具有大数据框的独立组的多重 t 检验答案

【问题标题】：Multiple t-test on independent group with a large dataframe具有大数据框的独立组的多重 t 检验
【发布时间】：2021-10-31 03:10:38
【问题描述】：

我看过很多类似的帖子，但绝大多数都至少 3 岁，我不确定它们是否适用于我的情况，所以我们开始吧。

一位同事向我寻求帮助，对她的项目进行多重 t 检验。

基本上，她有 20 个观察 x 30 个变量数据框，如下所示： |集团 |脂质 1 |脂质 2 | ... |脂质28|

| -------- | -------------- |

|一个 | |B | | | |B |

我们要做的是对每个脂质进行组比较（意味着对 A 组和 B 组之间的脂质 1 进行 t 检验，然后对脂质 2 进行 t 检验，依此类推）。

我们不想比较它们之间的脂质。

当然，我们不希望复制/粘贴相同的 3 行代码，特别是因为我们有另外 2 个具有相同变量但条件不同的数据框。

我尝试了我在这里看到的一种解决方案，但它给了我一个我不确定理解的错误：

sapply(foetal[,2:20], function(i) t.test(i ~ foetal$ID)) 
Error in if (stderr < 10 * .Machine$double.eps * max(abs(mx), abs(my))) stop("data are essentially constant") :  missing value where TRUE/FALSE needed In addition: Warning messages: 1: In mean.default(x) : l'argument n'est ni numérique, ni logique : renvoi de NA 2: In var(x) : NAs introduced by coercion 3: In mean.default(y) : l'argument n'est ni numérique, ni logique : renvoi de NA 4: In var(y) : Error in if (stderr < 10 * .Machine$double.eps * max(abs(mx), abs(my))) stop("data are essentially constant") :  missing value where TRUE/FALSE needed

我看到的另一种解决方案是使用收集函数来获取一列包含脂质，一列用于每个脂质的值，然后创建一个列表列，传播数据框并改变一个包含 p- 的新列t 检验的值。

tips %>% 
  select(tip, total_bill, sex) %>% 
  gather(key = variable, value = value, -sex) %>% 
  group_by(sex, variable) %>% 
  summarise(value = list(value)) %>% 
  spread(sex, value) %>% 
  group_by(variable) %>% 
  mutate(p_value = t.test(unlist(Female), unlist(Male))$p.value,
         t_value = t.test(unlist(Female), unlist(Male))$statistic)

(https://sebastiansauer.github.io/multiple-t-tests-with-dplyr/)

老实说，我不确定该怎么做。有没有人有小窍门之类的？

这是数据的 dput() ......虽然不太清楚为什么它是必要的......

