问题
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")
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.
What other ways can I run the multiple comparisons test?
回答1:
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')
with output
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
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).
回答2:
Since fifer is no longer maintained here is a solution with RVAideMemoire (described in more detail here 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 - - - - -
Category are replaced by the counts of each category.
I don't like corrections for multiple comparisons (see ref below for a discussion), but fdr is available.
Moran, M. D. (2003). Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos.
来源：https://stackoverflow.com/questions/42096271/post-hoc-tests-for-chi-sq-in-r