Goodness of fit test for grouped data | R Documentation |

Goodness of fit test for grouped data.

```
group.gof(g, ni, m, k, dist = "vm", rads = FALSE, R = 999, ncores = 1)
```

`g` |
A vector with the group points, either in radians or in degrees. |

`ni` |
The frequency of each or group class. |

`m` |
The mean direction in radians or in degrees. |

`k` |
The concentration parameter, |

`dist` |
The distribution to be tested, it can be either "vm" or "uniform". |

`rads` |
If the data are in radians, this should be TRUE and FALSE otherwise. |

`R` |
The number of bootstrap simulations to perform, set to 999 by default. |

`ncores` |
The number of cores to use. |

When you have grouped data, you can test whether the data come from the von Mises-Fisher distribution or from a uniform distribution.

This is an "htest"class object. Thus it returns a list including:

`statistic` |
The test statistic value. |

`parameter` |
Since this is a bootstrap based test, there are no degrees of freedom, hence this is "NA". |

`p.value` |
The p-value of the test. |

`alternative` |
A character with the alternative hypothesis. |

`method` |
A character with the test used. |

`data.name` |
A character vector with two elements. |

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Arthur Pewsey, Markus Neuhauser, and Graeme D. Ruxton (2013). Circular Statistics in R.

```
pvm, circ.summary, rvonmises
```

```
x <- rvonmises(100, 2, 10)
g <- seq(min(x) - 0.1, max(x) + 0.1, length = 6)
ni <- as.vector( table( cut(x, g) ) )
group.gof(g, ni, 2, 10, dist = "vm", rads = TRUE, R = 299, ncores = 1)
group.gof(g, ni, 2, 5, dist = "vm", rads = TRUE, R = 299, ncores = 1)
```

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