dput(dummy)
structure(list(ID = c("A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B"), 
    Lipid.1 = c(0.737, 0.419, 0.468, 0.805, 1.036, 0.825, 0.286, 
    1.166, 0.898, 0.504, 1.433, 0.41, 0.325, 0.866, 0.337, 0.876, 
    0.636, 0.953, 0.481, 0.602), Lipid.2 = c(0.001, 0.017, 0.013, 
    0.025, 0.018, 0.003, 0.007, NA, 0.01, 0.002, 0.01, 0.022, 
    0.005, NA, 0.018, NA, 0.015, 0.016, NA, 0.01), Lipid.3 = c(0.035, 
    0.018, 0.036, 0.024, 0.023, 0.027, 0.036, 0.037, 0.013, 0.037, 
    0.03, 0.04, 0.038, 0.033, 0.016, 0.034, 0.029, 0.033, 0.018, 
    0.029), Lipid.4 = c(NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_), Lipid.5 = c(0.09, 
    0.099, 0.12, 0.058, 0.136, 0.103, 0.153, 0.148, 0.047, 0.085, 
    0.098, 0.133, 0.099, 0.121, 0.084, 0.065, 0.11, 0.088, 0.065, 
    0.043), Lipid.6 = c(0.39, 0.555, 0.568, 0.6, 0.626, 0.378, 
    0.657, 0.57, 0.271, 0.41, 0.474, 0.617, 0.491, 0.738, 0.459, 
    0.365, 0.499, 0.388, 0.271, 0.275), Lipid.7 = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_), Lipid.8 = c(0.186, 0.197, 0.191, 0.125, 0.209, 
    0.107, 0.174, 0.143, 0.055, 0.134, 0.148, 0.193, 0.184, 0.213, 
    0.134, 0.085, 0.165, 0.215, 0.163, 0.061), Lipid.9 = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, "0,007"), Lipid.10 = c("0,242", "0,254", "0,134", 
    "0,226", "0,243", "0,122", "0,082", "0,119", "0,098", "0,093", 
    "0,27", "0,284", "0,258", "0,236", "0,173", "0,106", "0,138", 
    "0,066", "0,072", "0,081"), Lipid.11 = c("0,053", "0,114", 
    "0,038", "0,094", "0,073", "0,067", "0,028", "0,022", "0,021", 
    "0,05", "0,085", "0,102", "0,122", "0,096", "0,027", "0,03", 
    NA, "0,078", "0,066", NA), Lipid.12 = c(0.223, 0.261, 0.258, 
    0.212, 0.168, 0.101, 0.191, 0.09, 0.195, 0.082, 0.155, 0.2, 
    0.167, 0.231, 0.145, 0.089, 0.239, 0.141, 0.106, 0.124), 
    Lipid.13 = c(0.737, 0.763, 0.707, 0.587, 0.545, 0.317, 0.74, 
    0.602, 0.481, 0.531, 0.632, 0.448, 0.62, 0.766, 0.397, 0.623, 
    0.997, 0.578, 0.418, 0.412), Lipid.14 = c(0.683, 0.666, 0.507, 
    0.366, 0.443, 0.266, 0.493, 0.345, 0.368, 0.355, 0.432, 0.411, 
    0.491, 0.565, 0.357, 0.285, 0.604, 0.426, 0.538, 0.295), 
    Lipid.15 = c(0.911, 1.017, 0.503, 0.76, 0.741, 0.486, 0.648, 
    0.581, 0.955, 0.515, 0.932, 0.707, 0.626, 0.928, 0.836, 0.537, 
    0.654, 0.351, 0.498, 0.529), Lipid.16 = c(0.148, 0.116, 0.069, 
    0.104, 0.091, 0.064, 0.093, 0.123, 0.11, 0.097, 0.283, 0.076, 
    0.095, 0.194, 0.06, 0.061, 0.086, 0.051, 0.064, 0.059), Lipid.17 = c("0,155", 
    "0,274", "0,149", "0,127", "0,174", "nd", "0,109", "0,134", 
    "0,1", "0,09", "0,25", "0,112", "0,088", "0,243", "0,092", 
    "0,073", "0,153", "0,12", "0,14", "0,06"), Lipid.18 = c(3.143, 
    3.441, 4.359, 1.945, 2.573, 2.267, 3.585, 3.405, 2.296, 1.998, 
    3.468, 2.98, 3.626, 3.635, 3.236, 2.092, 2.586, 2.08, 1.718, 
    1.736), Lipid.19 = c(37.993, 36.148, 40.244, 30.395, 37.339, 
    35.742, 47.316, 47.555, 34.351, 32.377, 38.694, 39.413, 36.114, 
    41.235, 32.779, 32.222, 36.418, 36.918, 33.334, 31.421), 
    Lipid.20 = c(6.613, 5.913, 9.662, 3.789, 7.485, 6.297, 8.254, 
    8.07, 4.905, 5.686, 7.742, 7.533, 6.875, 7.908, 7.022, 5.446, 
    6.1, 6.782, 6.062, 6.089), Lipid.21 = c(7.235, 6.759, 8.331, 
    4.931, 6.558, 4.186, 5.99, 5.629, 3.066, 3.439, 7.102, 7.655, 
    6.606, 7.858, 5.804, 3.135, 3.218, 3.639, 2.975, 3.13), Lipid.22 = c(6.453, 
    6.664, 9.048, 4.341, 8.03, 7.599, 10.24, 10.954, 5.873, 6.687, 
    8.005, 8.908, 6.708, 8.06, 5.931, 6.083, 5.734, 5.587, 5.388, 
    6.088), Lipid.23 = c(4.943, 3.164, 5.153, 2.51, 4.071, 5.255, 
    7.636, 8.376, 4.726, 5.56, 4.762, 5.044, 4.549, 4.875, 4.57, 
    5.147, 4.396, 4.031, 3.556, 4.38), Lipid.24 = c(3.973, 4.279, 
    5.928, 3.066, 4.95, 4.667, 7.949, 7.268, 4.948, 3.72, 5.137, 
    5.539, 4.006, 5.276, 3.909, 4.163, 4.954, 5.02, 3.961, 4.201
    ), Lipid.25 = c(7.638, 5.224, 8.417, 3.902, 7.267, 6.007, 
    8.256, 7.457, 4.801, 4.86, 7.581, 8.173, 7.57, 8.591, 7.482, 
    5.091, 5.651, 6.577, 5.415, 5.76), Lipid.26 = c(10.225, 8.293, 
    13.188, 5.607, 10.993, 4.491, 5.767, 5.011, 3.589, 3.145, 
    11.471, 12.183, 9.686, 12.562, 9.697, 3.34, 4.186, 4.485, 
    3.23, 4.229), Lipid.27 = c(5.848, 4.856, 6.503, 3.534, 5.358, 
    8.933, 14.034, 12.806, 7.781, 8.094, 6.765, 6.867, 5.539, 
    7.772, 5.883, 7.832, 8.607, 7.586, 6.628, 7.563), Lipid.28 = c(32.941, 
    30.579, 31.358, 15.861, 30.353, 25.222, 35.662, 34.035, 20.338, 
    24.682, 30.698, 34.024, 31.608, 37.539, 24.901, 20.131, 23.126, 
    30.803, 25.639, 18.935)), class = "data.frame", row.names = c(NA, 
-20L))

