BonferroniTest {Analitica} | R Documentation |
Bonferroni-Corrected Pairwise t-Tests
Description
Performs pairwise t-tests with Bonferroni adjustment for multiple comparisons. This method controls the family-wise error rate by dividing the alpha level by the number of comparisons.
Usage
BonferroniTest(modelo, alpha = 0.05)
Arguments
modelo |
An object of class |
alpha |
Significance level (default is 0.05). |
Details
Advantages: - Very simple and easy to implement. - Strong control of Type I error. - Applicable to any set of independent comparisons.
Disadvantages: - Highly conservative, especially with many groups. - Can lead to low statistical power (increased Type II error). - Does not adjust test statistics, only p-values.
Value
An object of class "bonferroni"
and "comparaciones"
, containing:
-
Resultados
: Data frame with comparisons, mean differences, t-values, unadjusted and adjusted p-values, and significance. -
Promedios
: Named numeric vector of group means. -
Orden_Medias
: Group names ordered from highest to lowest mean. -
Metodo
: Name of the method used ("Bonferroni-adjusted t-test").
References
Dunn, O. J. (1964). Multiple Comparisons Using Rank Sums. Technometrics, 6(3), 241–252. doi:10.1080/00401706.1964.10490181
Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80–83. doi:10.2307/3001968
Examples
data(d_e, package = "Analitica")
mod <- aov(Sueldo_actual ~ as.factor(labor), data = d_e)
resultado <- BonferroniTest(mod)
summary(resultado)