为成对 t 检验编写函数
Writing a function for pairwise t-tests
我目前正尝试在 R 中编写一个函数，该函数允许我计算数据框中所有可能的成对 t 检验（我知道存在可以实现此目的的函数，但我也想学习如何成功编写函数）。我 运行 遇到了一个我不知道如何解决的问题。
数据：
library(combinat) # for generating pairwise combinations of variables
apple <- rnorm(100)
banana <- rnorm(100)
pear <- rnorm(100)
orange <- rnorm(100)
pineapple <- rnorm(100)
data <- data.frame(apple, banana, pear, orange, pineapple)
我的想法是使用for循环查找table列名组合中的每一对列名，使用匹配函数引用原始数据集中的关联列号，然后调用关联的列名称作为 t.test 函数中的元素。这个过程是独立工作的，但我 运行 在尝试迭代它时遇到了问题。
combinations <- combn2(names(data)) # creates a 2x10 table of all the combinations of the 5 column names
a<-match(combinations[8,1],colnames(data))
a<-data[,a]
b<-match(combinations[8,2],colnames(data))
b<-data[,b]
t.test(a, b)
# This works as expected
这是我尝试使用 for 循环自动执行此过程的尝试：
test <- function(data) {
names <- names(data)
combinations <- combinat::combn2(names(data))
num_rows <- NROW(combinations)
for (i in 1:num_rows) {
x<- match(combinations[i,1],colnames(data))
x<-data[,x]
y<- match(combinations[i,2],colnames(data))
y<-data[,y]
t.test(x, y)
}
}
test(data)
summary(test(data))
结果为空。我显然遗漏了一些东西，但我不确定如何进行。感谢任何帮助。
您需要为 t.test(x, y)
的输出分配一个引用
试试这个：
test <- function(data) {
names <- names(data)
combinations <- combinat::combn2(names(data))
num_rows <- nrow(combinations)
test_results <- vector(mode = "list", length = num_rows)
for (i in 1:num_rows) {
x <- match(combinations[i,1],colnames(data))
x <- data[,x]
y <- match(combinations[i,2],colnames(data))
y <- data[,y]
test_results[[i]] <- t.test(x, y)
}
return(test_results)
}
这将为您提供一个列表输出，其中每个条目都是根据您的要求对特定字段组合执行的不同 t 检验。
combn（不是combn2）的第三个参数接受一个可以应用于每个组合的函数。你可以简单地做
combn(data, 2L, \(d) {
syms <- lapply(names(d), as.symbol)
names(syms) <- c("x", "y")
eval(bquote(t.test(.(x), .(y)), syms), d)
}, FALSE)
输出
[[1]]
Welch Two Sample t-test
data: apple and banana
t = -0.11531, df = 197.6, p-value = 0.9083
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3017470 0.2684074
sample estimates:
mean of x mean of y
-0.03961686 -0.02294705
[[2]]
Welch Two Sample t-test
data: apple and pear
t = -0.78348, df = 197.86, p-value = 0.4343
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3841981 0.1657171
sample estimates:
mean of x mean of y
-0.03961686 0.06962364
[[3]]
Welch Two Sample t-test
data: apple and orange
t = -0.55681, df = 196.65, p-value = 0.5783
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3433412 0.1921482
sample estimates:
mean of x mean of y
-0.03961686 0.03597966
[[4]]
Welch Two Sample t-test
data: apple and pineapple
t = 0.038627, df = 197.99, p-value = 0.9692
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2739606 0.2849074
sample estimates:
mean of x mean of y
-0.03961686 -0.04509027
[[5]]
Welch Two Sample t-test
data: banana and pear
t = -0.64848, df = 196.99, p-value = 0.5174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3740876 0.1889462
sample estimates:
mean of x mean of y
-0.02294705 0.06962364
[[6]]
Welch Two Sample t-test
data: banana and orange
t = -0.4234, df = 194.84, p-value = 0.6725
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3334116 0.2155582
sample estimates:
mean of x mean of y
-0.02294705 0.03597966
[[7]]
Welch Two Sample t-test
data: banana and pineapple
t = 0.15274, df = 197.7, p-value = 0.8788
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2637425 0.3080290
sample estimates:
mean of x mean of y
-0.02294705 -0.04509027
[[8]]
Welch Two Sample t-test
data: pear and orange
t = 0.25138, df = 197.38, p-value = 0.8018
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2302948 0.2975828
sample estimates:
mean of x mean of y
0.06962364 0.03597966
[[9]]
Welch Two Sample t-test
data: pear and pineapple
t = 0.82024, df = 197.79, p-value = 0.4131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1610834 0.3905112
sample estimates:
mean of x mean of y
0.06962364 -0.04509027
[[10]]
Welch Two Sample t-test
data: orange and pineapple
t = 0.59521, df = 196.45, p-value = 0.5524
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1875381 0.3496780
sample estimates:
mean of x mean of y
0.03597966 -0.04509027
[[1]]
Welch Two Sample t-test
data: apple and banana
t = -0.11531, df = 197.6, p-value = 0.9083
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3017470 0.2684074
sample estimates:
mean of x mean of y
-0.03961686 -0.02294705
[[2]]
Welch Two Sample t-test
