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 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.

Value

An object of class "bonferroni_np" and "comparaciones", containing:

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)


[Package Analitica version 1.8.5 Index]