【问题讨论】：

请澄清您的具体问题或提供更多详细信息以准确突出您的需求。正如目前所写的那样，很难准确地说出你在问什么。

标签： r t-test hypothesis-test

【解决方案1】：

F你也可以使用R中的multtest库，用于多个二样本t-tests，如下代码所示：

library(multtest)
df <- as.data.frame(t(as.matrix(dummy)))
X <- apply(as.matrix.noquote(df[2:nrow(df),]), 2, as.numeric) 
cl <- ifelse(df[1,] == 'A', 1, 0) # class labels
welch_t_stat <- mt.teststat(X, cl, test='t')
welch_t_stat
# [1]  0.15843467 -0.86954194 -0.37680666          NA  0.92978706  0.72969094          NA -0.17962582          NA          NA          NAv
# [12]  0.69705527  0.16001073  0.15733921  0.59540273 -0.05557413          NA  0.52706460  0.99860493 -0.14561137  0.58894166  1.25114061
# [23]  1.03458080  0.86540315 -0.62788116 -0.28806189  0.60206042  0.12954702

从上面的结果可以看出，对于数据帧中的 28 种脂质，有 28 次 Welch t-tests 执行。

由于您获得了个人 t-statistics，现在，您可以计算 p-values 并使用 Bonferroni / Holm 应用 FWER 校正或使用 Benjamini & Hochberg 方法进行 FDR 校正（当您有大量测试时很有用）：

raw_p <- 2 * (1 - pnorm(abs(welch_t_stat))) # raw p-values assuming normal 
                                           # or use pt() with appropriate df
procedures <- c("Bonferroni", "Holm", "BH")
adjusted <- mt.rawp2adjp(raw_p, procedures)

