Cross validation with ESAG discrminant analysis | R Documentation |

Cross validation for estimating the classification rate of a discrminant analysis for directional data assuming an ESAG distribution.

```
esag.da(y, ina, fraction = 0.2, R = 100, seed = NULL)
```

`y` |
A matrix with the data in Eulcidean coordinates, i.e. unit vectors. The matrix must have three columns, only spherical data are currently supported. |

`ina` |
A variable indicating the groupings. |

`fraction` |
The fraction of data to be used as test set. |

`R` |
The number of repetitions. |

`seed` |
You can specify your own seed number here or leave it NULL. |

A repeated cross validation procedure is performed to estimate the rate of correct classification.

A list including:

`percent` |
The estimated percent of correct classification and two estimated standard deviations. The one is the standard devation of the rates and the other is assuming a binomial distribution. |

`ci` |
Three types of confidence intervals, the standard one, another one based on the binomial distribution and the third one is the empirical one, which calcualtes the upper and lower 2.5% of the rates. |

Michail Tsagris.

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

Tsagris M. and Alenazi A. (2019). Comparison of discriminant analysis methods on the sphere. Communications in Statistics: Case Studies, Data Analysis and Applications, 5(4), 467–491.

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.

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

```
vmf.da, vmfda.pred, dirknn
```

```
x <- rvmf(100, rnorm(3), 15)
ina <- rep(1:2, each = 50)
esag.da(x, ina, fraction = 0.2, R = 50)
```

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