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.

A list including:

`info` |
A vector with two elements, the test statistic value and the bootstrap p-value. |

`runtime` |
The runtime of the procedure. |

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>

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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