【讨论】：

【解决方案2】：

让我们从您粘贴的数据开始吧！你有丁字裤，而不是数字。例如Lipid.10

Lipid.10 = c("0,242", "0,254", "0,134", 
    "0,226", "0,243", "0,122", "0,082", "0,119", "0,098", "0,093", 
    "0,27", "0,284", "0,258", "0,236", "0,173", "0,106", "0,138", 
    "0,066", "0,072", "0,081")

此外，您的变量只包含 NA 值

Lipid.4 = c(NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_)

所以我不得不把它们清理一下。


structure(list(ID = c("A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B"), 
    Lipid.1 = c(0.737, 0.419, 0.468, 0.805, 1.036, 0.825, 0.286, 
    1.166, 0.898, 0.504, 1.433, 0.41, 0.325, 0.866, 0.337, 0.876, 
    0.636, 0.953, 0.481, 0.602), Lipid.2 = c(0.001, 0.017, 0.013, 
    0.025, 0.018, 0.003, 0.007, NA, 0.01, 0.002, 0.01, 0.022, 
    0.005, NA, 0.018, NA, 0.015, 0.016, NA, 0.01), Lipid.3 = c(0.035, 
    0.018, 0.036, 0.024, 0.023, 0.027, 0.036, 0.037, 0.013, 0.037, 
    0.03, 0.04, 0.038, 0.033, 0.016, 0.034, 0.029, 0.033, 0.018, 
    0.029), Lipid.4 = c(NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_), Lipid.5 = c(0.09, 
    0.099, 0.12, 0.058, 0.136, 0.103, 0.153, 0.148, 0.047, 0.085, 
    0.098, 0.133, 0.099, 0.121, 0.084, 0.065, 0.11, 0.088, 0.065, 
    0.043), Lipid.6 = c(0.39, 0.555, 0.568, 0.6, 0.626, 0.378, 
    0.657, 0.57, 0.271, 0.41, 0.474, 0.617, 0.491, 0.738, 0.459, 
    0.365, 0.499, 0.388, 0.271, 0.275), Lipid.7 = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_), Lipid.8 = c(0.186, 0.197, 0.191, 0.125, 0.209, 
    0.107, 0.174, 0.143, 0.055, 0.134, 0.148, 0.193, 0.184, 0.213, 
    0.134, 0.085, 0.165, 0.215, 0.163, 0.061), Lipid.9 = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 0.007), Lipid.10 = c(0.242, 0.254, 0.134, 0.226, 
    0.243, 0.122, 0.082, 0.119, 0.098, 0.093, 0.27, 0.284, 0.258, 
    0.236, 0.173, 0.106, 0.138, 0.066, 0.072, 0.081), Lipid.11 = c(0.053, 
    0.114, 0.038, 0.094, 0.073, 0.067, 0.028, 0.022, 0.021, 0.05, 
    0.085, 0.102, 0.122, 0.096, 0.027, 0.03, NA, 0.078, 0.066, 
    NA), Lipid.12 = c(0.223, 0.261, 0.258, 0.212, 0.168, 0.101, 
    0.191, 0.09, 0.195, 0.082, 0.155, 0.2, 0.167, 0.231, 0.145, 
    0.089, 0.239, 0.141, 0.106, 0.124), Lipid.13 = c(0.737, 0.763, 
    0.707, 0.587, 0.545, 0.317, 0.74, 0.602, 0.481, 0.531, 0.632, 
    0.448, 0.62, 0.766, 0.397, 0.623, 0.997, 0.578, 0.418, 0.412
    ), Lipid.14 = c(0.683, 0.666, 0.507, 0.366, 0.443, 0.266, 
    0.493, 0.345, 0.368, 0.355, 0.432, 0.411, 0.491, 0.565, 0.357, 
