Calculate pairwise multiple comparisons with one control according to Dunn.

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

`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. |

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 Kruskalâ€“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.

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. |

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.

Thorsten Pohlert

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.

`kruskal.test`

,
`friedman.test`

,
`posthoc.friedman.nemenyi.test`

,
`pnorm`

,
`p.adjust`

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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