r - 通过排列进行多组测试

Question

我有一个 df ，其中包含相对于两个实验的两组值(value_1 和 value_2)。

一个实验包含两组(0 和 1)，另一个实验包含三组(0、1、2)。

test    group   Value_1    Value_2
AA      0           15.1    11.2
AA      0           12.4    8.6
AA      1           9.6     22.5
AA      1           10.2    22
BB      0           12.11   11
BB      0           14      1.2
BB      1           11      13.2
BB      1           12.3    9
BB      2           14.2    12
BB      2           15      13

df <- structure(list(test = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L), .Label = c("AA", "BB"), class = "factor"), group = c(0L, 
0L, 1L, 1L, 0L, 0L, 1L, 1L, 2L, 2L), Value_1 = c(15.1, 12.4, 
9.6, 10.2, 12.11, 14, 11, 12.3, 14.2, 15), Value_2 = c(11.2, 
8.6, 22.5, 22, 11, 1.2, 13.2, 9, 12, 13)), .Names = c("test", 
"group", "Value_1", "Value_2"), class = "data.frame", row.names = c(NA, 
-10L))

我想对 value_1 和 value_2 应用置换测试 - 通过测试，按组 - 涵盖:

两组案例(0 vs 1)和

超过 2 个案例(0 对 1、0 对 2、1 对 2)。

到目前为止我所做的 - 分成几部分是:

当组只有两个时，我只需应用 oneway.test():

df %>%
  filter(test %in% 'AA') -> df_test_aa

df_test_aa_value_1 <- oneway.test(df_test_aa$Value_1~df_test_aa$group)
df_test_aa_value_1$p.value
[1] 0.2011234


df_test_aa_value_2 <- oneway.test(df_test_aa$Value_2~df_test_aa$group)
df_test_aa_value_2$p.value
[1] 0.05854026

每当组超过 2 时，我会测试所有可能的排列:

第一个 0 对 1:

df %>% filter(test %in% 'BB' & group %in% c(0,1)) -> df_test_bb_01

df_test_bb_01_value_1 <- oneway.test(df_test_bb_01$Value_1~df_test_bb_01$group)
df_test_bb_01_value_1$p.value
[1] 0.3585415

df_test_bb_01_value_2 <-    oneway.test(df_test_bb_01$Value_2~df_test_bb_01$group)
df_test_bb_01_value_2$p.value
[1] 0.4848446

然后 0 对 2:

 df %>%
 filter(test %in% 'BB' & group %in% c(0,2)) -> df_test_bb_02

 df_test_bb_02_value_1 <-       oneway.test(df_test_bb_02$Value_1~df_test_bb_02$group)
 df_test_bb_02_value_1$p.value
 [1] 0.3246012

 df_test_bb_02_value_2 <- oneway.test(df_test_bb_02$Value_2~df_test_bb_02$group)
 df_test_bb_02_value_2$p.value
 [1] 0.4142838

然后 1 对 2:

 df %>%
  filter(test %in% 'BB' & group %in% c(1,2)) -> df_test_bb_12

  df_test_bb_12_value_1 <- oneway.test(df_test_bb_12$Value_1~df_test_bb_12$group)
  df_test_bb_12_value_1$p.value
  [1] 0.08105404


  df_test_bb_12_value_2 <- oneway.test(df_test_bb_12$Value_2~df_test_bb_12$group)
  df_test_bb_12_value_2$p.value
  [1] 0.6245713

因此，我希望获得如下所示的 df:

test value  p_value_2sided  hypothesis
AA  Value_1   0.201         0,1
AA  Value_2   0.059         0,1
BB  Value_1   0.359         0,1
BB  Value_1   0.325         0,2
BB  Value_1   0.081         1,2
BB  Value_2   0.485         0,1
BB  Value_2   0.414         0,2
BB  Value_2   0.625         1,2

谢谢你的提示!

Answer 1

df <- structure(list(test = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L), .Label = c("AA", "BB"), class = "factor"), group = c(0L, 
0L, 1L, 1L, 0L, 0L, 1L, 1L, 2L, 2L), Value_1 = c(15.1, 12.4, 
9.6, 10.2, 12.11, 14, 11, 12.3, 14.2, 15), Value_2 = c(11.2, 
8.6, 22.5, 22, 11, 1.2, 13.2, 9, 12, 13)), .Names = c("test", 
"group", "Value_1", "Value_2"), class = "data.frame", row.names = c(NA, -10L))

library(tidyverse)

# reshape dataset
df2 = df %>% gather(value, v, -test, -group)

# function to compute p value
# vectorized version
f = function(t,val,x1,x2) {
  (df2 %>% 
     filter(test == t & value == val & group %in% c(x1,x2)) %>% 
     oneway.test(v~group, data = .))$p.value }
f = Vectorize(f)

df2 %>% 
  distinct(test, group, value) %>%       # get unique combinations
  group_by(test, value) %>%              # for each test and value
  nest() %>%                             # nest rest of columns
  mutate(d = map(data, ~data.frame(t(combn(.$group, 2)))),
         hypothesis = map(d, ~paste0(.$X1, ",", .$X2))) %>%  # get pairs/combinations of values
  unnest(d, hypothesis) %>%              # unnest data
  mutate(pval = f(test, value, X1, X2))  # apply vectorised function to get p value

# # A tibble: 8 x 6
#   test   value   hypothesis    X1    X2   pval
#   <fctr> <chr>   <chr>      <int> <int>  <dbl>
# 1 AA     Value_1 0,1            0     1 0.201 
# 2 BB     Value_1 0,1            0     1 0.359 
# 3 BB     Value_1 0,2            0     2 0.325 
# 4 BB     Value_1 1,2            1     2 0.0811
# 5 AA     Value_2 0,1            0     1 0.0585
# 6 BB     Value_2 0,1            0     1 0.485 
# 7 BB     Value_2 0,2            0     2 0.414 
# 8 BB     Value_2 1,2            1     2 0.625

您可以删除 X1和 X2如果你真的不需要它们。
但是，通过这种方式，您(也)将它们作为单独的数字变量，以防您想在分析的后期阶段在另一个过程(例如，过滤特定组)中使用它们。