常规进行t test检验时，需要首先进行方差齐性的F test检验，根据检验结果，确定t test检验时所使用的参数，对应地设置方差齐性，或者方差非齐性。 那么在R语言中如何实现一个函数，综合这两个检验呢？
Introduction
定义一个super.t函数，先做F test，然后再根据F test 的 p value结果，再进行做 t test。
Code
super.t <- function(form, data, level = 0.05) {
vareq <- var.test(form, data)[["p.value"]] >= level
t.test(form, data, var.equal = vareq)
}
ds <-
structure(
list(
Gender = structure(
c(2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L),
.Label = c("F", "M"),
class = "factor"
),
Ratings = c(4L, 1L, 3L, 4L, 5L, 5L, 5L, 3L, 1L, 5L, 4L, 5L)
),
.Names = c("Gender", "Ratings"),
class = "data.frame",
row.names = c(NA,-12L)
)
print(ds)
# output
Gender Ratings
1 M 4
2 M 1
3 M 3
4 M 4
5 M 5
6 M 5
7 F 5
8 F 3
9 F 1
10 F 5
11 F 4
12 F 5
typeof(ds)
# output
# list
# And then call it like:
super.t(Ratings ~ Gender, ds)
# output
Two Sample t-test
data: Ratings by Gender
t = 0.1857, df = 10, p-value = 0.8564
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.833149 2.166482
sample estimates:
mean in group F mean in group M
3.833333 3.666667