    0.285, 0.604, 0.426, 0.538, 0.295), Lipid.15 = c(0.911, 1.017, 
    0.503, 0.76, 0.741, 0.486, 0.648, 0.581, 0.955, 0.515, 0.932, 
    0.707, 0.626, 0.928, 0.836, 0.537, 0.654, 0.351, 0.498, 0.529
    ), Lipid.16 = c(0.148, 0.116, 0.069, 0.104, 0.091, 0.064, 
    0.093, 0.123, 0.11, 0.097, 0.283, 0.076, 0.095, 0.194, 0.06, 
    0.061, 0.086, 0.051, 0.064, 0.059), Lipid.17 = c(0.155, 0.274, 
    0.149, 0.127, 0.174, NA, 0.109, 0.134, 0.1, 0.09, 0.25, 0.112, 
    0.088, 0.243, 0.092, 0.073, 0.153, 0.12, 0.14, 0.06), Lipid.18 = c(3.143, 
    3.441, 4.359, 1.945, 2.573, 2.267, 3.585, 3.405, 2.296, 1.998, 
    3.468, 2.98, 3.626, 3.635, 3.236, 2.092, 2.586, 2.08, 1.718, 
    1.736), Lipid.19 = c(37.993, 36.148, 40.244, 30.395, 37.339, 
    35.742, 47.316, 47.555, 34.351, 32.377, 38.694, 39.413, 36.114, 
    41.235, 32.779, 32.222, 36.418, 36.918, 33.334, 31.421), 
    Lipid.20 = c(6.613, 5.913, 9.662, 3.789, 7.485, 6.297, 8.254, 
    8.07, 4.905, 5.686, 7.742, 7.533, 6.875, 7.908, 7.022, 5.446, 
    6.1, 6.782, 6.062, 6.089), Lipid.21 = c(7.235, 6.759, 8.331, 
    4.931, 6.558, 4.186, 5.99, 5.629, 3.066, 3.439, 7.102, 7.655, 
    6.606, 7.858, 5.804, 3.135, 3.218, 3.639, 2.975, 3.13), Lipid.22 = c(6.453, 
    6.664, 9.048, 4.341, 8.03, 7.599, 10.24, 10.954, 5.873, 6.687, 
    8.005, 8.908, 6.708, 8.06, 5.931, 6.083, 5.734, 5.587, 5.388, 
    6.088), Lipid.23 = c(4.943, 3.164, 5.153, 2.51, 4.071, 5.255, 
    7.636, 8.376, 4.726, 5.56, 4.762, 5.044, 4.549, 4.875, 4.57, 
    5.147, 4.396, 4.031, 3.556, 4.38), Lipid.24 = c(3.973, 4.279, 
    5.928, 3.066, 4.95, 4.667, 7.949, 7.268, 4.948, 3.72, 5.137, 
    5.539, 4.006, 5.276, 3.909, 4.163, 4.954, 5.02, 3.961, 4.201
    ), Lipid.25 = c(7.638, 5.224, 8.417, 3.902, 7.267, 6.007, 
    8.256, 7.457, 4.801, 4.86, 7.581, 8.173, 7.57, 8.591, 7.482, 
    5.091, 5.651, 6.577, 5.415, 5.76), Lipid.26 = c(10.225, 8.293, 
    13.188, 5.607, 10.993, 4.491, 5.767, 5.011, 3.589, 3.145, 
    11.471, 12.183, 9.686, 12.562, 9.697, 3.34, 4.186, 4.485, 
    3.23, 4.229), Lipid.27 = c(5.848, 4.856, 6.503, 3.534, 5.358, 
    8.933, 14.034, 12.806, 7.781, 8.094, 6.765, 6.867, 5.539, 
    7.772, 5.883, 7.832, 8.607, 7.586, 6.628, 7.563), Lipid.28 = c(32.941, 
    30.579, 31.358, 15.861, 30.353, 25.222, 35.662, 34.035, 20.338, 
    24.682, 30.698, 34.024, 31.608, 37.539, 24.901, 20.131, 23.126, 
    30.803, 25.639, 18.935)), row.names = c(NA, -20L), class = c("tbl_df", 
"tbl", "data.frame"))

