Analitica (version 2.2.0)

BonferroniNPTest: 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)

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)").

Arguments

formula

A formula of the form y ~ group.

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. - It is only useful in completely random or single-factor designs.

References

Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80–83. tools:::Rd_expr_doi("10.2307/3001968")

Dunn, O. J. (1964). Multiple Comparisons Using Rank Sums. Technometrics, 6(3), 241–252. tools:::Rd_expr_doi("10.1080/00401706.1964.10490181")

Shaffer, J. P. (1995). Multiple Hypothesis Testing. Annual Review of Psychology, 46(1), 561–584. tools:::Rd_expr_doi("10.1146/annurev.ps.46.020195.003021")

Examples

data(iris)
BonferroniNPTest(Sepal.Length ~ Species, data = iris)