Permutation based 2-sample mean test for circular data | R Documentation |

Permutation based 2-sample mean test for circular data.

```
hcfcirc.perm(u1, u2, rads = TRUE, B = 999)
hetcirc.perm(u1, u2, rads = TRUE, B = 999)
lrcirc.perm(u1, u2, rads = TRUE, B = 999)
hclrcirc.perm(u1, u2, rads = TRUE, B = 999)
embedcirc.perm(u1, u2, rads = TRUE, B = 999)
```

`u1` |
A numeric vector containing the data of the first sample. |

`u2` |
A numeric vector containing the data of the first sample. |

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

`B` |
The number of permutations to perform. |

The high concentration (hcfcirc.perm), log-likelihood ratio (lrcirc.perm), high concentration log-likelihood ratio (hclrcirc.perm), embedding approach (embedcirc.perm) or the non equal concentration parameters approach (hetcirc.perm) is used.

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

`statistic` |
The test statistic value. |

`parameter` |
The degrees of freedom of the test. Since these are permutation based tests 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.

Mardia K. V. and Jupp P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.

Rumcheva P. and Presnell B. (2017). An improved test of equality of mean directions for the Langevin-von Mises-Fisher distribution. Australian & New Zealand Journal of Statistics, 59(1): 119–135.

Tsagris M. and Alenazi A. (2022). An investigation of hypothesis testing procedures for circular and spherical mean vectors. Communications in Statistics-Simulation and Computation (Accepted for publication).

```
hcf.circaov, het.aov
```

```
u1 <- rvonmises(20, 2.4, 5)
u2 <- rvonmises(20, 2.4, 10)
hcfcirc.perm(u1, u2)
lrcirc.perm(u1, u2)
```

Directional documentation built on Oct. 12, 2023, 1:07 a.m.

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