剩下的很简单。

library(tidyverse)

ft = function(data){
  tryCatch(
    {tout = t.test(data$val ~ data$ID))
    tibble(
      t = tout$statistic,
      p = tout$p.value,
      stderr = tout$stderr
    )
    }, error = function(msg){
      return(tibble(t = NA, p = NA, stderr = NA))
    })
}

df %>% 
  pivot_longer(starts_with("Lipid"), names_to = "Lipid", values_to = "val") %>% 
  group_by(Lipid) %>% 
  nest() %>% 
  mutate(testt = map(data, ft)) %>% 
  select(Lipid, testt) %>% 
  unnest(testt)

输出

# A tibble: 28 x 4
# Groups:   Lipid [28]
   Lipid         t      p   stderr
   <chr>     <dbl>  <dbl>    <dbl>
 1 Lipid.1   0.158  0.876  0.142  
 2 Lipid.2  -0.870  0.399  0.00350
 3 Lipid.3  -0.377  0.711  0.00372
 4 Lipid.4  NA     NA     NA      
 5 Lipid.5   0.930  0.366  0.0143 
 6 Lipid.6   0.730  0.475  0.0614 
 7 Lipid.7  NA     NA     NA      
 8 Lipid.8  -0.180  0.859  0.0223 
 9 Lipid.9  NA     NA     NA      
10 Lipid.10 -0.200  0.844  0.0355 
# ... with 18 more rows

根据需要自定义ft 函数。我不得不在ft 中使用tryCatch 函数，因为变量只包含NA 值。

【讨论】：

谢谢马雷克！是的，我后来才看到数据不是数字，不知道为什么。可能来自高层（我们从另一家公司获得数据）。你的脚本中有很多我不知道的功能，我去查一下！
很高兴能帮上忙。但是请记住，在 Stack Overflow 上，您可以通过选择已批准的解决方案来表示感谢。

