Performs non-parametric pairwise comparisons based on rank-transformed data using the Conover-Iman procedure. This method is typically applied as a post hoc test following a significant Kruskal-Wallis test to identify specific group differences.
ConoverTest(formula, data, alpha = 0.05, method.p = "holm")An object of class "conover" and "comparaciones", containing:
Resultados: A data frame with pairwise comparisons, t-statistics, raw and adjusted p-values, and significance markers.
Promedios: A named numeric vector with mean ranks for each group.
Orden_Medias: A character vector with group names sorted from highest to lowest rank.
Metodo: A string describing the method used ("Conover (no parametrico)").
A formula of the form y ~ group, where y is a numeric variable
and group is a factor indicating group membership.
A data frame containing the variables specified in the formula.
Significance level for hypothesis testing (default is 0.05).
Method used to adjust p-values for multiple comparisons (default is "holm").
The Conover-Iman test uses rank-based t-statistics, offering improved statistical power over Dunn's test while maintaining flexibility in sample size.
Advantages: - More powerful than Dunn’s test, especially with moderate group differences. - Robust to non-normal data and suitable for ordinal or skewed distributions. - Allows for unequal sample sizes across groups.
Disadvantages: - Sensitive to heteroscedasticity (non-constant variances). - Requires appropriate p-value adjustment to control the family-wise error rate. - It is only useful in completely random or single-factor designs.
Conover, W. J. & Iman, R. L. (1979). "Multiple comparisons using rank sums." Technometrics, 21(4), 489–495.
data(d_e, package = "Analitica")
ConoverTest(Sueldo_actual ~ labor, data = d_e)
Run the code above in your browser using DataLab