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Dunn's multiple comparison test

Source: R/Dunn_test.R
Dunn_test.Rd

Performs Dunn's multiple comparison procedure for all pairwise comparisons of groups following a significant Kruskal-Wallis test.

Usage

Dunn_test(
  data,
  formula = NULL,
  alpha = 0.05,
  p_adjust_method = "holm",
  rounding = 4,
  verbose = TRUE
)

Arguments

data

A data frame, or an object returned by Kruskal_Wallis_test(), from which the response and grouping variables are obtained.

formula

A two-sided formula specifying the response and grouping variables in the form response ~ group. Ignored if data is a "oneway_aov" object.

alpha

A numeric value between 0 and 1 specifying the significance level. The default is 0.05.

p_adjust_method

A character string specifying the method used to adjust p-values for multiple comparisons. Must be one of stats::p.adjust.methods. The default is "holm".

rounding

An integer specifying the number of decimal places to display in the output. The default is 4.

verbose

Logical (default: TRUE). Show warnings and messages.

Value

A list containing the following components:

method

The name of the statistical procedure.

data

The input data used in the analysis.

pre_hoc

The Kruskal-Wallis test results.

post_hoc

Results of all pairwise Dunn comparisons, including differences in mean ranks, confidence intervals, Z statistics, unadjusted and adjusted p-values, and effect sizes.

summary

Descriptive statistics for each group, including compact letter displays (CLD).

Details

Dunn's test is a nonparametric post hoc procedure that compares group mean ranks after a significant Kruskal-Wallis test. The test statistic follows a normal approximation with an adjustment for tied ranks when ties are present.

Because multiple pairwise comparisons are performed, p-values should be adjusted to control the family-wise error rate or false discovery rate. The adjustment method is specified by p_adjust_method and is passed to stats::p.adjust().

If the data satisfy the assumptions of one-way ANOVA, parametric procedures such as Tukey_HSD_test(), REGWQ_test(), or Games_Howell_test() may be more appropriate.

References

Dinno, A. (2015). Nonparametric Pairwise Multiple Comparisons in Independent Groups using Dunn’s Test. The Stata Journal: Promoting Communications on Statistics and Stata, 15(1), 292-300. https://doi.org/10.1177/1536867X1501500117

Zar, J. H. (2014). Biostatistical analysis (5th edition). Pearson. Chapter 11: Multiple comparisons, pg. 255-256.

Examples

out <- Dunn_test(X_X_O, val ~ grp)
#> 
#> -------------------------------------
#> Dunn's multiple comparison procedure
#> -------------------------------------
#> Data: X_X_O ; Formula: val ~ grp
#> 
#>   GROUP CLD  N     AVG     SD     MED
#> 1    G1   a 20 11.9633 0.7985 11.9720
#> 2    G2  bc 20  8.1905 2.5801  8.8330
#> 3    G3   d 20  5.5526 0.5113  5.3966
#> 4    G4   b 20  9.7142 1.3761  9.5068
#> 5    G5  bc 20  9.2874 1.1111  9.1363
#> 6    G6  cd 20  7.7708 4.9047  6.9180
#>