费舍尔测验或X平方测验Rstudio

Question

此问题使用https://people.ucsc.edu/~mclapham/eart125/data/georoc.csv中的数据文件

流纹岩可分为高硅型(SiO2> 75%的流纹岩)和较典型的流纹岩(SiO2<75%)。板块内和汇流边缘构造环境中流纹岩类型的比例是否存在显著差异？输入下面的p-值：

我试图将这些信息与以下代码组合在一起：

kick <- matrix(c(georoc$tectonic.setting == "Intraplate" | georoc$tectonic.setting == "Convergent margin", georoc$SIO2), ncol = 2)
chisq.test(kick)

这就是我得到的

Pearson's Chi-squared test

data:  kick
X-squared = 380.59, df = 999, p-value = 1
Warning message:
In chisq.test(ckok) : Chi-squared approximation may be incorrect

我做错了什么，该如何解决？我对R.

Answer 1

我认为这是正确的，但不知道这个特定的领域，所以只是想提醒一下！

library(tidyverse)

data <- read_csv('https://people.ucsc.edu/%7Emclapham/eart125/data/georoc.csv')

data_tidy <- data %>% 
  filter(rock.type == "Rhyolite") %>% 
  mutate(high_SiO2 = SIO2 > 75) %>% 
  select("setting" = tectonic.setting, "type" = rock.type, high_SiO2) %>% 
  group_by(setting, type) %>% 
  count(high_SiO2) %>% 
  ungroup() %>% 
  filter(setting %in% c("Convergent margin", "Intraplate")) %>% 
  select(-type) %>% 
  pivot_wider(names_from = high_SiO2, values_from = n) %>% 
  select(setting, "low_SiO2" = `FALSE`, "high_SiO2" = `TRUE`) %>% 
  column_to_rownames(var = "setting") %>% 
  as.matrix()

这样做的结果如下：

                  low_SiO2 high_SiO2
Convergent margin       62        10
Intraplate              43        22

然后我们可以跑：

chisq.test(data_tidy)

..。得到：

    Pearson's Chi-squared test with Yates' continuity
    correction

data:  data_tidy
X-squared = 6.5263, df = 1, p-value = 0.01063

我最初担心的是，我混淆了矩阵的行和列，但我认为这在这个测试中并不重要。