GabrielTest: Gabriel’s Post Hoc Test for Multiple Comparisons v 2.0

In Analitica: Exploratory Data Analysis, Group Comparison Tools, and Other Procedures

Gabriel’s Post Hoc Test for Multiple Comparisons v 2.0

Description

A modification of Tukey's test for use with moderately unequal sample sizes.

Usage

GabrielTest(modelo, comparar = NULL, alpha = 0.05)

Arguments

modelo	An aov or lm object (full model: includes blocks, factors, etc.).
comparar	Character vector with the name(s) of the factor(s) to compare: - One name: main effect (e.g., "treatment" or "A") - Several names: interaction (e.g., c("A","B") for A:B) If omitted, it uses the first factor in modelo$xlevels.
alpha	Significance level (default 0.05).

Details

Advantages: - More powerful than Tukey for unequal group sizes. - Controls error rates effectively with moderate imbalance.

Disadvantages: - Can be anti-conservative with large differences in group sizes. - Less common in standard statistical software.

Value

An object of class "gabriel" and "comparaciones" containing:

• Resultados: a data.frame with columns Comparacion, Diferencia, SE, t_value, p_value (unadjusted), p_ajustada (gabriel), Valor_Critico (critical difference), and Significancia.

• Promedios: a named vector of group means as defined by comparar.

• Orden_Medias: group names ordered from highest to lowest mean.

• Metodo: "Gabriel t-test".

• Termino: the term being compared (e.g., "A", "B", or "A:B").

• MSerror, df_error, N: useful for plots with error bars.

References

Hochberg, Y., & Tamhane, A. C. (1987). Multiple Comparison Procedures.

Examples

# DCA
data(d_e, package = "Analitica")
mod1 <- aov(Sueldo_actual ~ as.factor(labor), data = d_e)
resultado <- GabrielTest(mod1)
summary(resultado)
plot(resultado)

# RCBD
mod2 <- aov(Sueldo_actual ~ as.factor(labor) + Sexo, data = d_e)
res <- GabrielTest(mod2, comparar = "as.factor(labor)")
summary(res); plot(res)

# Factorial
mod3 <- aov(Sueldo_actual ~ as.factor(labor) * Sexo, data = d_e)
resAB <- GabrielTest(mod3, comparar = c("as.factor(labor)","Sexo"))  # celdas A:B
summary(resAB, n = Inf); plot(resAB, horizontal = TRUE)

Analitica documentation built on Nov. 5, 2025, 5:13 p.m.