对数据框进行 chisq.test 以进行多次成对比较

Question

我有以下数据框：

species <- c("a","a","a","b","b","b","c","c","c","d","d","d","e","e","e","f","f","f","g","h","h","h","i","i","i")
category <- c("h","l","m","h","l","m","h","l","m","h","l","m","h","l","m","h","l","m","l","h","l","m","h","l","m")
minus <- c(31,14,260,100,70,200,91,152,842,16,25,75,60,97,300,125,80,701,104,70,7,124,24,47,251)
plus <- c(2,0,5,0,1,1,4,4,30,1,0,0,2,0,5,0,0,3,0,0,0,0,0,0,4)
df <- cbind(species, category, minus, plus)
df<-as.data.frame(df)

我想为每个类别-物种组合做一个 chisq.test，像这样：

物种 a，类别 h 和 l：p 值

物种 a，类别 h 和 m：p 值

物种 a，类别 l 和 m：p 值

物种 b，... 等等

使用以下 chisq.test（虚拟代码）：

chisq.test(c(minus(cat1, cat2),plus(cat1, cat2)))$p.value

我想最终得到一个表格，显示每个比较的每个 chisq.test p 值，如下所示：

Species   Category1  Category2   p-value
a         h          l           0.05
a         h          m           0.2
a         l          m           0.1
b...

其中 category 和 category 2 是 chisq.test 中比较的类别。

使用 dplyr 可以做到这一点吗？我已经尝试调整 here 和 here 中提到的内容，但正如我所见，它们并不真正适用于这个问题。

编辑：我还想看看如何为以下数据集完成此操作：

species <- c(1:11)
minus <- c(132,78,254,12,45,76,89,90,100,42,120)
plus <- c(1,2,0,0,0,3,2,5,6,4,0)

我想做一个chisq。将表中的每个物种与表中的每个其他物种进行比较（所有物种的每个物种之间的成对比较）。我想得到这样的结果：

species1  species2  p-value
1         2         0.5
1         3         0.7
1         4         0.2
...
11        10        0.02

我尝试将上面的代码更改为以下代码：

species_chisq %>%
do(data_frame(species1 = first(.$species),
            species2 = last(.$species),
            data = list(matrix(c(.$minus, .$plus), ncol = 2)))) %>%
mutate(chi_test = map(data, chisq.test, correct = FALSE)) %>%
mutate(p.value = map_dbl(chi_test, "p.value")) %>%
ungroup() %>%
select(species1, species2, p.value) %>%

但是，这仅创建了一个表，其中每个物种仅与自身进行比较，而不是与其他物种进行比较。我不太明白在@ycw 给出的原始代码中它指定了比较的位置。

编辑 2：

我通过here找到的代码设法做到了这一点。

Answer 1

来自dplyr 和purrr 的解决方案。请注意，我不熟悉卡方检验，但我遵循您在@Vincent Bonhomme 的帖子中指定的方式：chisq.test(test, correct = FALSE)。

另外，要创建示例数据框，不需要使用cbind，只需data.frame 就足够了。 stringsAsFactors = FALSE 对于防止列成为因素很重要。

# Create example data frame
species <- c("a","a","a","b","b","b","c","c","c","d","d","d","e","e","e","f","f","f","g","h","h","h","i","i","i")
category <- c("h","l","m","h","l","m","h","l","m","h","l","m","h","l","m","h","l","m","l","h","l","m","h","l","m")
minus <- c(31,14,260,100,70,200,91,152,842,16,25,75,60,97,300,125,80,701,104,70,7,124,24,47,251)
plus <- c(2,0,5,0,1,1,4,4,30,1,0,0,2,0,5,0,0,3,0,0,0,0,0,0,4)
df <- data.frame(species, category, minus, plus, stringsAsFactors = FALSE)

# Load packages
library(dplyr)
library(purrr)

# Process the data
df2 <- df %>%
  group_by(species) %>%
  slice(c(1, 2, 1, 3, 2, 3)) %>%
  mutate(test = rep(1:(n()/2), each = 2)) %>%
  group_by(species, test) %>%
  do(data_frame(species = first(.$species),
                test = first(.$test[1]),
                category1 = first(.$category),
                category2 = last(.$category),
                data = list(matrix(c(.$minus, .$plus), ncol = 2)))) %>%
  mutate(chi_test = map(data, chisq.test, correct = FALSE)) %>%
  mutate(p.value = map_dbl(chi_test, "p.value")) %>%
  ungroup() %>%
  select(species, category1, category2, p.value)

df2
# A tibble: 25 x 4
   species category1 category2   p.value
     <chr>     <chr>     <chr>     <dbl>
 1       a         h         l 0.3465104
 2       a         h         m 0.1354680
 3       a         l         m 0.6040227
 4       b         h         l 0.2339414
 5       b         h         m 0.4798647
 6       b         l         m 0.4399181
 7       c         h         l 0.4714005
 8       c         h         m 0.6987413
 9       c         l         m 0.5729834
10       d         h         l 0.2196806
# ... with 15 more rows

Answer 2

首先，您应该使用data.frame 创建您的data.frame，否则minus 和plus 列将变成factors。

species <- c("a","a","a","b","b","b","c","c","c","d","d","d","e","e","e","f","f","f","g","h","h","h","i","i","i")
category <- c("h","l","m","h","l","m","h","l","m","h","l","m","h","l","m","h","l","m","l","h","l","m","h","l","m")
minus <- c(31,14,260,100,70,200,91,152,842,16,25,75,60,97,300,125,80,701,104,70,7,124,24,47,251)
plus <- c(2,0,5,0,1,1,4,4,30,1,0,0,2,0,5,0,0,3,0,0,0,0,0,0,4)
df <- data.frame(species=species, category=category, minus=minus, plus=plus)

那么，我不确定是否有纯粹的dplyr 方式来做到这一点（很高兴看到相反的情况），但我认为这是一种部分-dplyr 方式来做到这一点：

df_combinations <-
  # create a df with all interactions
  expand.grid(df$species, df$category, df$category)) %>% 
  # rename columns
  `colnames<-`(c("species", "category1", "category2")) %>% 
  # 3 lines below:
  # manage to only retain within a species, category(1 and 2) columns
  # with different values
  unique %>% 
  group_by(species) %>% 
  filter(category1 != category2) %>% 
  # cosmetics
  arrange(species, category1, category2) %>%
  ungroup() %>% 
  # prepare an empty column
  mutate(p.value=NA)

# now we loop to fill your result data.frame
for (i in 1:nrow(df_combinations)){
  # filter appropriate lines
  cat1 <- filter(df,
                 species==df_combinations$species[i],
                 category==df_combinations$category1[i])
  cat2 <- filter(df,
                 species==df_combinations$species[i],
                 category==df_combinations$category2[i])
  # calculate the chisq.test and assign its p-value to the right line
  df_combinations$p.value[i] <- chisq.test(c(cat1$minus, cat2$minus,
                                             cat1$plus, cat2$plus))$p.value  

}

让我们看看生成的data.frame：

head(df_combinations)
# A tibble: 6 x 4
# A tibble: 6 x 4
# Groups:   species [1]
species category1 category2       p.value
<fctr>    <fctr>    <fctr>         <dbl>
1       a         h         l  3.290167e-11
2       a         h         m 1.225872e-134
3       a         l         h  3.290167e-11
4       a         l         m 5.824842e-150
5       a         m         h 1.225872e-134
6       a         m         l 5.824842e-150

检查第一行： chisq.test(c(31, 14, 2, 0))$p.value [1] 3.290167e-11

这是你想要的吗？