【解决方案3】：

如果您想获得完整的 t-test 输出，您可以遍历列：

如果我们从你的 df 开始：

data <- structure(list(ID = c("A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B"), 
    Lipid.1 = c(0.737, 0.419, 0.468, 0.805, 1.036, 0.825, 0.286, 
    1.166, 0.898, 0.504, 1.433, 0.41, 0.325, 0.866, 0.337, 0.876, 
    0.636, 0.953, 0.481, 0.602), Lipid.2 = c(0.001, 0.017, 0.013, 
    0.025, 0.018, 0.003, 0.007, NA, 0.01, 0.002, 0.01, 0.022, 
    0.005, NA, 0.018, NA, 0.015, 0.016, NA, 0.01), Lipid.3 = c(0.035, 
    0.018, 0.036, 0.024, 0.023, 0.027, 0.036, 0.037, 0.013, 0.037, 
    0.03, 0.04, 0.038, 0.033, 0.016, 0.034, 0.029, 0.033, 0.018, 
    0.029), Lipid.4 = c(NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_), Lipid.5 = c(0.09, 
    0.099, 0.12, 0.058, 0.136, 0.103, 0.153, 0.148, 0.047, 0.085, 
    0.098, 0.133, 0.099, 0.121, 0.084, 0.065, 0.11, 0.088, 0.065, 
    0.043), Lipid.6 = c(0.39, 0.555, 0.568, 0.6, 0.626, 0.378, 
    0.657, 0.57, 0.271, 0.41, 0.474, 0.617, 0.491, 0.738, 0.459, 
    0.365, 0.499, 0.388, 0.271, 0.275), Lipid.7 = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_), Lipid.8 = c(0.186, 0.197, 0.191, 0.125, 0.209, 
    0.107, 0.174, 0.143, 0.055, 0.134, 0.148, 0.193, 0.184, 0.213, 
    0.134, 0.085, 0.165, 0.215, 0.163, 0.061), Lipid.9 = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, "0,007"), Lipid.10 = c("0,242", "0,254", "0,134", 
    "0,226", "0,243", "0,122", "0,082", "0,119", "0,098", "0,093", 
    "0,27", "0,284", "0,258", "0,236", "0,173", "0,106", "0,138", 
    "0,066", "0,072", "0,081"), Lipid.11 = c("0,053", "0,114", 
    "0,038", "0,094", "0,073", "0,067", "0,028", "0,022", "0,021", 
    "0,05", "0,085", "0,102", "0,122", "0,096", "0,027", "0,03", 
    NA, "0,078", "0,066", NA), Lipid.12 = c(0.223, 0.261, 0.258, 
    0.212, 0.168, 0.101, 0.191, 0.09, 0.195, 0.082, 0.155, 0.2, 
    0.167, 0.231, 0.145, 0.089, 0.239, 0.141, 0.106, 0.124), 
    Lipid.13 = c(0.737, 0.763, 0.707, 0.587, 0.545, 0.317, 0.74, 
    0.602, 0.481, 0.531, 0.632, 0.448, 0.62, 0.766, 0.397, 0.623, 
    0.997, 0.578, 0.418, 0.412), Lipid.14 = c(0.683, 0.666, 0.507, 
    0.366, 0.443, 0.266, 0.493, 0.345, 0.368, 0.355, 0.432, 0.411, 
    0.491, 0.565, 0.357, 0.285, 0.604, 0.426, 0.538, 0.295), 
    Lipid.15 = c(0.911, 1.017, 0.503, 0.76, 0.741, 0.486, 0.648, 
    0.581, 0.955, 0.515, 0.932, 0.707, 0.626, 0.928, 0.836, 0.537, 
    0.654, 0.351, 0.498, 0.529), Lipid.16 = c(0.148, 0.116, 0.069, 
    0.104, 0.091, 0.064, 0.093, 0.123, 0.11, 0.097, 0.283, 0.076, 
    0.095, 0.194, 0.06, 0.061, 0.086, 0.051, 0.064, 0.059), Lipid.17 = c("0,155", 
    "0,274", "0,149", "0,127", "0,174", "nd", "0,109", "0,134", 
    "0,1", "0,09", "0,25", "0,112", "0,088", "0,243", "0,092", 
    "0,073", "0,153", "0,12", "0,14", "0,06"), Lipid.18 = c(3.143, 
    3.441, 4.359, 1.945, 2.573, 2.267, 3.585, 3.405, 2.296, 1.998, 
    3.468, 2.98, 3.626, 3.635, 3.236, 2.092, 2.586, 2.08, 1.718, 
    1.736), Lipid.19 = c(37.993, 36.148, 40.244, 30.395, 37.339, 
    35.742, 47.316, 47.555, 34.351, 32.377, 38.694, 39.413, 36.114, 
    41.235, 32.779, 32.222, 36.418, 36.918, 33.334, 31.421), 
    Lipid.20 = c(6.613, 5.913, 9.662, 3.789, 7.485, 6.297, 8.254, 
    8.07, 4.905, 5.686, 7.742, 7.533, 6.875, 7.908, 7.022, 5.446, 
    6.1, 6.782, 6.062, 6.089), Lipid.21 = c(7.235, 6.759, 8.331, 
    4.931, 6.558, 4.186, 5.99, 5.629, 3.066, 3.439, 7.102, 7.655, 
    6.606, 7.858, 5.804, 3.135, 3.218, 3.639, 2.975, 3.13), Lipid.22 = c(6.453, 
    6.664, 9.048, 4.341, 8.03, 7.599, 10.24, 10.954, 5.873, 6.687, 
    8.005, 8.908, 6.708, 8.06, 5.931, 6.083, 5.734, 5.587, 5.388, 
    6.088), Lipid.23 = c(4.943, 3.164, 5.153, 2.51, 4.071, 5.255, 
    7.636, 8.376, 4.726, 5.56, 4.762, 5.044, 4.549, 4.875, 4.57, 
    5.147, 4.396, 4.031, 3.556, 4.38), Lipid.24 = c(3.973, 4.279, 
    5.928, 3.066, 4.95, 4.667, 7.949, 7.268, 4.948, 3.72, 5.137, 
    5.539, 4.006, 5.276, 3.909, 4.163, 4.954, 5.02, 3.961, 4.201
    ), Lipid.25 = c(7.638, 5.224, 8.417, 3.902, 7.267, 6.007, 
    8.256, 7.457, 4.801, 4.86, 7.581, 8.173, 7.57, 8.591, 7.482, 
    5.091, 5.651, 6.577, 5.415, 5.76), Lipid.26 = c(10.225, 8.293, 
    13.188, 5.607, 10.993, 4.491, 5.767, 5.011, 3.589, 3.145, 
    11.471, 12.183, 9.686, 12.562, 9.697, 3.34, 4.186, 4.485, 
    3.23, 4.229), Lipid.27 = c(5.848, 4.856, 6.503, 3.534, 5.358, 
    8.933, 14.034, 12.806, 7.781, 8.094, 6.765, 6.867, 5.539, 
    7.772, 5.883, 7.832, 8.607, 7.586, 6.628, 7.563), Lipid.28 = c(32.941, 
    30.579, 31.358, 15.861, 30.353, 25.222, 35.662, 34.035, 20.338, 
    24.682, 30.698, 34.024, 31.608, 37.539, 24.901, 20.131, 23.126, 
    30.803, 25.639, 18.935)), class = "data.frame", row.names = c(NA, 
-20L))

