Performs Dunn's test for pairwise multiple comparisons of the ranked data. The mean rank of the different groups is compared. Used for post-hoc test following Kruskal-Wallis test.
The default of the rstatix::dunn_test() function is to perform a
two-sided Dunn test like the well known commercial softwares, such as SPSS
and GraphPad. This is not the case for some other R packages
(dunn.test and jamovi), where the default is to perform
one-sided test. This discrepancy is documented at
https://github.com/kassambara/rstatix/issues/50.
dunn_test(data, formula, p.adjust.method = "holm", detailed = FALSE)return a data frame with some of the following columns:
.y.: the y (outcome) variable used in the test.
group1,group2: the compared groups in the pairwise tests.
n1,n2: Sample counts.
estimate: mean ranks difference.
estimate1, estimate2: show the mean rank values of the two
groups, respectively.
statistic: Test statistic (z-value) used
to compute the p-value.
p: p-value.
p.adj: the
adjusted p-value.
method: the statistical test used to compare
groups.
p.signif, p.adj.signif: the significance level of
p-values and adjusted p-values, respectively.
The returned object has an attribute called args, which is a list holding the test arguments.
a data.frame containing the variables in the formula.
a formula of the form x ~ group where x is a
numeric variable giving the data values and group is a factor with
one or multiple levels giving the corresponding groups. For example,
formula = TP53 ~ cancer_group.
method to adjust p values for multiple comparisons. Used when pairwise comparisons are performed. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".
logical value. Default is FALSE. If TRUE, a detailed result is shown.
DunnTest performs the post hoc pairwise multiple comparisons procedure appropriate to follow up a Kruskal-Wallis test, which is a non-parametric analog of the one-way ANOVA. The Wilcoxon rank sum test, itself a non-parametric analog of the unpaired t-test, is possibly intuitive, but inappropriate as a post hoc pairwise test, because (1) it fails to retain the dependent ranking that produced the Kruskal-Wallis test statistic, and (2) it does not incorporate the pooled variance estimate implied by the null hypothesis of the Kruskal-Wallis test.
Dunn, O. J. (1964) Multiple comparisons using rank sums Technometrics, 6(3):241-252.
# Simple test
ToothGrowth %>% dunn_test(len ~ dose)
# Grouped data
ToothGrowth %>%
group_by(supp) %>%
dunn_test(len ~ dose)
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