data: apple and pear
t = -0.78348, df = 197.86, p-value = 0.4343
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3841981 0.1657171
sample estimates:
mean of x mean of y
-0.03961686 0.06962364
[[3]]
Welch Two Sample t-test
data: apple and orange
t = -0.55681, df = 196.65, p-value = 0.5783
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3433412 0.1921482
sample estimates:
mean of x mean of y
-0.03961686 0.03597966
[[4]]
Welch Two Sample t-test
data: apple and pineapple
t = 0.038627, df = 197.99, p-value = 0.9692
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2739606 0.2849074
sample estimates:
mean of x mean of y
-0.03961686 -0.04509027
[[5]]
Welch Two Sample t-test
data: banana and pear
t = -0.64848, df = 196.99, p-value = 0.5174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3740876 0.1889462
sample estimates:
mean of x mean of y
-0.02294705 0.06962364
[[6]]
Welch Two Sample t-test
data: banana and orange
t = -0.4234, df = 194.84, p-value = 0.6725
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3334116 0.2155582
sample estimates:
mean of x mean of y
-0.02294705 0.03597966
[[7]]
Welch Two Sample t-test
data: banana and pineapple
t = 0.15274, df = 197.7, p-value = 0.8788
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2637425 0.3080290
sample estimates:
mean of x mean of y
-0.02294705 -0.04509027
[[8]]
Welch Two Sample t-test
data: pear and orange
t = 0.25138, df = 197.38, p-value = 0.8018
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2302948 0.2975828
sample estimates:
mean of x mean of y
0.06962364 0.03597966
[[9]]
Welch Two Sample t-test
data: pear and pineapple
t = 0.82024, df = 197.79, p-value = 0.4131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1610834 0.3905112
sample estimates:
mean of x mean of y
0.06962364 -0.04509027
[[10]]
Welch Two Sample t-test
data: orange and pineapple
t = 0.59521, df = 196.45, p-value = 0.5524
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1875381 0.3496780
sample estimates:
mean of x mean of y
0.03597966 -0.04509027
[[1]]
Welch Two Sample t-test
data: apple and banana
t = -0.11531, df = 197.6, p-value = 0.9083
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3017470 0.2684074
sample estimates:
mean of x mean of y
-0.03961686 -0.02294705
[[2]]
Welch Two Sample t-test
data: apple and pear
t = -0.78348, df = 197.86, p-value = 0.4343
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3841981 0.1657171
sample estimates:
mean of x mean of y
-0.03961686 0.06962364
[[3]]
Welch Two Sample t-test
data: apple and orange
t = -0.55681, df = 196.65, p-value = 0.5783
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3433412 0.1921482
sample estimates:
mean of x mean of y
-0.03961686 0.03597966
[[4]]
Welch Two Sample t-test
data: apple and pineapple
t = 0.038627, df = 197.99, p-value = 0.9692
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2739606 0.2849074
sample estimates:
mean of x mean of y
-0.03961686 -0.04509027
[[5]]
Welch Two Sample t-test
data: banana and pear
t = -0.64848, df = 196.99, p-value = 0.5174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3740876 0.1889462
sample estimates:
mean of x mean of y
-0.02294705 0.06962364
[[6]]
Welch Two Sample t-test
data: banana and orange
t = -0.4234, df = 194.84, p-value = 0.6725
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3334116 0.2155582
sample estimates:
mean of x mean of y
-0.02294705 0.03597966
[[7]]
Welch Two Sample t-test
data: banana and pineapple
t = 0.15274, df = 197.7, p-value = 0.8788
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2637425 0.3080290
sample estimates:
mean of x mean of y
-0.02294705 -0.04509027
[[8]]
Welch Two Sample t-test
data: pear and orange
t = 0.25138, df = 197.38, p-value = 0.8018
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2302948 0.2975828
sample estimates:
mean of x mean of y
0.06962364 0.03597966
[[9]]
Welch Two Sample t-test
data: pear and pineapple
t = 0.82024, df = 197.79, p-value = 0.4131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1610834 0.3905112
sample estimates:
mean of x mean of y
0.06962364 -0.04509027
[[10]]
Welch Two Sample t-test
data: orange and pineapple
t = 0.59521, df = 196.45, p-value = 0.5524
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1875381 0.3496780
sample estimates:
mean of x mean of y
