Hypothesis test for IAG distribution over the ESAG distribution | R Documentation |

The null hypothesis is whether an IAG distribution fits the data well, where the altenrative is that ESAG distribution is more suitable.

```
iagesag(x, B = 1, tol = 1e-07)
```

`x` |
A numeric matrix with three columns containing the data as unit vectors in Euclidean coordinates. |

`B` |
The number of bootstrap re-samples. By default is set to 999. If it is equal to 1, no bootstrap is performed and the p-value is obtained throught the asymptotic distribution. |

`tol` |
The tolerance to accept that the Newton-Raphson algorithm used in the IAG distribution has converged. |

Essentially it is a test of rotational symmetry, whether the two `\gamma`

parameters are equal to zero.
This works for spherical data only.

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. If bootstrap was employed 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.

Paine P.J., Preston S.P., Tsagris M. and Wood A.T.A. (2018). An Elliptically Symmetric Angular Gaussian Distribution. Statistics and Computing, 28(3):689–697.

```
fishkent, iagesag, pc.test, esag.mle, kent.mle,
```

```
x <- rvmf(100, rnorm(3), 15)
iagesag(x)
fishkent(x, B = 1)
```

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