# Pairwise Test for Multiple Comparisons of Mean Rank Sums with one control (Dunn's-Test)

### Description

Calculate pairwise multiple comparisons with one control according to Dunn.

### Usage

1 | ```
dunn.test.control (x, g, p.adjust.method = p.adjust.methods, ...)
``` |

### Arguments

`x` |
a numeric vector of data values, or a list of numeric data vectors. |

`g` |
a vector or factor object giving the group for the
corresponding elements of |

`p.adjust.method` |
Method for adjusting p values (see |

`...` |
further arguments to be passed to or from methods. |

### Details

For one-factorial designs with samples that do not meet the assumptions
for one-way-ANOVA and subsequent post-hoc tests, the Kruskal-Wallis-Test
`kruskal.test`

can be employed that is also referred to as
the Kruskal–Wallis one-way analysis of variance by ranks. Provided that
significant differences were detected by this global test, one may be
interested in applying post-hoc tests according to Dunn for pairwise
multiple comparisons with one control.

See the vignette for details.

### Value

A list with class `"PMCMR"`

`method ` |
The applied method. |

`data.name` |
The name of the data. |

`p.value` |
The two-sided p-value of the standard normal distribution. |

`statistic` |
The estimated quantile of the standard normal distribution. |

`p.adjust.method` |
The applied method for p-value adjustment. |

### Note

A tie correction will be employed according to Glantz (2012).
As it is the case for multiple testing with one control using
`aov`

, the user must make sure that the control appears as the first
level in the group vector. There is no formula method enclosed.

### Author(s)

Thorsten Pohlert

### References

O.J. Dunn (1964). Multiple comparisons using rank sums. *Technometrics*, 6, 241-252.

S. A. Glantz (2012), *Primer of Biostatistics*. New York: McGraw
Hill.

S. Siegel, N. J. Castellan Jr. (1988), *Nonparametric Statistics
for The Behavioral Sciences*. New York: McGraw-Hill.

### See Also

`kruskal.test`

,
`friedman.test`

,
`posthoc.friedman.nemenyi.test`

,
`pnorm`

,
`p.adjust`

### Examples

1 2 3 4 5 6 7 8 | ```
##
require(stats)
data(PlantGrowth)
attach(PlantGrowth)
kruskal.test(weight, group)
dunn.test.control(weight,group, "bonferroni")
detach(PlantGrowth)
rm(PlantGrowth)
``` |

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