BonferroniNPTest {Analitica} | R Documentation |
Bonferroni-Corrected Mann-Whitney Tests (Non-Parametric)
Description
Performs all pairwise comparisons using the Wilcoxon rank-sum test (Mann-Whitney) with Bonferroni correction for multiple testing.
Usage
BonferroniNPTest(formula, data, alpha = 0.05)
Arguments
formula |
A formula of the form |
data |
A data frame containing the variables. |
alpha |
Significance level (default is 0.05). |
Details
Suitable for non-parametric data where ANOVA assumptions are violated.
Advantages: - Simple and intuitive non-parametric alternative to ANOVA post hoc tests. - Strong control of Type I error via Bonferroni correction. - Works with unequal group sizes.
Disadvantages: - Conservative with many groups. - Only valid for pairwise comparisons; does not support complex contrasts.
Value
An object of class "bonferroni_np"
and "comparaciones"
, containing:
-
Resultados
: Data frame with comparisons, W-statistics, raw and adjusted p-values, and significance levels. -
Promedios
: Mean ranks of each group. -
Orden_Medias
: Group names ordered from highest to lowest rank. -
Metodo
: Name of the method used ("Bonferroni (non-parametric)").
References
Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80–83. doi:10.2307/3001968
Dunn, O. J. (1964). Multiple Comparisons Using Rank Sums. Technometrics, 6(3), 241–252. doi:10.1080/00401706.1964.10490181
Shaffer, J. P. (1995). Multiple Hypothesis Testing. Annual Review of Psychology, 46(1), 561–584. doi:10.1146/annurev.ps.46.020195.003021
Examples
data(iris)
BonferroniNPTest(Sepal.Length ~ Species, data = iris)