r - R 数据框中不同组的卡方检验

Question

我有一个巨大的数据框，具有以下基本结构:

data <- data.frame(species = factor(c(rep("species1", 4), rep("species2", 4), rep("species3", 4))),
                 trap = c(rep(c("A","B","C","D"), 3)),
                 count=c(6,3,7,9,5,3,6,6,5,8,1,3))
data

我想要同时对每个物种的四个陷阱之间的物种计数数据进行卡方检验，而不是它们之间的卡方检验。对于每个单独的物种，可以使用以下代码来解决它，但由于我巨大的原始数据框，这对我来说不是合适的解决方案。

chi_species1 <- xtabs(count~trap, data, 
                       subset = species=="species1")
chi_species1
chisq.test(chi_species1)

感谢您的帮助!!

Answer 1

基础

df <- data.frame(species = factor(c(rep("species1", 4), rep("species2", 4), rep("species3", 4))),
                   trap = c(rep(c("A","B","C","D"), 3)),
                   count=c(6,3,7,9,5,3,6,6,5,8,1,3))
df
#>     species trap count
#> 1  species1    A     6
#> 2  species1    B     3
#> 3  species1    C     7
#> 4  species1    D     9
#> 5  species2    A     5
#> 6  species2    B     3
#> 7  species2    C     6
#> 8  species2    D     6
#> 9  species3    A     5
#> 10 species3    B     8
#> 11 species3    C     1
#> 12 species3    D     3

species <- unique(df$species)

chi_species <- lapply(species, function(x) xtabs(count~trap, df, 
                      subset = species== x))

chi_species <- setNames(chi_species, species)

lapply(chi_species, chisq.test)

#> $species1
#> 
#>  Chi-squared test for given probabilities
#> 
#> data:  X[[i]]
#> X-squared = 3, df = 3, p-value = 0.3916
#> 
#> 
#> $species2
#> 
#>  Chi-squared test for given probabilities
#> 
#> data:  X[[i]]
#> X-squared = 1.2, df = 3, p-value = 0.753
#> 
#> 
#> $species3
#> 
#>  Chi-squared test for given probabilities
#> 
#> data:  X[[i]]
#> X-squared = 6.2941, df = 3, p-value = 0.09815

^{由 reprex package 于 2022 年 4 月 25 日创建(v2.0.1)}

tidyverse

df %>% 
  group_by(species, trap) %>% 
  summarise(count = sum(count)) %>% 
  summarise(pvalue= chisq.test(count)$p.value) 

# A tibble: 3 × 2
  species  pvalue
  <fct>     <dbl>
1 species1 0.392 
2 species2 0.753 
3 species3 0.0981