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.

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`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. |

`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. |

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

Frank Konietschke

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.

For nonparametric all-pairs comparison see `gao_cs`

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