一组与多组的t检验
一组与多组的t检验
提问于 2020-04-06 15:47:41
我有下面的泰伯
test_tbl <- tibble(name = rep(c("John", "Allan", "George", "Peter", "Paul"), each = 12),
category = rep(rep(LETTERS[1:4], each = 3), 5),
replicate = rep(1:3, 20),
value = sample.int(n = 1e5, size = 60, replace = T))
# A tibble: 60 x 4
name category replicate value
<chr> <chr> <int> <int>
1 John A 1 71257
2 John A 2 98887
3 John A 3 87354
4 John B 1 25352
5 John B 2 69913
6 John B 3 43086
7 John C 1 24957
8 John C 2 33928
9 John C 3 79854
10 John D 1 32842
11 John D 2 19156
12 John D 3 50283
13 Allan A 1 98188
14 Allan A 2 26208
15 Allan A 3 69329
16 Allan B 1 32696
17 Allan B 2 81240
18 Allan B 3 54689
19 Allan C 1 77044
20 Allan C 2 97776
# … with 40 more rows我想要group_by(name, category)并执行三个t.test调用，比较category B、C和D与category A。
我想从输出中存储estimate和p.value。预期的结果如下：
# A tibble: 5 x 7
name B_vs_A_estimate B_vs_A_p_value C_vs_A_estimate C_vs_A_p_value D_vs_A_estimate D_vs_A_p_value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 John -0.578 0.486 0.198 0.309 0.631 0.171
2 Allan 0.140 0.644 0.728 0.283 0.980 0.485
3 George -0.778 0.320 -0.424 0.391 -0.154 0.589
4 Peter -0.435 0.470 -0.156 0.722 0.315 0.0140
5 Paul 0.590 0.0150 -0.473 0.475 0.681 0.407我更喜欢使用tidyverse和/或broom的解决方案。
Stack Overflow用户
发布于 2020-04-06 17:07:04
我们可以用
library(dplyr)
library(purrr)
library(stringr)
library(tidyr)
test_tbl %>%
split(.$name) %>%
map_dfr(~ {
Avalue <- .x$value[.x$category == 'A']
.x %>%
filter(category != 'A') %>%
group_by(category) %>%
summarise(out = t.test(value, Avalue)$p.value) %>%
mutate(category = str_c(category, '_vs_A_p_value'))}, .id = 'name') %>%
pivot_wider(names_from = category, values_from = out)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/61063661
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