gao {nparcomp}

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.

Note

The procedure can only be used to test hypotheses in terms of the distribution functions.

Author(s)

Frank Konietschke

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.

See Also

For nonparametric all-pairs comparison see gao_cs.

Examples

## Not run: 

data(liver)

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

 # Control= 3

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

## End(Not run)