Question

这是我尝试处理的数据框架：

m <- matrix(rnorm(108, mean = 5000, sd = 1000), nrow = 36) 
colnames(m) <- paste('V', 1:3, sep = '') 
df <- data.frame(type = factor(rep(c('T1', 'T2', 'T3', 'T4', 'T5', 
            'T6', 'T7', 'T8', 'T9'), each = 4)), 
            treatment = factor(rep(rep(c('C','P', 'N', 'S'), each = 1), 
            9)), 
            as.data.frame(m))

我想知道如何在每个＆＃34;类型＆＃34;中的行之间执行t检验。这是我想要的类型T1的t测试的一个例子：

t.test(df[1,3:5], df[2, 3:5])
t.test(df[1,3:5], df[3, 3:5])
t.test(df[1,3:5], df[4, 3:5])

t.test(df[1,3:5], df[3, 3:5])

t.test(df[1,3:5], df[4, 3:5])

我试图找出如何遍历所有行并从t检验中获取所有p值（以及用于识别的类型和处理），而不是手动计算每一行。任何帮助或建议将不胜感激。

Answer 1

这样的事情：

 public function addg()
{
            $this->load->model('catagories_model');
             $data['userdata'] = $this->session->all_userdata();
               $data['father'] = $this->catagories_model->get_catagories();
         $data['child'] = $this->catagories_model->get_catagories_child();
    $this->load->view('catagories/grand/add', $data);
}

<强>结果：

library(dplyr)
t_tests = df %>%
  split(.$type) %>%
  lapply(function(x){
    t(x[3:5]) %>%
      data.frame %>%
      setNames(x$treatment) %>%
      combn(2, simplify = FALSE) %>%
      lapply(function(x){
         data.frame(treatment = paste0(names(x), collapse = ", "), 
                   p_value = t.test(x[,1], x[,2])$p.value)
      }) %>%
      do.call(rbind, .) 
  }) %>% 
  do.call(rbind, .) %>%
  mutate(type = sub("[.].+", "", row.names(.)))

编辑（在数据集中添加额外级别＆＃34;文件＆＃34;）：

> head(t_tests, 10)
   treatment   p_value type
1       C, P 0.6112274   T1
2       C, N 0.6630060   T1
3       C, S 0.5945135   T1
4       P, N 0.9388568   T1
5       P, S 0.8349370   T1
6       N, S 0.9049995   T1
7       C, P 0.3274583   T2
8       C, N 0.9755364   T2
9       C, S 0.7391661   T2
10      P, N 0.3177871   T2

<强>结果：

library(dplyr)
t_tests = df %>%
  split(.$file) %>%
  lapply(function(y){
    split(y, y$type) %>%
    lapply(function(x){
      t(x[4:6]) %>%
        data.frame %>%
        setNames(x$treatment) %>%
        combn(2, simplify = FALSE) %>%
        lapply(function(x){
          data.frame(treatment = paste0(names(x), collapse = ", "), 
                     p_value = t.test(x[,1], x[,2])$p.value)
        }) %>%
        do.call(rbind, .) 
    }) %>% 
      do.call(rbind, .) %>%
      mutate(type = sub("[.].+", "", row.names(.)))
  }) %>% 
  do.call(rbind, .) %>%
  mutate(file = sub("[.].+", "", row.names(.)))

关于修改的注意事项：

OP希望将额外的顶级treatment p_value type file 1 C, P 0.3903450 T1 file1 2 C, N 0.3288727 T1 file1 3 C, S 0.0638599 T1 file1 4 P, N 0.6927599 T1 file1 5 P, S 0.1159615 T1 file1 6 N, S 0.2184015 T1 file1 7 C, P 0.1147805 T2 file1 8 C, N 0.4961888 T2 file1 9 C, S 0.9048607 T2 file1 10 P, N 0.4203666 T2 file1 11 P, S 0.3425908 T2 file1 12 N, S 0.7262478 T2 file1 13 C, P 0.6300293 T3 file1 14 C, N 0.8255837 T3 file1 15 C, S 0.7140522 T3 file1 16 P, N 0.4768694 T3 file1 17 P, S 0.3992130 T3 file1 18 N, S 0.8740219 T3 file1 19 C, P 0.2434270 T4 file1 20 C, N 0.2713622 T4 file1添加到数据中，最后可以添加另一个file + split和lapply。

新数据：

do.call

R：每个因子级别内的行之间的t检验

1 个答案:

编辑（在数据集中添加额外级别＆＃34;文件＆＃34;）：