清理df：

# remove the columns which only contain NA:
data$Lipid.4 <- NULL
data$Lipid.7 <- NULL
data$Lipid.9 <- NULL

# convert from string to numeric (I do it now manually with each column. You could use a for-loop)
data$Lipid.10 <- gsub(",", ".", data$Lipid.10)  # convert comma to dot
data$Lipid.10 <- as.numeric(data$Lipid.10) # convert from string to numeric
data$Lipid.11 <- gsub(",", ".", data$Lipid.11)
data$Lipid.11 <- as.numeric(data$Lipid.11)
data$Lipid.17 <- gsub(",", ".", data$Lipid.17)
data$Lipid.17 <- as.numeric(data$Lipid.17)

# get the lipid column names
all_lipids <- colnames(data)
all_lipids <-  all_lipids[all_lipids != "ID"] # we don't need the ID column for the loop

# now loop over each column an perform a t-test
for (column in all_lipids) {
  print(column)
  print(t.test(data[,column] ~ data$ID))
}

你得到每种脂质：

[1] "Lipid.1"

    Welch Two Sample t-test

data:  data[, column] by data$ID
t = 0.15843, df = 17.391, p-value = 0.8759
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.2766112  0.3216112
sample estimates:
mean in group A mean in group B 
         0.7144          0.6919

最后一点：您进行了很多比较。您可以考虑对多个测试进行更正。

【讨论】：

感谢洛伦兹！我也在考虑使用 for 循环，但我对自己的能力并不完全有信心，很高兴看到我离得不远^^如果我没有错，我们应该能够保存将循环的每次迭代的 p 值转换为数据框，对吗？是的，我们要用 Bonferroni 调整 p 值
for 循环在 R 中很少使用。这就是许多 map 函数的用途。但我很欣赏您将t.test 表示法的简单表示法为t.test(data[,column] ~ data$ID)。在我的例子中，它可以写成t.test(data$val ~ data$ID)，这肯定是一个更优雅和简洁的符号。现在我们只需要等待解决方案被@Yo Pomdpin 接受。