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")
我跑了一个chisq.test，结果重来了。我现在想要进行一些成对测试以了解其重要性。我尝试使用fifer包和chisq.post.test函数，但收到的错误是out of workspace。
我可以通过其他方式进行多重比较测试？
答案 0 :(得分：2)
这将有效（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，除了一对夫妇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（将control从默认fdr更改为bonferroni ）。
答案 1 :(得分：1)
由于fifer这里不再保持与RVAideMemoire的溶液（更详细地描述这里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代替。
我不喜欢用于多重比较校正（参见参考文献下面的讨论），但fdr是可用的。
Moran，M.D.（2003）。在生态学研究中拒绝序贯的Bonferroni的观点。 Oikos。