R中chi-sq的事后检验 [英] Post-Hoc tests for chi-sq in R

问题描述

我有一张看起来像这样的桌子.

I have a table that looks like this.

> 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")

我运行了 chisq.test 并且结果显着.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.

I ran a chisq.test and the results came back significant. I now want to run some pairwise tests to see where the significance lies. I tried to use the fifer package and the chisq.post.test function but i get an error that says out of workspace.

还有哪些其他方法可以运行多重比较测试?

What other ways can I run the multiple comparisons test?

推荐答案

This will work (try chisq.test 而不是默认的 fisher.test (exact) in事后测试):

This will work (try chisq.test instead of the default fisher.test (exact) in post hoc test):

(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，所有的成对测试都很重要，我们也可以使用不同的p-value-correction(通过改变默认的controlcode>fdr 到 bonferroni).

As we can see, all the pairwise tests except a couple are significant, we can use different p-value-correction too (by changing the control from default fdr to bonferroni).

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