0.03597966 -0.04509027
我目前正尝试在 R 中编写一个函数，该函数允许我计算数据框中所有可能的成对 t 检验（我知道存在可以实现此目的的函数，但我也想学习如何成功编写函数）。我 运行 遇到了一个我不知道如何解决的问题。
数据：
library(combinat) # for generating pairwise combinations of variables
apple <- rnorm(100)
banana <- rnorm(100)
pear <- rnorm(100)
orange <- rnorm(100)
pineapple <- rnorm(100)
data <- data.frame(apple, banana, pear, orange, pineapple)
我的想法是使用for循环查找table列名组合中的每一对列名，使用匹配函数引用原始数据集中的关联列号，然后调用关联的列名称作为 t.test 函数中的元素。这个过程是独立工作的，但我 运行 在尝试迭代它时遇到了问题。
combinations <- combn2(names(data)) # creates a 2x10 table of all the combinations of the 5 column names
a<-match(combinations[8,1],colnames(data))
a<-data[,a]
b<-match(combinations[8,2],colnames(data))
b<-data[,b]
t.test(a, b)
# This works as expected
这是我尝试使用 for 循环自动执行此过程的尝试：
test <- function(data) {
names <- names(data)
combinations <- combinat::combn2(names(data))
num_rows <- NROW(combinations)
for (i in 1:num_rows) {
x<- match(combinations[i,1],colnames(data))
x<-data[,x]
y<- match(combinations[i,2],colnames(data))
y<-data[,y]
t.test(x, y)
}
}
test(data)
summary(test(data))
结果为空。我显然遗漏了一些东西，但我不确定如何进行。感谢任何帮助。
您需要为 t.test(x, y)
试试这个：
test <- function(data) {
names <- names(data)
combinations <- combinat::combn2(names(data))
num_rows <- nrow(combinations)
test_results <- vector(mode = "list", length = num_rows)
for (i in 1:num_rows) {
x <- match(combinations[i,1],colnames(data))
x <- data[,x]
y <- match(combinations[i,2],colnames(data))
y <- data[,y]
test_results[[i]] <- t.test(x, y)
}
return(test_results)
}
这将为您提供一个列表输出，其中每个条目都是根据您的要求对特定字段组合执行的不同 t 检验。
combn（不是combn2）的第三个参数接受一个可以应用于每个组合的函数。你可以简单地做
combn(data, 2L, \(d) {
syms <- lapply(names(d), as.symbol)
names(syms) <- c("x", "y")
eval(bquote(t.test(.(x), .(y)), syms), d)
}, FALSE)
输出
[[1]]
Welch Two Sample t-test
data: apple and banana
t = -0.11531, df = 197.6, p-value = 0.9083
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3017470 0.2684074
sample estimates:
mean of x mean of y
-0.03961686 -0.02294705
[[2]]
Welch Two Sample t-test
data: apple and pear
t = -0.78348, df = 197.86, p-value = 0.4343
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3841981 0.1657171
sample estimates:
mean of x mean of y
-0.03961686 0.06962364
[[3]]
Welch Two Sample t-test
data: apple and orange
t = -0.55681, df = 196.65, p-value = 0.5783
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3433412 0.1921482
sample estimates:
mean of x mean of y
-0.03961686 0.03597966
[[4]]
Welch Two Sample t-test
data: apple and pineapple
t = 0.038627, df = 197.99, p-value = 0.9692
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2739606 0.2849074
sample estimates:
mean of x mean of y
-0.03961686 -0.04509027
[[5]]
Welch Two Sample t-test
data: banana and pear
t = -0.64848, df = 196.99, p-value = 0.5174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3740876 0.1889462
sample estimates:
mean of x mean of y
-0.02294705 0.06962364
[[6]]
Welch Two Sample t-test
data: banana and orange
t = -0.4234, df = 194.84, p-value = 0.6725
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3334116 0.2155582
sample estimates:
mean of x mean of y
-0.02294705 0.03597966
[[7]]
Welch Two Sample t-test
data: banana and pineapple
t = 0.15274, df = 197.7, p-value = 0.8788
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2637425 0.3080290
sample estimates:
mean of x mean of y
-0.02294705 -0.04509027
[[8]]
Welch Two Sample t-test
data: pear and orange
t = 0.25138, df = 197.38, p-value = 0.8018
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2302948 0.2975828
sample estimates:
mean of x mean of y
0.06962364 0.03597966
[[9]]
Welch Two Sample t-test
data: pear and pineapple
t = 0.82024, df = 197.79, p-value = 0.4131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1610834 0.3905112
sample estimates:
mean of x mean of y
0.06962364 -0.04509027
[[10]]
Welch Two Sample t-test
data: orange and pineapple
t = 0.59521, df = 196.45, p-value = 0.5524
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1875381 0.3496780
sample estimates:
mean of x mean of y
0.03597966 -0.04509027
[[1]]
Welch Two Sample t-test
data: apple and banana
t = -0.11531, df = 197.6, p-value = 0.9083
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3017470 0.2684074
sample estimates:
mean of x mean of y
-0.03961686 -0.02294705
[[2]]
Welch Two Sample t-test
data: apple and pear
