Dunn's Test of Multiple Comparisons
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.
dunn_test(data, formula, p.adjust.method = "holm", detailed = FALSE)
data |
a data.frame containing the variables in the formula. |
formula |
a formula of the form |
p.adjust.method |
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". |
detailed |
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.
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.
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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