prop.test函数（ 此处的文档 ）会根据我是否将matrix或向量传递给它而返回不同的结果？

在这里我传递它向量：

> prop.test(x = c(135, 47), n = c(1781, 1443))

    2-sample test for equality of proportions with
    continuity correction

data:  c(135, 47) out of c(1781, 1443)
X-squared = 27.161, df = 1, p-value = 1.872e-07
alternative hypothesis: two.sided
95 percent confidence interval:
 0.02727260 0.05918556
sample estimates:
    prop 1     prop 2 
0.07580011 0.03257103 

在这里，我创建一个matrix并将其传递给：

> table <- matrix(c(135, 47, 1781, 1443), ncol=2)
> prop.test(table)

    2-sample test for equality of proportions with
    continuity correction

data:  table
X-squared = 24.333, df = 1, p-value = 8.105e-07
alternative hypothesis: two.sided
95 percent confidence interval:
 0.02382527 0.05400606
sample estimates:
    prop 1     prop 2 
0.07045929 0.03154362 

为什么我会得到不同的结果？ 我希望返回两种方案的结果相同。

Answer 1

当x和n作为单独的向量输入时，它们分别被处理为成功次数和试验总次数。 但是当您输入矩阵时，第一列被视为成功次数，第二列被视为失败次数。 从prop.test的帮助：

 xa vector of counts of successes, a one-dimensional table with two entries, or a two-dimensional table (or matrix) with 2 columns, giving the counts of successes and failures, respectively. 

因此，要使用矩阵得到相同的结果，您需要将矩阵的第二列转换为失败次数（假设在您的示例中x是成功次数， n是试验次数）。

x = c(135, 47)
n = c(1781, 1443)

prop.test(x, n)  # x = successes; n = total trials

  2-sample test for equality of proportions with continuity correction data: x out of n X-squared = 27.161, df = 1, p-value = 1.872e-07 alternative hypothesis: two.sided 95 percent confidence interval: 0.02727260 0.05918556 sample estimates: prop 1 prop 2 0.07580011 0.03257103 

prop.test(cbind(x, n - x)) # x = successes; convert n to number of failures

  2-sample test for equality of proportions with continuity correction data: cbind(x, n - x) X-squared = 27.161, df = 1, p-value = 1.872e-07 alternative hypothesis: two.sided 95 percent confidence interval: 0.02727260 0.05918556 sample estimates: prop 1 prop 2 0.07580011 0.03257103