我有一张看起来像这样的桌子。
> 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chisq.test，结果回来了。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，但我收到一个错误消息out of workspace。
我还可以通过哪些其他方式运行多重比较测试？
由于fifer这里不再维护一个解决方案RVAideMemoire（此处更详细地描述https://rdrr.io/cran/RVAideMemoire/src/R/chisq.multcomp.R）：
install.packages("RVAideMemoire")
library(RVAideMemoire)
chisq.multcomp(theft_loc, p.method = "none")
> 7874 7909 7934 7971 7997 8106 8150 8191 8246 8277 8363 8437 8746 9090 10452 10848 11038 11265 11301
7909 0.78056 - - - - - - - - - - - - - - - - - -
7934 0.63321 0.84256 - - - - - - - - - - - - - - - - -
7971 0.44095 0.62272 0.76923 - - - - - - - - - - - - - - - -
7997 0.32889 0.48533 0.61768 0.83698 - - - - - - - - - - - - - - -
8106 0.06647 0.11954 0.17444 0.28701 0.39036 - - - - - - - - - - - - - -
8150 0.02923 0.05720 0.08854 0.15860 0.22857 0.73002 - - - - - - - - - - - - -
8191 0.01238 0.02625 0.04298 0.08354 0.12732 0.50552 0.74841 - - - - - - - - - - - -
8246 0.00339 0.00802 0.01417 0.03081 0.05073 0.27360 0.45342 0.66793 - - - - - - - - - - -
8277 0.00152 0.00382 0.00706 0.01637 0.02817 0.18156 0.32174 0.50276 0.80943 - - - - - - - - - -
8363 0.00012 0.00037 0.00078 0.00216 0.00422 0.04522 0.09741 0.18128 0.36396 0.50497 - - - - - - - - -
8437 1.0e-05 3.6e-05 8.5e-05 0.00027 0.00060 0.01007 0.02585 0.05643 0.13921 0.21586 0.56805 - - - - - - - -
8746 1.3e-11 8.8e-11 3.2e-10 2.0e-09 7.1e-09 8.2e-07 4.5e-06 2.0e-05 0.00013 0.00032 0.00341 0.01841 - - - - - - -
9090 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 6.2e-14 8.1e-13 8.0e-12 1.5e-10 6.9e-10 3.7e-08 8.1e-07 0.01000 - - - - - -
10452 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 < 2e-16 - - - - -
类别由每个类别的计数替换。我不喜欢对多重比较进行更正（请参阅下面的参考文献进行讨论），但fdr可以使用。
莫兰，医学博士（2003）。在生态研究中拒绝顺序 Bonferroni 的论点。奥伊科斯。
这将起作用（在事后测试中尝试chisq.test而不是默认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很重要，我们也可以使用不同的p-value-correction（通过将control默认值更改fdr为bonferroni）。