gao: Nonparametric multiple test procedure for many-to-one comparisons

Description

This function can be used to perform the nonparametric multiple tests for many-to-one comparisons by Gao et al. (2008). The multiple level is strongly controlled by the Hochberg-adjustment.

Usage

gao(formula, data, alpha = 0.05, control = NULL, silent = FALSE)

Arguments

formula

A two-sided 'formula' specifying a numeric response variable and a factor with more than two levels. If the factor contains less than 3 levels, an error message will be returned.

data

A dataframe containing the variables specified in formula.

alpha

The significance level (by default = 0.05).

control

Character string defining the control group in Dunnett comparisons. By default it is the first group by lexicographical ordering

silent

A logical indicating more informations should be print on screen.

Value

Info

Samples and sizes with estimated relative effects and variance estimators.

Analysis

Comparison: Distributions being compared, Estimator: Estimated effect, df: Degree of Freedom, Statistic: Teststatistic, P.Raw: Raw p-Value P.Hochberg: Adjusted p-Value by the Hochberg adjustment, Rejected: A logical indicating rejected hypotheses, P.Bonf: Bonferroni adjusted p-Values, P.Holm: Holm adjusted p-Value.

References

Gao, X. et al. (2008). Nonparametric Multiple Comparison Procedures for Unbalanced One-Way Factorial Designs. JSPI 138, 2574 - 2591.

Konietschke, F., Placzek, M., Schaarschmidt, S., Hothorn, L.A. (2014). nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals. Journal of Statistical Software, 61(10), 1-17.

Examples

Run this code

# NOT RUN {
data(liver)

gao(weight ~dosage, data=liver,alpha=0.05)

 # Control= 3

gao(weight ~dosage, data=liver,alpha=0.05,control="3")
# }