r - R中卡方的事后检验

Question

我有一张看起来像这样的 table 。

> dput(theft_loc)
structure(c(13704L, 14059L, 14263L, 14450L, 14057L, 15503L, 14230L, 
16758L, 15289L, 15499L, 16066L, 15905L, 18531L, 19217L, 12410L, 
13398L, 13308L, 13455L, 13083L, 14111L, 13068L, 19569L, 18771L, 
19626L, 20290L, 19816L, 20923L, 20466L, 20517L, 19377L, 20035L, 
20504L, 20393L, 22409L, 22289L, 7997L, 8106L, 7971L, 8437L, 8246L, 
9090L, 8363L, 7934L, 7874L, 7909L, 8150L, 8191L, 8746L, 8277L, 
27194L, 25220L, 26034L, 27080L, 27334L, 30819L, 30633L, 10452L, 
10848L, 11301L, 11494L, 11265L, 11985L, 11038L, 12104L, 13368L, 
14594L, 14702L, 13891L, 12891L, 12939L), .Dim = c(7L, 10L), .Dimnames = structure(list(
    c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", 
    "Friday", "Saturday"), c("BAYVIEW", "CENTRAL", "INGLESIDE", 
    "MISSION", "NORTHERN", "PARK", "RICHMOND", "SOUTHERN", "TARAVAL", 
    "TENDERLOIN")), .Names = c("", "")), class = "table")

我跑了一个 chisq.test结果显着。我现在想运行一些成对测试，看看重要性在哪里。我尝试使用 fifer包和 chisq.post.test函数，但我收到一个错误，提示 out of workspace .

我还可以通过哪些其他方式运行多重比较测试？

Answer 1

这将起作用(在事后测试中尝试 chisq.test 而不是默认的 fisher.test(精确)):

(Xsq <- chisq.test(theft_loc))  # Prints test summary, p-value very small,
#       Pearson's Chi-squared test
# data:  theft_loc
# X-squared = 1580.1, df = 54, p-value < 2.2e-16 # reject null hypothesis for independence

library(fifer)
chisq.post.hoc(theft_loc, test='chisq.test')

带输出

 Adjusted p-values used the fdr method.

               comparison  raw.p  adj.p
1       Sunday vs. Monday 0.0000 0.0000
2      Sunday vs. Tuesday 0.0000 0.0000
3    Sunday vs. Wednesday 0.0000 0.0000
4     Sunday vs. Thursday 0.0000 0.0000
5       Sunday vs. Friday 0.0000 0.0000
6     Sunday vs. Saturday 0.0000 0.0000
7      Monday vs. Tuesday 0.0000 0.0000
8    Monday vs. Wednesday 0.0000 0.0000
9     Monday vs. Thursday 0.0000 0.0000
10      Monday vs. Friday 0.0000 0.0000
11    Monday vs. Saturday 0.0000 0.0000
12  Tuesday vs. Wednesday 0.1451 0.1451
13   Tuesday vs. Thursday 0.0000 0.0000
14     Tuesday vs. Friday 0.0000 0.0000
15   Tuesday vs. Saturday 0.0000 0.0000
16 Wednesday vs. Thursday 0.0016 0.0017
17   Wednesday vs. Friday 0.0000 0.0000
18 Wednesday vs. Saturday 0.0000 0.0000
19    Thursday vs. Friday 0.0000 0.0000
20  Thursday vs. Saturday 0.0000 0.0000
21    Friday vs. Saturday 0.0000 0.0000

正如我们所看到的，除了一对之外的所有成对测试都是显着的，我们可以使用不同的 p-value-correction也是(通过将 control 从默认 fdr 更改为 bonferroni )。