两个数据集之间均值差的置信区间

Question

I'm working on two datasets, derrived fromm cats , an in-build R dataset.

> cats
    Sex Bwt  Hwt
1     F 2.0  7.0
2     F 2.0  7.4
3     F 2.0  9.5
4     F 2.1  7.2
5     F 2.1  7.3
6     F 2.1  7.6
7     F 2.1  8.1
8     F 2.1  8.2
9     F 2.1  8.3
10    F 2.1  8.5
11    F 2.1  8.7
12    F 2.1  9.8
...
137   M 3.6 13.3
138   M 3.6 14.8
139   M 3.6 15.0
140   M 3.7 11.0
141   M 3.8 14.8
142   M 3.8 16.8
143   M 3.9 14.4
144   M 3.9 20.5

I want to find the 99% Confidence Interval on the difference of means values between the Bwt of Male and Female specimens (Sex == M and Sex == F respectively)

I know that t.test does this, among other things, but if I break up cats to two datasets that contain the Bwt of Males and Females, t.test() complains that the two datasets are not of the same length, which is true. There's only 47 Females in cats , and 87 Males.

Is it doable some other way or am I misinterpreting data by breaking them up?

EDIT: I have a function suggested to me by an Answerer on another Question that gets the CI of means on a dataset, may come in handy:

ci_func <- function(data, ALPHA){
  c(
    mean(data) - qnorm(1-ALPHA/2) * sd(data)/sqrt(length(data)),
    mean(data) + qnorm(1-ALPHA/2) * sd(data)/sqrt(length(data))
    )
}

Answer 1

您应该使用公式接口应用t.test：

t.test(Bwt ~ Sex, data=cats, conf.level=.99)