t = -0.78348, df = 197.86, p-value = 0.4343
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3841981 0.1657171
sample estimates:
mean of x mean of y
-0.03961686 0.06962364
[[3]]
Welch Two Sample t-test
data: apple and orange
t = -0.55681, df = 196.65, p-value = 0.5783
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3433412 0.1921482
sample estimates:
mean of x mean of y
-0.03961686 0.03597966
[[4]]
Welch Two Sample t-test
data: apple and pineapple
t = 0.038627, df = 197.99, p-value = 0.9692
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2739606 0.2849074
sample estimates:
mean of x mean of y
-0.03961686 -0.04509027
[[5]]
Welch Two Sample t-test
data: banana and pear
t = -0.64848, df = 196.99, p-value = 0.5174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3740876 0.1889462
sample estimates:
mean of x mean of y
-0.02294705 0.06962364
[[6]]
Welch Two Sample t-test
data: banana and orange
t = -0.4234, df = 194.84, p-value = 0.6725
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3334116 0.2155582
sample estimates:
mean of x mean of y
-0.02294705 0.03597966
[[7]]
Welch Two Sample t-test
data: banana and pineapple
t = 0.15274, df = 197.7, p-value = 0.8788
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2637425 0.3080290
sample estimates:
mean of x mean of y
-0.02294705 -0.04509027
[[8]]
Welch Two Sample t-test
data: pear and orange
t = 0.25138, df = 197.38, p-value = 0.8018
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2302948 0.2975828
sample estimates:
mean of x mean of y
0.06962364 0.03597966
[[9]]
Welch Two Sample t-test
data: pear and pineapple
t = 0.82024, df = 197.79, p-value = 0.4131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1610834 0.3905112
sample estimates:
mean of x mean of y
0.06962364 -0.04509027
[[10]]
Welch Two Sample t-test
data: orange and pineapple
t = 0.59521, df = 196.45, p-value = 0.5524
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1875381 0.3496780
sample estimates:
mean of x mean of y
0.03597966 -0.04509027
[[1]]
Welch Two Sample t-test
data: apple and banana
t = -0.11531, df = 197.6, p-value = 0.9083
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3017470 0.2684074
sample estimates:
mean of x mean of y
-0.03961686 -0.02294705
[[2]]
Welch Two Sample t-test
data: apple and pear
t = -0.78348, df = 197.86, p-value = 0.4343
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3841981 0.1657171
sample estimates:
mean of x mean of y
-0.03961686 0.06962364
[[3]]
Welch Two Sample t-test
data: apple and orange
t = -0.55681, df = 196.65, p-value = 0.5783
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3433412 0.1921482
sample estimates:
mean of x mean of y
-0.03961686 0.03597966
[[4]]
Welch Two Sample t-test
data: apple and pineapple
t = 0.038627, df = 197.99, p-value = 0.9692
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2739606 0.2849074
sample estimates:
mean of x mean of y
-0.03961686 -0.04509027
[[5]]
Welch Two Sample t-test
data: banana and pear
t = -0.64848, df = 196.99, p-value = 0.5174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3740876 0.1889462
sample estimates:
mean of x mean of y
-0.02294705 0.06962364
[[6]]
Welch Two Sample t-test
data: banana and orange
t = -0.4234, df = 194.84, p-value = 0.6725
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3334116 0.2155582
sample estimates:
mean of x mean of y
-0.02294705 0.03597966
[[7]]
Welch Two Sample t-test
data: banana and pineapple
t = 0.15274, df = 197.7, p-value = 0.8788
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2637425 0.3080290
sample estimates:
mean of x mean of y
-0.02294705 -0.04509027
[[8]]
Welch Two Sample t-test
data: pear and orange
t = 0.25138, df = 197.38, p-value = 0.8018
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2302948 0.2975828
sample estimates:
mean of x mean of y
0.06962364 0.03597966
[[9]]
Welch Two Sample t-test
data: pear and pineapple
t = 0.82024, df = 197.79, p-value = 0.4131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1610834 0.3905112
sample estimates:
mean of x mean of y
0.06962364 -0.04509027
[[10]]
Welch Two Sample t-test
data: orange and pineapple
t = 0.59521, df = 196.45, p-value = 0.5524
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1875381 0.3496780
sample estimates:
mean of x mean of y
0.03597966 -0.04509027